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

Fire images classification using high order statistical features

Wildfires and forest fires have devastated millions of hectares of forest across the world over the years. Computer vision-based fire classification, which classifies fire pixels from non-fire pixels in image or video datasets, has gained popularity as a result of recent innovations. A conventional machine learning-based approach or a deep learning-based approach can be used to distinguish fire pixels from an image or video. Deep learning is currently the most prominent method for detecting forest fires. Although deep learning algorithms can handle large volumes of data, typically ignore the differences in complexity among training samples, limiting the performance of training models. Moreover, in real-world fire scenarios, deep learning techniques with little data and features underperform. The present study utilizes a machine learning-based approach for extracting features of higher-order statistical methods from pre-processed images from publicly available datasets: Corsican and FLAME, and a private dataset: Firefront Gestosa. It should be noted that handling multidimensional data to train a classifier in machine learning applications is complex. This issue is addressed through feature selection, which eliminates duplicate or irrelevant data that has an effect on the model's performance. A greedy feature selection criterion is adopted in this study to select the most significant features for classification while reducing computational costs. The Support Vector Machine (SVM) is a conventional machine classifier that works on discriminative features input obtained using the MIFS, feature selection technique. The SVM uses a Radial Basis Function (RBF) kernel to classify fire and non-fire pixels, and the model's performance is assessed using assessment metrics like overall accuracy, sensitivity, specificity, precision, recall, F-measure, and G-mean.publishersversionpublishe

Time-Frequency Analysis of GPR Signal Using a Modified Hilbert Huang Approach

In this paper, we will discuss the efficiency of a modified Hilbert Huang transform (HHT) approach [1, 2], which is largely applied to fault diagnosis for rolling bearing and vibration analysis, for the time-frequency analysis of the GPR signals. The aim of this approach is to improve the readability of the HHT spectrum and hence improving the visibility of all the events and reducing the effects of interference terms. An improved Hilbert-Huang transform (HHT) combined with wavelet packet transform (WPT) is proposed for recognizing reflected echoes in A-scans profiles. The HHT consists of Empirical Mode Decomposition (EMD) and Hilbert-Huang spectrum. Firstly, the Wavelet Packet Transform (WPT) decomposes the signal into a set of narrow band signals, then a series of Intrinsic Mode Functions (IMFs) can be obtained after application of the EMD. Where after, a screening processes is conducted on the IMFs of each narrow band signal to remove unrelated IMFs. Hilbert Transform is then employed to calculate the Hilbert amplitude spectrum. The results show that the proposed method has better discriminability than the traditional HHT and the Continuous Wavelet Transform (CWT) among different states. The proposed algorithm has the potentiality to trace the different changes, which paves a way for a better interpretation of radar profiles

Review of Recent Trends

This work was partially supported by the European Regional Development Fund (FEDER), through the Regional Operational Programme of Centre (CENTRO 2020) of the Portugal 2020 framework, through projects SOCA (CENTRO-01-0145-FEDER-000010) and ORCIP (CENTRO-01-0145-FEDER-022141). Fernando P. Guiomar acknowledges a fellowship from “la Caixa” Foundation (ID100010434), code LCF/BQ/PR20/11770015. Houda Harkat acknowledges the financial support of the Programmatic Financing of the CTS R&D Unit (UIDP/00066/2020).MIMO-OFDM is a key technology and a strong candidate for 5G telecommunication systems. In the literature, there is no convenient survey study that rounds up all the necessary points to be investigated concerning such systems. The current deeper review paper inspects and interprets the state of the art and addresses several research axes related to MIMO-OFDM systems. Two topics have received special attention: MIMO waveforms and MIMO-OFDM channel estimation. The existing MIMO hardware and software innovations, in addition to the MIMO-OFDM equalization techniques, are discussed concisely. In the literature, only a few authors have discussed the MIMO channel estimation and modeling problems for a variety of MIMO systems. However, to the best of our knowledge, there has been until now no review paper specifically discussing the recent works concerning channel estimation and the equalization process for MIMO-OFDM systems. Hence, the current work focuses on analyzing the recently used algorithms in the field, which could be a rich reference for researchers. Moreover, some research perspectives are identified.publishersversionpublishe

Sign language gesture recognition with bispectrum features using SVM

Wi-Fi based sensing system captures the signal reflections due to human gestures as Channel State Information (CSI) values in subcarrier level for accurately predicting the fine-grained gestures. The proposed work explores the Higher Order Statistical (HOS) method by deriving bispectram features (BF) from raw signal by adopting a Conditional Informative Feature Extraction (CIFE) technique from information theory to form a subset of informative and best features. Support Vector Machine (SVM) classifier is adopted in the present work for classifying the gesture and to measure the prediction accuracy. The present work is validated on a secondary dataset, SignFi, having data collected from two different environments with varying number of users and sign gestures. SVM reports an overall accuracy of 83.8%, 94.1%, 74.9% and 75.6% in different environments/scenarios.Taylor's University through its TAYLOR'S PhD SCHOLARSHIP Programmeinfo:eu-repo/semantics/publishedVersio

Fire segmentation using a Deeplabv3+ architecture

In the last decade, the number of forest _res events is growing due to the fast change of earth's climate. Hence, more automatized _re _ghting actions had become necessary. Deep learning had drawn interesting results for pixel level classi_cation for smoke detection, but few systems are proposed for _re ame detection. In this paper, a semantic segmentation approach using Deeplabv3+ architecture for wild_re detection is proposed. The network uses Deeplabv3 architecture as encoder and Atrous Spatial Pyramid Pooling (ASPP) which allows to encode multi-scale information and boost the network performance. In fact, the ASPP block concatenates a stack of parallel Atrous convolutions with graduating rates, which produces a multi-scale feature map that is further resized. The tests were performed on a public dataset, Corsican _re dataset, which contains 1135 RGB images and 640 infrared pictures. The experiments were conducted on two customized datasets, one using the whole dataset within a single channel information (red and infra-red), and another using only the RGB image set that contains information coded in 3 channels. The used dataset is unbalanced, which could induce high precision with very low sensitivity. Therefore, to measure the performance, Dice similarity and Tversky loss functions with cross-entropy are adopted. The capability of Deeplabv3+ was tested with two di_erent backbones, ResNet-18 and ResNet-50, and compared to a very simple Convolutional Neural Network (CNN) architecture with dilated convolution. Four di_erent metrics were used to evaluate the segmentation capability: Accuracy, mean Intersection over Union (IoU), Mean Boundary F1 (BF) Score, and Mean Dice coe_cient. The experimental results demonstrate that the Deeplabv3+ with ResNet-50 backbone and a loss function type Dice or Tversky can accurately detect the _re ame. The given results are very encouraging for further study using deep learning approaches.info:eu-repo/semantics/publishedVersio

Assessing the impact of the loss function and encoder architecture for fire aerial images segmentation using deeplabv3+

Wildfire early detection and prevention had become a priority. Detection using Internet of Things (IoT) sensors, however, is expensive in practical situations. The majority of present wildfire detection research focuses on segmentation and detection. The developed machine learning models deploy appropriate image processing techniques to enhance the detection outputs. As a result, the time necessary for data processing is drastically reduced, as the time required rises exponentially with the size of the captured pictures. In a real-time fire emergency, it is critical to notice the fire pixels and warn the firemen as soon as possible to handle the problem more quickly. The present study addresses the challenge mentioned above by implementing an on-site detection system that detects fire pixels in real-time in the given scenario. The proposed approach is accomplished using Deeplabv3+, a deep learning architecture that is an enhanced version of an existing model. However, present work fine-tuned the Deeplabv3 model through various experimental trials that have resulted in improved performance. Two public aerial datasets, the Corsican dataset and FLAME, and one private dataset, Firefront Gestosa, were used for experimental trials in this work with different backbones. To conclude, the selected model trained with ResNet-50 and Dice loss attains a global accuracy of 98.70%, a mean accuracy of 89.54%, a mean IoU 86.38%, a weighted IoU of 97.51%, and a mean BF score of 93.86%.info:eu-repo/semantics/publishedVersio

GPR target detection using a neural network classifier designed by a multi-objective genetic algorithm

Ground Penetrating Radar (GPR) is an electromagnetic sensing technology employed for localization of underground utilities, pipes, and other types of objects. The radargrams typically obtained have a high dimensionality, containing a number of signatures with hyperbolic pattern shapes, and can be processed to retrieve information about the target's locations, depths and material type of underground soil. The classical Hough Transform approach used to reconstruct these hyperbola shapes is computationally expensive, given the large dimensionality of the radargrams. In literature, several approaches propose to first approximate the location of hyperbolas to small segments through a classification stage, before applying the Hough transform over these segments. However, the published classifiers designed for this task present a relatively complex architecture. Aiming at an improved target localization, we propose an alternative classification methodology. The goal is to classify windows of GPR radargrams into two classes (with or without target) using a neural network radial basis function (RBF), designed via a multi-objective genetic algorithm (MOGA). To capture samples' fine details, high order statistic cumulant features (HOS) were used. Feature selection was performed by MOGA, with an optional prior reduction using a mutual information (MIFS) approach. The obtained results demonstrate improvement of the classification performance when compared with other models designed with the same data and are among the best results available in the literature, albeit the large reduction in classifier complexity. (C) 2019 Elsevier B.V. All rights reserved.Portuguese Erasmus National Agency [2015-01-PT01-KA107-04276]Portuguese Foundation for Science and Technology, through IDMEC, under LAETA [UID/EMS/50022/2019]info:eu-repo/semantics/publishedVersio

A Survey on MIMO-OFDM Systems: Review of Recent Trends

MIMO-OFDM is a key technology and a strong candidate for 5G telecommunication systems. In the literature, there is no convenient survey study that rounds up all the necessary points to be investigated concerning such systems. The current deeper review paper inspects and interprets the state of the art and addresses several research axes related to MIMO-OFDM systems. Two topics have received special attention: MIMO waveforms and MIMO-OFDM channel estimation. The existing MIMO hardware and software innovations, in addition to the MIMO-OFDM equalization techniques, are discussed concisely. In the literature, only a few authors have discussed the MIMO channel estimation and modeling problems for a variety of MIMO systems. However, to the best of our knowledge, there has been until now no review paper specifically discussing the recent works concerning channel estimation and the equalization process for MIMO-OFDM systems. Hence, the current work focuses on analyzing the recently used algorithms in the field, which could be a rich reference for researchers. Moreover, some research perspectives are identified

Abstracts of 1st International Conference on Computational & Applied Physics

This book contains the abstracts of the papers presented at the International Conference on Computational & Applied Physics (ICCAP’2021) Organized by the Surfaces, Interfaces and Thin Films Laboratory (LASICOM), Department of Physics, Faculty of Science, University Saad Dahleb Blida 1, Algeria, held on 26–28 September 2021. The Conference had a variety of Plenary Lectures, Oral sessions, and E-Poster Presentations. Conference Title: 1st International Conference on Computational & Applied PhysicsConference Acronym: ICCAP’2021Conference Date: 26–28 September 2021Conference Location: Online (Virtual Conference)Conference Organizer: Surfaces, Interfaces, and Thin Films Laboratory (LASICOM), Department of Physics, Faculty of Science, University Saad Dahleb Blida 1, Algeria