
Shelly VishwakarmaUniversity of Southampton · Department of Electronics and Computer Science (ECS)
Shelly Vishwakarma
PhD
About
46
Publications
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Introduction
I am a Lecturer in the Electronics and Computer Science School at the University of Southampton. My research focuses on designing and developing hardware and software frameworks that aim to advance state-of-the-art opportunistic sensing using radio frequency (RF) signals arising from WiFi transmissions for contextual sensing applications, including concurrent physical activity recognition and indoor localization.
Additional affiliations
February 2020 - July 2022
August 2015 - August 2019
January 2014 - July 2015
Education
August 2015 - January 2020
Publications
Publications (46)
Voice identification is being increasingly adopted in various domains, including security infrastructures, intelligent home systems, and personalised digital assistants. Notably, it harbours significant promise in transforming healthcare, especially in electronic health record detecting and speech impairment monitoring such as aphasia. Current stra...
Motion tracking systems based on optical sensors typically suffer from poor lighting conditions, occlusion, limited coverage, and may raise privacy concerns. More recently, radiofrequency (RF) based approaches using commercial WiFi devices have emerged which offer low-cost ubiquitous sensing whilst preservin privacy. However, RF sensing systems typ...
This work introduces an attention mechanism that can be integrated into any standard convolution neural network to improve model sensitivity and prediction accuracy with minimal computational overhead. The attention mechanism is introduced in a lightweight network – Alexnet and its classification performance for human micro‐Doppler signatures is ev...
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 p...
This paper presents a comprehensive dataset intended to evaluate passive Human Activity Recognition (HAR) and localization techniques with measurements obtained from synchronized Radio-Frequency (RF) devices and vision-based sensors. The dataset consists of RF data including Channel State Information (CSI) extracted from a WiFi Network Interface Ca...
Software Defined Radar (SDRadar) is a unique radar system, where most of its processing, like filtering, correlation, signal generation etc. is performed by software. This means SDRadar can be flexibly deployed for different purposes and with a relative short development process. In this paper, we present a generic SDRadar system that can operate i...
This paper presents an indoor joint communication and sensing system that consists of synchronized off-the-shelf wireless network interface cards (NIC) and Raspberry Pis. There exists a significant body of research that uses the channel state information (CSI) reported by wireless network interface cards for sensing, but only the amplitude and phas...
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 oper...
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...
Radio-frequency based non-cooperative monitoring 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...
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 u...
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...
This paper presents a comprehensive dataset intended to evaluate passive Human Activity Recognition (HAR) and localization techniques with measurements obtained from synchronized Radio-Frequency (RF) devices and vision-based sensors. The dataset consists of RF data including Channel State Information (CSI) extracted from a WiFi Network Interface Ca...
This work presents an application of Integrated sensing and communication (ISAC) system for monitoring human activities directly related to healthcare. 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 hum...
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...
Narrowband and broadband indoor radar images significantly deteriorate in the presence of target-dependent and target-independent static and dynamic clutter arising from walls. A stacked and sparse denoising autoencoder (StackedSDAE) is proposed for mitigating the wall clutter in indoor radar images. The algorithm relies on the availability of clea...
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 simula...
This work considers the use of a passive WiFi radar
(PWR) to monitor human activities. Real-time uncooperative
monitoring of people has numerous applications ranging from
smart cities and transport to IoT and security. In e-healthcare,
PWR technology could be used for ambient assisted living and
early detection of chronic health conditions. Large t...
Micro-Doppler analysis has become increasingly popular in recent years owning to the ability of the technique to enhance classification strategies. Applications include recognising everyday human activities, distinguishing drone from birds, and identifying different types of vehicles. However, noisy time-frequency spectrograms can significantly aff...
Narrowband and broadband indoor radar images significantly deteriorate in the presence of target dependent and independent static and dynamic clutter arising from walls. A stacked and sparse denoising autoencoder (StackedSDAE) is proposed for mitigating wall clutter in indoor radar images. The algorithm relies on the availability of clean images an...
This work presents a simulation framework to generate human micro-Dopplers in WiFi based passive radar scenarios, wherein we simulate IEEE 802.11g complaint WiFi transmissions using MATLAB's WLAN toolbox and human animation models derived from a marker-based motion capture system. We integrate WiFi transmission signals with the human animation data...
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...
We present an ultra-short range IEEE 802.11ad-based automotive joint radar-communications (JRC) framework, wherein we improve the radar's Doppler resilience by incorporating Prouhet-Thue-Morse sequences in the preamble. The proposed processing reveals detailed micro-features of common automotive objects verified through extended scattering center m...
Radar images of humans and other concealed objects are considerably distorted by attenuation, refraction, and multipath clutter in indoor through-wall environments. Although several methods have been proposed for removing target-independent static and dynamic clutter, there still remain considerable challenges in mitigating target-dependent clutter...
Radar images of humans and other concealed objects are considerably distorted by attenuation, refraction and multipath clutter in indoor through-wall environments. While several methods have been proposed for removing target independent static and dynamic clutter, there still remain considerable challenges in mitigating target dependent clutter esp...
We present an ultra-short range IEEE 802.11ad-based automotive joint radar-communications (JRC) framework, wherein we improve the radar's Doppler resilience by incorporating Prouhet-Thue-Morse sequences in the preamble. The proposed processing reveals detailed micro-features of common automotive objects verified through extended scattering center m...
This dataset comprises of measured time domain micro-Dopplers returns from multiple humans undergoing different activities using coherent radar sensors in line-of-sight conditions. We collected measurement data using a Doppler radar configured with an N9926A Field-Fox vector network analyzer (VNA) and two linearly polarized horn antennas (HF907). T...
The radar signals gathered in urban environments typically consist of the superposition
of signals from multiple moving targets along with clutter from the propagation environment. Therefore, the radar signals must be suitably processed before they can be used for inferring human activity. This dataset comprises of the following target classes -a h...
Recently, several machine learning algorithms have been applied for classifying micro-Doppler signatures from different human motions. However, these algorithms must demonstrate versatility in handling diversity in test and training data to be used for real-life scenarios. For example, situations may arise where the propagation channel or the prese...
Radar returns from dynamic human motions are usually modeled using primitive based techniques. While the method is computationally simple and reasonably accurate in generating micro-Doppler signatures of humans, it is unreliable for predicting the radar cross-section (RCS) of the human especially at high frequencies. On the other hand, the shooting...
The detection and identification of humans and concealed objects by through wall radars is affected by wall propagation effects such as attenuation and multipath. Several works, in the past, have provided solutions for mitigating wall effects based on either prior information of the wall parameters or signal processing solutions for separating wall...
Radar returns from dynamic human motions are usually modeled using primitive based techniques. While the method is computationally simple and reasonably accurate in generating micro-Doppler signatures of humans, it is unreliable for predicting the radar cross-section (RCS) of the human especially at high frequencies. On the other hand, the shooting...
Studies have demonstrated the usefulness of micro-Doppler signatures for classifying dynamic radar targets such as humans, helicopters, wind turbines etc. However, these classification works are based on the assumption that the propagation channel consists of only a single moving target. When multiple targets move simultaneously in the channel, the...
Micro-Doppler signatures of dynamic targets such as humans, animals and vehicles are very effective feature vectors for classification based on machine learning algorithms. In the existing works, the test data have been measured in nearly identical operating conditions to the training data that were gathered for the classifiers. However, this assum...
Narrowband through-wall radars have been researched for detecting and classifying indoor movers on the basis of their micro-Doppler signatures. These radars usually operate in the unlicensed 2.4GHz ISM band and are therefore susceptible to interference from WiFi networks operating with the IEEE 802.11g protocol. In this work, we show, through exper...
Micro-Doppler signatures of dynamic indoor targets (such as humans and fans) serve as a useful tool for classification. However, all the current classification methods are limited by the assumption that only a single target is present in the channel. In this work, we propose a method to classify multiple targets that are simultaneously present in t...