The advent of Smart Cities and its extension to Smart Regions requires seamless interaction of systems as well as with users, in a context where a great deal of devices exhibit potential network connectivity. Wireless systems are key elements in order to enable high interactivity, with multiple different systems operating simultaneously within a given region. Multiple network coordination and analysis is compulsory in order to enhance coverage/capcity relations, whilst achieving required bit rates and Quality of Service demands. In this work, the analysis of multiple wireless systems, based on the combination of WLAN/WBAN/NFC will be analyzed in the context of Smart Cities, examining inter-operation performance and overall deployment considerations.

 Solid Oxide Fuel Cells (SOFC´s) are devices for the chemical to electrical energies conversion. Traditional SOFC´s have been applied in stationary power generation systems based on their high efficiencies (~ 60%) and high operating temperatures (~ 1000 °C). They present the flexibility to be operated with a variety of fuels (e.g. hydrogen, alcohols, methane). Among the different architectures that they can present, microtubular SOFC´s have lower operating temperatures, higher tolerance to thermal cycles, faster start-up capacity and higher volumetric power densities, compared to conventional tubular SOFC´s. The present contribution includes results of the performance evaluation of microtubular SOFC´s. Prototypes were fabricated based on a dip coating technique; a cermet of Ni-Ce_0.8Gd_0.2O_2-x was used as the anode, Ce_0.8Gd_0.2O_2-x as the electrolyte, and La_0.8Sr_0.2Co_0.2Fe_0.8O₃ as the cathode. The performance of the cells was evaluated based on the polarization curves obtained by linear sweep voltammetry measurements in the 500-600 °C range, using hydrogen as a fuel. Electrical Impedance Spectroscopy measurements were also performed. Failure of the cells can be predicted from electrical measurements and could be related to formation of fractures in the cells or the cement sealant. During the evaluation, safety relies on gas sensors to detect fuel leaks.

General coordination of the formation is a critical asset in swarm robotics. When specialized agents are considered, with robots dedicated to specific tasks, formation control and efficient transition in the leadership of the swarm must be achieved. A task switching approach is presented in this paper and is formulated by evolving the definition of specialization to match with targets recognition, such as detecting special landmarks via embedded sensors, or the presence of an object in the environment. Specialization zones are generally defined as circular areas around each detected target corresponding to a specialized task to be dealt with by a specific robot. Entering within the influence area of a specialization zone triggers the switching of the leader of the formation. Under the assumption of specialized agents, each robot in the swarm is the specialist associated with one given specialized target. Appropriateness, smoothness and safety of the transition remain the major factors of performance considered. The framework is further refined by making the targets, and therefore the corresponding zone of influence, dynamic, which leads to the consideration of combined specialization areas. The robots control and leader switching processes are studied in relation with targets recognition, and adapted to accommodate real-time dynamic leadership and formation control as targets move and interact one with each other. The proposed system is validated in simulation to demonstrate that the group of robots effectively coordinate themselves around targets and dynamically allocate the appropriate specialized agent.

Nanotechnologies provide new materials for the consolidation and protection of the Cultural Heritage: innovative solutions are represented by ethyl silicate or silica nanoparticles dispersed in aqueous colloidal suspensions mixed to titanium dioxide in nanometric form. The challenge of this work is to provide a quick and non-invasive survey method able to evaluate the titanium dioxide amount in the coatings applied on the treated stones. In fact, the titanium dioxide weight percentage incorporate into the coating depends on both application phase and, over time, environmental biological and chemical conditions. In this paper, we show the preliminary results obtained by spectroradiometric survey carried out on marble samples coated through nanoparticle films. The coatings were prepared increasing weight percentage of the titanium dioxide from 0w% to 8w%. The data obtained through a field hyperspectral sensors shown spectral signatures depending on the content of titanium dioxide. In fact, the samples are characterized by different spectral shapes in the wavelength range 350-450nm, especially. The results are useful to develop a procedure for checking the application phase of coatings on the tangible Cultural Heritage. Moreover, the same method can be used, also, both to analyze the effect of the nanoparticle product on the base stone, before its application, and to verify the efficiency of the coating, over time.

Graphene provides a unique way for sensing local pH level of substances, with important implications in the monitoring of cellular metabolic activities where protonic excretion occurs in suitable conditions [1]. Doping modifications of graphene,  induced by the contact of the graphene sheet with different pH solutions were investigated by micro-Raman spectroscopy in order to develop a pH biosensor. A small amount of liquid (of the order of few microliters) was dropped on a graphene surface and the Raman response collected by using He-Ne laser light excitation. Depending on the doping level, the energy of the Raman  G mode of graphene significantly changes in the range of 1580-1610 cm^-1. The Raman response of graphene exposed to known pH aqueous solutions was investigated in order to provide a calibration curve for the sensor. In the aim to test the developed biosensor with  real biological systems, the pH values of cell culture media in different conditions were evaluated. The obtained results suggested that the proposed biosensing scheme could be adopted also for monitoring more complex biological systems as a single cell at the micrometer scale.

[1]   G. L. C. Paulus et al. “A graphene-based physiometer array for the analysis of single biological cells” Sci. Reports 4 (2014) 6865.

In recent years, the sector of catering services has increased the number of contracts up to 33% of firms or collective organizations in the EU. This represents an annual turnover of about 24 billion euro from educational institutions, healthcare and social sectors, prisons and private companies.

However, large economic losses appear every year at each stage of the food value chain. Entities such as the Food and Agriculture Organization of the United Nations (FAO) and the European Parliament (EP) have already launched several policies and initiatives to avoid these losses.

Following to these efforts, temperature control has come up as a key factor in the distribution sector of the whole food supply chain. As a counterpart, food distribution groups frozen, fresh and even cooked food so the temperature monitoring system has to face different scenarios with unalike ambient temperature. This variety of scenarios affects on the consumption of the electronics. In most of the electronics’ datasheet, power consumption values are given for an ambient temperature of 25°C, which differs from the conditions of interest.

This work presents the effects that ambient temperature causes on the SL900A sensor tag’s current consumption. Temperature conditions inside a metallic tray, transporting ready meal for the service food industry, are replicated through a climatic chamber whereas the current consumption is measured using a DC power analyser. Both active and data logging modes of operation have been used and the correlation between their current consumption and the temperature variation has been analysed.

Here we present a standalone point-of-use immunosensor device for food safety. Gluten was used as reference allergen to develop and test the device. Celiac disease is a gluten-sensitive enteropathy and one of the most common autoimmune disease affecting 1% of individuals in Europe

According to the international food standards, to label a food as gluten-free, it must contain less than 20mg/kg of gluten (20ppm). And according to the same standard, the method to detect the amount of gluten must have a detection limit of 10ppm.

In this work, we present an automatic and standalone microfluidic-based immunosensor to detect food allergens for food safety. The system has been developed as an application experiment in the SMARTER-SI European project. It comprises a microfluidic cartridge fabricated in cyclic olefin polymer by injection molding, the fluidic components to drive the reagents, two alternative optical detection systems based on fluorescent or colorimetric detection, respectively, and the electronics to manage all the components. As a proof-of-concept, gluten was immunodetected in a sandwich format by using R5 monoclonal antibody as capturing and detector probe. Gluten -antibody binding was revealed by using R5 mAb labelled to Alexa Fluor 647 or conjugated to horseradish peroxidase (HRP) for the fluorescence or the colorimetric version, respectively. Gluten were assayed at different concentrations from 50 to 0.1ppm. After 3minutes of developing and incubation, it appears to be capable to discriminate the intensity signals from a gluten concentration range between 1 and 50 ppm.

Sensors based on the measurement of nuclear magnetic resonance (NMR) relaxation times have been increasing in popularity, due in part to developments in permanent magnet technology. Such sensors tend to measure the spin-lattice (longitudinal) relaxation time T₁, or the effective spin-spin (transverse) relaxation time T₂^eff. It is important when developing sensors that there are a range of safe and repeatable calibration samples to aid in their calibration and testing. For the spin-spin relaxation times different viscosities of PDMS oil provide a suitable range of safe test materials. However, for the spin-lattice relaxation times, available options are not as safe to use and typically consist of different concentrations of Nickel Sulphate or Copper Sulfate solutions. In this work we report the use of solutions and gels comprising full fat milk powder as a safe and inexpensive material that can affect the longitudinal relaxation Time over a very wide range of values. We demonstrate that concentrations in distilled water from 5% to 64% give T₁ values from 1.8s down to 348 ms respectively. In addition to demonstrating their effectiveness for magnetic resonance sensors, validation of the range of T₁ values is undertaken on a high field clinical MRI system.

Monitoring of mechanical structures is a Big Data challenge and includes Structural Health Monitoring (SHM) and Non-destructive Testing (NDT). The sensor data produced by common measuring techniques, e.g., guided wave propagation analysis, is characterized by a high dimensionality in the temporal and spatial domain. There are off- and on-line methods applied at maintenance- or run-time, respectively. On-line methods (SHM) usually are constrained by low-resource processing platforms, sensor noise, unreliability, and real-time operation requiring advanced and efficient sensor data processing. Commonly, structural monitoring is a task that maps high-dimensional input data on low-dimensional output  data (information, that is feature extraction), e.g., in the simplest case a Boolean output variable “Damaged”. Machine Learning (ML), e.g., supervised learning, can be used to derive such a mapping function. But ML quality and performance depends strongly on the input data size. Therefore, adaptive and reliable input data reduction (that is feature selection) is required at the first layer of an automatic structural monitoring system. Assuming some kind of two-dimensional sensor data (or n-dimensional data in general), image segmentation can be used to identify Regions of Interest (ROI), e.g., of wave propagation fields. Wave propagation in materials underlie reflections that must be distinguished, especially in hybrid materials (e.g., combining metal and fibre-plastic composites) there are complex wave propagation fields.  The image segmentation is one of the most crucial part of image processing (Mishra, 2011). Major difficulties in image segmentation are noise and the differing homogeneity (fuzziness and signal gradients) of regions, complicating the definition of suitable threshold conditions for the edge detection or region splitting/clustering. Many traditional image segmentation algorithms are constrained by this issue. Artificial Intelligence can aid to overcome this limitation by using autonomous agents as an adaptive and self-organizing software architecture, presented in this work. Using a collection of co-operating agents decomposes a large and complex problem in smaller and simpler problems with a Divide-and-Conquer approach. Related to the image segmentation scenario, agents are working mostly autonomous (de-coupled) on dynamic bounded data from different regions of an image (i.e., distributed with simulated mobility), adapted to the locality, being reliable and less sensitive to noisy sensor data. In this work, different agent behaviour and segmentation approaches are introduced and evaluated with measured high-dimensional data from piezo-electric acusto-ultrasonic sensors recording wave propagation in plate-like structures. Commonly, SHM deploys only a small set of sensors and actuators at static positions delivering only a few temporal resolved sensor signals (1D), whereas NDT methods additionally can use spatial scanning to create images of wave signals (2D). Both one-dimensional temporal  and two-dimensional spatial segmentation is considered to find characteristic ROIs.

Conventional methods for diagnosing gastrointestinal (GI) diseases have involved analysis of headspace volatile organic compounds (VOCs) present in the breath, urine, or fecal samples of patients. Most previous diagnostic testing methods have utilized purely metabolomic approaches to analyze VOCs with analytical instruments such as gas chromatography-mass spectroscopy (GC-MS), nuclear magnetic resonance (NMR) metabolomics, selected ion flow tube-mass spectrometry (SIFT-MS), proton transfer reaction-mass spectrometry (PTR-MS), and field asymmetric ion mobility spectroscopy (FAIMS). These sophisticated and expensive methods usually involve the use of large immobile (non-portable) benchtop instruments, requiring extensive data manipulations and analyses along with advanced modeling procedures to achieve diagnostic interpretations of complex chemical data. Other approaches such as colonoscopies and biopsies are more invasive and discourage patient participation in prophylactic GI-disease screenings. The more recent availability of portable electronic gas-sensing devices, developed with the aim of simplifying disease diagnoses by analysis of headspace VOC mixtures collectively using multi-sensor arrays, allow the production of disease-specific aroma signatures (smellprints) based on detection of precise complex mixtures of disease biomarker metabolites. Electronic-nose (e-nose) devices provide very fast results, are easy to operate, and are more readily applicable to clinical practice. This paper summarizes some very recent e-nose technologies being developed and tested for GI-disease diagnostic applications, including ones with dual-technology and multi-technology sensor arrays for both pattern recognition and identification of key-metabolite chemical species. In addition, novel portable electronic devices, developed with new operational mechanisms and sensor types, are described which offer possibilities of providing new means of diagnosing GI-tract diseases.