Proof of Presence: Novel Vehicle Detection System

The detection of vehicle presence in parking slots is a fundamental part of smart parking systems. The problem is commonly approached with devices composed of specific sensors, which usually are either magnetic or infrared. The sensor is continuously sampled by an onboard microcontroller capable of determining the slot status (available or occupied) and sending the information to a central collection and gathering system by means of a wireless technology. Nevertheless, cost and power consumption are still an issue. Based on the consideration that the vehicle slot status is only a single bit of information, in this article an alternative low-power and cost-effective approach is proposed. Specifically, two novel vehicle presence detectors, one battery-powered and based on 868 MHz LoRa technology and one solar-cell-powered and adopting BAP 866 MHz UHF RFID technology, are presented, designed, realized, and tested. The obtained results demonstrate the appropriateness of the proposed approach since the same functionalities of conventional devices at lower cost and lower consumption are reached

Impact of RFID and EPCglobal on Critical Processes of the Pharmaceutical Supply Chain

The need to implement and guarantee effective item-level tracing systems is becoming more and more important for a wide range of business applications, such as manufacturing, logistics, healthcare, and anti-counterfeiting. Among these, the pharmaceutical supply chain, with millions of medicines moving around the world and needing to be traced at item level, represents a very interesting reference scenario. Furthermore, the growing counterfeiting problem raises a significant threat within the supply chain system. Recently, several international institutions (e.g. Food and Drug Administration, European Medicines Agency, European Federation of Pharmaceutical Industries and Associations, GS1) are encouraging the use of innovative solutions in healthcare and in the pharmaceutical supply chain, to improve patient safety and enhance the efficiency of the pharmaceutical supply chain, with better worldwide drug traceability

MAC Layer QoS Mechanisms for a Geostationary Satellite Network

An efficient resource management is crucial in supporting multimedia traffic in satellite networks. To this, Dynamic Bandwidth Allocation Capabilities mechanisms can be exploited to deliver the required QoS while optimising the bandwidth utilization. This paper just deals with the design of innovative algorithms for scheduling and sending the resource requests queued on a EuroSkyWay [1,2] satellite terminal to a Traffic Resource Manager (TRM). The effectiveness of the defined mechanisms has been evaluated through computer simulations. Particularly, by considering different mixes of MPEG-2 traffic and HTTP traffic, the Average Waiting Time (AWT) of the requests and their Losses Percentage have been calculated and compared with those ones obtained using algorithms proposed in MAC layer EuroSkyWay specifications

Animal models of compulsive eating behavior

In industrialized nations, overeating is a significant problem leading to overweight, obesity, and a host of related disorders; the increase in these disorders has prompted a significant amount of research aimed at understanding their etiology. Eating disorders are multifactorial conditions involving genetic, metabolic, environmental, and behavioral factors. Considering that compulsive eating in the face of adverse consequences characterizes some eating disorders, similar to the way in which compulsive drug intake characterizes drug-addiction, it might be considered an addiction in its own right. Moreover, numerous review articles have recently drawn a connection between the neural circuits activated in the seeking/intake of palatable food and drugs of abuse. Based on this observation, “food addiction” has emerged as an area of intense scientific research and accumulating evidence suggests it is possible to model some aspects of food addiction in animals. The development of well-characterized animal models would advance our understanding of the etiologic neural factors involved in eating disorders, such as compulsive overeating, and it would permit to propose targeted pharmacological therapies. However, to date, little evidence has been reported of continued food seeking and intake despite its harmful consequences in rats and mice

An innovative AAL system based on neural networks and IoT-aware technologies to improve the quality of life in elderly people

Nowadays more and more elderly people need support in daily activities. This is due to the increase of cognitive diseases and other conditions which lead the elderly to not being self-sufficient. Considering this, providing an Ambient Assisted Living system could improve significantly people life quality and could support caregivers' tasks. The combination of Ambient Assisted Living systems and information and communication technologies achieve this purpose perfectly. They exploit internet of things and artificial intelligence paradigms to make daily challenges easier for people with neurodegenerative diseases. This work melds technologies mentioned above providing a smart system for elderly to manage goods and fill in shopping lists. It was possible using software, hardware, and cloud systems combined with a neural network aimed to recognise products. The proposed system has been validated both from a functional point of view through a proof-of-concept and quantitatively by a performance analysis of its components

Location Based Indoor and Outdoor Lightweight Activity Recognition System

In intelligent environments one of the most relevant information that can be gathered about users is their location. Their position can be easily captured without the need for a large infrastructure through devices such as smartphones or smartwatches that we easily carry around in our daily life, providing new opportunities and services in the field of pervasive computing and sensing. Location data can be very useful to infer additional information in some cases such as elderly or sick care, where inferring additional information such as the activities or types of activities they perform can provide daily indicators about their behavior and habits. To do so, we present a system able to infer user activities in indoor and outdoor environments using Global Positioning System (GPS) data together with open data sources such as OpenStreetMaps (OSM) to analyse the user’s daily activities, requiring a minimal infrastructure

Behavior modeling for a beacon-based indoor location system

In this work we performed a comparison between two different approaches to track a person in indoor environments using a locating system based on BLE technology with a smartphone and a smartwatch as monitoring devices. To do so, we provide the system architecture we designed and describe how the different elements of the proposed system interact with each other. Moreover, we have evaluated the system’s performance by computing the mean percentage error in the detection of the indoor position. Finally, we present a novel location prediction system based on neural embeddings, and a soft-attention mechanism, which is able to predict user’s next location with 67% accuracy

An IoT-Aware Smart System Exploiting the Electromagnetic Behavior of UHF-RFID Tags to Improve Worker Safety in Outdoor Environments

Recently, different solutions leveraging Internet of Things (IoT) technologies have been adopted to avoid accidents in agricultural working environments. As an example, heavy vehicles, e.g., tractors or excavators, have been upgraded with remote controls. Nonetheless, the community continues to encourage discussions on safety issues. In this framework, a localization system installed on remote-controlled farm machines (RCFM) can help in preventing fatal accidents and reduce collision risks. This paper presents an innovative system that exploits passive UHF-RFID technology supported by commercial BLE Beacons for monitoring and preventing accidents that may occur when ground-workers in RCFM collaborate in outdoor agricultural working areas. To this aim, a modular architecture is proposed to locate workers, obstacles and machines and guarantees the security of RCFM movements by using specific notifications for ground-workers prompt interventions. Its main characteristics are presented with its main positioning features based on passive UHF-RFID technology. An experimental campaign discusses its performance and determines the best configuration of the UHF-RFID tags installed on workers and obstacles. Finally, system validation demonstrates the reliability of the main components and the usefulness of the proposed architecture for worker safety

Cadmium ions and cadmium sulphide particles in γ-titanium dihydrogen phosphate. Synthesis, thermal behaviour and X-ray characterization

AbstractLayered compounds with CdS particles supported on the ion-exchanger, γ-titanium dihydrogen phosphate, were prepared by the stepwise reaction of the ion-exchanger and cadmium solution, followed by reaction with H2S gas. The CdS content on the ion-exchanger is dependent on the timeframe of the H2S gas flow. The materials obtained were layered, as shown by the X-ray measurements that exhibit both precursor and CdS diffraction peaks. The thermal treatment of the material obtained gives evidence of its stability (≤ 320°C) before the CdS decomposition, which occurs in a single step

Wearable Technologies and AI at the Far Edge for Chronic Heart Failure Prevention and Management: A Systematic Review and Prospects

Smart wearable devices enable personalized at-home healthcare by unobtrusively collecting patient health data and facilitating the development of intelligent platforms to support patient care and management. The accurate analysis of data obtained from wearable devices is crucial for interpreting and contextualizing health data and facilitating the reliable diagnosis and management of critical and chronic diseases. The combination of edge computing and artificial intelligence has provided real-time, time-critical, and privacy-preserving data analysis solutions. However, based on the envisioned service, evaluating the additive value of edge intelligence to the overall architecture is essential before implementation. This article aims to comprehensively analyze the current state of the art on smart health infrastructures implementing wearable and AI technologies at the far edge to support patients with chronic heart failure (CHF). In particular, we highlight the contribution of edge intelligence in supporting the integration of wearable devices into IoT-aware technology infrastructures that provide services for patient diagnosis and management. We also offer an in-depth analysis of open challenges and provide potential solutions to facilitate the integration of wearable devices with edge AI solutions to provide innovative technological infrastructures and interactive services for patients and doctors