Higher order feature extraction and selection for robust human gesture recognition using CSI of COTS Wi-Fi devices

Device-free human gesture recognition (HGR) using commercial o the shelf (COTS) Wi-Fi devices has gained attention with recent advances in wireless technology. HGR recognizes the human activity performed, by capturing the reflections ofWi-Fi signals from moving humans and storing them as raw channel state information (CSI) traces. Existing work on HGR applies noise reduction and transformation to pre-process the raw CSI traces. However, these methods fail to capture the non-Gaussian information in the raw CSI data due to its limitation to deal with linear signal representation alone. The proposed higher order statistics-based recognition (HOS-Re) model extracts higher order statistical (HOS) features from raw CSI traces and selects a robust feature subset for the recognition task. HOS-Re addresses the limitations in the existing methods, by extracting third order cumulant features that maximizes the recognition accuracy. Subsequently, feature selection methods derived from information theory construct a robust and highly informative feature subset, fed as input to the multilevel support vector machine (SVM) classifier in order to measure the performance. The proposed methodology is validated using a public database SignFi, consisting of 276 gestures with 8280 gesture instances, out of which 5520 are from the laboratory and 2760 from the home environment using a 10 5 cross-validation. HOS-Re achieved an average recognition accuracy of 97.84%, 98.26% and 96.34% for the lab, home and lab + home environment respectively. The average recognition accuracy for 150 sign gestures with 7500 instances, collected from five di erent users was 96.23% in the laboratory environment.Taylor's University through its TAYLOR'S PhD SCHOLARSHIP Programmeinfo:eu-repo/semantics/publishedVersio

The University Defence Research Collaboration In Signal Processing

This chapter describes the development of algorithms for automatic detection of anomalies from multi-dimensional, undersampled and incomplete datasets. The challenge in this work is to identify and classify behaviours as normal or abnormal, safe or threatening, from an irregular and often heterogeneous sensor network. Many defence and civilian applications can be modelled as complex networks of interconnected nodes with unknown or uncertain spatio-temporal relations. The behavior of such heterogeneous networks can exhibit dynamic properties, reflecting evolution in both network structure (new nodes appearing and existing nodes disappearing), as well as inter-node relations. The UDRC work has addressed not only the detection of anomalies, but also the identification of their nature and their statistical characteristics. Normal patterns and changes in behavior have been incorporated to provide an acceptable balance between true positive rate, false positive rate, performance and computational cost. Data quality measures have been used to ensure the models of normality are not corrupted by unreliable and ambiguous data. The context for the activity of each node in complex networks offers an even more efficient anomaly detection mechanism. This has allowed the development of efficient approaches which not only detect anomalies but which also go on to classify their behaviour

Robust Inference in Wireless Sensor Networks

This dissertation presents a systematic approach to obtain robust statistical inference schemes in unreliable networks. Statistical inference offers mechanisms for deducing the statistical properties of unknown parameters from the data. In Wireless Sensor Networks (WSNs), sensor outputs are transmitted across a wireless communication network to the fusion center (FC) for final decision-making. The sensor data are not always reliable. Some factors may cause anomaly in network operations, such as malfunction, corruption, or compromised due to some unknown source of contamination or adversarial attacks. Two standard component failure models are adopted in this study to describe the system vulnerability: the probabilistic and static models. In probabilistic models, we consider a widely known ε−contamination model, where each node has ε probability of malfunctioning or being compromised. In contrast, the static model assumes there is up to a certain number of malfunctioning nodes. It is assumed that the decision center/network operator is aware of the presence of anomaly nodes and can adjust the operation rule to counter the impact of the anomaly. The anomaly node is assumed to know that the network operator is taking some defensive actions to improve its performance. Considering both the decision center (network operator) and compromised (anomalous) nodes and their possible actions, the problem is formulated as a two-player zero-sum game. Under this setting, we attempt to discover the worst possible failure models and best possible operating strategies. First, the effect of sensor unreliability on detection performance is investigated, and robust detection schemes are proposed. The aim is to design robust detectors when some observation nodes malfunction. The detection problem is relatively well known under the probabilistic model in simple binary hypotheses testing with known saddle-point solutions. The detection problem is investigated under the mini-max framework for the static settings as no such saddle point solutions are shown to exist under these settings. In the robust estimation, results in estimation theory are presented to measure system robustness and performance. The estimation theory covers probabilistic and static component failure models. Besides the standard approaches of robust estimation under the frequentist settings where the interesting parameters are fixed but unknown, the estimation problem under the Bayes settings is considered where the prior probability distribution is known. After first establishing the general framework, comprehensive results on the particular case of a single node network are presented under the probabilistic settings. Based on the insights from the single node network, we investigate the robust estimation problem for the general network for both failure models. A few robust localization methods are presented as an extension of robust estimation theory at the end

Online Optimal State Feedback Control of Linear Systems over Wireless MIMO Fading Channels

We consider the optimal control of linear systems over wireless MIMO fading channels, where the MIMO wireless fading and random access of the remote controller may cause intermittent controllability or uncontrollability of the closed-loop control system. We formulate the optimal control design over random access MIMO fading channels as an infinite horizon average cost Markov decision process (MDP), and we propose a novel state reduction technique such that the optimality condition is transformed into a time-invariant reduced-state Bellman optimality equation. We provide the closed-form characterizations on the existence and uniqueness of the optimal control solution via analyzing the reduced-state Bellman optimality equation. Specifically, in the case that the closed-loop system is almost surely controllable, we show that the optimal control solution always exists and is unique. In the case that MIMO fading channels and the random access of the remote controller destroy the closed-loop controllability, we propose a novel controllable and uncontrollable positive semidefinite (PSD) cone decomposition induced by the singular value decomposition (SVD) of the MIMO fading channel contaminated control input matrix. Based on the decomposed fine-grained reduced-state Bellman optimality equation, we further propose a closed-form sufficient condition for both the existence and the uniqueness of the optimal control solution. The closed-form sufficient condition reveals the fact that the optimal control action may still exist even if the closed-loop system suffers from intermittent controllability or almost sure uncontrollability

Statistical Traffic State Analysis in Large-scale Transportation Networks Using Locality-Preserving Non-negative Matrix Factorization

Statistical traffic data analysis is a hot topic in traffic management and control. In this field, current research progresses focus on analyzing traffic flows of individual links or local regions in a transportation network. Less attention are paid to the global view of traffic states over the entire network, which is important for modeling large-scale traffic scenes. Our aim is precisely to propose a new methodology for extracting spatio-temporal traffic patterns, ultimately for modeling large-scale traffic dynamics, and long-term traffic forecasting. We attack this issue by utilizing Locality-Preserving Non-negative Matrix Factorization (LPNMF) to derive low-dimensional representation of network-level traffic states. Clustering is performed on the compact LPNMF projections to unveil typical spatial patterns and temporal dynamics of network-level traffic states. We have tested the proposed method on simulated traffic data generated for a large-scale road network, and reported experimental results validate the ability of our approach for extracting meaningful large-scale space-time traffic patterns. Furthermore, the derived clustering results provide an intuitive understanding of spatial-temporal characteristics of traffic flows in the large-scale network, and a basis for potential long-term forecasting.Comment: IET Intelligent Transport Systems (2013