Quantum particle on hyperboloid

We present quantization of particle dynamics on one-sheet hyperboloid embedded in three dimensional Minkowski space. Taking account of all global symmetries enables unique quantization. Making use of topology of canonical variables not only simplifies calculations but also gives proper framework for analysis.Comment: 7 pages, no figures, revtex

Simple model of big-crunch/big-bang transition

We present classical and quantum dynamics of a test particle in the compactified Milne space. Background spacetime includes one compact space dimension undergoing contraction to a point followed by expansion. Quantization consists in finding a self-adjoint representation of the algebra of particle observables. Our model offers some insight into the nature of the cosmic singularity.Comment: 17 pages, no figures, RevTeX4, accepted for publication in Class. Quantum Gra

UWB and WiFi Systems as Passive Opportunistic Activity Sensing Radars

Human Activity Recognition (HAR) is becoming increasingly important in smart homes and healthcare applications such as assisted-living and remote health monitoring. In this paper, we use Ultra-Wideband (UWB) and commodity WiFi systems for the passive sensing of human activities. These systems are based on a receiver-only radar network that detects reflections of ambient Radio-Frequency (RF) signals from humans in the form of Channel Impulse Response (CIR) and Channel State Information (CSI). An experiment was performed whereby the transmitter and receiver were separated by a fixed distance in a Line-of-Sight (LoS) setting. Five activities were performed in between them, namely, sitting, standing, lying down, standing from the floor and walking. We use the high-resolution CIRs provided by the UWB modules as features in machine and deep learning algorithms for classifying the activities. Experimental results show that a classification performance with an F1-score as high as 95.53% is achieved using processed UWB CIR data as features. Furthermore, we analysed the classification performance in the same physical layout using CSI data extracted from a dedicated WiFi Network Interface Card (NIC). In this case, maximum F1-scores of 92.24% and 80.89% are obtained when amplitude CSI data and spectrograms are used as features, respectively

Physical Activity Sensing via Stand-Alone WiFi Device

WiFi signals for physical activity sensing shows promising potential for many healthcare applications due to its potential for recognising various everyday activities, non-invasive nature and low intrusion on privacy. Traditionally, WiFi-based sensing uses the Channel State Information (CSI) from an offthe- shelf WiFi Access Point (AP) which transmits signals that have high pulse repetition frequencies. However, when there are no users on the communication network only beacon signals are transmitted from the WiFi AP which significantly deteriorates the sensitivity and specificity of such systems. Surprisingly WiFi based sensing under these conditions have received little attention given that WiFi APs are frequently in idle state. This paper presents a practical system based on passive radar technique which does not require any special setup or preset firmware and able to work with any commercial WiFi device. To cope with the low density of beacon signal, a modified Cross Ambiguity Function (CAF) has been proposed to reduce redundant samples in the recorded. In addition, an external device has been developed to send WiFi probe request signals and stimulate an idle AP to transmit WiFi probe responses thus generate usable transmission signals for sensing applications without the need to authenticate and join the network. Experimental results prove that proposed concept can significantly improve activity detection and is an ideal candidate for future healthcare and security applications

Passive WiFi Radar for Human Sensing Using A Stand-Alone Access Point

Human sensing using WiFi signal transmissions is attracting significant attention for future applications in ehealthcare, security and the Internet of Things (IoT). The majority of WiFi sensing systems are based around processing of Channel State Information (CSI) data which originates from commodity WiFi Access Points (AP) that have been primed to transmit high data-rate signals with high repetition frequencies. However, in reality, WiFi APs do not transmit in such a continuous uninterrupted fashion, especially when there are no users on the communication network. To this end, we have developed a passive WiFi radar system for human sensing which exploits WiFi signals irrespective of whether the WiFi AP is transmitting continuous high data-rate OFDM signals, or periodic WiFi beacon signals whilst in an idle status (no users on the WiFi network). In a data transmission phase, we employ the standard cross ambiguity function (CAF) processing to extract Doppler information relating to the target, whilst a modified version is used for lower data-rate signals. In addition, we investigate the utility of an external device that has been developed to stimulate idle WiFi APs to transmit usable signals without requiring any type of user authentication on the WiFi network. In the paper we present experimental data which verifies our proposed methods for using any type of signal transmission from a stand-alone WiFi device, and demonstrate the capability for human activity sensing

Families of exact solutions of a 2D gravity model minimally coupled to electrodynamics

Three families of exact solutions for 2-dimensional gravity minimally coupled to electrodynamics are obtained in the context of ${\cal R}=T$ theory. It is shown, by supersymmetric formalism of quantum mechanics, that the quantum dynamics of a neutral bosonic particle on static backgrounds with both varying curvature and electric field is exactly solvable.Comment: 13 pages, LaTeX, to be published in JM

Using RF Transmissions from IoT Devices for Occupancy Detection and Activity Recognition

IoT ecosystems consist of a range of smart devices that generated a plethora of Radio Frequency (RF) transmissions. This provides an attractive opportunity to exploit already-existing signals for various sensing applications such as e-Healthcare, security and smart home. In this paper, we present Passive IoT Radar (PIoTR), a system that passively uses RF transmissions from IoT devices for human monitoring. PIoTR is designed based on passive radar technology, with a generic architecture to utilize various signal sources including the WiFi signal and wireless energy at the Industrial, Scientific and Medical (ISM) band. PIoTR calculates the phase shifts caused by human motions and generates Doppler spectrogram as the representative. To verify the proposed concepts and test in a more realistic environment, we evaluate PIoTR with four commercial IoT devices for home use. Depending on the effective signal and power strength, PIoTR performs two modes: coarse sensing and fine-grained sensing. Experimental results show that PIoTR can achieve an average of 91% in occupancy detection (coarse sensing) and 91.3% in activity recognition (fine-grained sensing)