Reducing Phase Cancellation Effect with ASK-PSK Modulated Stamp in Augmented UHF RFID Indoor Localization System

AbstractIn this paper, we propose exploiting ASK-PSK modulated stamp in receiving path selection technique to lessen the phase cancellation effect in augmented ultra-high-frequency (UHF) radio frequency identification (RFID) indoor localization system (AURIS). In AURIS, a tag-like semi-passive RFID component (referred as sensatag) can capture backscatter signal of other proximal tags with presence of RF source. According to the principle of backscatter radio link, the received signal at sensatag antenna is the superposition of backscatter signal of tags and continuous carrier wave (CW) from RF source. However, due to phase difference between tag's backscatter signal and RF CW, the modulated backscatter signal could be cancelled. We refer this effect as phase cancellation effect. Exploiting the spatial diversity of dual-antenna's two receiving paths, the likelihood of phase cancellation occurrence could be reduced. But the developed technique with two co-operating paths is not energy efficient. Therefore, this paper proposes to inject a ASK-PSK modulated stamp sequence in the pilot tone of backscatter signal as a signature of phase cancellation for ASK modulated data frame, which could be identified by the receiving sensatag. With the knowledge of occurrence of phase cancellation, sensatag could activate the alternative receiving path. This technique fully exploits the space diversity of dual-antenna, and also reduces the power consumption by reducing one receiving path in operation. We demonstrate the performance of stamp based receiving path selection technique with data obtained from computer simulation

Capsfall: Fall detection using ultra-wideband radar and capsule network

Radar technology for at home health-care has many advantages such as safety, reliability, privacy-preserving, and contact-less sensing nature. Detecting falls using radar has recently gained attention in smart health care. In this paper, CapsFall, a new method for fall detection using an ultra-wideband radar that leverages the recent deep learning advances is proposed. To this end, a radar time series is derived from the radar back-scattered matrix and its time-frequency representation is obtained and used as input to the capsule network for automatic feature learning. In contrast to other existing methods, the proposed CapsFall method relies on multi-level feature learning from radar time-frequency representations. In particular, the proposed method utilizes a capsule network for automating feature learning and enhancing model discriminability. The experiments are conducted using a set of radar signals collected from ten subjects when performing various activities in a room environment. The performance of the proposed CapsFall method is evaluated in terms of classification metrics and compared with those of the other existing methods based on convolutional neural network, multi-layer perceptron, decision tree, and support vector machine. The results show that the proposed CapsFall method outperforms the other methods in terms of accuracy, precision, sensitivity, and specificity values

Algorithm design for parallel implementation of the SMC-PHD filter

The sequential Monte Carlo (SMC) implementation of the probability hypothesis density (PHD) filter suffers from low computational efficiency since a large number of particles are often required, especially when there are a large number of targets and dense clutter. In order to speed up the computation, an algorithmic framework for parallel SMC-PHD filtering based on multiple processors is proposed. The algorithm makes full parallelization of all four steps of the SMC-PHD filter and the computational load is approximately equal among parallel processors, rendering a high parallelization benefit when there are multiple targets and dense clutter. The parallelization is theoretically unbiased as it provides the same result as the serial implementation, without introducing any approximation. Experiments on multi-core computers have demonstrated that our parallel implementation has gained considerable speedup compared to the serial implementation of the same algorithm

Segmental Spectral Decomposition as a Time Persistent Method of BioImpedance Spectroscopy Feature Extraction

BioImpedance Spectroscopy (BIS) have been investigated in many research areas as a method to detect changes in living tissues. However, BIS measurements are known to be hardly reproducible in clinical applications. This article proposes segmental spectral decomposition as a method of extracting reproducible parameters from raw BIS. The efficiency of this method is then compared to conventional Cole-Cole parameter extraction in a classification task

Human Breathing Rate Estimation from Radar Returns Using Harmonically Related Filters

Radar-based noncontact sensing of life sign signals is often used in safety and rescue missions during disasters such as earthquakes and avalanches and for home care applications. The radar returns obtained from a human target contain the breathing frequency along with its strong higher harmonics depending on the target’s posture. As a consequence, well understood, computationally efficient, and the most popular traditional FFT-based estimators that rely only on the strongest peak for estimates of breathing rates may be inaccurate. The paper proposes a solution for correcting the estimation errors of such single peak-based algorithms. The proposed method is based on using harmonically related comb filters over a set of all possible breathing frequencies. The method is tested on three subjects for different postures, for different distances between the radar and the subject, and for two different radar platforms: PN-UWB and phase modulated-CW (PM-CW) radars. Simplified algorithms more suitable for real-time implementation have also been proposed and compared using accuracy and computational complexity. The proposed breathing rate estimation algorithms provide a reduction of about 81% and 80% in the mean absolute error of breathing rates in comparison to the traditional FFT-based methods using strongest peak detection, for PN-UWB and PM-CW radars, respectively

Architectures for Efficient Implementation of Particle Filters

Particle filters are sequential Monte Carlo methods that are used in numerous problems where time-varying signals must be presented in real time and where the objective is to estimate various unknowns of the signal and/or detect events described by the signals. The standard solutions of such problems in many applications are based on the Kalman filters or extended Kalman filters. In situations when the problems are nonlinear or the noise that distorts the signals is non-Gaussian, the Kalman filters provide a solution that may be far from optimal. Particle filters are an intriguing alternative to the Kalman filters due to their excellent performance in very di#cult problems including communications, signal processing, navigation, and computer vision. Hence, particle filters have been the focus of wide research recently and immense literature can be found on their theory. Most of these works recognize the complexity and computational intensity of these filters, but there has been no e#ort directed toward the implementation of these filters in hardware. The objective of this dissertation is to develop, design, and build e#cient hardware for particle filters, and thereby bring them closer to practical applications. The fact that particle filters outperform most of the traditional filtering methods in many complex practical scenarios, coupled with the challenges related to decreasing their computational complexity and improving real-time performance, makes this work worthwhile. The mai

IMPROVED ISE IDENTIFICATION UNDER HARDWARE CONSTRAINT

The three Instruction Set Extension (ISE) enumeration algorithms described in this paper are Subgraph Enumeration (SE), Subgraph Removal (SR), and Lucky Subgraph Removal (LSR). SE exhaustively enumerates all convex subgraphs of a dataflow graph. SR iteratively finds the highest gain subgraph and then locks the related nodes out of the solution space for the next iteration of the search. Finally, LSR represents our tunable approach where both SE and SR are used to trade compiler execution time for solution quality in a hardware constrained design space. In this paper we present the mechanics behind these three ISE enumeration algorithms, and an instruction selection algorithm compatible with all three approaches. Index Terms — Instruction set extension, instruction enumeration, instruction selection, configurable processor, ASIP, ISE 1