Abstract—High costs of urban services, namely, waterworks, transportation, waste collection, wastewater collection and treatment, energy, and public lighting, require their optimization in management. This optimization can be mostly achieved using dedicated technology and strategy by “building” smart cities and smart grids. This paper illustrates findings related to the application of a designed distributed edge computing system for supervising a network of sensors, located on a special configuration of a pipeline, to detect leaks. The plant to be supervised is a zigzag waterworks with leaks to be simulated by opening and closing taps. The pressure variation is detected by magnetic sensors, which convert pressure variation into electric signal to be processed on-line thanks to an advanced and robust algorithm called a filter diagonalization method that performs a spectral analysis. In this paper, we have also developed a 2-D representation of the leak within the pipeline or waterworks, which is a robust way to see the dimensions or the expansion of the leak in a specific space.

A semi-analytical methodology is presented for the accurate analysis of time-domain radiation characteristics of antenna sensors. A locally conformal finite-difference time-domain technique is adopted to derive a minimal pole/residue spherical harmonic expansion of the equivalent currents excited on a suitable Huygens surface enclosing the sensing device. In this way, by using the singularity expansion method, the time-domain gain of the structure can be evaluated in closed form as the superposition of non-uniform spherical wave contributions attenuating along with the time and space according to the complex poles accounting for the natural resonant processes occurring in the device.

The IEC 62670-3 standard recommends the open-circuit voltage method to calculate the cell temperature inside high-concentrator photovoltaic (HCPV) modules. This method requires knowledge of the temperature coefficient of open-circuit voltage (b), and the same standard provides a procedure to get this parameter. In this paper, an alternative method for the thermal characterisation of HCPV modules is proposed. As an advantage, it allows obtaining both the b parameter and the internal thermal resistance (r) of the device from outdoor measurements. No internal sensor for measuring the cell temperature is required as in the case of the IEC 62670-3 standard. Knowing the r parameter allows a more accurate characterisation of the cell temperature. The proposed procedure is applied to a real HCPV module. An outdoor experimental campaign of two months in Jaen (Southern Spain) was carried out. The b value was underestimated in a 0.50% and the r value was overestimated in a 4.18%. When applying the estimated parameters for the prediction of the cell temperature, the open-circuit voltage method gave a root mean square error (RMSE) of 1.41 C, while the internal thermal resistance method gave a RMSE of 0.62 C.

Current methods for ultrasound (US) molecular imaging suffer the lack of image processing techniques specifically designed to identify the newer nanosized contrast agents (CAs). The available pulse sequences and signal analysis methods for US contrast detection, in fact, were developed for the older microbubble CAs, whose acoustic properties differ significantly from those of nanoparticles. This work illustrates the implementation and experimental testing of a new contrast detection scheme, tailored to enhance the contribution of solid nanosized CAs in echographic images. The proposed protocol, including a novel pulse sequence and a two-step image processing algorithm, was evaluated on a phantom consisting of silica nanospheres dispersed into an agarose gel matrix that was imaged through a conventional echographic transducer. Obtained results demonstrated the capability of selectively suppressing non-contrast echoes, without any loss in spatial resolution and maintaining the characteristics of real-time imaging, therefore showing very promising perspectives for clinical applications.

This paper presents a preliminary analysis of a class of non-linear filters for enhancement of mammogram lesions. A non-linear filtering approach employing polynomial model of non-linearity is designed by second order truncation of Volterra series expansion. The proposed filter response is a linear combination of Type-0 and Type-II Volterra filters. Type-0 filter provides contrast enhancement, suppressing the ill-effects of background noise. On the other hand, Type-II filter employs edge enhancement. The objective analysis of the proposed filter is carried out by estimating values of quality parameters like CEM and PSNR on mammograms from MIAS and DDSM databases.

Current imaging methods for catheter position monitoring during minimally invasive surgery do not provide an effective support to surgeons, often resulting in the choice of more invasive procedures. This study was conducted to demonstrate the feasibility of non-ionizing monitoring of endovascular devices through embedded quantitative ultrasound (QUS) methods, providing catheter self-localization with respect to selected anatomical structures. QUS-based algorithms for real-time automatic tracking of device position were developed and validated on in vitro and ex vivo phantoms. A trans-esophageal ultrasound probe was adapted to simulate an endovascular device equipped with an intravascular ultrasound probe. B-mode images were acquired and processed in real time by means of a new algorithm for accurate measurement of device position. After off-line verification, automatic position calculation was found to be correct in 96% and 94% of computed frames in the in vitro and ex vivo phantoms, respectively.

Application specific information processing (ASIP) unit in smart cameras requires sophisticated image processing algorithms for image quality improvement and extraction of relevant features for image understanding and machine vision. The improvement in performance as well as robustness can be achieved by intelligent moderation of the parameters both at algorithm (image resolution, contrast, compression, and so on) as well as hardware levels (camera orientation, field of view, and so on). This paper discusses the employment of ISO/IEC/IEEE 21451 smart transducer standards for performance improvement of smart cameras. The standardized transducer electronic data sheets (TEDS-by IEEE 21450) provide the self description of sensors, of which the calibration details are of vital importance to yield a smart and reconfigurable imaging system. This is possible by exercising intelligent control over the TEDS (smart camera) calibration details as well as automated tuning of algorithm parameters (in ASIP) based on decisions by perceptually efficient image quality assessment (IQA) tool. Estimation of distortion based on reduced reference IQA has been highlighted as a reliable methodology for this purpose. The proposed IQA approach uses wavelets for features extraction followed by estimation of luminance, contrast, and divergence parameters to obtain the proposed distortion measure (Q). The computational complexity in the process has been catalyzed using integral image and gradient magnitude approaches. The validation of Q metric is carried out by evaluating the image quality for various types of distortions on images from Content-based Strategies of Image Quality assessment (CSIQ) and Information Visualization CyberInfrastructure (IVC) databases. Simulation results yield a healthy correlation of Q and the subjective human opinions.

Although today water is becoming more and more precious, its major waste is caused by transportation. The authorities in charge of the management of water pipes indicate double-digit percentage of waste, sometimes it even exceeds 50% the amount of water mostly lost by inefficiency of distribution waterworks. In this study, the authors present an alternative method of spectral analysis, used for detecting leaks in water pipes, with respect to classical spectral methods as direct Fourier transform/fast Fourier transform. They have used decimated Padè approximant (DPA), where the input time signal points or auto-correlation functions are given via measurements or computations, and the task is to reconstruct the unknown components as the harmonic variables in terms of the fundamental complex frequencies and amplitudes. They have also introduced decimated linear predictor technique as direct consequence of the DPA, since they differ only in one step, namely the calculation of the amplitudes.

This paper presents a non-linear framework employing a robust Polynomial filter for accomplishing enhancement of mammographic abnormalities outcoming from biomedical instrumentation, that is, X-rays instrumentation. The approach proposed in this work utilizes a linear combination of Type-0 and Type-II Polynomial filters as a generalized filtering solution to achieve enhancement of mammographic masses and calcifications irrespective of the nature of background tissues. A Type-0 filter provides contrast enhancement, suppressing the ill effects of background noise. On the other hand, Type-II filter performs edge enhancement leading to preservation of finer details. Contrast Improvement Index (CII) is used as a performance measure to quantify the degree of improvement in contrast of the region-of interest (ROI). In addition, estimation of signal-to-noise ratio (in terms of PSNR and ASNR) is carried out to account for the suppression in background noise levels and over-enhancements of the processed mammograms. These measures are used as a mechanism to optimally select the filter parameters and also serve as a quantifying platform to compare the performance of the proposed filter with other non-linear enhancement techniques to be used for diverse biomedical image sensors.

In this paper, the authors present a mechatronics system consisting of an intelligent robotic arm able to sort ball bearings having the same colour and shape drawing advantage from vision. After acquiring and processing an image from a camera, two almost concentric and circular regions are extracted from the image and their areas are calculated as number of pixels belonging to them. The center of these regions provides the point that the end-effector has to reach in order to grip a cylindrical transport structure where the bearing is placed. Since the size measurements of image regions are very repeatable and the depth between the camera and the object is known, the bearing is recognized from the area. For the sake of automatically appreciating the effectiveness of the proposed approach, a RFID (Radio-Frequency Identification) tag is attached to the transport structure that supports the object. The tag contains stored information on the specific bearing for verifying the success in recognition making use of a reader device. Several experimental tests confirmed that the suggested strategy may be applied to track spare parts in assembly lines.

This research work regards the design and realization of an absorption spectrophotometer based on a LED light source in place of the usually employed Xenon lamp. The advantage of the use of LED technology resides in several factors such as the reducing of the analyte temperature variations and thus noise generation, which occur if a Xenon light source is used, beside of the high luminous efficiency, reliability, operating duration, lower maintenance and a lower power consumption. This last factor allows to supply the entire designed apparatus using a solar panel thus making the system easly portable for use even in places where the electricity network is absent. An optical filtering system was realized in order to detect the analyte absorption for each wavelength range selected by the optical filters. A PC-interfaced PIC-based control unit used to manage the different functionalities required by the spectophotometer was realized and tested. The control unit acquires and processes, via the developed firmware, the raw data provided by different sensors employed in the system. The sensors are used to monitor analyte temperature and humidity values, to control the analyte pressure and to acquire the luminous intensity value of the light beam before and after passing through the analyte. Finally, the realized electronic control unit actuates different mechanical sections (stepper motor, solenoid valve), sincronyzing and controlling the data exchange between hardware sections, microcontroller and the PC.

Postural and motor changes in patients affected by abnormalities of movement are the results of the interactions of nervous system, musculoskeletal system and sensorial system. Gait analysis is a good technique to characterize various gait pathologies, and a high accuracy of the results is necessary in order to describe, in quantitative way, functional limitation related to the pathology. In this paper the model for modeling the kinematics of human body is implemented by considering data acquired from a camera of optoelectronic system capable of measuring three-dimensional coordinates of reflective markers placed in known positions on the patient. Kinematic model is reported for pelvis and accuracy of its trajectories acquired by cameras is also investigated. Moreover, since coordinate system of the other joints (hip, ankle, foot) are relative respect pelvic coordinate system, accuracy in its model can reduce errors in evaluation of essential angles to define human motion.

Many industrial and transportation applications use combustion in dedicated chambers. Combustion implies, depending upon the nature and the amount of precursors, production of carbon dioxide, pollutants and dusts in terms of particulate matters. With the aim of reducing emission, lean combustion is of great interest. However flame stability within the combustor chamber is a key issue under lean conditions. In fact under lean conditions burners exhibit flame instability, flashback or lean blow out, until the flame extinction. Hence the online monitoring of these phenomena related to combustion instability is essential. One the most used techniques is to check temperature and flame stability by means of sensing probes resisting to high temperatures. Increasing the number of probes, it is possible to perform a 2D and 3D monitoring. However since these probes are costly and require heavy maintenance procedures, it could be wise to exploit imaging processing through cameras directed to portholes across which we can see inner parts, and atmosphere of the furnace/chamber. This paper illustrates findings related to monitoring the flame behaviour different operating conditions chamber by an advanced image processing. A specific algorithm has been developed to characterize the flame, hence, to perform measurements. Myriad filters have been utilized to enhance flame features.

Advances in technological devices unveil new architectures for instrumentation and improvements in measurement techniques. Sensing technology, related to biomedical aspects, plays a key role in nowadays applications; it promotes different advantages for: healthcare, solving difficulties for elderly persons, clinical analysis, microbiological characterizations, etc.. This book intends to illustrate and to collect recent advances in biomedical measurements and sensing instrumentation, not as an encyclopedia but as clever support for scientists, students and researchers in other to stimulate exchange and discussions for further developments.

Nowadays telecardiology is an important tool in cardiac diagnosis from a remote location. During Electrocardiogram (ECG) or Cardiac Signal acquisition several artifacts strongly affect the ST segment, degrade the signal quality, frequency resolution, produce large amplitude signals in ECG that can resemble PQRST waveforms and mask the tiny features that are important for clinical monitoring and diagnosis. So the extraction of high-resolution cardiac signals from recordings contaminated with artifacts is an important issue to investigate. In this paper, various novel block based time–frequency domain adaptive filter structures for cardiac signal enhancement are presented. These filters estimate the deterministic components of the cardiac signal and remove the noise component. The Block Leaky Least Mean Square (BLLMS) algorithm, being the solution of the steepest descent strategy for minimizing the mean squared error in a complete signal occurrence, is shown to be steady-state unbiased and with a lower variance than the LMS algorithm. To improve the filtering capability some variants of BLLMS, Block Normalized LLMS (BNLLMS) and Block Error Normalized LLMS (BENLLMS) algorithms are implemented in both time domain (TD) and frequency domains (FD). Finally, we have applied these algorithms on real cardiac signals obtained from the MIT-BIH data base and compared their performance with the conventional LLMS algorithm. The results show that the performance of the block based algorithms is superior to the LLMS counterparts in terms of signal to noise ratio improvement (SNRI), excess mean square error (EMSE) and misadjustment (M). Among all the algorithms FDBENLLMS achieves higher SNRI than other techniques. These values are 25.8713 dB, 20.1548 dB, 21.6718 dB and 20.7131 dBs for power line interference (PLI), baseline wander (BW), muscle artifacts (MA) and electrode motion artifacts (EM) removal.

In this paper, the authors introduce an innovative and complete tool for the automatic diagnostic and detection of small notches on analyzed components. The tool is based on the analysis of the data of a nanocomposite optical sensor accurately moved by a robotic arm, whose gripper catches and moves the optical sensor, for scanning without contact the external surface of mechanical components. The recent optical sensor is composed of an optical fiber source made up of a nanocomposite material, and it allows detecting the characteristics of the target by monitoring the backscattered light. The innovative tool here presented is based on the analysis of several experiments carried out, considering small notches of different lengths. The proposed algorithm, together with the used device, is able to point out the presence or not of a small notch on the scanned component and also to estimate with a good precision its length and position.

The aim of this paper was to optimize the employment of a novel algorithm for acquisition and processing of medical ultrasound (US) signals to facilitate its clinical translation. The implemented procedure is dedicated to selective enhancement of nanoparticle (NP) contrast agents in echographic images and is based on the differences in US signal backscatter between NP-containing targets and more homogeneous objects. Previous preliminary studies verified the feasibility of this approach on silica nanospheres (SiNSs) dispersed at a constant volume concentration (0.7%) in agarose gel samples. The present extended these evaluations, addressing two issues of direct clinical interest: 1) safety: SiNSs were coated with a biocompatible layer made of polyethylene glycol (PEG) and the adopted NP volume concentration was reduced to 0.2%, which is in the nontoxic range and 2) reproducibility: a different phantom configuration was used, to verify the independence of algorithm performance from a specific target region shape. The obtained results demonstrated that the proposed method can be effectively applied to enhance the presence of PEG-coated SiNSs in the diameter range 160–660 nm at a low and biocompatible volume concentration: the combined employment of a phantom with a different geometry and a lower concentration of PEG-coated NPs, in fact, caused only slight variations in the suppression patterns of noncontrast echoes, without affecting the final diagnostic effectiveness of the investigated contrast detection scheme. This approach also provides specific advantages with respect to the available measurement techniques dedicated to the enhancement of targeted US contrast agents for molecular imaging purposes.

The genesis of epileptic seizures is nowadays still mostly unknown. The hypothesis that most of scientist share is that an abnormal synchronization of different groups of neurons seems to trigger a recruitment mechanism that leads the brain to the seizure in order to reset this abnormal condition. If this is the case, a gradual transformation of the characteristics of the EEG can be hypothesized. It is therefore necessary to find a parameter that is able to measure the synchronization level in the EEG and, since the spatial dimension has to be taken into account if we aim to find out how the different areas in the brain recruit each other to develop the seizure, a spatio-temporal analysis of this parameter has to be carried out. In the present paper, a spatio-temporal analysis of EEG synchronization in 24 patients affected by absence seizure is proposed and the results are hereby reported and compared to the results obtained with a group of 40 healthy subjects. The spatio-temporal analysis is based on Permutation Entropy (PE). We found out that, ever since the interictal stages, fronto-temporal areas appear constantly associated to PE levels that are higher compared to the rest of the brain, whereas the parietal/occipital areas appear associated to low-PE. The brain of healthy subjects seems to behave in a different way because we could not see a recurrent behaviour of PE topography.

Epileptic seizures are generated and evolve through an underlying anomaly of synchronization in the activity of groups of neuronal populations. The related dynamic scenario of state transitions is revealed by detecting changes in the dynamical properties of Electroencephalography (EEG) signals. The recruit- ment procedure ending with the crisis can be analyzed by means of a spatial-temporal plot from which to extract suitable descriptors that are able to monitor and quantify the evolving synchronization level from the EEG tracings. In this paper, a spatial-temporal analysis of EEG synchronization based on the concept of Permutation Entropy (PE) is proposed. The performance of PE are tested on a database of 24 patients affected by absence (generalized) seizures. The results achieved are compared to the dynamical behavior of the EEG of 40 healthy subjects. Being PE a feature which is dependent on two-parameters, an extensive study of the sensitivity of the performance of PE with respect to the parameters’ setting was carried out on scalp EEG. Once the optimal PE configuration was determined, its ability to detect the different brain states was evaluated. One relevant result of the study is that, in contrast to the widely accepted interpretation of the transition to absence seizure as an abrupt change, within the limits of the analyzed database, the “jump” transition to the epileptic status is heralded well before the seizure on- set. Indeed, ever since the interictal stages, the frontal-temporal scalp areas appear constantly associated to PE levels that are higher compared to the remaining electrodes, whereas the parieto-occipital areas appear associated to lower-PE values. The EEG of healthy subjects does not show any similar dynamic behavior nor exhibits any recurrent portrait in PE topography.

Halloysite Nanotubes (HNTs) are nanomaterials composed of double layered aluminosilicate minerals with a predominantly hollow tubular structure in submicron range. HNTs are characterized by a wide range of applications in anticancer therapy, sustained agent delivery, being particularly interesting because of their tunable release rates and fast adsorption rates. However systematic investigations of their acoustic properties are still poorly documented. This paper shows a quantitative assessment of the effectiveness of HNTs as scatterers at conventional ultrasonic frequencies (5.7 -7 MHz) in low range of concentrations (1.5-5 mg/mL). Different samples of HNT (diameter: 40-50 nm; length: 0.5 to 2 microns, empty lumen diameter: 15-20 nm) containing agarose gel were imaged through a commercially available echographic system and acquired data were processed through a dedicated prototypal platform in order to extract the average ultrasonic signal amplitude associated to the considered sample. Relationships have been established among backscatter, HNT concentration and the employed echographic frequency. Our results demonstrated that improvement in image backscatter could be achieved incrementing HNT concentration, determining a non-linear signal enhancement due to the fact that they are poly-disperse in length. On the other hand the effect of different echographic frequencies used was almost constant at all concentrations, specifically using higher values of echographic frequency allows yielding a signal enhanced of a factor 1.75±0.26.

The authors present in this paper some interesting results relating to an innovative device for automatic diagnostic. The system is composed by a robotic arm that accurately moves an optical sensor for automatic diagnostic through the inspection on the presence of notches on a mechanical beam. The optical sensor is composed of an optical fiber source made up of nanocomposite material, a PDMS-Au tip that may enhance the light and a receiver optical fiber sheaf. The new system allows analyzing the characteristics of the target by monitoring the backscattered light. The sensor motion is realized by using a robotic arm whose gripper catches and moves the optical sensor. The device has been evaluated in order to analyze its ability for detecting small notches on a mechanical component.

Halloysite clay Nanotubes (HNTs) are nanomaterials composed of double layered aluminosilicate minerals with a hollow tubular structure in the submicron range. They are characterized by a wide range of applications in anticancer therapy as agent delivery. In this work we aim to investigate the automatic detection features of HNTs through advanced quantitative ultrasound imaging employing different concentrations (3-5 mg/mL) at clinical conventional frequency, i.e. 7 MHz. Different tissue mimicking samples of HNT containing agarose gel were imaged through a commercially available echographic system, that was opportunely combined with ultrasound signal analysis research platform for extracting the raw ultrasound radiofrequency (RF) signals. Acquired data were stored and analyzed by means of an in-house developed algorithm based on wavelet decomposition, in order to identify the specific spectrum contribution of the HNTs and generate corresponding image mapping. Sensitivity and specificity of the HNT detection were quantified. Average specificity (94.36%) was very high with reduced dependency on HNT concentration, while sensitivity showed a proportional increase with concentration with an average of 46.78%. However, automatic detection performances are currently under investigation for further improvement taking into account image enhancement and biocompatibility issues

Breast sonograms are more effective towards differentiation of cysts from solid tumours; if they could be post-processed for minimization of speckle content without blurring of edges. The approach presented in this paper consists of a bilateral filtering in homogeneity domain so that the despeckling process do not compromises the texture and features of masses. The proposed despeckled approach decomposes the input image into homogeneous and non-homogeneous regions; which are then selectively processed using the bilateral filter. The domain filtering component is made dominant when applied to homogeneous pixels providing smoothening while the range filter dominates on the non-homogeneous pixels leading to edge preservation. Simulations carried out on breast ultrasound images depict satisfactory speckle filtering supported with improvement in values of performance parameters (PSNR, SSIM & SSI).

Brain activity can be recorded by means of EEG (Electroencephalogram) electrodes placed on the scalp of the patient. The EEG reflects the activity of groups of neurons located in the head, and the fundamental problem in neurophysiology is the identification of the sources responsible of brain activity, especially if a seizure occurs and in this case it is important to identify it. The studies conducted in order to formalize the relationship between the electromagnetic activity in the head and the recording of the generated external field allow to know pattern of brain activity. The inverse problem, that is given the sampling field at different electrodes the underlying asset must be determined, is more difficult because the problem may not have a unique solution, or the search for the solution is made difficult by a low spatial resolution which may not allow to distinguish between activities involving sources close to each other. Thus, sources of interest may be obscured or not detected and known method in source localization problem as MUSIC (MUltiple SIgnal Classification) could fail. Many advanced source localization techniques achieve a best resolution by exploiting sparsity: if the number of sources is small as a result, the neural power vs. location is sparse. In this work a solution based on the spatial sparsity of the field signal is presented and analyzed to improve MUSIC method. For this purpose, it is necessary to set a priori information of the sparsity in the signal. The problem is formulated and solved using a regularization method as Tikhonov, which calculates a solution that is the better compromise between two cost functions to minimize, one related to the fitting of the data, and another concerning the maintenance of the sparsity of the signal. At the first, the method is tested on simulated EEG signals obtained by the solution of the forward problem. Relatively to the model considered for the head and brain sources, the result obtained allows to have a significant improvement compared to the classical MUSIC method, with a small margin of uncertainty about the exact location of the sources. In fact, the constraints of the spatial sparsity on the signal field allow to concentrate power in the directions of active sources, and consequently it is possible to calculate the position of the sources within the considered volume conductor. Later, the method is tested on the real EEG data too. The result is in accordance with the clinical report even if improvements are necessary to have further accurate estimates of the positions of the sources.

In the field of radiology, mammographic screened images (i.e., X-ray image sensing) are very challenging and difficult to interpret. The expert radiologist visually hunts the mammograms for any specific abnormality. However, human factor causes a low degree of precision that often results in biopsy and anxiety for the patient involved. This paper proposes a novel computer-aided detection (CAD) system to reduce the human factor involvement and to help the radiologist in automatic diagnosis of malignant/nonmalignant breast tissues by utilizing polar complex exponential transform (PCET) moments as texture descriptors. The input region of interest is extracted manually and subjected to further number of preprocessing stages. Both magnitude and phase of PCET moments are used for feature extraction of suspicious region. Moreover, a new classifier adaptive differential evolution wavelet neural network is introduced to improve the classification accuracy of the proposed CAD system. The proposed system is tested on the mammographic images from Mammographic Image Analysis Society database. The designed system attains a fair accuracy of 97.965% with 98.196% sensitivity and 97.194% specificity. The best area under the receiver operational characteristics curve for the proposed classifier is found to be 0.984 with confidence interval from 0.968 to 0.999 and ±0.0108 standard error

A cavitating two-phase flow of water in a pipe with area shrinkage was experimentally investigated, acquiring at high sampling rate pressure signals and images of the cavitating flow field. The time series of the pressure fluctuations was analyzed in terms of power spectral density and related to the cavitation regimes. Furthermore, the fluctuations of the pressure measurements were also decomposed using the wavelet transform to analyze the frequency distribution of the signals energy with respect to the flow behavior. The energy content at each frequency band of the acquire signals is well related to cavitation flow-field behavior. Moreover, the artificial neural network and the least squares support vector machine (LS-SVM) were implemented to identify the cavitation regime, using, as inputs, the power spectral density distributions of the pressure fluctuations, and some features of the decomposed signals, as the wavelet energy for each decomposition level and wavelet entropy. Results indicate the most accurate model to be used in the cavitation regime identification, underlining the enhanced capability of LS-SVM trained with the input data set based on the wavelet decomposition features

In this paper the authors present an application of an innovative device consisting in a new optical sensor accurately moved by driving a controlled robotic arm. The new sensor is made up of nanocomposite material and it is composed of an optical fiber source, an optical fiber bundle receiver and a PDMS-Au tip able to enhance the light. The device allows to reconstruct the target characteristics taking advantage of the backscattered light. The motion of the sensor is obtained by means of a robot manipulator which gripper grasps and moves the sensor. The realized device has been tested in order to evaluate its ability to provide useful information on colours, surface opacity and profile of the detected objects.

This paper aims to describe two intelligent systems, microprocessor based, capable of monitoring, both locally o remotely, more photovoltaic strings. In particular, the electronic board called CS097 helps to detect environmental parameters such as temperature and solar radiation and to calculate average power and energy produced by the solar system, in order to monitor the efficiency and electricity production of the photovoltaic field. It can control up to four photovoltaic strings, acquiring voltage and current values for each string. Transducers, installed on the same board, detect currents lower than 20 amps and voltages lower than 1000 V. The acquired data have a maximum error of 1% compared to true current and voltage values generated by the strings. It’s also provided a galvanic isolation between the measuring circuits and the acquisition ones. The CS083 and CS088 electrical boards, instead, work as an alarm system which indicates a critical condition in case of an electrical continuity loss or a not-justified rapid variation of the voltage read on each string.

Leak detection is an important issue in piping that deals with the management of water resources; nowadays large amounts of water in the network are dispersed as reported in current scientific literature. Among the methods for leak detection in water pipes, spectral analysis is very interesting. A classical spectral method is fast Fourier transform, but in this paper, we present an alternative method of spectral analysis, which has higher performance in terms of resolution and fast processing, namely decimated signal diagonalization (DSD). It is a nonlinear, parametric method for fitting time domain signals represented in terms of exponentially damped time signals. The aim is to reconstruct the unknown components as the harmonic variables, estimating the fundamental complex frequencies, and amplitudes. The DSD method partly uses the principles of the filter diagonalization method (FDM), which constructs matrices of a generalized eigenvalue problem directly from measured time signals of arbitrary length. However, the DSD because of its windowing technique produces a considerable reduction of size of the original data matrix, and consequently acquisition time can be shorter. We have tested the DSD method for leak detection problem in an experimental zigzag pipeline. We show as the DSD method produces good results in terms of resolution than FDM one.

In this paper, we present the implementation of a new class of optical pressure sensors in a robotic tactile-sensing system based on polydimethylsiloxane (PDMS). The sensor consists of a tapered optical fiber, where an optical signal goes across, embedded into a PDMS–gold nanocomposite material (GNM). By applying different pressure forces onto the PDMS-based nanocomposite, changes in the optical transmittivity of the fiber can be detected in real time due to the coupling between the GNM and the tapered fiber region. The intensity reduction of a transmitted light is correlated to the pressure force magnitude. Light intensity is converted into an electric signal by a system suitable for robotic implementation. High sensitivity using forces by applying weights of a few grams is proved. Sensitivity on the order of 5 g is checked. A detailed algorithm for the detection of roughness and shapes by means of a robotic finger is proposed.

The design and full-wave analysis of piezoelectric micro- needle antenna sensors for minimally invasive near-¯eld detection of cancer-related anomalies of the skin is presented. To this end, an accurate locally conformal ¯nite-di®erence time-domain procedure is adopted. In this way, an insightful understanding of the physical processes detecting the characteristics of the considered class of devices is achieved. This is important to improve the structure reliability, so optimizing the design cycle. In this regard, a suitable sensor layout is described, and discussed in detail. The major benefit of the proposed system stems from the potential for obtaining a superior performance in terms of input impedance matching and efficiency, in combination with an electronically tunable steering property of the near-field radiation intensity which can be profitably used to enhance the illumination and, hence, the localization of possible malignant lesions in the host medium. By using the detailed modeling approach, an extensive parametric study is carried out to analyze the effect produced on the sensor response by variations of the complex permittivity of the skin due to the presence of anomalous cells, and thus useful heuristic discrimination formulas for the evaluation of the exposure level to cancer risk are derived.

Sea water monitoring is becoming a great area of research in the field of measurements and instrumentation. For many years, the sea monitoring interest was within the framework of climate changes due to sudden temperature variations, for example, El Niño phenomenon. But, with the increasing of maritime traffic, development of port activities as well as discharged urban waters after dedicated treatment, sea water needs to be monitored using, in general, spectroscopic and fluorescence methods for detecting specific pollutants. All equipments, in case of monitoring directly on sea, must obey to specific requirements in order to avoid misrepresentation of pollutant concentrations. We present a design of a special buoy for hosting instrumentation for marine monitoring. The architecture of the instrumentation is presented and an anti-shock simulation is also presented. A spectroscopy method using light is presented in this research for monitoring marine water.

The quality of combustion process has an impact on combustion itself and mainly on emissions. This1 latter is one of the major concerns in an environmental viewpoint; for instance, the amount of oxygen is an indicator of bad and good combustion. It is also a constraint for regulating pollutant production, in particular dust that is also a vector transporting harmful micropollutants. The paper illustrates combustion quality detection by means of imaging. The work aims at retrieving possible precursors of combustion deterioration, and instability and allowing decision makers to provide accordingly. Images have been taken from an experimental setup.

The protection of environment, in a specific manner air component, is one of the most important objectives established by national and international organizations. Air pollutants are generally detected by electronic instrumentation containing dedicated units from capturing to processing and displaying. With the deterioration of anthropic activities due to, for example, increasing of car traffic, increasing of heavy industrial activities, and subsequent greenhouse effects, the nature of required value becomes very complex and attention must be paid to ‘‘stealth” pollutants and mechanisms that produce them. That is why, beyond traditional pollutants, attention must be paid to specific classes of pollutants such as dusts vectoring fixed in inodorous gases, and other components included in gaseous substances as dioxins and furans. This paper illustrates results of experimental activities within a review approach for detecting pollutants based on advanced technology using nanoparticle through helium microwave-induced plasma, sensors exploiting perovskite structures and thermography for detecting harmful gases. Longterm detection of dioxins is also illustrated.

Aim of this paper is the design of an absorption spectrophotometer based on LED technology presenting several advantages such as high luminous efficiency, reliability, long operating duration, low maintenance and low power consumption besides the reduction of analyte temperature variations which occur if Xenon light source is used. An optical filtering system was realized to detect analyte absorption for each wavelength range selected by proper optical filters; also to characterize filtered light beam in terms of its coherence length, thus correlating measured absorption spectrum with light source characteristics, the Michelson interferometer was used. Realized white LED-based spectrophotometer can be used to monitor air quality in hospital rooms or to detect atmospheric pollution deriving from vehicular traffic and different typology of pollutants (e.g., heavy metals deriving by industrial activities). A PC-interfaced control unit acquires and processes raw data provided by sensors (pressure, temperature, humidity, luminosity) and manages the optical filtering system motion by actuating a stepper motor. Whole system operation was tested and obtained results confirm the proper functioning and correct interaction, through PC terminal, between user and control unit.

The joint study of respiratory and cardiac activity suggests indirect methods to derive the respiratory signal by electrocardiogram (ECG) processing. Potential advantages of such methods are low cost, high convenience, and continuous noninvasive respiratory monitoring. Recent works show that the respiratory signal can be accurately evaluated by single-channel ECG processing. The aim of this paper is to introduce a new method based on the Empirical Mode Decomposition (EMD) for the respiratory signal evaluation. A comparison versus popular algorithms for the respiratory signal extraction is also shown. Preliminary results confirm that EMD algorithm provides better performances, with respect to others, especially in the case of respiratory waveform reconstruction.

The proposed research illustrates an innovating implantable micro-apparatus to be encompassed under the scalp for monitoring and retrieving electrical cerebral activities. The illustrated system considers its theoretical realization including, design of circuital electronic components and energy harvesting, 3D package, chemical aspects concerning the utilization of UHMWPE (Ultra High Molecular Weight Polyethylene) polymeric materials for packaging including mechanical simulations and comparison with titanium material, and electromagnetic aspects regarding the Wi-Fi radiation. A full description of necessary circuitry is included. Moreover, for chemical viewpoint, requirements of polymeric nanomaterials, embedding silver or copper nanoparticles to be used for its fabrication, are discussed illustrating antibacterial and electromagnetic wall barrier properties. The study of the proposed work concerns the whole design of the system.

Installation of renewal energy plant is a vital question for safeguarding cities and human agglomerations against pollution and helping them in the effort to save conventional energy contribution. As it is a widespread issue, PV plants can be located everywhere even in a severe conditions on the proviso that no external depositions, covering and coating the solar module, can alter the photovoltaic efficiency. To solve the problem, practically speaking, diverse solutions are envisaged and among them there is a continuous cleaning of dust by means of water and special liquids. The research proposes a modelling of the effect of dust on efficiency using experimental measurements provided through MPPT (maximum power point tracker) installed in the measuring architecture. Dust covering the PV module reduces the solar irradiance affecting the energy conversion. A comparison has been performed between a clean PV module under MPPT variations and another one of the same technology (CdTe, cadmium telluride) with dust. Both acquisitions have been carried out simultaneously for around one month. Both measurement campaigns agree with the scientific literature.

Life satisfaction has been widely used in recent studies to evaluate the effect of environmental factors on individuals’ well-being. In the last few years, many studies have shown that the potential impact of climate change on cities depends on a variety of social, economic, and environmental determinants. In particular, extreme events, such as flood and heat waves, may cause more severe impacts and induce a relatively higher level of vulnerability in populations that live in urban areas. Therefore, the impact of climate change and related extreme events certainly influences the economy and quality of life in affected cities. Heat wave frequency, intensity, and duration are increasing in global and local climate change scenarios. The association between high temperatures and morbidity is well-documented, but few studies have examined the role of meteo-climatic variables on hospital admissions. This study investigates the effects of temperature, relative humidity, and barometric pressure on health by linking daily access to a Matera (Italy) hospital with meteorological conditions in summer 2012. Extreme heat wave episodes that affected most of the city from 1 June to 31 August 2012 (among the selected years 2003, 2012, and 2017) were analyzed.

In real time clinical environment, the brain signals which doctor need to analyze are usually very long. Such a scenario can be made simple by partitioning the input signal into several blocks and applying signal conditioning. This paper presents various block based adaptive filter structures for obtaining high resolution electroencephalogram (EEG) signals, which estimate the deterministic components of the EEG signal by removing noise. To process these long duration signals, we propose Time domain Block Least Mean Square (TDBLMS) algorithm for brain signal enhancement. In order to improve filtering capability, we introduce normalization in the weight update recursion of TDBLMS, which results TD-B-normalized-least mean square (LMS). To increase accuracy and resolution in the proposed noise cancelers, we implement the time domain cancelers in frequency domain which results frequency domain TDBLMS and FD-B-Normalized-LMS. Finally, we have applied these algorithms on real EEG signals obtained from human using Emotive Epoc EEG recorder and compared their performance with the conventional LMS algorithm. The results show that the performance of the block based algorithms is superior to the LMS counter-parts in terms of signal to noise ratio, convergence rate, excess mean square error, misadjustment, and coherence.

This paper proposes several efficient and less complex signal conditioning algorithms for brain signal enhancement in remote healthcare monitoring applications. In clinical environment during electroencephalogram (EEG) recording, several artifacts encounter and mask tiny features underlying brain wave activity. Especially in remote clinical monitoring, low computational complexity filters are desirable. Hence, in our paper, we propose various efficient and computationally simple adaptive noise cancelers for EEG enhancement. These schemes mostly employ simple addition and shift operations, and achieve considerable speed over the other conventional realizations. We have tested the proposed implementations on real brain waves recorded using emotive EEG system. Our experiments show that the proposed realization gives better performance compared with existing realizations in terms of signal to noise ratio, computational complexity, convergence rate, excess mean square error, misadjustment, and coherence.

In this work we present an electronic board for driving and control of High Intensity Discharge (HID) lamps and Light Emitting Diode (LED) lamps. In the last fifteen years we have seen a big expansion of HID lamps for public lighting utilizations. In these last years in the same way the LED technology is developing in public lighting. For these two reasons we will need more and more an electronic device which can drive both HID and LED lamps. The presented electronic board is able to drive six lamps by means of six outputs reconfigurable for HID or LED lamp; in particular five outputs are dedicated to drive only LED lamps, while one output can be set up for HID or LED lamps by user. In this work particular effort was made for energy saving problems. Additionally a communication module is developed for remote control. The presented board is developed with discrete components; in order to minimise the board’s cost and PCB’s area, it is working in progress another board with a fully-integrated ASIC with all the control logic systems, communication module and power factor correction inside.

The respiratory signal can be accurately evaluated by single-channel electrocardiogram (ECG) processing, as shown in recent literature. Indirect methods to derive the respiratory signal from ECG can benefit from a simultaneous study of both respiratory and cardiac activities. These methods lead to major advantages such as low cost, high efficiency, and continuous noninvasive respiratory monitoring. The aim of this paper is to reconstruct the waveform of the respiratory signal by processing single-channel ECG. To achieve these goals, two techniques of decomposition of the ECG signal into suitable bases of functions are proposed, such as the empirical mode decomposition (EMD) and the wavelet analysis. The results highlight the main differences between them in terms of both theoretical foundations, and performance achieved by applying these algorithms to extract the respiratory waveform shape from single-channel ECG are presented. The results also show that both algorithms are able to reconstruct the respiratory waveform, although the EMD is able to break down the original signal without a preselected basis function, as it is necessary for wavelet decomposition. The EMD outperforms the wavelet approach. Some results on experimental data are presented.

Abstract—Electroencephalogram (EEG) remains the most immediate, simple, and rich source of information for understanding phenomena related to brain electrical activities. It is certainly a source of basic and interesting information to be extracted using specific and appropriate techniques. The most important aspect in processing EEG signals is to use less co-lateral assets and instrumentation in order to carried out a possible diagnosis; this is the approach of early diagnosis. Advanced estimate spectral analysis can reveal new information encompassed in EEG signals by means of specific parameters or indices. The research proposes a multidimensional approach with a combined use of decimated signal diagonalization (DSD) as basis from which it is possible to work by finding appropriate signal windows for revealing expected information and overcoming signal processing limitations encountered in quantitative EEG. Important information, about the state of the patient under observation, must be extracted from calculated DSD bispectrum. For this aim, it is useful to define an assessment index about the dynamic process associated with the analyzed signal. This information is measured by means of entropy, since the degree of order/disorder of the recorded EEG signal will be reflected in the obtained DSD bispectrum.

Breathing plays a key role in human health, especially in terms of fatigue and energy. Oxygen maximum consumption is important for lung. Lung functional performance is characterized by: its ventilator capacity, to bring air (thus O2) into alveoli, and its capacity to transfer O2 and CO2 into and from the pulmonary capillary bed. Hence, the O 2 and CO2 diffusion coefficients as well as the O 2 consumption rate and the CO2 production rate represent the lung performance indices. Both gases have a relevant importance in human body energy expenditure; one of the best instrumentation used for this purpose is ergospirometer. It allows to know the oxygen maximum consumption or the aerobic maximum power VO2, that is the maximum amount of energy (moles of ATP) produced by different substances (carbohydrates, lipids and proteins) per unit of time. This research proposes a signal processing approach for improving the quality of data recovered from the ergospirometric sessions. The ergospirometric is used to evaluate the efficiency of walking for two main reasons: rehabilitation and cough impact on a patient before a surgery. Cough can be an index of muscular and lung problems on human body. Before a surgery, it could be necessary to discriminate some pathologies in presence of cough. The results offered by the research are characterized by a good accuracy and they help clinicians in knowing précised values of oxygen maximum consumption, hence, human body expenditure. A Cosmed K4b2, as ergospirometer, is used in this research.

Aim of this paper was to assess the diagnostic accuracy of a novel ultrasound (US) approach for femoral neck densitometry. A total of 173 female patients (56–75 years) were recruited and all of them underwent a dual X-ray absorptiometry (DXA) of the proximal femur and an US scan of the same anatomical district. Acquired US data were analysed through a novel algorithm that performed a series of spectral and statistical analyses in order to calculate bone mineral density employing an innovative method. Diagnostic accuracy of US investigations was quantitatively assessed through a direct comparison with DXA results. The average diagnostic agreement resulted pretty good (85.55%), with a maximum (88.00%) in correspondence of the youngest investigated patients (56–60 y). Overall, diagnostic accuracy showed only minimal variations with patient age, indicating that the proposed approach has the potential to be effectively employable for osteoporosis diagnosis in the whole considered age interval.

There is a strong demand of implantable devices for diverse applications. Most of them are dedicated and are used for therapy activators, and others for monitoring physiological parameters. The aforementioned demand must be correlated to the utilization of new materials and their reliability during use. This research proposes a design of a complete implantable packaging for encompassing neuro-recording electroencephalogram signals. The content of all system is also illustrated in terms of inner devices and simulated in terms of thermal behavior. Finally the neuro-packaging has been built in polylactic acid and tested in appropriate experimental setup for a specific characterization by measuring temperature, humidity and deformation. However, since PLA is a bioadsorbable polymer, a further design has been performed covering the neuro-packaging with another biocompatible nanomaterial called ultra-high-molecular-weight polyethylene in order to avoid the bioabsorption.

Wind generators (WGs) or wind turbine generator systems (WTGSs) are produced by a lot of manufacturers all over the world. Many of them are outside western countries and even if they deliver appropriate technical certification for complete testing of the WGs, it is sometimes necessary to perform testing either after direct buying or after maintenance operations. A check facility or testing bench is necessary in order to verify the foreseen parameters for the correct operating mode of the electric machine. A particular issue arises when it is necessary to change the WG on the basis of power hence the diameter. This research presents a way of designing and building a testing bench for WGs in a range from 10 kW up to 25 kW. More specifically, the bench has been used for testing a 20 kW WG. An appropriate measurement series has been traced out to verify the operating mode of the WG and the bench. A total harmonic distortion has been also evaluated and Energy Production per Year has been determined by applying the Kriging technique.

Subterranean waters are often polluted by industrial and anthropic effluents that are drained in subsoil. To prevent and control pollution, legislations of different developed countries require an online monitoring measurement, especially for detecting organic solvents (chlorinated and unchlorinated ones). Online measurements include both real-time and no real-time measurements. In general, it is difficult to implement real-time measurements in stricto sensu for online acquisitions on aqueous effluents since they need to be processed by a modeling. This research presents an experimental measurement system based on infrared (IR) spectroscopy for aqueous effluents containing hydrocarbons and capable of displaying excellent values of pollutant concentrations even in instable conditions; the system is able to detect pollutants either in laminar or turbulent flow. The results show the possibility of avoiding the use of “Pitot tube” that is employed to create a stagnation point in order to convert kinetic energy into potential one. This conversion allows the transformation of a turbulent flow in a laminar flow making easy measurement of pollutants included in an aqueous effluent. Obviously, “Pitot tube” is also used for other fluid effluents. The obtained results have been compared with those produced by means of sophisticated IR instrumentation for laboratory applications.

Many researches generally encompass experimental and dedicated plants for scientific purposes. They display outputs with limited uses. On the contrary, measurements performed on real plants could be very significant because they reflect the reality and the milieu where the plants are located. This paper illustrates a method of characterizing a real plant based on a pre-characterization of a sample of photovoltaic module and on an “ex-post” characterization of a general plant where the same photovoltaic module is used. The photovoltaic pre-characterization is an outdoor one performed in several months in order to know the response of the module due to solar exposure. Outdoor characterizations are necessary beside flash test with sun simulator. Power matrixes are a useful method for predicting and verifying the performances of a photovoltaic module. The research offers the opportunity to calculate the efficiency and the energy production for a 50 kWp plant.

This paper describes the operation and calibration modes for an experimental monitoring system of a photovoltaic (PV) plant with greater emphasis on the Maximum Power Point Tracker (MPPT). In order to optimize the energy production and therefore the economic convenience, it is very important that MPPT device works properly and that the measurement of PV electrical quantities is not affected by excessive errors. In this regard, the operation and related features of the MPPT3000, multifunction testing device used in the proposed measuring chain, are described in detail. More specifically, in addition to multi-parameter data acquisition system for PV plants, an experimental setup for calibration and operation check of MPPT devices has been developed, during their normal operation directly connected to the PV panels. From the collected data, it is observed a low measurement error (less than 1,5%) and verified the correct functioning of MPPT3000 devices, able to maximize the PV produced power even in case of abrupt variations in the solar irradiation level.

An interesting technique, used in nuclear magnetic resonance data processing for tackling FFT limitations, FDM (Filter Diagonalization Method), can be used, by considering the pipeline, especially complex configurations, as a vascular apparatus with arteries, veins, capillaries, etc.. The thrombosis, for human vascular apparatus, that might be occur, can be considered as a leakage for the complex pipeline. The choice of eigenvalues in FDM or in Spectra-based techniques is a key issue in recovering the solution of the main equation (for FDM) or frequency domain transformation (for FFT) in order to determine the accuracy in detecting leaks in pipelines. This paper illustrates a robust technique in assessing the problem of eigenvalues making it less experimental and more analytical using Tikhonov – based regularization techniques.

The paper illustrates findings regarding the design and realization of flexible GUI (graphical user interface) to be used on current and past instrumentation, especially for instruments using Michelson interferometer. It was designed for an affordable spectrophotometer constructed in the laboratory of Measurement and Instrumentation of the Department of Innovating Engineering/University of Salento. As it is well-known, in the milieu of scientists and researchers working in the area of Fourier Transform InfraRed (FTIR) instrument, the quality of interferogram is a key issue on detecting the pollutant or material under test by means of specific frequency.

Development of artificial or robotic skin implementing optical touch sensors is considered. Integration of polymeric optical fibers in elastomeric nanocomposite matrix biomimicking human skin specification as flexibility and largearea sensing is presented. The sensing principle is directly linked to deformation of the polymeric optical fiber itself under applied weight solicitations.

An adaptive initialization method was developed to produce fully automatic processing frameworks based on graph-cut and gradient flow active contour algorithms. This method was applied to abdominal Computed Tomography (CT) images for segmentation of liver tissue and hepatic tumors. Twenty-five anonymized datasets were randomly collected from several radiology centres without specific request on acquisition parameter settings nor patient clinical situation as inclusion criteria. Resulting automatic segmentations of liver tissue and tumors were compared to their reference standard delineations manually performed by a specialist.

In this paper, the design and testing of a PC-interfaced PIC-based control unit used to manage an absorption spectrophotometer, employing a white LED as light source, are described. LED technology allows to perform the absorption measurements reducing the analyte temperature variations and thus noise generation, which occur if a Xenon light source, usually employed, is used; also thanks to LED technology, the system results low cost, easy to use and with a low power consumption. The realized spectrophotometer can be used for atmospheric and industrial pollutant detection or for indoor air monitoring (e.g., in hospital rooms), being able to detect particulate matter, pesticides, volatile organic compounds as well as pollution produced by heavy metals. The realized system manages the different required functionalities, such as acquisition and processing, via firmware, of raw data provided by sensors, actuation of mechanical devices (stepper motor and solenoid valve), and synchronizing and controlling the data exchange between hardware sections, microcontroller, and PC. Both hardware and software sections were designed carrying out the appropriate tests to verify their proper operation. Results confirm the correct system functioning and interaction, via PC terminal, between user and the realized control unit.

The aim of the present work was to demonstrate the possibility of selective detection of nanoparticle contrast agents (NPCAs) on diagnostic echographic images by exploiting the second harmonic component they introduce in the spectra of corresponding ultrasound signals, as a consequence of nonlinear distortion during ultrasound propagation. We employed silica nanospheres (SiNSs) of variable diameter (160 nm, 330 nm, and 660 nm) dispersed in different volume concentrations (range 0.07–0.8%) in agarose gel samples that were automatically scanned through a digital ecograph using narrow-band ultrasound pulses at 6.6 MHz and variable mechanical index (MI range 0.2–0.6).

Visual Evoked Potentials (VEPs) are referred to electrical potentials due to brief visual stimuli which can be recorded from scalp overlying visual cortex. A way to measure VEPs is through encephalogram (EEG). VEPs are very important because they can quantify functional integrity of the optic pathway. Their study allows to detect abnormalities that affect the visual pathways or visual cortex in the brain, and so methods that permit to identify VEPs components in EEG signals must be defined. However, the background activity measured from EEG hides VEPs components because they have a low voltage. So it is necessary to define a robust method to extract features, which best describe these potentials of interest. In this work Empirical Mode Decomposition (EMD) method is used to separate the EEG components and to detect VEPs. EMD decomposes a signal into components named Intrinsic Mode Functions (IFM). The results, obtained from the study of EEG records of a normal person, suggest that IMFs may be used to determine VEPs in EEG and to obtain important information related to brain activity by a time and frequency analysis of IMF components. It is well comparable with the known Wavelet Transform method, but it is characterized from a greater simplicity of implementation because the basis used in the analysis is generated by the same analyzed signal.

Transportation of liquids is a primary aspect of human beings’ life. The most important infrastructure used accordingly is pipeline. It serves as asset for transporting different liquids and strategic ones. The latter goods are for example: chemical substances, oil, gas and water. So, it is necessary to monitor such infrastructures by means of specific tools. Leakage detection methods are used to reveal liquid leaks in pipelines for many applications, namely, waterworks, oil pipelines, industry heat exchangers, etc.. The configuration of pipeline is a key issue because it impacts on the effectiveness of the method to be used and, consequently, on the results to be counterchecked. This research illustrated an improvement of impedance method for zigzag pipeline by carrying out an experimental frequency analysis that has been compared with other methods based on frequency response. Hence, impedance method is generally used for simple (straight) pipeline configurations because complicated pipelines with many curves introduce difficulties and major uncertainties in the calculation of characteristic impedance and in the statement of boundary conditions. The paper illustrates the case of a water pipeline where the leakage is acquired thanks to pressure transducers.

Seizure detection and monitoring are generally carried out by electroencephalogram (EEG) instrumentation with electrodes located on the scalp. For 24 h monitoring, it is possible to use an EEG recorder brought by the patient positioned on a bed or normally moving in the hospital and doing everything. This paper presents findings in the design of a neurocase for hosting a neurorecording system and its conformity to IEEE 24451 limited to wireless aspects related to data transmission. The recording system also provides for suppressing spikes and surges in EEG signals since these signals can be considered as a series of voltages with a relationship with space and time. Nanotechnology solutions relative to materials have been illustrated. Moreover, a stress simulation has been also performed in order to verify the sustainability of the design. The studied system considers the implementation of the neurorecording system including, design of circuital electronic components and thermoelectric power board supply, and 3-D package.

Non-Linear Polynomial Filters (NPF) consists of a framework of weighted coefficients of low-pass and high pass filters. This paper explores the applicability of NPF for the contrast enhancement of breast tumors in mammograms. NPF algorithm in the present work has been improved to provide controlled background suppression during the mammogram enhancement. This is because, in the process to control overshoots and visualization of tumor margins; the uncontrolled background suppression may lead to loss of finer details in the vicinity of the lesion region. Simulation results have shown that the response of the proposed NPF has been reasonably good on mammograms containing tumors embedded in different types of background tissues.

Computer aided detection of mammographic masses can be improved to a greater extent employing non-linear filters for image enhancement. The present work proposes a truncated Volterra filter combination to provide contrast enhancement as well as texture based processing of masses in digital mammograms. Noteworthy improvement in visualization of masses has been observed in the simulation results carried out on cases from DDSM database. The improved performance of the proposed filtering approach is well supported with calculated values of objective evaluation parameters.

Recent literature has reported increasing interest in using contrast agents for ultrasound imaging, in the form of shelled gas microbubbles, for innovative advanced purposes such as noninvasive targeted imaging and drug delivery. Effectiveness of such agents is time-dependent and is determined by microbubble dissolution behavior, a complex phenomenon whose knowledge is still limited. In the present study, we monitored the microbubbles of an experimental phospholipid-shelled perfluorobutane contrast agent through time-scheduled size distribution measurements. The diameter-time curve we obtained for shelled perfluorobutane microbubbles showed a rapid diameter increment up to about 1.4 times the initial value, followed by a slow decrement towards bubble disappearance. This behavior is qualitatively similar to the one theoretically predicted by Kabalnov’s model for unshelled bubbles, with an extended lifetime due to shell effect. Kabalnov’s model, devised for spontaneous dissolution of unshelled microbubbles, was consequently modified in order to get a proper prediction of experimental results also in the case of encapsulated bubbles. A theoretical diameter-time curve was then derived from this new model and fitted to our experimental data points, to estimate microbubble surface tension and to determine the value of an empirical parameter accounting for the shell effect. The proposed model has the potential to predict the dissolution behavior of all kinds of microbubble contrast agents for ultrasound imaging and the adopted experimental methodology represents a new and simple way to estimate microbubble surface tension, essential also for predicting microbubble oscillation performance.

Incremental learning could be really useful for fault detection and anticipation in non-destructive testing and evaluation. The real-time monitoring could be proficiently exploited when an early warning system is required for the human safety. This is the case of aeronautic transportation of persons and goods. Here, an automated neural-based system for defect detection in aeronautic composites is proposed. The entire system consists of a stand-alone defect classifier based on a Bayesian neural network (BNN) combined with advantages of Very Large Scale Integration (VLSI )implementation. Exploiting a parallel implementation is worthwhile when high computational speed, special operating conditions, portability, limited physical size, low-power dissipation and reliability are required. This study shows how hardware-based neural network can increase processing speed and defect identification rate. Secondary random access memory-based field programmable gate arrays represent a suitable platform to realise these models, since their re-programmability can rapidly change the parameters of the network if a new training is needed. With the hardware-based BNN, 100% of delamination bottom/top, inclusion bottom/middle/top, porosity and 99.6% of delamination middle were correctly identified. The achieved results highlight the efficient design of the hardware network, obtained also using a new circuit to compute the activation function of neurons.

Indoor air quality requirements are a hard constraint for workers’ rooms and close locations, and many legislations require frequently measurements for safety. In presence of possible leaks and room climate deterioration, it is required a permanent and online measurements by means of appropriate instrumentation. For hospital rooms, and in particular for surgery rooms, air monitoring of gaseous components is mandatory. In general, there are two main techniques: photo-acoustic spectroscopy (PAS) and chromatography. The first is a portable instrument for quick monitoring even if it is accurate. The latter is not a portable instrument but it is for sensitive and accurate measurements. The paper proposes an architecture of measurements, based on LED spectroscopy for monitoring surgery rooms. In particular, it illustrates the control system based on pulse width modulation. Since the architecture is not PAS and it is not based on chromatography, it could not monitor a large number of pollutants. But conversely, it would be able to measure with high accuracy a small number of chemical species included in pollutants. We only report a deep analysis and experimental activities of control systems.

This work deals with a useful joined application of Kalman filter and Kriging technique for a continuous and accurate environmental monitoring in a disaster scenario or generally in a critical event, necessary to assure an efficient and timely risk management. A suitable Decision Support Systems (DSS) is proposed to provide assistance for the “early warning" of a critical situation, so to improve the "first response" to the happened event. Using the proposed modelling techniques in data environmental analysis permits both the characterization and validation of all measured big data coming from a suitable Space- Aided Distributed Sensor System (SADSS). In particular, the proposed technique is able also to predict the values of the monitored environmental parameters, so it results a very useful analysis tool, especially when there are many missing ,erroneous or invalid data.

Detection of leakages in pipelines is a matter of continuous research because of the basic importance for a waterworks system is finding the point of the pipeline where a leak is located and − in some cases − a nature of the leak. There are specific difficulties in finding leaks by using spectral analysis techniques like FFT (Fast Fourier Transform), STFT (Short Term Fourier Transform), etc. These difficulties arise especially in complicated pipeline configurations, e.g. a zigzag one. This research focuses on the results of a new algorithm based on FFT and comparing them with a developed STFT technique. Even if other techniques are used, they are costly and difficult to be managed. Moreover, a constraint in the leak detection is the pipeline diameter because it influences accuracy of the adopted algorithm. FFT and STFT are not fully adequate for complex configurations dealt with in this paper, since they produce ill-posed problems with an increasing uncertainty. Therefore, an improved Tikhonov technique has been implemented to reinforce FFT and STFT for complex configurations of pipelines. Hence, the proposed algorithm overcomes the aforementioned difficulties due to applying a linear algebraic approach.

Recording neural signal from a living human body is a complex task and it is an important research issues for neuroscientists and researchers in biomedical engineering. The major issue to over- come in the design of a system that is aimed at being implant into the human body is having a low power consumption, low noise circuit and small dimension to minimize tissue damage. In this paper, specific issues of the most important part of such a neural acquisition system are presented; in particular, the design of a low-power amplifier, for a fully implantable neural recording system, is described. The amplifier uses a differential pair as input stage. Given that neural amplifiers must include differen- tial input pair to achieve a high common-mode ratio rejection (CMRR). T

HRV (Heart Rate Variability) is an indicator that can be related to different human organs and systems: breathing, heart, brain, pulmonary system, etc. In cardiac clinic, physical exertion can be pre-assessed thanks to HR (Heart Rate) response using appropriate tests to rule out eventual cardiac dysfunction prior to undergo patient to further exams, surgical operations and rehabilitation activities. HR assessment must determine the capability of patient to continue exertion up to a certain level without having angina pain symptoms and brain dysfunctions. The variability of HR is a marker of dynamic load because it is sensitive and responsive to acute stress. Moreover it is also a marker of a cumulative wear and tear because it declines with advancing age. In this paper we propose combined measurements of EEG-Ergospirometry and ECG for patient’s cardio-pulmonary condition assessment for allowing doctors to make a decision on rehabilitation or surgical operation for people suspected of suffering from epilepsy seizures. Measurements assessed using frequency domain parameters have permitted the determination of low and high frequencies that are related to sympathetic and parasympathetic activities respectively.

Diagnosis by imaging is one of the most important findings in biomedical imaging because it allows not only the diagnosing of a specific pathology but to perform online and offline surgical operations using imaging as it is noticed in interventional radiology. This paper illustrates the use Hough transform in identifying pathological structures included in CT (Computer Tomography) and HRCT (High Resolution Computer Tomography) images related to patients suffering from lung disease. These abnormal areas appear as bulges of the trophic vessels and they are similar to circular structures with level of lighter gray near to white. Circular Hough transform (CHT) identifies regions with a circular shape. However, a metrics is defined in order to understand if the pointed out area has a pathological morphology. CHT is used here for helping to detect possible events of indolent tumors or undetermined significance pathologies for lung apparatus. For this aim, we use entropy approach with CHT because it measures the scatter of the directional elements in an image. In fact a high entropy value is related to areas with a strong contrast in grayscale, and abnormalities in the image are present as a set of points with more lighter than the dark background. The results have shown, by means of an accuracy true table, rendering a comparison between clinicians’diagnosis and CHT detection, it is possible to indicate, with a better accuracy, potential areas of undetermined significance pathologies. Finally, a receiver operational curve (ROC) is used as an accuracy index for evaluating the positive impact of entropy on diagnosis.

The MRI signal, in terms of amplitude and phase, was simulated for susceptibility-based brain venography (BV) without and with administration of contrast agent (CA) under standard clinical parameters and field magnitude of 1.5 T (Tesla). Then, in order to compare the enhancement due to CA and that one obtained through susceptibility weighted magnitude image (SWI) processing, exploiting only the intrinsic magnetic susceptibility differences between blood and surrounding tissue, the simulated signal was processed with a well-known phase masking technique to increase signal differences as a function of tissue type, thus generating better image contrast. The simulation was carried out adopting a well-established bi-compartmental model, described by a set of analytical formulas. The based simulated signal was processed with the SWI technique considering a conventional masking procedure available in literature and a different masking method designed and described in our previous work. The resulting enhanced signals in terms of image contrast obtained with these two different phase masking procedures and with the administration of conventional CA were compared. The new designed mask (DM), compared with the conventional one, allows to get better results in terms of image contrast for all blood volume fractions considered in the simulation. Additionally, better results can be obtained also with respect to the case of CA administration only. Results from model simulations demonstrate the effectiveness of masking techniques to improve image contrast regarding all vessel sizes of clinical interest in the high resolution susceptibility-based brain venography possibly avoiding, in some cases, the need for CA administration.

The glucose-insulin regulatory system is characterized by a dynamic that includes some delays in the processes acting inside. This aspect is basically a key issue for people using open-loop or closed-loop artificial pancreas. This paper will focus on the influence that the above delays have in the latest mathematical models, and in particular on those resulting from the delivering of insulin at the subcutaneous level. Such models analyze the fluctuations in the concentration of intravenous glucose and insulin in a system regulated by an open loop or a closed loop (Artificial Pancreas). The paper introduces new features in terms of mathematical aspects to solve the problem of the actual delivering of glucose by using Retarded Differential Equations and Impulsive Differential Equations (in cases of micro-infusion), that analytically describe these dynamics.

A cavitating two-phase flow of water in a pipe with area shrinkage was experimentally investigated, acquiring at high sampling rate pressure signals and images of the cavitating flow-field. The fluctuations of the pressure measurements in the internal orifice and of the intensity level of the images' pixels were decomposed using the wavelet transform to analyze the frequency distribution of the signals energy with respect to the flow behavior and to highlight the influence of temperature on the phenomena under investigation. It was shown that the energy content at each frequency band of the acquired signals is well related to cavitation flow-field behavior.

Halloysite nanotubes (HNTs) are nanomaterials composed of double layered aluminosilicate minerals characterized by a wide range of medical applications. Nonetheless, systematic investigations of their imaging potential are still poorly documented. This paper shows a parametric assessment of the effectiveness of HNTs as scatterers for safe ultrasound (US)-based molecular imaging. Quantitative evaluation of average signal enhancement produced by HNTs with varying set up configuration was performed. The influence of different levels of power (20%, 50%, and 80%) of the signal emitted by clinical equipment was determined, to assess the efficacy of different HNT concentrations (1.5, 3, and 5 mg/mL) at conventional ultrasonic frequencies (5.7–7 MHz), even in case of specific limitation regarding US mechanical interaction with target tissues. Different samples of HNT containing agarose gel were imaged through a commercially available echographic system and acquired data were processed through a dedicated prototypal platform to extract the average ultrasonic signal amplitude. The rate of signal enhancement achieved by different concentration values was quantified and the contribution of frequency increment was separately evaluated. Despite influencing the level of mechanical excitation on HNTs and tissues, our results demonstrated how increasing the power of the emitted signal negatively affected the measured backscatter.

Multimodal medical image fusion is effectuated to minimize the redundancy while augmenting the necessary information from the input images acquired using different medical imaging sensors. The sole aim is to yield a single fused image, which could be more informative for an efficient clinical analysis. This paper presents a two-stage multimodal fusion framework using the cascaded combination of stationary wavelet transform (SWT) and non sub-sampled Contourlet transform (NSCT) domains for images acquired using two distinct medical imaging sensor modalities (i.e., magnetic resonance imaging and computed tomography scan). The major advantage of using a cascaded combination of SWT and NSCT is to improve upon the shift variance, directionality, and phase information in the finally fused image. The first stage employs a principal component analysis algorithm in SWT domain to minimize the redundancy. Maximum fusion rule is then applied in NSCT domain at second stage to enhance the contrast of the diagnostic features. A quantitative analysis of fused images is carried out using dedicated fusion metrics. The fusion responses of the proposed approach are also compared with other state-of-the-art fusion approaches; depicting the superiority of the obtained fusion results.

Computer based diagnosis of Alzheimer's disease can be performed by dint of the analysis of the functional and structural changes in the brain. Multispectral image fusion deliberates upon fusion of the complementary information while discarding the surplus information to achieve a solitary image which encloses both spatial and spectral details. This paper presents a Non-Sub-sampled Contourlet Transform (NSCT) based multispectral image fusion model for computer-aided diagnosis of Alzheimer's disease. The proposed fusion methodology involves color transformation of the input multispectral image. The multispectral image in YIQ color space is decomposed using NSCT followed by dimensionality reduction using modified Principal Component Analysis algorithm on the low frequency coefficients. Further, the high frequency coefficients are enhanced using non-linear enhancement function. Two different fusion rules are then applied to the low-pass and high-pass sub-bands: Phase congruency is applied to low frequency coefficients and a combination of directive contrast and normalized Shannon entropy is applied to high frequency coefficients. The superiority of the fusion response is depicted by the comparisons made with the other state-of-the-art fusion approaches (in terms of various fusion metrics).

The detection of neurophysiological features by means of electroencephalogram (EEG) is one of the most recurrent medical exams to be performed on human beings. As it stands, EEG trials are not always sufficient to deliver a clear and precise diagnosis for much pathology. Hence, it must be integrated with other exams. However, we can use all additional instrumental exams to improve the quality of the diagnosis because there are other constraints, namely, financial, medical, and individual. This paper presents an original implementation of EEG signal processing using filter diagonalization method to build a bispectrum and contour representation to discover possible abnormalities hidden in the signal for aided-diagnosis. Two different EEG signals are used for this scope. EEG signals are acquired simultaneously with electrocardiograms (ECG) and ergospirometric ones. ECG signals are also processed along with EEGs. A comparison is made with high order spectra approach. All experimental data regarding EEG, ECG, and ergospirometry are acquired during suspected-patient walking along a path of ∼32 m for verifying the impact of fatigue on neurophysiological processes and vice versa.

The electromagnetic characterization of piezoelectric micro-needle antenna sensors for fully non-invasive detection of cancer-related anomalies of the skin is presented. To this end, a full-wave finite-difference time-domain procedure is adopted to analyze the performance of the considered class of devices in terms of circuital characteristics and near-field radiation properties as a function of the curvature radius of the relevant sensing probe. This analysis is, in turn, useful to gain a physical insight into the processes which affect the behavior of the structure and, hence, the accuracy in the detection of possible malignant lesions of the skin. In particular, by using the mentioned modeling approach, an extensive parametric study is carried out to analyze the effect produced on the sensor response by variations of the complex permittivity of the skin due to the presence of anomalous cells and, in this way, obtain useful discrimination diagrams for the heuristic evaluation of the exposure level to the cancer risk.

Multispectral image fusion deliberates upon fusion of the complementary information while discarding the surplus information to achieve a solitary image which encloses both spatial and spectral details. This paper presents a Non-subsampled Contourlet Transform (NSCT) based multispectral image fusion model which integrates Principal Component Analysis (PCA), Phase congruency, directive contrast and entropy. The proposed methodology involves color transformation of input multispectral image. Two different fusion rules are then applied to the high-pass and low-pass subbands: Phase congruency is applied to low frequency coefficients and a combination of directive contrast and normalized Shannon entropy is applied to high frequency coefficients. The superiority of the fusion response is depicted by the comparisons made with the other state-of-the-art fusion approaches (in terms of various fusion metrics).

Exploration of the world of micro-/nanotechnology, thanks to recent developments, has given an added value to optoelectronics. The main goal of this book is to provide tools and methods for applied research in this specific field. All topics will be connected with each other in the book in order to guide the researcher in the creation of a research platform based on available facilities. A reliable approach to creating an operating platform of research is to combine technological know-how, design, and experimental aspects. All fields communicate by feedback systems. Design represents the first step and starts after a preliminary study of the given device considering the available technology and the final application. Fabrication will introduce new techniques on the basis of innovative materials, and technological optimization will guarantee a standard procedure regarding the final production of the device. Finally, experimentation on and accurate analysis of the measured outputs will round out quality control of the device. Feedback systems are applied to implement devices with well-defined input/output characteristics. The book will provide approaches to the implementation of stable optoelectronic devices where the stability can be verified by the simultaneous agreement of experimental, theoretical, and numerical results. In particular, theoretical tools will define the application and a proper layout of the device, in addition to the numerical results, will provide solutions including the prediction of fabrication errors (analysis of error margins) such as geometrical resolutions and layer thicknesses. Finally, experimental setups and signal processing will complete the research activity. The proposed research platform procedures can be applied to devices such as electromagnetic waveguides, photonic crystals, microelectromechanical systems (MEMS), optoelectronic systems, and applied electronics.

The release of petroleum liquids in water, such as marine, riverine, and lacustrine basins, is a matter of concern that shoves authorities and experts to adopt technical approaches for preventing damages, monitoring oil content in water and cleaning up environmental aqueous matrices. In this paper, a dedicated system for oil spill detection is presented. The proposed system consists of an optical fiber sensor and an image processing unit useful to steer and optimize the measurement process. An optical fiber-based antenna sensor is used to detect the oil concentration in water. The sensor consists of two slanted optical probes acting as transmitter and receiver, respectively. Both probes are completely immersed into water being analyzed. The sensing approach is based on the measurement of the light coupling level affected by the reflectivity of the oil layer floating on the water surface. The experimental measurement of different types of oil is performed to assess the sensitivity of the developed system. Special attention is put on the image postelaboration useful to derive the characteristics of the oil distribution on the water surface. In this respect, two different image processing techniques are considered: the first one is based on a suitable energy-minimizing spline fitting procedure subject to external constraint forces, whereas a judicious use of the Hough transform is made in the second one.

Aim of this work is to describe the electronic driving system and the entire experimental setup realized in order to photo-ignite a gaseous fuel/air mixture enriched with Multi-wall carbon nanotubes (MWCNTs) with added metal impurities, makers of photo-ignition process. The realized electronic boards present different features such as variable flash brightness, pulse duration and high flash rate, allowing to fully characterize the combustion process under investigation. Varying the Xenon light source’s parameters, the needed light energy/power to ignite MWCNT/Fe mixtures with different weight ratio was found. Experimental results show that lower energy thresholds are required with increasing MWCNTs amount respect to ferrocene. Then, the photo-induced ignition of CNTs mixed with nanoparticles was used in a properly realized experimental setup for triggering the combustion of different CNT-enriched air/fuel mixtures (CH4, Liquid Propane and H2). The combustion tests triggered by MWCNTs/ferrocene photo-ignition show better performances (shorter ignition delays, higher peak pressure values and a higher fuel burning rate), for all used gaseous fuels and all tested air / fuel ratios, compared with those obtained by using a traditional spark plug.

Flow measurements, in particular water flow pressures, are topics of great interest since the issue has an important impact on our daily life. Flow measurements can be performed by means of different devices and instruments. But, in some cases, instead of measuring in direct way the flow, it could be necessary to have pressure by means of dedicated sensors and transducers. In case of hazardous liquids, as suggested by anti-explosion liquids, any measure must be adopted to guarantee measurement and conveying in a safety conditions. So a contactless flow measurement could be a good solution thanks to the use of videomeasurements. The paper points out an experimental design and construction of a realtime system for measuring frames of water flow to be composed for determining velocity and changes. This system is suitable for pipeline grids and waterworks where it is difficult to install appropriate devices for the purposes.

Air monitoring plays a key role in measuring atmospheric pollutant concentrations in different locations of the same region or of distinct ones. This paper describes results of processing volatile organic compounds (VOCs) using an interesting genetic algorithm and also processing benzo(a)pyrene(BaP) data. Both pollutants are collected from a network of sensors located in an industrial area and in a city respectively.

Pulmonary oedema is a life-threatening disease that requires special attention in the area of research and clinical diagnosis. Computer-based techniques are rarely used to quantify the intrathoracic fluid volume (IFV) for diagnostic purposes. This paper discusses a software program developed to detect and diagnose pulmonary oedema using LabVIEW. The software runs on anthropometric dimensions and physiological parameters, mainly transthoracic electrical impedance (TEI). This technique is accurate and faster than existing manual techniques. The LabVIEW software was used to compute the parameters required to quantify IFV. An equation relating per cent control and IFV was obtained. The results of predicted TEI and measured TEI were compared with previously reported data to validate the developed program. It was found that the predicted values of TEI obtained from the computer-based technique were much closer to the measured values of TEI. Six new subjects were enrolled to measure and predict transthoracic impedance and hence to quantify IFV. A similar difference was also observed in the measured and predicted values of TEI for the new subjects.

Electronic knee is new opportunity for people having one or two amputated legs. Differently from traditional prosthesis, electronic knee is the first one to be driven by a microprocessor with an hydraulic control for the static and dynamic steps. It is become a positive key issue for the comfort and movement freedom for transfemoral amputed people. Despite the use of microprocessor, many difficulties arise during connection on human being and from calibration. This paper presents a technique of predicting calibration operations and stabilometric measurements in order to allow a better connection between electronic knee and the amputated patient. The technique is based on a novel genetic doped algorithm capable of producing the postural positions and correct calibration.

This special issue titled “New Developments and Applications in Sensing Technology” in the book series of “Lecture Notes in Electrical Engineering” contains invited papers from renowned experts working in the field of sensing technology. A total of 17 chapters describe the advancement in the area of smart sensors and sensor networks design, measurement techniques, signal processing, and efficient algorithms in recent times.

Monitoring networks are essential tools for the effective management of vulnerable or limited environmental resources. Cost and logistics constraints often suggest to reduce the number of monitoring sites while minimizing the loss of information determined by these changes. The problem can be rigorously addressed through the optimization of one or more objective functions that represent the managerial goals associated to the network. However, the use of objective functions is based on assumptions that in practical cases can be inaccurate. To overcome this problem, we have developed a retrospective analysis procedure that validates the degree of acceptability of the optimal reduced configuration at a local and global level. The procedure has been applied to a case study in Apulia, Italy, finding that the optimal reduced network was unable to recover the measured values of the monitored parameter of two discarded locations, making it unable to accomplish its monitoring goals.

Aim of this work was to perform simulated measurements of the magnetic behavior of a novel class of bimodal nanosized contrast agents (CAs), made of a silica core covered by smaller superparamagnetic nanoparticles (NPs) and designed to be detected through both ultrasound and magnetic resonance imaging (MRI), in order to compare their performance in terms of MRI signal enhancement with that of the superparamagnetic NPs alone. The considered bimodal nanocomposites consisted of 330-nm silica nanospheres covered by either superparamagnetic iron oxide NPs or dumbbell-like FePt-IO nanocrystals. We simulated the MRI signal generated by each of the considered CAs during a brain venography in standard clinical conditions. Quantitative assessments of signal enhancement were carried out as a function of the main model parameters. The performed numerical simulations showed that the magnetic response of the novel nanocomposites was similar or better compared to that of the superparamagnetic NPs alone for echo times longer than 20 ms, leading to an easier detection of smaller vessels. Obtained results suggest that the bimodal NPs have an exciting potential for the development of innovative clinical protocols for multimodal imaging, combining quantitative measurements of cerebral blood flow and targeted molecular imaging of specific diseases.

The development of electrical trains has been requiring advances in the construction of components that are usually supervised by smart systems. Safety aspects are given to redundant capabilities of the new equipments to overcome failures. The new components must obey to internationally recognized standards. For that reason, the electrical supply and the mechanical contacts between train and rails are the key issuesto be supervised. The current ISO/IEC/IEEE 1451 is basically helpful for the electrical supply by means of the dedicated transducers. In this paper, a new voltage/current transducer for the train supply is shown and analyzed paying the attention on its reliability, availability, maintainability, and safety approach performed in an innovative way with a specific reference to guidelines of the ISO/IEC/IEEE 1451. At this aim, an extensive theoretical and experimental analysis has been performed using dedicated algorithms developed for the purposes of this research considering reliability aspects according to commercial and defense industry needs.

Solar energy is available almost everywhere but in some circumstances and locations it is necessary to optimize the dimensions of plants, avoiding large surface of PV modules installed on the ground, preferring modules located on solar trackers to increase the efficiency by at least 35%. However, even in this latter case, there are still margins for increasing the PV plant’s efficiency. For this purpose, we have developed and tested an electronic system for controlling and driving bi-axial solar trackers of a PV plant, managed by a PC software application with a user-friendly graphical interface. The designed software is able to calculate the sun circadian orbit and consequently to move the solar panels in order to maintain the panel’s surface always perpendicular to solar rays, improving the efficiency in energy production. In particular, the object of this work consists in optimizing an existing, designed by us, fully-hardware setup which didn’t allow a simple and rapid plant management, completely replacing the Master electronic board with the designed software, so as to be able to communicate, by means of PC’s RS232 serial port, with Slave electronic boards for tracker motors driving.

The goal of array processing is to gather information from propagating radio-wave signals, as their Direction Of Arrival (DOA). The estimation of the DOA can be carried out by extracting the information of interest from the steering vector relevant to the adopted antenna sensor array. Such task can be accomplished in a number of different ways. However, in source estimation problems, it is essential to make use of a processing algorithm which feature not only good accuracy under ideal working conditions, but also robustness against non-idealities such as noise, limitations in the amount of collectible data, correlation between the sources, and modeling errors. In this work particular attention is devoted to spectrum estimation approaches based on sparsity. Conventional algorithms based on Beamforming fail wherein the radio sources are not within Rayleigh resolution range which is a function of the number of sensors and the dimension of the array. DOA estimation techniques such as MUSIC (MUltiple Signal Classifications) allow having a larger spatial resolution compared to Beamforming-based procedures, but if the sources are very close and the Signal to Noise Ratio (SNR) level is low, the resolution turns to be low as well. A better resolution can be obtained by exploiting sparsity: if the number of sources is small, the power spectrum of the signal with respect to the location is sparse. In this way, sparsity can enhance the accuracy of the estimation. In this paper, an estimation procedure based on the sparsity of the radio signals and useful to improve the conventional MUSIC method is presented and analyzed. The sparsity level is set in order to focus the signal energy only along the actual direction of arrival. The obtained numerical results have shown an improvement of the spatial resolution as well as a reduced error in DOA estimation with respect to conventional techniques.

Synthetic Aperture Radar (SAR) is a tool of coherent imagery utilized for meteorological and astronomical purposes. But, these images are contaminated with speckle noise which degrades the image quality and automatic information extraction becomes difficult. This paper presents an improved filtering technique which combines the Wavelets and proposed Anisotropic Diffusion (AD) filter for despeckling SAR images. The speckled image is initially decomposed into sub-bands using 2D-Discrete Wavelet Transform (2D-DWT) followed by application of modified AD filter. The diffusion coefficient presented in this modified AD filter consists of a combination of gradient and Laplacian operators. The spatial variation of this diffusion coefficient occurs in such a way that it prefers forward diffusion to backward diffusion resulting in effective reconstruction of structural content and detection of weak edges. The filtered sub-bands are then reconstructed after soft thresholding. Based on the simulation results as well as the values of image quality metrics; filtered SAR images obtained by the proposed speckle suppression methodology can be claimed better in comparison to other recent works.

Spirometry deals with finding and predicting respiratory system pathologies through instrumentation that mainly carries out measurements on the volume and the air flow expired from lungs. In many cases, during spirometric and pneumotachographic trials in hospital, there are people who are not able to begin or to complete their tests because of diverse difficulties due to presumable pathologies. Hence, these trials may be lost if they are not recovered and post-processed in adequately, at least to display the expiration trend and step. This work presents rapid techniques of helping physiopathologists to extract information from a non-complete expiration curve as spirometric post-processing. The two techniques are based on WOB (Work Of Breath) and CGA (Controlled Genetic Algorithm) respectively. A comparison is performed between the two techniques; the WOB is calculated by assuming classes of fixed resistance R according to the age, to the sex, to the previous pathologies, etc. while the CGA technique provides a strict monitoring of GA steps in order to reduce uncertainty of final results.

Environmental monitoring networks are essential for understanding environmental dynamics of a given region if dedicated sensors and sensing systems are used. They are sensitive to external and internal fluctuations that can produce loss of capabilities of correct detection and retrieval of desired environmental quantities. Recent findings, in terms of smart grids and Internet of Things, have allowed the implementation of testing and characterization of such networks using new computation and simulation platforms that can deliver excellent results. This paper presents a procedure for evaluating the informative content of an actual network and a new approach of implementing a characterization of hydrological and environmental networks of sensors to be “stress-tested.” The proposed characterization is implemented by using Ptolemy II tool. It is an open-source simulation and modeling tool intended for experimenting with system design techniques, particularly those that involve combinations of different types of models. It is user friendly as described in final comments. This paper does not deal with power systems, intended as smart grid, but with smart sensors networking.

Background. Tactile interfaces that stimulate the plantar surface with vibrations could represent a step forward toward the development of wearable, inconspicuous, unobtrusive, and inexpensive assistive devices for people with visual impairments. Objective. To study how people understand information through their feet and to maximize the capabilities of tactile-foot perception for assisting human navigation. Methods. Based on the physiology of the plantar surface, three prototypes of electronic tactile interfaces for the foot have been developed. With important technological improvements between them, all three prototypes essentially consist of a set of vibrating actuators embedded in a foam shoe-insole. Perceptual experiments involving direction recognition and real-time navigation in space were conducted with a total of 60 voluntary subjects. Results. The developed prototypes demonstrated that they are capable of transmitting tactile information that is easy and fast to understand. Average direction recognition rates were 76%, 88.3%, and 94.2% for subjects wearing the first, second, and third prototype, respectively. Exhibiting significant advances in tactile-foot stimulation, the third prototype was evaluated in navigation tasks. Results show that subjects were capable of following directional instructions useful for navigating spaces. Conclusion. Footwear providing tactile stimulation can be considered for assisting the navigation of people with visual impairments.

Photovoltaic (PV) characterization is a topic of major interest in the field of renewable energy. Monocrystalline and polycrystalline modules are mostly used and, hence characterized since many laboratories have data of them. Conversely, cadmium telluride (CdTe), as thin film module are, in some circumstances, difficult to be used for energy prediction. This work covers outdoor testing of photovoltaic modules, in particular that regarding CdTe ones. The scope is to obtain temperature coefficients that best predict the energy production. A First Solar (K-275) module has been used for the purposes of this research. Outdoor characterizations were performed at Department of Innovation Engineering, University of Salento, Lecce (Italy). The location of Lecce city represents a typical site in the South Italy. The module was exposed outdoor and tested under clear sky conditions as well as under cloudy sky ones. During testing, the global-inclined irradiance varied between 0 and 1500 W/m2. About 37000 I-V characteristics were acquired, allowing to process temperature coefficients as a function of irradiance and ambient temperature. The module was characterized by measuring the full temperature-irradiance matrix in the range from 50 to 1300 W/m2 and from -1 to 40 W/m2 from October 2011 to February 2012. Afterwards, the module energy output, under real conditions, was calculated with the ‘Matrix Method’ of SUPSI-ISAAC and the results were compared with the five months energy output data of the same module measured with the outdoor energy yield facility in Lecce.

The aim of this paper is to demonstrate the saving of electrical energy and therefore the reduction of greenhouse gas emissions (GHG) in smart spaces. Such saving was achieved through an automated system of blinds or curtains that allow the entrance of natural light into a room of a building. The paper discusses the advantages of using an automated system in comparison to a conventional or mechanical one. The prototype system makes use of a logical and electromechanical architecture for its operation. The automation of this system seeks to maintain a level of comfort and adequate lighting based on the Mexican normative (NOM-025-STPS-2008) of the Ministry of Labor and Social Welfare. The system has three operating modes: manual, automatic, and programmed. In manual mode, the user can adjust the level (height) of the blinds according to a specific need. In the automatic mode, the user establishes a required set point of illumination and, through a photo-resistance module, the system seeks to reach such illumination level. In the programmed mode and upon the use of a calendar, the user decides the opening percentage of the blinds throughout the day and the week. In this paper, energy and emissions have been calculated based on the 2017 Mexican energy mix.

Aim of this study was to perform a detailed clinical validation of a new fully automatic algorithm for vertebral interface segmentation in echographic images. Abdominal echographic scans of lumbar vertebrae L1–L4 were carried out on 150 female subjects with variable age and body mass index (BMI). Acquired datasets were automatically processed by the algorithm and the accuracy of the obtained segmentations was then evaluated by three independent experienced operators. Obtained results showed a very good specificity in vertebra detection (93.3%), coupled with a reasonable sensitivity (68.1%), representing a suitable compromise between the detection of a sufficient number of vertebrae for reliable diagnoses and the limitation of the corresponding computation time. Importantly, there was only a minimum presence of ‘false vertebrae’ detected (2.8%), resulting in a very low influence on subsequent diagnostic analyses. Furthermore, the algorithm was specifically tuned to provide an improved sensitivity (up to 73.1%) with increasing patient BMI, to keep a suitable number of correctly detected vertebrae even when the acquisition was intrinsically more difficult because of the augmented thickness of abdominal soft tissues. The proposed algorithm will represent an essential added value for developing echographic methods for the diagnosis of osteoporosis on lumbar vertebrae.

The main idea of this paper is to assess a simpler and faster procedure leading to the evaluation of the fluid flow rate through a pipe. Currently, several methods are available and they involve ad-hoc instruments. All these methods are characterized by high accuracies and dynamic responses, but they are intended to be inserted within the pipe under investigation, bringing to well-known insertion effects, compromising the reliability of the measurements performed. The authors illustrate a newer methodology for the measurement of flow rates by means of the processing of the vibration signals of pipe walls, inferred by the flow turbulence. Previous studies of the same authors showed a linear dependence between the amplitude of the most prominent peak of the vibration spectra and the flow rate. In this work, the authors relate the power content of the processed signals (by introducing the signal Root Mean Square value) to the flow rate.

Wearable and autonomous devices and systems for biomedical applications are assuming a specific importance to preserve human beings’ activities. Some applications are namely: energy harvesting for supplying autonomous devices, artificial limb and related robotics, on-line and off-line control of physiological parameters, wearable tissues and devices, wireless portable components, implantable items etc.. Software applications are also welcomed. The book aims to illustrate new specific techniques for theoretical studies, designing, building, characterizing and testing the aforementioned devices and systems. The book also intends to suggest new issues for researchers, master degree students and scientists.

This paper describes an intelligent system for monitoring photovoltaic plants, detecting thefts or malfunctions and optimizing energy production by algorithm to drive solar trackers. Sensing/processing board detects environmental parameters and calculates produced power/energy for monitoring efficiency while anti-theft system reveals any critical condition. Designed board controls biaxial trackers calculating sun position and following solar orbit to optimize energy production. Monitoring and anti-tampering systems communicate with PC or remote stations by wireless modules. Finally, wireless monitoring system of household facilities measures absorbed currents viewing consumption values on web-page. Depending on light/presence sensors, system can switch on/off monitored facilities obtaining energy savings.

This paper describes a wireless monitoring smart system of household electrical facilities, with ZigBee / WiFi transceivers, able to detect absorbed current from electrical loads, to calculate dissipated power and energy by means of PIC based software and to view the calculated consumption values on web page properly realized for user’s remote control. Depending on light/presence sensors’ signal, the realized system can switch on/off the monitored electrical loads for obtaining energy savings and user satisfaction. Also by sending DALI-standard commands to slave loads (e.g lighting facilities based on LEDs), the user can monitor, remotely by using a tablet/smartphone connected to internet, the operation’s state and adjust the light intensity of each light point for achieving different lighting scenarios.

Soil and top soil contaminations are generally produced by air deposition and subterranean leaks due to diverse factors, namely, industrial activities and natural phenomena (e.g. aerosols). However, a continuous monitoring is needed to assess eventual contaminants that can be on top soil. When the soil extension is very high, it is very difficult to perform accurate analysis because of excessive cost of characterization and successive analytical measurements. But in some cases, analytical data could not be available for all co-ordinates located in the area under test. Geostatistical approach could help in solving the missing data problem or helping in finding data in case of large meshes applied on the area under test. The research illustrates the opportunity of recovering data making a prediction by means of Kriging techniques. The application has been performed on data coming from deposimeters used for collecting atmospheric deposition on soil. PCBs (polychlorinated byphenils) are pollutants that are necessary to determine on soil, especially where industrial activities are carried out. The paper also illustrates the optimal conditions for increasing accuracy in recovering data thanks to fact that once a few numbers of point are known, it is possible to predict the trend of values of PCBs in unknown locations of the considered area

Beamforming is one of the most interesting techniques used to know distance systems in order to detect punctual, widespread obstacles. If correctly associated to DOA (Difference of Arrival), it can allow the description of obstacle shape. Distance ranging, for mobile and fixed systems, namely cars, vehicles, vessels and airplanes, that is a key issue for demands of nowadays. Distance between cars and from obstacles can be established and measured using laser and ultrasound. Cloudy and foggy conditions are very important requirements for testing distance ranging facilities. If based on acoustic waves, they can be easily integrated by sophisticated on-board software in order to perform new features. This research presents interesting aspects of defining new requirements for an acoustic scanning capable of reconstructing fixed obstacle features by targeting them using a special array of sensors. The term “acoustic scanning” is intended here as an aspect of sound ranging and reproduction regarding spatial locations of the obstacle, that is spatial shaping. The paper illustrates first an experimental system from which it is possible to derive parameters for setting spatial shaping of scenarios and after a clear identification of DOAs.

Volatile organic compounds (VOCs) belong to special pollutants included in Kyoto protocol and in its updated versions. They are responsible for great and dangerous air pollution. The volatility of VOCs make them difficult to be computed and to be measured by means of appropriate sensors in terms of accuracy. Nowadays different methods have been adopted and approved by specific authorities. One of the most important is EPA25 issued by the American Environmental Protection Agency. As a matter of fact, EPA25 only works on set of complete data. In case of noncomplete set of data, we mean missing data issue due to different troubles, namely dysfunction concerning networks of sensors, thermal drifts, etc., EPA25 as well as other methods are not able to overcome the above issue that affects the prediction and the reliability. This research proposes an alternative and effective way, based on cognitive approach, to process VOC data delivered by a network of sensors by using a mixed genetic algorithm with an additional fuzzy-based procedure. A comparison with other techniques based on neural networks is also envisaged. The proposed modified genetic algorithm offers the best accuracy