CID Survey Report Satellite Imagery and Associated Services used by the JRC. Current Status and Future Needs

The Agriculture and Fisheries Unit (IPSC) together with the Informatics, Networks and Library Unit (ISD) has performed this inventory called the Community Image Data portal Survey (the CID Survey); 20 Actions from 4 different Institutes (ISD, IPSC, IES, and IHCP) were interviewed. The objectives of the survey were to make an inventory of existing satellite data and future requirements; to obtain an overview of how data is acquired, used and stored; to quantify human and financial resources engaged in this process; to quantify storage needs and to query the staff involved in image acquisition and management on their needs and ideas for improvements in view of defining a single JRC portal through which imaging requests could be addressed. Within the JRC there are (including 2006) more than 700 000 low resolution (LR) and 50 000 medium resolution (MR) images, with time series as far back as 1981 for the LR data. There are more than 10 000 high resolution (HR) images and over 500 000 km2 of very high resolution (VHR) images. For the LR and MR data, cyclic global or continental coverage dominates, while the majority of HR and VHR data is acquired over Europe. The expected data purchase in the future (2007, 2008) known which enables good planning. Most purchases of VHR and HR data are made using the established FCs with common licensing terms. Otherwise multiple types of licensing govern data usage which emphasizes the need for CID to establish adequate means of data access. The total amount of image data stored (2006 inclusive) is 55 TB, with an expected increase of 80% in 2 years. Most of the image data is stored on internal network storage inside the corporate network which implies that the data is accessible from JRC, but difficulties arise when access is to be made by external users via Internet. In principle current storage capacity in the JRC could be enough, but available space is fragmented between Actions which therefore implies that a deficit in storage could arise. One solution to this issue is the sharing of a central storage service. Data reception is dominated by FTP data transfer which therefore requires reliable and fast Internet transfer bandwidth. High total volume for backup requires thorough definition of backup strategy. The user groups at JRC are heterogeneous which places requirements on CID to provide flexible authentication mechanisms. There is a requirement for a detailed analysis of all metadata standards needed for reference in a catalogue. There is a priority interest for such Catalogue Service and also for a centralized storage. The services to implement for data hosted on central storage should be WCS, WMS, file system access. During the analysis of the results mentioned above, some major areas could be identified as a base for common services to be provided to interested Actions, such as: provision of a centralized data storage facility with file serving functionality including authentication service, image catalogue services, data visualization and dissemination services. Specialized data services that require highly customized functionality with respect to certain properties of the different image types will usually remain the sole responsibility of the individual Actions. An orthorectification service for semi-automated orthorectification of HR and VHR data will be provided to certain Actions. At the end of the report some priorities and an implementation schedule for the Community Image Data portal (CID) are given.JRC.G.3-Agricultur

Exploiting the PIR Sensor Analog Behavior as Thermoreceptor: Movement Direction Classification Based on Spiking Neurons

Pyroelectric infrared sensors (PIR) are widely used as infrared (IR) detectors due to their basic implementation, low cost, low power, and performance. Combined with a Fresnel lens, they can be used as a binary detector in applications of presence and motion control. Furthermore, due to their features, they can be used in autonomous intelligent devices or included in robotics applications or sensor networks. In this work, two neural processing architectures are presented: (1) an analog processing approach to achieve the behavior of a presynaptic neuron from a PIR sensor. An analog circuit similar to the leaky integrate and fire model is implemented to be able to generate spiking rates proportional to the IR stimuli received at a PIR sensor. (2) An embedded postsynaptic neuron where a spiking neural network matrix together with an algorithm based on digital processing techniques is introduced. This structure allows connecting a set of sensors to the post-synaptic circuit emulating an optic nerve. As a case study, the entire neural processing approach presented in this paper is applied to optical flow detection considering a four-PIR array as input. The results validate both the spiking approach for an analog sensor presented and the ability to retrieve the analog information sent as spike trains in a simulated optic nerve.Los sensores infrarrojos piroeléctricos (PIR) se utilizan ampliamente como detectores de infrarrojos (IR) debido a su implementación básica, bajo costo, baja potencia y rendimiento. Combinados con una lente Fresnel, se pueden utilizar como detector binario en aplicaciones de control de presencia y movimiento. Además, por sus características, pueden utilizarse en dispositivos inteligentes autónomos o incluirse en aplicaciones de robótica o redes de sensores. En este trabajo, se presentan dos arquitecturas de procesamiento neuronal: (1) un enfoque de procesamiento analógico para lograr el comportamiento de una neurona presináptica a partir de un sensor PIR. Se implementa un circuito analógico similar al modelo de integración y disparo con fugas para poder generar tasas de picos proporcionales a los estímulos IR recibidos en un sensor PIR. (2) Una neurona postsináptica integrada donde se introduce una matriz de red neuronal con picos junto con un algoritmo basado en técnicas de procesamiento digital. Esta estructura permite conectar un conjunto de sensores al circuito postsináptico emulando un nervio óptico. Como estudio de caso, todo el enfoque de procesamiento neuronal presentado en este artículo se aplica a la detección de flujo óptico considerando una matriz de cuatro PIR como entrada. Los resultados validan tanto el enfoque de picos para un sensor analógico presentado como la capacidad de recuperar la información analógica enviada como trenes de picos en un nervio óptico simulado.This research was partially supported by the Spanish grant MINDROB (PID2019-105556GB-C33) and by the CHIST-ERA H2020 grant SMALL (CHIST-ERA-18-ACAI-004)

Wi-Fi Enabled Healthcare

Focusing on its recent proliferation in hospital systems, Wi-Fi Enabled Healthcare explains how Wi-Fi is transforming clinical work flows and infusing new life into the types of mobile devices being implemented in hospitals. Drawing on first-hand experiences from one of the largest healthcare systems in the United States, it covers the key areas associated with wireless network design, security, and support. Reporting on cutting-edge developments and emerging standards in Wi-Fi technologies, the book explores security implications for each device type. It covers real-time location services and emerging trends in cloud-based wireless architecture. It also outlines several options and design consideration for employee wireless coverage, voice over wireless (including smart phones), mobile medical devices, and wireless guest services. This book presents authoritative insight into the challenges that exist in adding Wi-Fi within a healthcare setting. It explores several solutions in each space along with design considerations and pros and cons. It also supplies an in-depth look at voice over wireless, mobile medical devices, and wireless guest services. The authors provide readers with the technical knowhow required to ensure their systems provide the reliable, end-to-end communications necessary to surmount today’s challenges and capitalize on new opportunities. The shared experience and lessons learned provide essential guidance for large and small healthcare organizations in the United States and around the world. This book is an ideal reference for network design engineers and high-level hospital executives that are thinking about adding or improving upon Wi-Fi in their hospitals or hospital systems

The Role of Visual Features in Text-Based CAPTCHAs: An fNIRS Study for Usable Security

To mitigate dictionary attacks or similar undesirable automated attacks to information systems, developers mostly prefer using CAPTCHA challenges as Human Interactive Proofs (HIPs) to distinguish between human users and scripts. Appropriate use of CAPTCHA requires a setup that balances between robustness and usability during the design of a challenge. The previous research reveals that most usability studies have used accuracy and response time as measurement criteria for quantitative analysis. The present study aims at applying optical neuroimaging techniques for the analysis of CAPTCHA design. The functional Near-Infrared Spectroscopy technique was used to explore the hemodynamic responses in the prefrontal cortex elicited by CAPTCHA stimulus of varying types. )e findings suggest that regions in the left and right dorsolateral and right dorsomedial prefrontal cortex respond to the degrees of line occlusion, rotation, and wave distortions present in a CAPTCHA. The systematic addition of the visual effects introduced nonlinear effects on the behavioral and prefrontal oxygenation measures, indicative of the emergence of Gestalt effects that might have influenced the perception of the overall CAPTCHA figure.Comment: 24 pages, 32 figures, Computational Intelligence and Neuroscienc

Deep Models and Shortwave Infrared Information to Detect Face Presentation Attacks

This paper addresses the problem of face presentation attack detection using different image modalities. In particular, the usage of short wave infrared (SWIR) imaging is considered. Face presentation attack detection is performed using recent models based on Convolutional Neural Networks using only carefully selected SWIR image differences as input. Conducted experiments show superior performance over similar models acting on either color images or on a combination of different modalities (visible, NIR, thermal and depth), as well as on a SVM-based classifier acting on SWIR image differences. Experiments have been carried on a new public and freely available database, containing a wide variety of attacks. Video sequences have been recorded thanks to several sensors resulting in 14 different streams in the visible, NIR, SWIR and thermal spectra, as well as depth data. The best proposed approach is able to almost perfectly detect all impersonation attacks while ensuring low bonafide classification errors. On the other hand, obtained results show that obfuscation attacks are more difficult to detect. We hope that the proposed database will foster research on this challenging problem. Finally, all the code and instructions to reproduce presented experiments is made available to the research community

Development of a simulation tool for measurements and analysis of simulated and real data to identify ADLs and behavioral trends through statistics techniques and ML algorithms

openCon una popolazione di anziani in crescita, il numero di soggetti a rischio di patologia è in rapido aumento. Molti gruppi di ricerca stanno studiando soluzioni pervasive per monitorare continuamente e discretamente i soggetti fragili nelle loro case, riducendo i costi sanitari e supportando la diagnosi medica. Comportamenti anomali durante l'esecuzione di attività di vita quotidiana (ADL) o variazioni sulle tendenze comportamentali sono di grande importanza.With a growing population of elderly people, the number of subjects at risk of pathology is rapidly increasing. Many research groups are studying pervasive solutions to continuously and unobtrusively monitor fragile subjects in their homes, reducing health-care costs and supporting the medical diagnosis. Anomalous behaviors while performing activities of daily living (ADLs) or variations on behavioral trends are of great importance. To measure ADLs a significant number of parameters need to be considering affecting the measurement such as sensors and environment characteristics or sensors disposition. To face the impossibility to study in the real context the best configuration of sensors able to minimize costs and maximize accuracy, simulation tools are being developed as powerful means. This thesis presents several contributions on this topic. In the following research work, a study of a measurement chain aimed to measure ADLs and represented by PIRs sensors and ML algorithm is conducted and a simulation tool in form of Web Application has been developed to generate datasets and to simulate how the measurement chain reacts varying the configuration of the sensors. Starting from eWare project results, the simulation tool has been thought to provide support for technicians, developers and installers being able to speed up analysis and monitoring times, to allow rapid identification of changes in behavioral trends, to guarantee system performance monitoring and to study the best configuration of the sensors network for a given environment. The UNIVPM Home Care Web App offers the chance to create ad hoc datasets related to ADLs and to conduct analysis thanks to statistical algorithms applied on data. To measure ADLs, machine learning algorithms have been implemented in the tool. Five different tasks have been identified. To test the validity of the developed instrument six case studies divided into two categories have been considered. To the first category belong those studies related to: 1) discover the best configuration of the sensors keeping environmental characteristics and user behavior as constants; 2) define the most performant ML algorithms. The second category aims to proof the stability of the algorithm implemented and its collapse condition by varying user habits. Noise perturbation on data has been applied to all case studies. Results show the validity of the generated datasets. By maximizing the sensors network is it possible to minimize the ML error to 0.8%. Due to cost is a key factor in this scenario, the fourth case studied considered has shown that minimizing the configuration of the sensors it is possible to reduce drastically the cost with a more than reasonable value for the ML error around 11.8%. Results in ADLs measurement can be considered more than satisfactory.INGEGNERIA INDUSTRIALEopenPirozzi, Michel

Real-time localisation system for GPS denied open areas using smart street furniture

Real-time measurement of crowd dynamics has been attracting significant interest, as it has many applications including real-time monitoring of emergencies and evacuation plans. To effectively measure crowd behaviour, an accurate estimate for pedestrians’ locations is required. However, estimating pedestrians’ locations is a great challenge especially for open areas with poor Global Positioning System (GPS) signal reception and/or lack of infrastructure to install expensive solutions such as video-based systems. Street furniture assets such as rubbish bins have become smart, as they have been equipped with low-power sensors. Currently, their role is limited to certain applications such as waste management. We believe that the role of street furniture can be extended to include building real-time localisation systems as street furniture provides excellent coverage across different areas such as parks, streets, homes, universities. In this thesis, we propose a novel wireless sensor network architecture designed for smart street furniture. We extend the functionality of sensor nodes to act as soft Access Point (AP), sensing Wifi signals received from surrounding Wifi-enabled devices. Our proposed architecture includes a real-time and low-power design for sensor nodes. We attached sensor nodes to rubbish bins located in a busy GPS denied open area at Murdoch University (Perth, Western Australia), known as Bush Court. This enabled us to introduce two unique Wifi-based localisation datasets: the first is the Fingerprint dataset called MurdochBushCourtLoC-FP (MBCLFP) in which four users generated Wifi fingerprints for all available cells in the gridded Bush Court, called Reference Points (RPs), using their smartphones, and the second is the APs dataset called MurdochBushCourtLoC-AP (MBCLAP) that includes auto-generated records received from over 1000 users’ devices. Finally, we developed a real-time localisation approach based on the two datasets using a four-layer deep learning classifier. The approach includes a light-weight algorithm to label the MBCLAP dataset using the MBCLFP dataset and convert the MBCLAP dataset to be synchronous. With the use of our proposed approach, up to 19% improvement in location prediction is achieved