Buried Object Sensing Considering Curved Pipeline

This letter presents design and implementation of a system solution, where light weight wireless devices are used to identify a moving object within underground pipeline for maintenance and inspection. The devices such as transceiver operating at S-band are deployed for underground settings. Finer-grained channel information in conjunction with leaky-wave cable (LWC) detects any moving entity. The processing of the measured data over time is analyzed and used for reporting the disturbances. Deploying an LWC as the receiver has benefits in terms of a wider coverage area, covering blind and semiblind zones. The system fully exploits the variances of both amplitude and phase information of channel information as the performance indicators for motion detection. The experimental results demonstrate greater level of accuracy

Dynamic indoor localization using maximum likelihood particle filtering

A popular approach for solving the indoor dynamic localization problem based on WiFi measurements consists of using particle filtering. However, a drawback of this approach is that a very large number of particles are needed to achieve accurate results in real environments. The reason for this drawback is that, in this particular application, classical particle filtering wastes many unnecessary particles. To remedy this, we propose a novel particle filtering method which we call maximum likelihood particle filter (MLPF). The essential idea consists of combining the particle prediction and update steps into a single one in which all particles are efficiently used. This drastically reduces the number of particles, leading to numerically feasible algorithms with high accuracy. We provide experimental results, using real data, confirming our claim.Fil: Wang, Wenxu. Guangdong University of Technology; ChinaFil: Marelli, Damian Edgardo. Guangdong University of Technology; China. Centro Científico Nacional e Internacional Francés Argentino de Ciencias de la Información y Sistemas; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas; ArgentinaFil: Fu, Minyue. Universidad de Newcastle; Australia. Guangdong University of Technology; Chin

S-Band Sensing-Based Motion Assessment Framework for Cerebellar Dysfunction Patients

© 2018 IEEE. Cerebellar dysfunction (CD) is a neurological disorder that involves a number of abnormalities that affect the movement of various parts of the body such as gait abnormality or tremors in limbs such as hands or feet while reaching out for something. A user-friendly tool that can objectively evaluate the aforementioned body movements in CD patients can aid the clinicians for an objective assessment in clinical settings. The objective of this paper is to develop a method that quantifies the gait abnormality and tremors in hand using a S -band sensing technique. The S -band sensing essentially leverages small wireless devices such as network interface card, omnidirectional antenna, and router operating at 2.4 GHz to record the wireless channel data. Specifically, the aim is to use the variances of amplitude and phase information induced due to the human body movements. Each body movement leaves a unique imprint in the form of wireless channel information that is used to identify abnormalities in body motions. The proposed framework applied a linear transformation on raw phase data for calibrations since the data retrieved using the interface card contain noise and is inapplicable for motion detection. The support vector machine used to classify the data achieved high classification accuracy