Multi-Modes Phonon Softening in Two-Dimensional Electron-Lattice System

Phonon dispersion in a two-dimensional electron-lattice system described by a two-dimensional square-lattice version of Su-Schrieffer-Heeger's model and having the half-filled electronic band is studied theoretically at temperatures higher than the mean field critical temperature of the Peierls transition. When the temperature is lowered from the higher region down to the critical one, softening of multi phonon modes which have wave vectors equal to the nesting vector \vv{Q}=(\pi/a,\pi/a) with $a$ the lattice constant or parallel to \vv{Q} is observed. Although both of the transverse and longitudinal modes are softened at the critical temperature in the case of the wave vector equal to \vv{Q}, only the transverse modes are softened for other wave vectors parallel to \vv{Q}. This behavior is consistent with the Peierls distortions at lower temperatures.Comment: 10 pages, 5 Figure

High Energy Quark-Antiquark Elastic scattering with Mesonic Exchange

We studies the high energy elastic scattering of quark anti-quark with an exchange of a mesonic state in the $t$ channel with $-t/\Lambda^{2} \gg 1$. Both the normalization factor and the Regge trajectory can be calculated in PQCD in cases of fixed (non-running) and running coupling constant. The dependence of the Regge trajectory on the coupling constant is highly non-linear and the trajectory is of order of $0.2$ in the interesting physical range.Comment: 29 page

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

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)

SimHumalator: An Open Source End-to-End Radar Simulator For Human Activity Recognition

Radio-frequency based non-cooperative monitor ing of humans has numerous applications ranging from law enforcement to ubiquitous sensing applications such as ambient assisted living and bio-medical applications for non-intrusively monitoring patients. Large training datasets, almost unlimited memory capacity, and ever- increasing processing speeds of computers could drive forward the data- driven deep-learning focused research in the above applications. However, generating and labeling large volumes of high-quality, diverse radar datasets is an onerous task. Furthermore, unlike the fields of vision and image processing, the radar community has limited access to databases that contain large volumes of experimental data. Therefore, in this article, we present an open-source motion capture data-driven simulation tool, SimHumalator, that can generate large volumes of human micro-Doppler radar data in passive WiFi scenarios. The simulator integrates IEEE 802.11 WiFi standard(IEEE 802.11g, n, and ad) compliant transmissions with the human animation data to generate the micro-Doppler features that incorporate the diversity of human motion characteristics and the sensor parameters. The simulated signatures have been validated with experimental data gathered using an in-house-built hardware prototype. This article describes simulation methodology in detail and provides case studies on the feasibility of using simulated micro-Doppler spectrograms for data augmentation tasks

On CSI and Passive WiFi Radar for Opportunistic Physical Activity Recognition

The use of Wi-Fi signals for human sensing has gained significant interest over the past decade. Such techniques provide affordable and reliable solutions for healthcare-focused events such as vital sign detection, prevention of falls and long-term monitoring of chronic diseases, among others. Currently, there are two major approaches for Wi-Fi sensing: (1) passive Wi-Fi radar (PWR) which uses well established techniques from bistatic radar, and channel state information (CSI) based wireless sensing (SENS) which exploits human-induced variations in the communication channel between a pair of transmitter and receiver. However, there has not been a comprehensive study to understand and compare the differences in terms of effectiveness and limitations in real-world deployment. In this paper, we present the fundamentals of the two systems with associated methodologies and signal processing. A thorough measurement campaign was carried out to evaluate the human activity detection performance of both systems. Experimental results show that SENS system provides better detection performance in a line-of-sight (LoS) condition, whereas PWR system performs better in a non-LoS (NLoS) setting. Furthermore, based on our findings, we recommend that future Wi-Fi sensing applications should leverage the advantages from both PWR and SENS systems

Role of Lipoxygenase and Allene Oxide Synthase in Wound-Inducible Defense Response of Pea

Lipoxygenase (LOX) and allene oxide synthase (AOS) are key enzymes in the jasmonic acid (JA) biosynthesis pathway. In this work, the role of LOX and AOS in defense response induced by wounding was investigated using pea (Pisum sativum L., cv. Ning Xia) seedlings as the material. The results showed that wound-induced "JA burst" was accompanied by the activation of LOX and AOS and the accumulation of their mRNAs; applied JA also stimulated the accumulation of LOX and AOS. Further experiments conducted with inhibitors demonstrated that the wound-induced JA was regulated by LOX and AOS at both transcriptional and enzymic levels. Their activation was necessary in wound-mediated defense response and could enhance the tolerance of pea seedlings to wounding

Scattering length for helium atom-diatom collision

We present results on the scattering lengths of ^4He--^4He_2 and ^3He--^4He_2 collisions. We also study the consequence of varying the coupling constant of the atom-atom interaction.Comment: Contribution to Proceedings of the International Workshop ``Critical Stability of Few-Body Quantum Systems'' (Dresden, October 17--22, 2005

Motion Correction Using Deep Learning Neural Networks - Effects of Data Representation

An in-silico investigation of the effects of ultrasound data representation on the accuracy of the motion prediction made using deep learning neural networks was carried out. The representations studied include: linear (‘envelope’), log compressed, linear with phase and log compressed with phase. A UNet model was trained to predict non-rigid deformation field using a fixed and a moving image pair as the input. The results illustrate that the choice of the representation plays an important role on the accuracy of motion estimation. Specifically, representations with phase information outperform the representations without phase. Furthermore, log-compressed data yielded predictions with higher accuracy than the linear data

Augmenting Experimental Data with Simulations to Improve Activity Classification in Healthcare Monitoring

Human micro-Doppler signatures in most passive WiFi radar (PWR) scenarios are captured through real-world measurements using various hardware platforms. However, gathering large volumes of high quality and diverse real radar datasets has always been an expensive and laborious task. This work presents an open-source motion capture data-driven simulation tool SimHumalator that is able to generate human microDoppler radar data in PWR scenarios. We qualitatively compare the micro-Doppler signatures generated through SimHumalator with the measured real signatures. Here, we present the use of SimHumalator to simulate a set of human actions. We demonstrate that augmenting a measurement database with simulated data, using SimHumalator, results in an 8% improvement in classification accuracy. Our results suggest that simulation data can be used to augment experimental datasets of limited volume to address the cold-start problem typically encountered in radar research