Towards sustainable energy-efficient communities based on a scheduling algorithm

The Internet of Things (IoT) and Demand Response (DR) combined have transformed the way Information and Communication Technologies (ICT) contribute to saving energy and reducing costs, while also giving consumers more control over their energy footprint. Unlike current price and incentive based DR strategies, we propose a DR model that promotes consumers reaching coordinated behaviour towards more sustainable (and green) communities. A cooperative DR system is designed not only to bolster energy efficiency management at both home and district levels, but also to integrate the renewable energy resource information into the community's energy management. Initially conceived in a centralised way, a data collector called the "aggregator" will handle the operation scheduling requirements given the consumers' time preferences and the available electricity supply from renewables. Evaluation on the algorithm implementation shows feasible computational cost (CC) in different scenarios of households, communities and consumer behaviour. Number of appliances and timeframe flexibility have the greatest impact on the reallocation cost. A discussion on the communication, security and hardware platforms is included prior to future pilot deployment.Comunidad de Madri

A wearable closed-loop insulin delivery system based on low-power SoCs

The number of patients living with diabetes has increased significantly in recent years due to several factors. Many of these patients are choosing to use insulin pumps for their treatment, artificial systems that administer their insulin and consist of a glucometer and an automatic insulin supply working in an open loop. Currently, only a few closed-loop insulin delivery devices are commercially available. The most widespread systems among patients are what have been called the “Do-It-Yourself Hybrid Closed-Loop systems.” These systems require the use of platforms with high computing power. In this paper, we will present a novel wearable system for insulin delivery that reduces the energy and computing consumption of the platform without affecting the computation requirements. Patients’ information is obtained from a commercial continuous glucose sensor and a commercial insulin pump operating in a conventional manner. An ad-hoc embedded system will connect with the pump and the sensor to collect the glucose data and process it. That connection is accomplished through a radiofrequency channel that provides a suitable system for the patient. Thus, this system does not require to be connected to any other processor, which increases the overall stability. Using parameters configured by the patient, the control system will make automatic adjustments in the basal insulin infusion thereby bringing the patient’s glycaemia to the target set by a doctor’s prescription. The results obtained will be satisfactory as long as the configured parameters faithfully match the specific characteristics of the patient. Results from the simulation of 30 virtual patients (10 adolescents, 10 adults, and 10 children), using a python implementation of the FDA-approved (Food and Drug Administration) UVa (University of Virginia)/Padova Simulator and a python implementation of the proposed algorithm, are presented

A new framework for deep learning video based Human Action Recognition on the edge

Nowadays, video surveillance systems are commonly found in most public and private spaces. These systems typically consist of a network of cameras that feed into a central node. However, the processing aspect is evolving towards distributed approaches, leveraging edge-computing. These distributed systems are capable of effectively addressing the detection of people or events at each individual node. Most of these systems, rely on the use of deep-learning and segmentation algorithms which enable them to achieve high performance, but usually with a significant computational cost, hindering real-time execution. This paper presents an approach for people detection and action recognition in the wild, optimized for running on the edge, and that is able to work in real-time, in an embedded platform. Human Action Recognition (HAR) is performed by using a Recurrent Neural Network (RNN), specifically a Long Short-Term Memory (LSTM). The input to the LSTM is an ad-hoc, lightweight feature vector obtained from the bounding box of each detected person in the video surveillance image. The resulting system is highly portable and easily scalable, providing a powerful tool for real-world video surveillance applications (in the wild and real-time action recognition). The proposal has been exhaustively evaluated and compared against other state-of-the-art (SOTA) proposals in five datasets, including four widely used (KTH, WEIZMAN, WVU, IXMAX) and a novel one (GBA) recorded in the wild, that includes several people performing different actions simultaneously. The obtained results validate the proposal, since it achieves SOTA accuracy within a much more complicated video surveillance real scenario, and using a lightweight embedded hardware.European CommissionAgencia Estatal de InvestigaciónUniversidad de Alcal

Accuracy and precision of agents orientation in an indoor positioning system using multiple infrastructure lighting spotlights and a PSD sensor

In indoor localization there are applications in which the orientation of the agent to be located is as important as knowing the position. In this paper we present the results of the orientation estimation from a local positioning system based on position-sensitive device (PSD) sensors and the visible light emitted from the illumination of the room in which it is located. The orientation estimation will require that the PSD sensor receives signal from either 2 or 4 light sources simultaneously. As will be shown in the article, the error determining the rotation angle of the agent with the on-board sensor is less than 0.2 degrees for two emitters. On the other hand, by using 4 light sources the three Euler rotation angles are determined, with mean errors in the measurements smaller than 0.35◦ for the x- and y-axis and 0.16◦ for the z-axis. The accuracy of the measurement has been evaluated experimentally in a 2.5 m-high ceiling room over an area of 2.2 m2 using geodetic measurement tools to establish the reference ground truth values.Junta de Comunidades de Castilla-La Manch

Analysis of Multiple-Access Discrimination Techniques for the Development of a PSD-Based VLP System

There are several technologies and techniques available when developing indoor positioning systems (IPS). Recently, the development of positioning systems based on optical signals has aroused great interest, mainly those using visible light from the lighting infrastructure. In this work, we analyze which techniques give better results to lay the foundations for the development of a Visible Light Positioning system (VLP). Working only with a receiver, it is analyzed what the result of determining the position of different emitters is when they emit simultaneously and without any synchronism. The results obtained by Frequency Division Multiple Access (FDMA) (with digital bandpass filters, I/Q demodulation, and FFT) and Code Division Multiple Access (CDMA) are compared. The interference between signals when emitted simultaneously from multiple emitters is analyzed as well as the errors they cause and how these effects can be mitigated. As a result of the research, the advantages and disadvantages using different multiple-access determination techniques are determined. In addition, advantages and disadvantages of using FDMA and CDMA techniques as well as hardware requirements that make one more feasible than the other are presented. The system behavior, in terms of errors, is established using FDMA and different configurations such as: I/Q, RMS, or FFT. The work also determines the error rates that can be obtained with the different FDMA and CDMA configurations, considering different error scenarios and integration time. Synthetic emulations and empirical tests were performed, which concluded that IPS systems based on optical signals and PSD sensors can achieve very high measurement accuracies and a high measurement rate. Obtained positioning errors in a room of 3 m height are less than 1 cm when working in noisy environments.Agencia Estatal de Investigació

A nonrecursive GR algorithm to extract road networks in high-resolution images from remote sensing

A number of studies address the development of algorithms based on the Growing Region (GR) technique adaptations for extracting road networks in images. However, these algorithms are high-computationally demanding and time-consuming while processing high-resolution images. The aim of this study is to introduce a modified version of the GR algorithm, named Nonrecursive Growing Region (NRGR), to extract road networks in high-resolution images from remote sensing. This study describes how the NRGR algorithm works to perform the extractions in a faster way. The proposed algorithm was developed taking into consideration the reduction of the data dependence between its tasks in order to allow the GR algorithm to process these tasks with the help of Graphical Processor Units (GPUs). The experiments were conducted to demonstrate the ability of the NRGR to process low or high spatial resolution images with or without the help of GPUs. Results achieved by experiments performed in this study suggest that the NRGR algorithm is less complex and faster than previous adaptations versions tested of the GR algorithm to process images. The NRGR was able to process the tested images with less than 30% of the time used by the recursive algorithm, reaching values below 10% in some cases. The NRGR algorithm can be used as software or hardware-software system"s co-design solutions to develop maps of road networks for Cartography

Accuracy and Precision Assessment of AoA-Based Indoor Positioning Systems Using InfrastructureLighting and a Position-Sensitive Detector

Unlike GNSS-based outdoor positioning, there is no technological alternative for Indoor Positioning Systems (IPSs) that generally stands out from the others. In indoor contexts, the measurement technologies and localization strategies to be used depend strongly on the application requirements and are complementary to each other. In this work, we present an optical IPS based on a Position-Sensitive Detector (PSD) and exploiting illumination infrastructure to determine the target position by Angle of Arrival (AoA) measurements. We combine the proposed IPS with different positioning strategies depending on the number of visible emitters (one, two, or more) and available prior or additional information about the scenario and target. The accuracy and precision of the proposal is assessed experimentally for the different strategies in a 2.47 m high space covering approximately 2.2 m2, using high-end geodetic equipment to establish the reference ground truth. When the orientation of the target is known from external measurements, an average positioning error of 8.2 mm is obtained using the signal received from only one emitter. Using simultaneous observations from two emitters, an average positioning error of 9.4 mm is obtained without external information when the target movement is restricted to a plane. Conversely, if four signals are available, an average positioning error of 4.9 cm is demonstrated, yielding the complete 3D pose of the target free of any prior assumption or additional measurements. In all cases, a precision (2s) better than 5.9 mm is achieved across the complete test space for an integration time of 10 ms. The proposed system represents a prospectively useful alternative for indoor positioning applications requiring fast and reliable cm-level accuracy with moderate cost when smart illumination infrastructure is available in the environment

Calibrage d'une caméra zoom. Applications aux tourelles tilt-pan pour la robotique

CLERMONT FD-BCIU Sci.et Tech. (630142101) / SudocSudocFranceF

Cooperative demand response framework for a smart community targeting renewables: testbed implementation and performance evaluation

Demand response (DR) is emerging as the workhorse of achieving energy efficiency and reducing our carbon footprint, which persists as a major challenge amongst all the different energy-chain players, i.e., the utility providers, policy makers, consumers, and the technology sector. For instance, the Internet-of-Things (IoT) paradigm and network-enabled appliances/devices have escalated the expectations of what technology could do for the acceptance of DR programs. In this work, we design, deploy on a scalable pilot testbed, and evaluate a collaboration-based approach to the demand-side management of a community of electricity consumers that jointly targets green consumption. The design of the framework architecture is centralized via the so-called aggregator, which optimizes the demand scheduled by consumers along with their time frame preferences towards the maximization of the consumption of renewables. On the pilot, we opt for lightweight, yet efficient platforms such as Raspberry Pi boards,and evaluate them over a series of network protocols, i.e., MQTT-TLS and CoAP-DTLS, paying special attention to the security and privacy of the communications over Z-Wave, ZigBee, andWiFi. The experiments conducted are configured using two active Living Labs datasets from which we extract three community scenarios that vary according to the flexibility or rigidity of the appliances’ operation time frame demand. During the performance evaluation, processing and communication overheads lie within feasible ranges, i.e., the aggregator requires less than 2 s to schedule a small consumer community with four appliances, whereas the latency of its link to households’ controllers adds less than 100 ms. In addition, we demonstrate that our implementations running over WiFi links and UDP sockets on Raspberry Pi 4 boards are fast, though insecure. By contrast, secure CoAP (with DTLS) offers data encryption, automatic key management, and integrity protection, as well as authentication with acceptable overheads.Comunidad de Madri

New Applications and Architectures Based on FPGA/SoC

Nowadays, the digital world demands continuous technological evolutions [...