Experimental validation of a quasi-realtime human respiration detection method via UWB radar

In this paper, we propose a quasi-realtime human respiration detection method via UWB radar system in through-wall or similar condition. With respect to the previous proposed automatic detection method, the new proposed method assures competitive performance in the human respiration motion detection and effective noise/clutter rejection, which have been proved by experimental results in actual scenario. This new method has also been implemented in a UWB through-wall life-detection radar prototype, and its time consuming is about 2 s, which can satisfy the practical requirement of quasi-realtime for through-wall sequential vital sign detection. Therefore, it can be an alternative for through-obstacles static human detection in antiterrorism or rescue scenarios

Human Respiration Localization Method Using UWB Linear Antenna Array

Human respiration is the basic vital sign in remote monitoring. There has been remarkable progress in this area, but some challenges still remain to obtain the angle-of-arrival (AOA) and distinguish the individual signals. This paper presents a 2D noncontact human respiration localization method using Ultra-Wideband (UWB) 1D linear antenna array. The imaging reconstruction based on beamforming is used to estimate the AOA of the human chest. The distance-slow time 2D matrix at the estimated AOA is processed to obtain the distance and respiration frequency of the vital sign. The proposed method can be used to isolate signals from individual targets when more than one human object is located in the surveillance space. The feasibility of the proposed method is demonstrated via the simulation and experiment results

Probing resistive switching in HfO2/Al2O3 bilayer oxides using in-situ transmission electron microscopy

In this work, we investigate the resistive switching in hafnium dioxide (HfO2) and aluminum oxide (Al2O3) bilayered stacks using in-situ transmission electron microscopy and X-ray energy dispersive spectroscopy. Conductance of the HfO2/Al2O3 stack changes gradually upon electrical stressing which is related to the formation of extended nanoscale physical defects at the HfO2/Al2O3 interface and the migration and re-crystallization of Al into the oxide bulk. The results suggest two competing physical mechanisms including the redistribution of oxygen ions and the migration of Al species from the Al electrode during the switching process. While the HfO2/Al2O3 bilayered stack appears to be a good candidate for RRAM technology, the low diffusion barrier of the active Al electrode causes severe Al migration in the bi-layered oxides leading to the device to fail in resetting, and thereby, largely limiting the overall switching performance and material reliability

Structure-property relationships in graphene-based strain and pressure sensors for potential artificial intelligence applications

Wearable electronic sensing devices are deemed to be a crucial technology of smart personal electronics. Strain and pressure sensors, one of the most popular research directions in recent years, are the key components of smart and flexible electronics. Graphene, as an advanced nanomaterial, exerts pre-eminent characteristics including high electrical conductivity, excellent mechanical properties, and flexibility. The above advantages of graphene provide great potential for applications in mechatronics, robotics, automation, human-machine interaction, etc.: graphene with diverse structures and leverages, strain and pressure sensors with new functionalities. Herein, the recent progress in graphene-based strain and pressure sensors is presented. The sensing materials are classified into four structures including 0D fullerene, 1D fiber, 2D film, and 3D porous structures. Different structures of graphene-based strain and pressure sensors provide various properties and multifunctions in crucial parameters such as sensitivity, linearity, and hysteresis. The recent and potential applications for graphene-based sensors are also discussed, especially in the field of human motion detection. Finally, the perspectives of graphene-based strain and pressure sensors used in human motion detection combined with artificial intelligence are surveyed. Challenges such as the biocompatibility, integration, and additivity of the sensors are discussed as well

An Overview of Plant Phenolic Compounds and Their Importance in Human Nutrition and Management of Type 2 Diabetes

In this paper, the biosynthesis process of phenolic compounds in plants is summarized, which includes the shikimate, pentose phosphate and phenylpropanoid pathways. Plant phenolic compounds can act as antioxidants, structural polymers (lignin), attractants (flavonoids and carotenoids), UV screens (flavonoids), signal compounds (salicylic acid and flavonoids) and defense response chemicals (tannins and phytoalexins). From a human physiological standpoint, phenolic compounds are vital in defense responses, such as anti-aging, anti-inflammatory, antioxidant and anti-proliferative activities. Therefore, it is beneficial to eat such plant foods that have a high antioxidant compound content, which will cut down the incidence of certain chronic diseases, for instance diabetes, cancers and cardiovascular diseases, through the management of oxidative stress. Furthermore, berries and other fruits with low-amylase and high-glucosidase inhibitory activities could be regarded as candidate food items in the control of the early stages of hyperglycemia associated with type 2 diabetes

Scheduling for in-network sensor query processing

In-network query processing for sensor networks is a cross-layer paradigm, in which database-style queries are injected into networked sensor nodes and query results over online sensory data are produced by these nodes. In this thesis work, we take a scheduling approach to improve the performance and to provide quality guarantees for in-network sensor query processing. Specifically, we propose the following three scheduling schemes: (1) DCS (Distributed, Cross-layer Scheduling), (2) AHS (Adaptive, Holistic Scheduling), and (3) QAS (Quality-Aware Scheduling). Each of the three schemes addresses a distinct aspect of the problem; collectively, they serve a wide arrange of applications. In DCS, each node utilizes cross-layer information to determine time slots for query processing and arranges its own sleep and communication timings in coordination with its neighbors for reliable query result reporting. AHS further adapts the schedules of both query operators and wireless communication to runtime dynamics for continual efficiency. QAS maximizes the total quality profit of a multi-query workload through prioritizing individual queries and modeling quality status. Our results on both real-world and simulated sensor networks demonstrate the efficiency of the proposed schemes