805 research outputs found
Classification of Multipartite Entanglement via Negativity Fonts
Partial transposition of state operator is a well known tool to detect
quantum correlations between two parts of a composite system. In this letter,
the global partial transpose (GPT) is linked to conceptually multipartite
underlying structures in a state - the negativity fonts. If K-way negativity
fonts with non zero determinants exist, then selective partial transposition of
a pure state, involving K of the N qubits (K leq N) yields an operator with
negative eigevalues, identifying K-body correlations in the state. Expansion of
GPT interms of K-way partially transposed (KPT) operators reveals the nature of
intricate intrinsic correlations in the state. Classification criteria for
multipartite entangled states, based on underlying structure of global partial
transpose of canonical state, are proposed. Number of N-partite entanglement
types for an N qubit system is found to be 2^{N-1}-N+2, while the number of
major entanglement classes is 2^{N-1}-1. Major classes for three and four qubit
states are listed. Subclasses are determined by the number and type of
negativity fonts in canonical state.Comment: 5 pages, No figures, Corrected typo
Realistic Micro-Doppler Database Generation Through Neural Style Transfer Framework
Real-time monitoring of humans can assist professionals in providing healthy living enabling technologies to ensure the health, safety, and well-being of people of all age groups. To enhance the human activity recognition performance, we propose a style-transfer neural framework to generate realistic synthetic micro-Doppler signature dataset. The proposed network extracts environmental effects such as noise, multipath, and occlusions effects directly from the measurement data
and transfers these features to the clean simulated signatures generated through our simulator called SimHumaLator. This results in more realistic-looking signatures qualitatively and quantitatively. We use these enhanced signatures to augment our measurement data and observe an improvement in the classification performance by 5% compared to no augmentation case
Design of high‐speed software defined radar with GPU accelerator
Software defined radar (SDRadar) systems have become an important area for future radar development and are based on similar concepts to Software defined radio (SDR). Most of the processing like filtering, frequency conversion and signal generation are implemented in software. Currently, radar systems tend to have complex signal processing and operate at wider bandwidth, which means that limits on the available computational power must be considered when designing a SDRadar system. This paper presents a feasible solution to this potential limitation by accelerating the signal processing using a GPU to enable the development of a high speed SDRadar system. The developed system overcomes the limitation on the processing speed by CPU-only, and has been tested on three different SDR devices. Results show that, with GPU accelerator, the processing rate can achieve up to 80 MHz compared to 20 MHz with the CPU-only. The high speed processing makes it possible to run in real-time and process full bandwidth across the WiFi signal acquired by multiple channels. The gains made through porting the processing to the GPU moves the technology towards real-world application in various scenarios ranging from healthcare to IoT, and other applications that required significant computational processing
FMNet: Latent Feature-wise Mapping Network for Cleaning up Noisy Micro-Doppler Spectrogram
Micro-Doppler signatures contain considerable information about target
dynamics. However, the radar sensing systems are easily affected by noisy
surroundings, resulting in uninterpretable motion patterns on the micro-Doppler
spectrogram. Meanwhile, radar returns often suffer from multipath, clutter and
interference. These issues lead to difficulty in, for example motion feature
extraction, activity classification using micro Doppler signatures (-DS),
etc. In this paper, we propose a latent feature-wise mapping strategy, called
Feature Mapping Network (FMNet), to transform measured spectrograms so that
they more closely resemble the output from a simulation under the same
conditions. Based on measured spectrogram and the matched simulated data, our
framework contains three parts: an Encoder which is used to extract latent
representations/features, a Decoder outputs reconstructed spectrogram according
to the latent features, and a Discriminator minimizes the distance of latent
features of measured and simulated data. We demonstrate the FMNet with six
activities data and two experimental scenarios, and final results show strong
enhanced patterns and can keep actual motion information to the greatest
extent. On the other hand, we also propose a novel idea which trains a
classifier with only simulated data and predicts new measured samples after
cleaning them up with the FMNet. From final classification results, we can see
significant improvements
MDPose:Human Skeletal Motion Reconstruction Using WiFi Micro-Doppler Signatures
Motion tracking systems based on optical sensors typically often suffer from
issues, such as poor lighting conditions, occlusion, limited coverage, and may
raise privacy concerns. More recently, radio frequency (RF)-based approaches
using commercial WiFi devices have emerged which offer low-cost ubiquitous
sensing whilst preserving privacy. However, the output of an RF sensing system,
such as Range-Doppler spectrograms, cannot represent human motion intuitively
and usually requires further processing. In this study, MDPose, a novel
framework for human skeletal motion reconstruction based on WiFi micro-Doppler
signatures, is proposed. It provides an effective solution to track human
activities by reconstructing a skeleton model with 17 key points, which can
assist with the interpretation of conventional RF sensing outputs in a more
understandable way. Specifically, MDPose has various incremental stages to
gradually address a series of challenges: First, a denoising algorithm is
implemented to remove any unwanted noise that may affect the feature extraction
and enhance weak Doppler signatures. Secondly, the convolutional neural network
(CNN)-recurrent neural network (RNN) architecture is applied to learn
temporal-spatial dependency from clean micro-Doppler signatures and restore key
points' velocity information. Finally, a pose optimising mechanism is employed
to estimate the initial state of the skeleton and to limit the increase of
error. We have conducted comprehensive tests in a variety of environments using
numerous subjects with a single receiver radar system to demonstrate the
performance of MDPose, and report 29.4mm mean absolute error over all key
points positions, which outperforms state-of-the-art RF-based pose estimation
systems
Occupancy Detection and People Counting Using WiFi Passive Radar
Occupancy detection and people counting technologies have important uses in many scenarios ranging from management of human resources, optimising energy use in intelligent buildings and improving public services in future smart cities. Wi-Fi based sensing approaches for these applications have attracted significant attention in recent years because of their ubiquitous nature, and ability to preserve the privacy of individuals being counted. In this paper, we present a Passive Wi-Fi Radar (PWR) technique for occupancy detection and people counting. Unlike systems which exploit the Wi-Fi Received Signal Strength (RSS) and Channel State Information (CSI), PWR systems can directly be applied in any environment covered by an existing WiFi local area network without special modifications to the Wi-Fi access point. Specifically, we apply Cross Ambiguity Function (CAF) processing to generate Range-Doppler maps, then we use Time-Frequency transforms to generate Doppler spectrograms, and finally employ a CLEAN algorithm to remove the direct signal interference. A Convolutional Neural Network (CNN) and sliding-window based feature selection scheme is then used for classification. Experimental results collected from a typical office environment are used to validate the proposed PWR system for accurately determining room occupancy, and correctly predict the number of people when using four test subjects in experimental measurements
People counting using multistatic passive WiFi radar with a multi-input deep convolutional neural network
Accurately counting numbers people is useful in many applications. Currently, camera-based systems assisted by computer vision and machine learning algorithms represent the state-of-the-art. However, they have limited coverage areas and are prone to blind spots, obscuration by walls, shadowing of individuals in crowds, and rely on optimal positioning and lighting conditions. Moreover, their ability to image people raises ethical and privacy concerns. In this paper we propose a distributed multistatic passive WiFi radar (PWR) consisting of 1 reference and 3 surveillance receivers, that can accurately count up to six test subjects using Doppler frequency shifts and intensity data from measured micro-Doppler (µ-Doppler) spectrograms. To build the person-counting processing model, we employ a multi-input convolutional neural network (MI-CNN). The results demonstrate a 96% counting accuracy for six subjects when data from all three surveillance channels are utilised
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