MEDUSA: Scalable Biometric Sensing in the Wild through Distributed MIMO Radars

Radar-based techniques for detecting vital signs have shown promise for continuous contactless vital sign sensing and healthcare applications. However, real-world indoor environments face significant challenges for existing vital sign monitoring systems. These include signal blockage in non-line-of-sight (NLOS) situations, movement of human subjects, and alterations in location and orientation. Additionally, these existing systems failed to address the challenge of tracking multiple targets simultaneously. To overcome these challenges, we present MEDUSA, a novel coherent ultra-wideband (UWB) based distributed multiple-input multiple-output (MIMO) radar system, especially it allows users to customize and disperse the $16 \times 16$ into sub-arrays. MEDUSA takes advantage of the diversity benefits of distributed yet wirelessly synchronized MIMO arrays to enable robust vital sign monitoring in real-world and daily living environments where human targets are moving and surrounded by obstacles. We've developed a scalable, self-supervised contrastive learning model which integrates seamlessly with our hardware platform. Each attention weight within the model corresponds to a specific antenna pair of Tx and Rx. The model proficiently recovers accurate vital sign waveforms by decomposing and correlating the mixed received signals, including comprising human motion, mobility, noise, and vital signs. Through extensive evaluations involving 21 participants and over 200 hours of collected data (3.75 TB in total, with 1.89 TB for static subjects and 1.86 TB for moving subjects), MEDUSA's performance has been validated, showing an average gain of 20% compared to existing systems employing COTS radar sensors. This demonstrates MEDUSA's spatial diversity gain for real-world vital sign monitoring, encompassing target and environmental dynamics in familiar and unfamiliar indoor environments.Comment: Preprint. Under Revie

Distributed manufacturing systems and the internet of things : a case study

In order to stay competitive in today's global market, manufacturing companies need to be flexible. To ensure flexible production, shorten processing times, and reduce time-tomarket, companies are utilizing the distributed manufacturing system paradigm, wherein geographically distributed, local resources are used for product development and production. In this context, the Internet of Things (IoT) has emerged as a concept which uses existing communication technologies, such as local wireless networks and the Internet to ensure visibility of anything from anywhere and at any time. In the paper, a case study of applying the IoT to the manufacturing domain is discussed. A distributed agent-based system for virtual monitoring and control of 3-axis CNC milling machine tools is designed and developed. The machines' 3D models and process states are shown through a web interface in real-time. The potential and challenges of implementing this system and the basic building blocks for decentralized value creation are discussed

Laser Chemosensor with Rapid Responsivity and Inherent Memory Based on a Polymer of Intrinsic Microporosity

This work explores the use of a polymer of intrinsic microporosity (PIM-1) as the active layer within a laser sensor to detect nitroaromatic-based explosive vapors. We show successful detection of dinitrobenzene (DNB) by monitoring the real-time photoluminescence. We also show that PIM-1 has an inherent memory, so that it accumulates the analyte during exposure. In addition, the optical gain and refractive index of the polymer were studied by amplified spontaneous emission and variable-angle ellipsometry, respectively. A second-order distributed feedback PIM-1 laser sensor was fabricated and found to show an increase in laser threshold of 2.5 times and a reduction of the laser slope efficiency by 4.4 times after a 5-min exposure to the DNB vapor. For pumping at 2 times threshold, the lasing action was stopped within 30 s indicating that PIM-1 has a very fast responsivity and as such has a potential sensing ability for ultra-low-concentration explosives

Distribution automation on LV and MV using distributed intelligence

Nowadays, the electrical energy distribution network is considered to be a critical infrastructure in industrially developed societies. Its protection regarding safety and security threats is increasing awareness and concern.The fact that this infrastructure is geographically spread across large areas brings difficult technological challenges for fault management (real-time prevention, detection, precise location and isolation of anomalies). The distribution automation and fault location in the distribution network is a key task for any operator. Distribution Automation (DA) and electric network monitoring and sensing can reduce outage and repair times, optimise voltage profiles and improve asset management. Advanced DA processes real-time information from sensors, smart meters, Distributed Generation (DG) and network topology for fault location, automatic reconfiguration of feeders, voltage and reactive power optimisation. This paper addresses this subject and describes the technology and functionalities being implemented on a DA pilot project promoted by EDP Distribuição (EDP Group), Portugal and under InovGrid initiative together with EFACEC and Silver Spring Networks (SSN)

BIG MOBILITY DATA ANALYTICS FOR TRAFFIC MONITORING AND CONTROL

With the overpopulation of large cities, the problems with citizens’ mobility, transport inefficiency, traffic congestions and environmental pollution caused by the heavy traffic require advanced ITS solutions to be overcome. Recent advances and wide proliferation of mobile and Internet of Things (IoT) devices, carried by people, built in vehicles and integrated in a road infrastructure, enable collection of large scale data related to mobility and traffic in smart cities, still with a limited use in real world applications. In this paper, we propose the traffic monitoring, control and adaptation platform, named TrafficSense, based on Big Mobility Data processing and analytics. It provides a continuous monitoring of a traffic situation and detection of important traffic parameters, conditions and events, such as travel times along the street segments and traffic congestions in real time. Upon detecting a traffic congestion on an intersection, the TrafficSense application leverages the feedback control loop mechanism to provide a traffic adaptation based on the dynamic configuration of traffic lights duration in order to increase the traffic flows in critical directions at the intersections. We tested and evaluated the developed application on the distributed cloud computing infrastructure. By varying the streaming workload and the cluster parameters we show the feasibility and applicability of our approach and the platform

Preliminary spectral analysis of near-real-time radon data

Fast Fourier analysis of the near-real-time radon data collected since 1977 by the Caltech automated radon-thoron monitoring system has been carried out in order to determine if any characteristic frequency components are present that can be associated either with precursors to seismicity or with environmental factors. Preliminary results indicate that during "quiet" periods with low seismicity and no rainfall the spectral power is distributed as 1/f. Before four local earthquakes a departure from this 1/f behavior was observed at low frequency. During periods of heavy rainfall an increase in both low and high frequency power was observed. The spectral power of the large radon anomaly observed prior to the October 15, 1979 Imperial Valley earthquake was found to have a 1/f distribution but with power at all frequencies about four times greater than that of data from "quiet" periods