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

Fast Fourier sampling for ultra-wide band digital receiver applications

The fast Fourier sampling (FFS) method is related to the new sampling paradigm, compressive sampling (CS). This paper explores the application of the FFS method in an ultra-wide band digital receiver. The aim of the study is to quickly detect sparsely distributed carrier frequencies in an ultra-wide frequency band using fewer digital sampled data when compared to ubiquitous methods, such as the fast Fourier transform (FFT). Study shows that the FFS method can be applied to ultra-wide band sparse radar signal detection using randomly selected data from conventional analog-todigital converter and has the added advantage that it can be implemented on DSP hardware using a short-length of FFT

Identification of regions of fastest mixing in a system of point vortices

This paper describes a numerical method for efficiently identifying the regions of fastest mixing of a passive dye in a flow due to a system of point vortices. Results obtained from computations are presented for systems of three and four point vortices, both in the unbounded domain and inside a circular cylinder. The flow is two-dimensional and the fluid is incompressible. The regions where mixing is possible are found by studying the largest Lagrangian Lyapunov exponent distribution with respect to various initial positions of tracer particles. The regions of fastest mixing are then identified from the Lyapunov exponent distribution at small times. The results of the method are verified by quantifying the mixing by using a traditional box counting technique. The technique is then applied to several different initial configurations of vortices and some interesting results are obtained. Some numerical findings about the nature of the exponents computed are also discussed. Copyrigh

Hodograph transformation

Mapping of curves from the physical to the hodograph plane, when the Jacobian vanishes along a curve is discussed in detail here. A certain quadratic expression occuring in the equations has been identified as the important factor in the transformation. The nature of the transformation depends on whether the roots are complex or real distinct and so on. A geometrical representation of the referred quadratic expression is also presented, resulting in a better understanding of the transformation technique

Random superharmonic and subharmonic response: Multiple time scaling of a duffing oscillator

Narrowband random third order superharmonic and one third order subharmonic response of a Duffing oscillator is discussed. A multiple time scale approach is used. The work is a more rigorous approach than that already published on random superharmonic response by the authors, and the results obtained corroborate the previous ones. In particular, random jumps in amplitude can occur, but are less likely to occur as the bandwidth of the excitation is increased. The results are further corroborated by comparison with digital simulation. In addition, new results on random subharmonic response are also presented, obtained by using the same approach

RANDOM SUPERHARMONIC RESPONSE OF A DUFFING OSCILLATOR.

The third order superharmonic response of a Duffing oscillator to narrow band random excitation is analyzed. The analysis shows the effect of excitation bandwidth on the response and stability of the non-linear oscillator. In particular the analysis shows that multivalued superharmonic response can occur only when the excitation bandwidth is small. Stability of the response is examined by constructing a mean square phase plane from a time dependent Fokker-Planck equation and also by perturbing the stationary solutions

Engineering estimates of normal loads on slender airbreathing bodies

A method is developed to obtain quick engineering estimates for the normal load on slender airbreathing shapes. Estimates of the normal load can be obtained using a Trefftz plane analysis. This computation, although two-dimensional and subsonic, agrees fairly well with experimental data (from NASA and AGARD) for three-dimensional slender bodies in supersonic flows. The computational technique is simple and extremely fast

MapReduce-based techniques for multiple object tracking in video analytics

Multiple Object Tracking (MOT) has been deployed effectively in various applications including automated video surveillance, self-driving vehicles, robotics, and medical imaging. Despite improvements in the qualitative performance (accuracy) of the existing state-of-The-Art MOT methods through complex image analysis and global optimization techniques, a high computational cost is still a performance limitation. This paper focuses on achieving a high computational speed and proposes three parallel MOT techniques based on MapReduce. This paper introduces techniques that provide a parallel solution which effectively handles the challenges of time-dependencies among the various sections of the video file processed during MOT. Through performance analysis of a prototype deployed on the Amazon EC2 cloud, this paper shows that the proposed techniques provide a scalable solution for parallelizing the MOT methods and achieves an efficiency and speedup of up to 77% and 17 respectively, for a large video file, on a 20 node Hadoop cluster