Distributed networks of sensors have been recognized to be a powerful tool for developing fully automated systems that monitor environments and human activities. Nevertheless, problems such as active control of heterogeneous sensors for high-level scene interpretation and mission execution are open. This paper presents the authors' ongoing research about design and implementation of a distributed heterogeneous sensor network that includes static cameras and multi-sensor mobile robots. The system is intended to provide robot-assisted monitoring and surveillance of large environments. The proposed solution exploits a distributed control architecture to enable the network to autonomously accomplish general-purpose and complex monitoring tasks. The nodes can both act with some degree of autonomy and cooperate with each other. The paper describes the concepts underlying the designed system architecture and presents the results obtained working on its components, including some simulations performed in a realistic scenario to validate the distributed target tracking algorithm.

In the last decades, sensor networks have received significant attention in the field of Ambient Intelligence (AmI)for surveillance and assisted living applications, as they provide a powerful tool to capture relevant information aboutenvironments and people activities. Mobile robots hold the promise to enhance the potential of sensor networks, towards thedevelopment of intelligent systems that are able not only to detect events, but also to actively intervene on the environmentaccordingly. This paper presents a Distributed Ambient Intelligence Architecture (DAmIA) aiming at integrating multi-sensorrobotic platforms with Wireless Sensor Networks (WSNs). It is based on the Robot Operating System (ROS), and providesa flexible and scalable software infrastructure extendible to different AmI scenarios. The paper describes the proposedarchitecture and presents experimental tests, showing the feasibility of the system in the context of Ambient Assisted Living(AAL).

An innovative Computer Vision System (CVS) that extracts color features discriminating the qualitylevels occurring during fresh-cut radicchio storage in air or modified atmosphere packaging was proposed. It self-configures the parameters normally set by operators and completely automates thefollowing steps adapting to the specific product at hand: color-chart detection, foreground extractionand color segmentation for features extraction and selection. Results proved the average value of a* 20 over the white part and the percentage of light white with respect to the whole visible surface to be the most discriminating color features to significantly separate (P <= 0.05) the three desired quality levels(high, middle and poor) occurring during fresh-cut radicchio storage 23 (whose true value was verifiedon the base of ammonium content and human evaluated visual quality). The proposed procedure significantly simplify the CVS design and the optimization of its performance, limiting the subjective human intervention to the minimum.

Quality loss during storage is often associated to changes in relevant product colors and/or to the appearanceof new pigments. Computer Vision System (CVS) for non-destructive quality evaluation often relies on human knowledgeprovided by operators to identify these relevant colors and their features. The approach described in this paperautomatically identifies the most significant colors in unevenly colored products to evaluate their quality level. Itsperformance was compared with results obtained by exploiting human training. The new method improved qualityevaluation and reduced the subjectivity and the inconsistency potentially induced by operators.

In green leafy vegetables, the retention of green colour is one of the most generally used index to evaluatethe overall quality and freshness and it is associated to total chlorophyll content.Destructive chemical techniques and non-destructive chlorophyll meters represent the state-of-the-artmethods to accomplish such critical task. The former are effective and robust but also expensive and timeconsuming. The latter are cheaper and faster but exhibit lower reliability, require the probe to touch theleaves and heavily depend on the positions chosen for sampling the leaf's surface. In this paper, a newapproach to non-destructively predict total chlorophyll content of fresh-cut rocket leaves without contactis proposed. Fresh-cut rocket leaves were analysed for total chlorophyll content by spectrophotometerand SPAD-502 (used as reference values) and acquired by a computer vision system using a machinelearningmodel (Random Forest Regression) to predict total chlorophyll content. Finally, the trainedand validated model will be used for on-line prediction of total chlorophyll content of unseen freshcutrocket leaves. The proposed system can match the physical and timing constraints of a real industrialproduction line and its performance (R2 = 0.90), measured on the case study of fresh-cut rocket leaves,outperformed the results of the SPAD chlorophyll meter (R2 = 0.79).

Consumer acceptance of fresh-cut nectarines may be related to several characteristics (freshness, overall browning) that can be measured with suitable sensors. In this paper, digital image analysis was used toassess quality and marketability of fresh-cut nectarines, stored in air. Visual appearance and colour were measured using a conventional colorimeter and a computer vision system. In addition, anumber of fruit were analyzed for pH, titratable acidity, total soluble solids, total phenols and antioxidant content. During storage, nectarine slices showed a loss in appearance; after 12 d slices which were brown,having lost their freshness, showed a mean 30% decrease in total phenolics and a mean 10% increase in antioxidant activity with respect to fresh slices. Visual appearance correlated significantly with the browning score (R2 = 0.78), titratable acidity (R2 = 0.45) and pH (R2 = 0.85). Correlation of the visual appearance score with the colour parametersb* and chroma measured by the computer vision system was higher (R2 = 0.76) than that obtained using a colorimeter (R2 = 0.57). The results showed that the computer vision system was more effective than the colorimetric method. Measurements by means of the computer vision system resulted in a reliable tool to determine quality and marketability of fresh-cut nectarines in an objective and quantitative way. Moreover, the proposed computer vision system is suitable for real-time application in the food processing industry.

White table grape cv. Italia is a typical component of the Mediterranean diet and a source of phenolic compounds, particularly abundant in the skin portion. The aim of this study was to characterize the phenolic profile of the table grape skin and to assess its stability after the in vitro digestion process. The main phenolic compounds identified by the HPLC-DAD analysis were: procyanidin B1, caftaric acid, catechin, coutaric acid, quercetin 3-glucuronide and quercetin 3-glucoside. All compounds showed a good stability after in vitro digestion (from 43 to 80%). Moreover, the influence of grape skin polyphenols on the modulation of ROS and GSH levels was evaluated in basal and in stressed conditions on human intestinal cells (HT-29). In basal conditions, a higher polyphenol concentrations exerted pro-oxidant effect corresponding to high ROS level and low GSH content. This effect was probably due to the polyphenolic oxidation in cell culture condition with consequent production of hydrogen peroxide. Otherwise, in stressed conditions, grape skin polyphenols exerted antioxidant effects up to 1.3×10-6 ?g/g and restored the stress-related GSH reduction. The in vitro digestion process attenuated the biological effect of grape skin polyphenols on intestinal cell line (HT-29). In conclusion, grape skin polyphenols showed different behavior in relation to their concentrations and to the intracellular ROS levels.

In recent years, the study of sensor and robot networks for Ambient Assisted Living applications has gained a growing attention. In this work, a multimodal user interface for a distributed ambient intelligence architecture is presented. The user interface has been designed with in mind the specific characteristics of each user and the easiness of use of functions even for people without familiarity with technology. The system has been tested with elderly users: the acquired experience allows the analysis of problems like user acceptance, usability and suitability of these systems. The tests have shown a good users' feeling towards the interface, especially in relation to the voice interface.

The relationships between colour parameters obtained by a Computer Vision System (CVS) and both antioxidant activity (AA) and total phenol contents (TP) on coloured carrots were expressed as multivariate models obtained by multiple linear regression. The AA and TP predicted by the proposed models showed a good correlation with the real AA (R2 = 0.97, P ? 0.001) and TP (R2 = 0.94, P ? 0.001) measurements on the data set including internal and external parts of carrots. The predictions on the data set including only the internal (unevenly pigmented) parts of the carrots exhibited lower determination coefficients (R2 = 0.93 for AA and R2 = 0.86 for TP, P ? 0.001). The effectiveness of the models was checked also on the colour information provided by a colorimeter whose measures proved to be more sensitive to the uneven pigmentation of the carrots. Finally, the proposed models were able to successfully estimate the AA and the TP contents of pigmented carrots when applied to colours measured by the CVS.

Quality rating is currently accomplished by non-destructive and subjective sensory evaluation or by objectiveand destructive analytical techniques. There is a strong need of an objective non-destructive contactless qualityevaluation system to monitor fruit and vegetable along the whole supply chain. This paper proposes a Computervision system to satisfy this request. Image processing and machine learning techniques have been combined todevelop a Computer vision system whose configuration and tuning has been strongly simplified: that makes easierits deployment in real applications. The system has been verified on two white table grape cultivars (Italiaand Victoria) against three different classification tasks. The first considered five quality levels (5, 4, 3, 2, 1); thesecond separated the higher fully marketable quality levels (5 and 4) from the boundary (3) and the waste (2and 1); the third separated the higher fully marketable quality levels (5 and 4) from the other three (3, 2 and1). The system achieved a cross-validation classification accuracy up to 92% on the cultivar Victoria and up to100% on the cultivar Italia for binary or binomial classification between fully marketable and residual qualitylevels. The obtained results support its capability of powerfully, flexibly and continuously monitoring the qualityof the complete production along the whole supply chain

Non-destructive evaluation of vegetables by Computer Vision Systems (CVSs) makes possible to check their quality level in an objective and consistent way along the whole supply chain up to the final users. CVSs have been proven to be successful when applied to unpackaged products.The proposed approach aimed to enable this analysis on packaged fresh-cut lettuce with minimum constraints on the acquisition phase and without any care to flatten the surface of the bag facing the camera. A deep-learning architecture, based on Convolutional Neural Networks (CNNs), was used to identify regions of the image where the vegetable was visible with minimum colour distortions due to packaging. To meaningfully assess the performance of the system, each lettuce's sample was acquired both through packaging material and without packaging material. The image analysis was applied to both the resulting images to automatically grade their quality level. The results showed that the performance loss due to the presence of packaging is negligible (83% instead of 86%) and that the proposed system can be used to monitor the quality level of fresh-cut lettuce regardless of packaging at all the critical check points along the supply chain.

The paper describes the developed hardware and software components of a computer vision systemthat extractscolour parameters from calibrated colour images and identifies non-destructively the different quality levels exhibitedby lettuce (either whole or fresh-cut) during storage. Several colour parameters extracted by computervision system have been evaluated to characterize the product quality levels. Among these, brown on total andbrown on white proved to achieve a good identification of the different quality levels on whole and fresh-cut lettuce(P-value b 0.0001). In particular, these two parameters were able to discriminate three levels: very good orgood products (quality levels from 5 to 4), samples at the limit of marketability (quality level of 3) and wasteitems (quality levels from 2 to 1). Quality levels were also chemically and physically characterized. Among theparameters analysed, ammonia content proved to discriminate the marketable samples from the waste in bothproduct's typologies (either fresh-cut or whole); even the two classes of waste were well discriminated byammonia content (P-value b 0.0001).A function that infers quality levels from the extracted colour parameters has been identified using a multiregressionmodel (R2 = 0.77). Multi-regression also identified a function that predicts the level of ammonia(an indicator of senescence) in the iceberg lettuce from a colour parameter provided by the computer visionsystem (R2 = 0.73), allowing a non-destructive evaluation of a chemical parameter that is particularly usefulfor the objective assessment of lettuce quality.The developed computer vision system offers flexible and simple non-destructive tool that can be employed inthe food processing industry to monitor the quality and shelf life of whole and fresh-cut lettuce in a reliable,objective and quantitative way.

Consumer acceptance of fresh-cut nectarines may be related to several characteristics (freshness, overallbrowning) that can be measured with suitable sensors. In this paper, digital image analysis was used toassess quality and marketability of fresh-cut nectarines, stored in air for 12 d at 5 oC. Visual appearanceand colour were measured using a conventional colorimeter and a computer vision system. In addition, anumber of fruit were analyzed for pH, titratable acidity, total soluble solids, total phenols and antioxidantcontent. During storage, nectarine slices showed a loss in appearance; after 12 d slices which were brown,having lost their freshness, showed a mean 30% decrease in total phenolics and a mean 10% increase inantioxidant activity with respect to fresh slices (13.7 ± 4.3 mg gallic acid 100 g-1 f.w. and 53.5 ± 5.2 molTE 100 g-1 f.w.). Visual appearance correlated significantly with the browning score (R2 = 0.78), titratableacidity (R2 = 0.45) and pH (R2 = 0.85). Correlation of the visual appearance score with the colour parametersb* and chroma measured by the computer vision system was higher (R2 = 0.76) than that obtainedusing a colorimeter (R2 = 0.57). The results showed that the computer vision system was more effectivethan the colorimetric method. Measurements by means of the computer vision system resulted in a reliabletool to determine quality and marketability of fresh-cut nectarines in an objective and quantitativeway. Moreover, the proposed computer vision system is suitable for real-time application in the foodprocessing industry.