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Machine learning techniques aim to mimic the human ability to automatically learn how to perform tasks through training examples. They have proven capable of tasks such as prediction, learning and adaptation based on experience and can be used in virtually any scientific application, ranging from biomedical, robotic, to business decision applications, and others. However, the lack of domain knowledge for a particular application can make feature extraction ineffective or even unattainable. Furthermore, even in the presence of pre-processed datasets, the iterative process of optimizing Machine Learning parameters, which do not translate from one domain to another, maybe difficult for inexperienced practitioners. To address these issues, we present in this paper a Vectorized Automated ML Pre-processIng and post-pRocEssing framework, approximately named (VAMPIRE), which implements feature extraction algorithms capable of converting large time-series recordings into datasets. Also, it introduces a new concept, the Activation Engine, which is attached to the output of a Multi Layer Perceptron and extracts the optimal threshold to apply binary classification. Moreover, a tree-based algorithm is used to achieve multi-class classification using the Activation Engine. Furthermore, the internet of things gives rise to new applications such as remote sensing and communications, so consequently applying Machine Learning to improve operation accuracy, latency, and reliability is beneficial in such systems. Therefore, all classifications in this paper were performed on the edge in order to reach high accuracy with limited resources. Moreover, forecasts were applied on three unrelated biomedical datasets, and on two other pre-processed urban and activity detection datasets. Features were extracted when required, and training and testing were performed on the Raspberry Pi remotely, where high accuracy and inference speed were achieved in every experiment. Additionally, the board remained competitive in terms of power consumption when compared with a laptop which was optimized using a Graphical Processing Unit.

Medical educators in residency programs have unique opportunities to teach health inequities, social determinants of health (SDOH), and implicit bias. However, faculty are not adequately trained to effectively teach these topics. The aim is to assess the effectiveness of a faculty-level workshop to teach health inequity.An interactive workshop was designed by an interprofessional faculty from a major urban teaching hospital, addressing SDOH, implicit bias, an "Enhanced Social History," and the benefits of interprofessional care. Before and after completion, workshop participants completed surveys regarding comfort in teaching these concepts. Survey results were analyzed to assess benefits of the intervention.Sixty-four percent of participants completed preworkshop and postworkshop surveys. Participants reported increased contemplation and improved comfort in teaching SDOH, barriers to medical care, and implicit bias.Faculty comfort in teaching health inequity increased after this workshop. This may help bridge the gap between the expectation of clinical faculty to evaluate trainee practice of patient-centered, culturally competent care, and faculty possession of and confidence in health inequity teaching skills in clinical settings. Future research should focus on learner- and patient-based outcomes, including teaching time and impact on delivery of care.

This article compares different rainfall observing techniques available in the City of Genoa (Italy): the Ligurian regional tipping-bucket rain gauge (TBRG) network and the Monte Settepani long-range weather radar (WR) operated by the Ligurian Regional Environmental Protection Agency (ARPAL) and Smart Rainfall System (SRS), a network of microwave sensors for satellite down-links developed by the University of Genoa and Artys Srl. SRS and WR are the two indirect monitoring systems considered in this analysis. The respective performance of the rainfall measurements was assessed by comparison with the reference observations made in situ by seven TBRG stations selected within the boundary of the City of Genoa. The analysis was performed by considering the main precipitation events that occurred between January 2017 and December 2018. The results show an agreement between the precision of the 10-min rainfall intensity (RI) measurements, quantified by the root mean square errors (RMSEs), made by the WR and the SRS system for the seven sites. Similar performance has been also observed when comparing the peak of RI and the total rain accumulation with the reference TBRG values for the selected precipitation events. An assessment of the WR and SRS accuracy in providing the 2-D RI fields was also performed by computing the map of the RMSE differences over the City of Genoa territory. It is shown that the SRS RI maps are generally associated with a lower level of RMSE with respect to the WR in the areas that are characterized by a higher density of sensors.

The management of tandem extracranial internal carotid artery and intracranial large vessel occlusion during endovascular thrombectomy (EVT) for acute ischemic stroke (AIS) has been under-investigated. We sought to investigate outcomes of AIS patients with tandem occlusion (TO) treated with carotid artery stenting (CAS) compared to those not treated with CAS (no-CAS) during EVT.We performed a cohort study using data from AIS patients enrolled in the Italian Registry of Endovascular Treatment in Acute Stroke. Outcomes were 3 months' mortality, functional outcome, complete and successful recanalization, any intracranial hemorrhage, parenchymal hematoma and symptomatic intracerebral hemorrhage.Among 466 AIS patients with TO, CAS patients were 122 and no-CAS patients were 226 (118 excluded). After adjustment for unbalanced variables, CAS was associated with a lower rate of 3 months' mortality (OR 0.407, 95% CI 0.171-0.969, p = 0.042). After adjustment for pre-defined variables, CAS was associated with a lower rate of 3 months' mortality (aOR 0.430, 95% CI 0.187-0.989, p = 0.047) and a higher rate of complete recanalization (aOR 1.986, 95% CI 1.121-3.518, p = 0.019), successful recanalization (aOR 2.433, 95% CI 1.263-4.686, p = 0.008) and parenchymal hematoma (aOR 2.876, 95% CI 1.173-7.050, p = 0.021). CAS was associated with lower 3 months mortality (OR 0.373, 95% CI 0.141-0.982, p = 0.046) and higher rates of successful recanalization (OR 2.082, 95% CI 1.099-3.942, p = 0.024) after adjustment for variables associated with 3 months' mortality and successful recanalization, respectively.Among AIS patients with TO, CAS during EVT was associated with a higher rate of successful reperfusion and a lower rate of 3 months' mortality.

PurposePalliative sedation (PS) plays a critical role to give suffering relief from refractory symptoms at the end of life. Our audit aimed to assess and improve quality of PS at the Department of Oncology and Hematology of University Hospital of Modena, to verify the adherence to international guidelines, the cooperation among members of care team, focusing with attention on family’s perception of this delicate situation. MethodsFrom December 2016 to June 2019, data of patients undergoing PS in the Department were collected by an electronic folder tool, “Sedation Tool” (ST), that recorded clinical and PS informations, D-PaP, Rudkin Score and family’s perception. Results245 patients were enrolled. 82% had a Karnofsky Performance Status 10-20%. The most common cancer types were lung and gastro-intestinal carcinomas (27% and 21% respectively). Refractory symptoms observed were confusion and agitation (76%), dyspnea (39%), pain (15%), delirium (10%), psychological distress (5%). Midazolam was the drug of choice for PS. Most of patients had Rudkin score 5 after 24 hours and 33% had terminal event within a period of 24 hours from the beginning of PS. During PS most of Patient’s relatives reported peacefulness (65%), agitation/impatience in 6% of cases, concern for suffering (16%). ConclusionPS is used in case of worsening general conditions at the end-stage disease to relieve refractory symptoms with dignity. The ST can become a simple instrument to evaluate and improve PS quality, providing more attention on the impact of PS on relatives to then possibly develop new supportive procedures for patients and their families.

Coast erosion is a process that degrades a coastal profile and is mainly due to natural factors (e.g., related to climate change) and overcrowding (e.g., urbanization and massive tourism). While the first cause can be considered as a slow process, the growing presence of humans is leading to rapid aging of coasts. The Mediterranean Sea authorities are focusing on the necessity for a systematic and comprehensive approach to the management of littoral areas. In this context, Italy is searching for a promising solution to safeguard coasts but, at the same time, to manage in an intelligent and “green” way the big amount of tourists. Research communities all over the world indicated the Internet of Things (IoT) as a valid technology to develop solutions in order to try solving or mitigating the coastal erosion problem. IoT-based techniques allow to manage heterogeneous and massive data for real-time monitoring and decision making and can be used for coastal environment and crowd level monitoring. This article presents a monitoring system based on the Social IoT (SIoT), a new paradigm that defines a network where every node is an object capable of establishing the social relationships with other things in an autonomous way according to specific rules. Thanks to social relationships, all involved devices in the monitoring system (i.e., sensors, cameras, and smartphones) are able to collect and exchange information. The proposed system, developed and installed in Cagliari (Italy), is able to evaluate the occupational state of a beach considering environmental and crowding data collected by devices and feedback sent by users.

This study aims to know the basis of sulfate-reducing bacteria (SRB) and chitin source relationship for the development of a biotreatment system for mine-impacted water (MIW). The MIW consists of river water impacted by coal acid mine drainage (AMD), an extremely acid effluent, rich in sulfate and dissolved metal ions, with a high pollutant potential. Chitin was used as metal ion sorbent and biostimulant of SRB, whose anaerobic dissimilatory metabolism reduces sulfate to sulfide. Microcosms were built in an oxygen-free atmosphere using chitin from two different sources: commercial chitin and shrimp shell waste, which contains calcium carbonate, an acidity removal agent, in addition to chitin. The results indicate that the shrimp shell performs best in removing sulfate (99.75%), iron (99.04%), aluminum (98.47%), and manganese (100%) ions. The iron ion sorption kinetics of the sediments were also studied; pseudo-second order behavior was observed. High-throughput sequencing analysis revealed the present bacterial community and its abundance in the microcosms after 11 and 30 treatment days: SRB were detected but were not the majority. Thus, this research aims to contribute to the sustainable treatment MIW through the employment of an abundant and low-cost biomaterial.

The determination of the chemical characterization and the order of ink crossing by ballpoint pens have received increasing and important attention in forensic analysis due to forgery or alteration of documents strongly related to their use. The examination of these cases must be using non-destructive methods, in order to preserve the integrity of the evidence present in the documents. Laser desorption ionization mass spectrometry (LDI-MS) and LDI mass spectrometry imaging (LDI-MSI) have been widely applied in forensic document analysis by offering specific information on the chemical composition of the material from the mass to charge ratio (m/z) of the dyes without destroying the spatial location of the analytes. In this work, the combination of LDI data with statistical treatment, such as principal components analysis, classified the blue and black pen inks according to their 11 brands and 18 models studied. The interpretation of the LDI mass spectra allowed a chemical profile analysis relating the concept of relative ion intensity of the following dyes: methyl violet (m/z 372), ethyl violet (m/z 456), basic blue 26 (m/z 470), and basic blue 7 (m/z 478). In addition, the order of ink crossing (pen over pen, pen over printer and vice versa) was determined by monitoring the ions: methyl violet dye (m/z 372), their homologues (m/z 358, 344, 330, 316) and basic blue 7 (m/z 478) for the pens; and the ions of m/z 285 and 355, for inkjet and laserjet printers, respectively, by the LDI (+) MSI.

This paper describes the results obtained by the application of an innovative environmental monitoring technique able to estimate rainfall intensity in real time by processing the attenuation of microwave satellite link signal measured by low cost sensors. The satellite that has been used during our work, Turksat 42° E, belongs to the plethora of satellites operating for television and radio channel broadcasting. Each sensor exploits off the shelf components, is equipped with a radio frequency power-measuring unit, and provides connectivity to the server over a wide area network. A validation of the approach with a field comparison experiment at the urban scale, comprising three measurement sites equipped with such sensors, was established since autumn 2016 in the municipality of Genoa, Italy. Point-scale rainfall intensity measurements made by two calibrated tipping-bucket rain gauges constitute the reference for the comparative analysis of the microwave sensors performance. The dynamic calibration of the rain gauges was carried out by using an automatic calibration rig and the measurements have been processed with advanced algorithms to reduce counting errors. The experimental setup allowed a full characterization of the microwave signal trends as a function of different precipitation. The results showed a strong correlation between the microwave signal attenuation and the reference rainfall observations and demonstrated the possibility to retrieve ten-minute rain accumulations from the microwave links by adopting a proper electromagnetic model. The comparison between the different measuring systems is performed by computing the time series statistics and the frequency of the rain conditions for each precipitation event.

This work deals with vehicular monitoring within smart cities through Automatic License Plate Recognition (ALPR) techniques and vehicle discrimination object detection (YOLO), in order to obtain timely statistical data. The combined use of the two techniques allows to obtain much more refined data than a simple vehicle counter. Moreover, the collected data undergoes a process of anonymization in accordance with the European regulations for the protection of personal data (GDPR). The use of convolutional neural networks (CNN) made it possible to obtain vehicle tracking statistics, returning daily, weekly, monthly and yearly habits with the ultimate goal of allowing a monitoring and control of the city traffic conditions. The results obtained showed a high accuracy in the classification of vehicles and a wide range of statistics concerning the occurrences of each vehicle within the area of interest.

This paper addresses vehicle tracking using an approach based on the $\mathbf{DAB }+$ standard, which is becoming increasingly popular in private vehicles as an alternative to the old FM radio standard. The digital standard allows the use of different contents useful to the driver. On the other hand, the bidirectional channel of the $\mathbf{DAB }+$ standard could allow the vehicle to transmit generic information. The aim of this work is to transmit some parameters of the radio channel to allow the identification and tracking of vehicle position. By means of a neural network it is possible to reconstruct the path of the vehicles through a maximum likelihood comparison between data contained in a data-set and the information offered by the vehicles in real time. Experimental tests conducted on the road have shown how it is possible to obtain a high accuracy of vehicle traffic in a transparent manner and with respect for the processing of personal data in accordance with current European legislation.

Within the frame of the home environment the television set has occupied a primordial place in the past few years, at a tight pace with another technological paradigm: the Smart Home concept. Within this context, an interesting application is the integration of user feedback mechanisms and health monitoring devices which could allow the entire home eco-system to actively participate to the monitoring of the users' health status. This paper proposes an architecture to integrate health monitoring devices, user feedback and the smart home environment with a HbbTV - enabled television, on the purpose of combining the TV/SH experience with extended health monitoring.

This paper addresses the use of machine learning techniques for the determination of subgroups within 5G networks. Currently, the burden of determining the subgroups falls uniquely on the gNB. The aim of this work is to lighten the computation burden of the gNB in estimating the evaluation of the position and mobility of users, with the ultimate aim of determining the optimal modulation and coding scheme (MCS). This work proposes an innovative approach based on machine learning techniques that are interposed among user and gNB, helping the latter to determine the network configuration. The results obtained show how direct communication between UEs and neural network speeds up the determination of the MCS and the allocation of resources to subgroups within 5G technology.

Localization based on wireless signals has been introduced using a positioning method that relies on satellite navigation positioning systems, such as the Global Positioning System (GPS). The main issue with a passive localization system, such as this, is that although we would only gather anonymous information, we still need the user's approval. In this paper, a fingerprint-based localization approach based on Long-Term Evolution (LTE) Advanced Pro Signal is proposed. The authors analyzed received signal strength indication (RSSI) related to the communication between the base station and the user equipment (UE). Using a machine learning approach (i.e., a generative adversarial network - GAN), the proposed system allows understanding the correlation between RSSI and the position of the user in the space. The state of art related to fingerprinting-based localization has been drawn up as a costly and time-consuming offline training phase. The GAN will generate new models (RSSI, localization), and the experimental results show improvement in accuracy when compared with the effort to perform the offline phase. The results indicate great accuracy, 0.4 meters and 10 meters, respectively indoor, and outdoor environments case and the measurement campaign was fast and time saver.

Nowadays, the number of simultaneously active devices in urban environment is rapidly increasing and ever more in need of new services. In this context, service providers aim to optimize the use of resources in order to guarantee adequate quality of service to as many users as possible. The fifth generation (5G) wireless technology, thanks to its technical characteristics and promising potential, is pushing the research community to propose new solutions and to diversify services to take full advantage of the network. This paper presents a new method for massive concurrent uplink of video content in a restrained area. Next generation NodeB (gNB)‘s ability to handle uplink requests from a large number of users in terms of minimum video quality versus number of users has been investigated. In the proposed scenario, user's live videos from various point of views at a sport event is actively used by the provider/broadcaster to offer new emerging services.

The field of mobility represents one of the key aspects in the study of smart cities with increasing urbanization. In this article the phenomenon related to the flows of people moving within the city has been studied. In fact, the phenomena of road congestion and excessively crowded areas are increasing. The tools used in the monitoring phase concern Wi-Fi sniffing and video camera systems for the classification of vehicles, people, bicycles, and so on. Real-time monitoring allows citizens to know in advance the road conditions with a high level of reliability, overcoming the limits of a single technological system that offers imprecise monitoring. Results obtained from the systems based on a single technology have been merged into a machine learning solution using neural networks with a high degree of accuracy.

Optical coherence tomography (OCT) angiography (OCTA) is a non-invasive tool for the in-vivo study of the intraretinal vascular network. It is based on the analysis of motion particles within the retina to reconstruct the paths followed by the erythrocytes, i.e. retinal capillaries. To date, qualitative and quantitative information are based on the morphological features disclosed by retinal capillaries. In the present study, we proposed new quantitative functional metrics, named Total Flow Intensity (TFI), Active Flow Intensity (AFI), and Volume-related Flow Intensity (VFI), based on the processing of the blood flow signal detected by OCTA. We studied these metrics in a cohort of healthy subjects, and we assessed their clinical utility by including a cohort of age-matched patients affected by Stargardt disease. Moreover, we compared TFI, AFI, and VFI to the widely used vessel density (VD) parameter. TFI, AFI, and VFI were able to describe in detail the different properties of the retinal vascular compartment. In particular, TFI was intended as the overall amount of volumetric retinal blood flow. AFI represented a selective measure of voxels disclosing blood flow signal. VFI was developed to put in relationship the volumetric blood flow information with the not vascularized retinal volume. In conclusion, TFI, AFI, and VFI were proposed as feasible functional OCTA biomarkers based on the analysis of retinal blood flow signal.

With the rise of Internet of Things (IoT) and Edge Computing, which are technologies that rely on smart and low power computing nodes with adequate processing power and storage capabilities, it is expected that Artificial Intelligence and machine learning will play a role in the continuous spreading of their application fields.

Detection and classification of moving targets is an essential feature in many applications like road surveillance systems, autonomous cars, and smart gate systems. Multi-chirp sequence Frequency Modulated Continuous Wave (FMCW) radars with a 2D FFT processing can be used to produce a Range-Doppler images (R-D maps) containing the signature of the target. However, in low-cost FMCW radars, these images suffer from many problems like low-resolution and ambiguity. Such problems can make the image look unrealistic as well as hard to process and classify. In this paper, we propose a human-vehicle classification method based on Transfer Learning. The classification is done by processing the R-D maps generated by a low-cost short range 24 GHz FMCW radar with a convolutional Neural Network (CNN). The adopted CNN succeeded to reach a 96.5% accuracy in discriminating humans from vehicles.

Smart cities are characterized by smart heterogeneous devices that can interact and cooperate with each other by exchanging regularly low amounts of data in the context of IoT. Lately, there has been an increasing interest in enhancing the IoT paradigm to support exchange of multimedia data. This paper focuses on the concept of Urban Mobility Services and in particular on proposing a solution to enable best QoS and load balance in a 5G network context. The paper introduces a novel algorithm for MobilIty Services uSer vIrtualizatiON (MISSION). MISSION employs cloud computing and broadcast of multimedia content in order to reduce the network load, the number of interactions, and user device energy consumption. It also relies on rating of network reputation in the 5G heterogeneous network environment and performing network selection in the quest to maximize QoS parameters. The performance of the proposed solution is compared against that of a TraffictYpe-based DifferEntiated Reputation (TYDER) algorithm. This performance was evaluated in terms of QoS parameters such as delay, latency, packet loss and prediction error. The results show how MISSION outperforms TYDER in urban mobility scenario.

One of the main objectives of the United Nations towards sustainable energy consumption and production is the design and retrofit of buildings to be considered nearly Zero Energy Buildings (nZEB). In this paper, a user-friendly tool that provides recommendations on the most suitable Renewable Energy Source (RES) and Battery Energy Storage System (BESS) combinations and sizes for the consider building is proposed. Based on the answer to a few simple questions, the tool aims to propose the RES-BESS system that maximizes the building's self-consumption, thus minimizing the required investment to get a nZEB. An implementation of the first prototype is presented. Preliminary performance results are promising and show that significant savings can be achieved.

The rise of interest in Building Information Modelling (BIM) during the last years has led to the introduction of complex data structures. Among all this information, a precise definition of structures in the form of point clouds is still an open research area. Consequently, an accurate, fast and easy to use point cloud acquisition system is needed. In this paper, we present a hardware-software prototype for automated indoor environments scanning through the acquisition of point clouds using multiple low-cost depth sensors. The acquisition and processing phase are described in detail, as well as the test environments used to assess the system functionality.

The Internet of Things (IoT) has disrupted the IT landscape drastically, and Long Range Wide Area Network (LoRaWAN) is one specification that enables these IoT devices to have access to the Internet. Former security analyses have suggested that the gateways in LoRaWAN in their current state are susceptible to a wide variety of malicious attacks, which can be notoriously difficult to mitigate since gateways are seen as obedient relays by design. These attacks, if not addressed, can cause malfunctions and loss of efficiency in the network traffic. As a solution to this unique problem, this paper presents a novel certificate authentication technique that enhances the cyber security of gateways in the LoRaWAN network. The proposed technique considers a public key infrastructure (PKI) solution that considers a two-tier certificate authority (CA) setup, such as a root-CA and intermediate-CA. This solution is promising, as the simulation results validate that about 66.67% of the packets that are arriving from an illegitimate gateway (GW) are discarded in our implemented secure and reliable solution.

This paper presents a high linearity, wide dynamic range, calibration-less readout circuit for metal oxide resistive gas sensors. The proposed interface is based on resistance-to-time conversion. Such conversion represents a suitable signal conditioning to interface resistive sensors varying over a wide range. The output waveform of time-based circuits can be easily interfaced when connected to a counter or a microcontroller. The readout circuit designed in AMS 0.35 μm-dual 1.65 V CMOS technology shows that the wide resistance range varying from 10 kΩ to 10GΩ is converted into time domain with a relative linearity error lower than 0.9% and achieves a dynamic range of 161 dB over 6-decades at transistor level simulation. Such high dynamic range is required for outdoor air quality monitoring and at the same time to overcome the sensor drift.

The potential of three different kind of reactors and active biomass to be used as inoculum for nitrogen removal was verified. Sludge samples were collected from a Membrane Bioreactor (MBR), a previous tank of a Moving Bed Biofilm Reactor (MBBR) and a Trickling Filter (TF). Samples were compared according to bacterial activity in batch tests and their microbiology (16 s rRNA sequences). The microorganisms examined were: AOB, NOB, anammox bacteria and OHO. Results showed that the richest sample was from MBR (Chao value equals 581). However, the bacterial activity was greater in MBBR sample (qAOO,NH4 equals 0.002 mgN·mgVSS−1·h−1; qNOO,NO2_NO3 equals 0.001 mgN·mgVSS−1·h−1 and qNOX_N2,SB equals 10.0 mgN·mgVSS−1·h−1). Therefore, MBBR WWTP was shown to have the best inoculum and operating conditions for nitrogen conversion and removal. Besides, if aeration is provided as low as necessary for AOB to start the activity in denitrification tank, simultaneous partial nitrification, and denitrification (SPND) can occur.

The medicinal species Richardia brasiliensis Gomes has secondary metabolites with considerable pharmacological activities. This study aims to determine the antiproliferative and genotoxic capacity of extracts and fractions obtained from the aerial parts of R. brasiliensis , in the Allium cepa test, on which one can detect the damages on chromosomes and the infl uence on cell proliferation during the mitotic cycle. The antiproliferative and genotoxic eff ect from extracts and fractions were evaluated at concentrations of 10 and 500 µg/mL and the mitotic index (MI) and the percentage of chromosomal alterations (CA) were calculated and used on the χ² test. It was observed antiproliferative capacity, the best results were from the autumn crude extract, and the hexane and butanol fractions with lower MI than the negative control. All treatments were genotoxic with CA, but with lower values than the ones found in the positive control.

Digital holography needs efficient coding tools that facilitate storage and transmission of this type of data in order to reach practical applications. This paper presents an experimental analysis of the performance of different coding tools for the compression of digital holograms. During the experiments, a dedicated compression architecture is employed in order to transform the holographic data in a representation suitable to be provided to the encoders, and for performing an objective quality evaluation of the obtained results. Several state-of-the-art image and video codecs are evaluated on different reference datasets, comprising different types of digital holograms. The evaluation is carried out on the reconstructed images with different metrics, and obtained results are critically analyzed and discussed.

The selection of a natural bacterial consortium from an activated sludge system and the verification of the capability of these organisms for hormone biodegradation are important to the was...

In this paper, we analyze the radar echo signals of a 24 GHz FMCW radar obtained from two different groups of moving targets (humans and vehicles). Range and Doppler information are obtained with a 2D FFT analysis. The aim of this paper is to describe the experimental setup, the adopted approach for the extraction of the Doppler-based features for human-vehicle classification, and the results obtained so far. In particular, the differences between pedestrians and vehicles in the Doppler domain will be reported and discussed.

This paper goes beyond classic Internet of Things (IoT) and focuses on the concept of Multimedia-based Internet of Things (M-IoT). Smart heterogeneous multimedia things can interact and cooperate with each another and with other things connected to the Internet to facilitate multimedia-based services and applications that are globally available to the users. Nowadays, the access network selection for heterogeneous multimedia devices is not managed taking into account the reputation that a given network has at a given instant. In order to solve these issues ReMIoT, a Reputation-based M-IoT architecture is introduced in this paper and presented alongside a novel solution to improve multimedia content delivery quality. The solution includes three distinct operational stages: (i) multimedia service detection, (ii) network reputation detection, and (iii) M-IoT network selection. The performance of the proposed solution is compared against that of a traffic type-based differentiated reputation algorithm for radio resource allocation (TYDER). This performance was evaluated in terms of QoS parameters such as throughput, packet delay and packet loss ratio.

In the past few years, it is noticed a fast adoption and increase of mobile devices that are able to retrieve information quickly, regardless of the user’s physical environment. Thus, application developers have leveraged it by developing a variety of applications using mobile Augmented Reality (AR) in domains such as cultural heritage education, shopping, gaming, and more. AR technology overlays a virtual object into the real world that enables users to have virtual and real-world experience at the same time. During a visit to a cultural heritage site such as a museum or an art gallery, the learning experience and enjoyment is necessary for a visitor and AR introduces new opportunities to improve them. We propose an AR-based framework for cultural heritage education. We aim to promote cultural heritage education by enriching user’s experience and at the same time to offer the knowledge with the most enjoyable way. In order to evaluate our framework, we have developed a server application and an AR-based smartphone application.

In this work, a power management system for the fluttering wind energy harvester FLEHAP is presented and tested. The proposed system uses a low power microcontroller running a simple power management algorithm, and an optimised self-powered circuit consisting of a buck-boost converter and an auxiliary circuit, which is capable of providing enough power for the functionality of the components. The primary goal of the system is to ensure the power delivery capability and self-start-up, while the secondary goal is to provide high power efficiency on the output. This approach gives an advantage over the commercial converters tested on the same harvester in terms of efficiency, as well as programmability and flexibility in the power monitoring and management, allowing the system to be either used with different type of harvesters, or being a part of more advanced power management scheme through the ability of communicating data.

The need for low cost power sources was always the scope of researchers, especially for applications such as Wireless sensor networks (WSN), where the maintenance and replacement of energy storage units is costly. In this sense, energy harvesting (EH) is more reliable and cheaper due to its unlimited availability. Though, the environment conditions are usually unpredictable, which requires a specialized circuit to be used with the energy harvesters to ensure the efficient power transfer and to adapt to the variable conditions. The proposed system is a specialized EH circuit for the fluttering wind harvester. It consists of an AC/DC buck-boost converter, controlled by a low power microcontroller which adapts the performance according the variable wind conditions, giving an edge over commercial circuits for the same purpose.

Holography is an acquisition and reproduction technique of visual contents which allows, theoretically, the reconstruction of the acquired scene without any difference with its real-world counterpart. Digital holograms are characterized by a huge amount of data, having different features compared to conventional image and video content. This paper presents a statistical analysis of computer-generated holograms data with the aim of providing a better understanding of the features and characteristics of holographic data, to be used as a basis for research and design of novel coding solutions specifically tailored for holographic contents.

Short-range terrestrial radio communications, in low-power and low-cost configurations are the current enabler for the IoT and M2M applications. The main drawback of such applications is the need of a gateway that enables them to communicate with the world. Satellite gateways have become in recent years more affordable, opening the possibility to the aforementioned applications to get even more present in the current technological development, but still the combined equipment and especially the access costs are still prohibitive for large scale deployments. The equipment costs for a satellite gateway could be substantially lowered using the Software Defined Radio (SDR) concept which can provide a high degree of flexibility in dynamically operating multiple wireless interfaces, especially combined with the plethora of terrestrial communication standards. The design of an embedded hardware device based on SDR technologies would therefore significantly lower the overall costs of satellite communication for IoT and M2M applications. Combining the presented elements, the main goal of this paper is to analyze the requirements of the satellite part of a SDR-based gateway for M2M and IoT applications and to present a hardware implementation of the gateway together with its operational characteristics.

In the past few years, the Smart Cities concept has become one of the main driving forces for the urban transition towards a low carbon environment, sustainable economy, and mobility. Tourism, as one of the fastest growing industries, is also an important generator of carbon emissions; therefore, the recently emerging sustainable tourism concept is envisioned as an important part of the Smart Cities paradigm. Within this context, the Internet-of-Things (IoT) concept is the key technological point for the development of smart urban environments through the use of aggregated data, integrated in a single decisional platform. This paper performs the first analysis on the feasibility of the use of an IoT approach and proposes a specific architecture for a sustainable tourism application. The architecture is tailored for the optimisation of the movement of cruise ship tourists in the city of Cagliari (Italy), by taking into consideration factors such as transport information and queue waiting times. A first set of simulations is performed using 67-point of interest, real transportation data, and an optimisation algorithm.

Correct regulation of meteoric surface and subsurface flow waters is a fundamental goal for the sustainable development of the territories. A new system, aimed at real-time monitoring of the rainfall and of the cumulated rainfall, is introduced and discussed in the present paper. The system implements a Sensor Network based on the IoT paradigm and can cover safety-critical “hot spots” with a relatively small number of sensors, strategically placed, in areas not covered by traditional weather radars and rain gauges, and lowering the costs of deployment and maintenance with respects to these devices. A real application case, based on the implementation of the pilot plant at the Monte Scarpino landfill (Genoa, Italy), is presented and discussed. The system performances are assessed on the basis of comparisons with data provided by a polarimetric weather radar and by a traditional rain gauge.

Nowadays, cities can be perceived as increasingly dangerous places. Usually, CCTV is one of the main technologies used in a modern security system. However, poor light situations or bad weather conditions (rain, fog, etc.) limit the detection capabilities of image-based systems. Microwave radar detection systems can be an answer to this limitation and take advantage of the results obtained by low-cost technologies for the automotive market. Transportation by car may be dangerous, and every year car accidents lead to the fatalities of many individuals. Humans require automated assistance when driving through detecting and correctly classifying approaching vehicles and, more importantly, pedestrians. In this paper, we present the application of machine learning to data collected by a 24 GHz short-range radar for urban classification. The training and testing take place on a Raspberry Pi as an edge computing node operating in a client/server arrangement. The software of choice is Rulex, a high-performance machine learning package controlled through a remote interface. Forecasts with a varying number of classes were performed with one, two, or three classes for vehicles and one for humans. Furthermore, we applied a single forecast for all four classes, as well as cascading forecasts in a tree-like structure while varying algorithms, cascading the block order, setting class weights, and varying the data splitting ratio for each forecast to improve prediction accuracy. In the experiments carried out for the validation of the presented approach, an accuracy of up to 100% for human classification and 96.67% for vehicles, in general, was obtained. Vehicle sub-classes were predicted with 90.63% accuracy for motorcycles and 77.34% accuracy for both cars and trucks.

This paper comprises several assays aiming to identify the basis for the bioremediation of mine-impacted water (MIW). To do so, the conditions for build anoxic microcosms for treating this effluent were varied, containing MIW, and a source of chitin, to biostimulate sulfate-reducing bacteria (SRB). The chitin sources were: commercial chitin (CHIT) and shrimp shell (SS), which in addition to chitin, contains CaCO3, and proteins in its composition. The CHIT assays were not successful in sulfate-reduction, even when the pH was increased with CaCO3. However, in all SS assays the SRB development was successful (85% sulfate removal for assay 3), including the metal-free (MF-SS) assay (75% for assay 5). High-throughput sequencing analysis revealed the structure of bacterial community in the SS assay: the most abundant genera were Clostridium and Klebsiella, both fermentative and chitinase producers; a few SRB from the genera Desulfovibrio and Desulfosporosinus were also detected. In the MF-SS assay, Desulfovibrio genuswas detected but Comamonas was dominant. It could be deduced that SS is a suitable substrate for SRB development, but CHIT is not. The sulfate-reduction process was provided by the cooperation between fermentative/chitinase-producer bacteria together with SRB, which leads to efficient MIW treatment, removing sulfate and metallic ions.

Muller cells (MC) represent a key element for the metabolic and functional regulation of the vertebrate retina. The aim of the present study was to test the feasibility of a new method for the in-vivo detection and quantification of extrafoveal MC in human retina. We developed a new approach to isolate and analyse extrafoveal MC in vivo, starting from structural optical coherence tomography data. Our pilot investigation was based on the optical properties of MC, which are known to not interfere with the light reaching the outer retinal structures. We reconstructed MC in the macular region of 18 healthy subjects and the quantitative analyses revealed ~42,000/9 mm2 cells detected. Furthermore, we included 2 patients affected by peripheral intraocular melanoma, with macular sparing, needing surgical enucleation. We used these two eyes to perform a qualitative comparison between our reconstructions and histological findings. Our study represents the first pilot investigation dedicated on the non-invasive isolation and quantification of MC, in-vivo, in human retina. Although we are aware that our study has several limitations, first of all related with the proper detection of foveal MC, because of the peculiar z-shape morphology, this approach may open new opportunities for the non-invasive in vivo analysis of MC, providing also potential useful perspectives in retinal diseases.

Bacterial profile, nutrients removal performance, and membrane fouling behavior were evaluated in an anoxic/oxic electro-membrane bioreactor (A/O e-MBR) treating municipal wastewater at dif...

This paper extends to 4K or Ultra High Definition (UHD) the scope of a parametric model for perceptual video quality estimation, which has been previously designed for resolutions from Quarter Common Intermediate Format (QCIF) up to High Definition (HD).