This work copes with the design and implementation of a wireless sensors network architecture to automatically and continuously monitor, for the first time, the manufacturing process of Sardinian Carasau bread. The case of a traditional bakery company facing the challenge of the Food-Industry 4.0 competitiveness is investigated. The process was analyzed to identify the most relevant variables to be monitored during the product manufacturing. Then, a heterogeneous, multi-tier wireless sensors network was designed and realized to allow the real-time control and the data collection during the critical steps of dough production, sheeting, cutting and leavening. Commercial on-the-shelf and cost-effective integrated electronics were employed, making the proposed approach of interest for many practical cases. Finally, a user-friendly interface was provided to enhance the understanding, control and to favor the process monitoring. With the wireless senors network (WSN) we designed, it is possible to monitor environmental parameters (temperature, relative humidity, gas concentrations); cinematic quantities of the belts; and, through a dedicated image processing system, the morphological characteristics of the bread before the baking. The functioning of the WSN was demonstrated and a statistical analysis was performed on the variables monitored during different seasons. offered by Internet-of-Things (IoT) solutions to meet sustainability (or waste reduction) criteria [2]. The food industry is managing critical changes related to consumer needs, to health and safety concerns and to the demand of food products which should be differentiated and high-quality [5]. However, the quality of these products can change suddenly during the production process; thus, leading to the need for ad hoc, reliable and real-time strategies to monitor the manufacturing process [5,6]. Therefore, to satisfy customer demands, the digital monitoring of the supply chain is required to provide a deep knowledge of the crucial production steps, in order to detect the weaknesses of and permit the optimization of the whole process, to reduce the maintenance difficulties and lower the costs [5,6]. Moreover, this digitalization trend in food industry can favor the automatic data collection, the lowering of paperwork and the enabling the development of real-time, robust feedback strategies [2]. Furthermore, the challenge of ensuring acceptable adoption costs of new information and communication technologies (ICT) by small and medium size activities calls for a reasonable and effective answers [4].As a solution to these problems, the use of wireless senors networks (WSNs) was proposed [4]. WSNs are recognized as a relevant technology of the 21st century. A WSN can be defined as a low-cost platform which connects large networks of sensors [7][8][9]. They are systems which comprise radio-frequency (RF) transceivers, sensors, micro-controller or processor and power sources [5,6]. WSNs are a novel and interesting manifestation of the IoT technology [10][11][12]....

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MRI can favor clinical diagnosis providing morphological and functional information of several neurological disorders. This paper deals with the problem of exploiting both data, in a combined way, to develop a tool able to support clinicians in the study and diagnosis of Alzheimer’s Disease (AD). In this work, 69 subjects from the ADNI open database, 33 AD patients and 36 healthy controls, were analyzed. The possible existence of a relationship between brain structure modifications and altered functions between patients and healthy controls was investigated performing a correlation analysis on brain volume, calculated from the MRI image, the clustering coefficient, derived from fRMI acquisitions, and the Mini Mental Score Examination (MMSE). A statistically-significant correlation was found only in four ROIs after Bonferroni’s correction. The correlation analysis alone was still not sufficient to provide a reliable and powerful clinical tool in AD diagnosis however. Therefore, a machine learning strategy was studied by training a set of support vector machine classifiers comparing different features. The use of a unimodal approach led to unsatisfactory results, whereas the multimodal approach, i.e., the synergistic combination of MRI, fMRI, and MMSE features, resulted in an accuracy of 95.65%, a specificity of 97.22%, and a sensibility of 93.93%.

In this paper, a microwave imaging approach based on artificial neural networks (ANNs) for neck tumor detection is presented. The aim of this technique is to retrieve the geometric and dielectric properties of the neck to identify the possible presence of tumors, starting from scattered electric field data. A fully-connected neural network is developed to test the feasibility of the proposed approach. Moreover, a numerical model including the main features of a cross section of the neck is specifically designed in order to create a suitable training dataset. Subsequently, for the optimization of the ANN architecture and performance evaluation, a numerical analysis is conducted. A set of simulated cases, based on realistic neck phantoms, is tested to evaluate the robustness of the network. Preliminary results show the possibility to identify and locate neck tumors.

The ubiquitous nature and great potential of Wireless Sensors Network has not yet been fully exploited in automotive applications. This work deals with the choice of the cost-effective hardware required to face the challenges and issues proposed by the new trend in the development of intelligent transportation systems. With this aim, a preliminary WSN architecture is proposed. Several commercially available open-source platforms are compared and the Raspberry Pi stood out as a suitable and viable solution. The sensing layer is designed with two goals. Firstly, accelerometric, temperature, and relative humidity sensors were integrated on a dedicated PCB to test if mechanical or environmental stresses during bus rides could be harmful to the device or to its performances. The physical quantities are monitored automatically to alert the driver, thus improving the quality of service. Then, the rationale and functioning of the management and service layer is presented. The proposed cost-effective WSN node was employed and tested to transmit messages and videos, while investigating if any quantitative relationship exists between these operations and the environmental and operative conditions experienced by the hardware.