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

Multilevel Weighted Support Vector Machine for Classification on Healthcare Data with Missing Values

This work is motivated by the needs of predictive analytics on healthcare data as represented by Electronic Medical Records. Such data is invariably problematic: noisy, with missing entries, with imbalance in classes of interests, leading to serious bias in predictive modeling. Since standard data mining methods often produce poor performance measures, we argue for development of specialized techniques of data-preprocessing and classification. In this paper, we propose a new method to simultaneously classify large datasets and reduce the effects of missing values. It is based on a multilevel framework of the cost-sensitive SVM and the expected maximization imputation method for missing values, which relies on iterated regression analyses. We compare classification results of multilevel SVM-based algorithms on public benchmark datasets with imbalanced classes and missing values as well as real data in health applications, and show that our multilevel SVM-based method produces fast, and more accurate and robust classification results.Comment: arXiv admin note: substantial text overlap with arXiv:1503.0625

Multilevel SVM and AI based Transformer Fault Diagnosis using the DGA Data

The Dissolved Gas Analysis (DGA) is utilized as a test for the detection of incipient prob-lems in transformers, and condition monitoring of transformers using software-based diagnosis tools has become crucial. This research uses dissolved gas analysis as an intel-ligent fault classification of a transformer. The Multilayer SVM technique is used to de-termine the classification of faults and the name of the gas. The learned classifier in the multilayer SVM is trained with the training samples and can classify the state as normal or fault state, which contains six fault categories. In this paper, polynomial and Gaussi-an functions are utilized to assess the effectiveness of SVM diagnosis. The results demonstrate that the combination ratios and graphical representation technique is more suitable as a gas signature, and that the SVM with the Gaussian function outperforms the other kernel functions in diagnosis accuracy

Detecting P2P Botnet in Software Defined Networks

Software Defined Network separates the control plane from network equipment and has great advantage in network management as compared with traditional approaches. With this paradigm, the security issues persist to exist and could become even worse because of the flexibility on handling the packets. In this paper we propose an effective framework by integrating SDN and machine learning to detect and categorize P2P network traffics. This work provides experimental evidence showing that our approach can automatically analyze network traffic and flexibly change flow entries in OpenFlow switches through the SDN controller. This can effectively help the network administrators manage related security problems

Fast Machine Learning Algorithms for Massive Datasets with Applications in the Biomedical Domain

The continuous increase in the size of datasets introduces computational challenges for machine learning algorithms. In this dissertation, we cover the machine learning algorithms and applications in large-scale data analysis in manufacturing and healthcare. We begin with introducing a multilevel framework to scale the support vector machine (SVM), a popular supervised learning algorithm with a few tunable hyperparameters and highly accurate prediction. The computational complexity of nonlinear SVM is prohibitive on large-scale datasets compared to the linear SVM, which is more scalable for massive datasets. The nonlinear SVM has shown to produce significantly higher classification quality on complex and highly imbalanced datasets. However, a higher classification quality requires a computationally expensive quadratic programming solver and extra kernel parameters for model selection. We introduce a generalized fast multilevel framework for regular, weighted, and instance weighted SVM that achieves similar or better classification quality compared to the state-of-the-art SVM libraries such as LIBSVM. Our framework improves the runtime more than two orders of magnitude for some of the well-known benchmark datasets. We cover multiple versions of our proposed framework and its implementation in detail. The framework is implemented using PETSc library which allows easy integration with scientific computing tasks. Next, we propose an adaptive multilevel learning framework for SVM to reduce the variance between prediction qualities across the levels, improve the overall prediction accuracy, and boost the runtime. We implement multi-threaded support to speed up the parameter fitting runtime that results in more than an order of magnitude speed-up. We design an early stopping criteria to reduce the extra computational cost when we achieve expected prediction quality. This approach provides significant speed-up, especially for massive datasets. Finally, we propose an efficient low dimensional feature extraction over massive knowledge networks. Knowledge networks are becoming more popular in the biomedical domain for knowledge representation. Each layer in knowledge networks can store the information from one or multiple sources of data. The relationships between concepts or between layers represent valuable information. The proposed feature engineering approach provides an efficient and highly accurate prediction of the relationship between biomedical concepts on massive datasets. Our proposed approach utilizes semantics and probabilities to reduce the potential search space for the exploration and learning of machine learning algorithms. The calculation of probabilities is highly scalable with the size of the knowledge network. The number of features is fixed and equivalent to the number of relationships or classes in the data. A comprehensive comparison of well-known classifiers such as random forest, SVM, and deep learning over various features extracted from the same dataset, provides an overview for performance and computational trade-offs. Our source code, documentation and parameters will be available at https://github.com/esadr/

Localização de faltas de alta impedância monofásicas por minimização de erros em sistemas de distribuição

High impedance faults have as their main characteristic a low fault current, which means that, in most cases, conventional protection systems are not sensitive. In recent years, high impedance faults have been the subject of works in the literature, and most of them define methods for detecting these faults. In addition to detection, locating faults is important so that the maintenance team can carry out repairs more quickly and reduce the number of consumers impacted by the interruption in the energy supply. For this reason, this work proposes a methodology for locating single-phase high impedance faults in distribution systems using synchronized RMS measurements by minimizing measurement errors. This minimization consists of solving a mixed integer nonlinear programming problem. The method is evaluated for unbalanced distribution systems, with load uncertainties and measurement errors. The proposed methodology presented good results when estimating the location of the fault, but it was verified that the precision of the results is linked to the errors added to the data. The IEEE 34-node test feeder and IEEE 123-node test feeder were used in the tests.As faltas de alta impedância têm como principal característica uma baixa corrente de defeito, fazendo com que, na maioria das vezes, os sistemas de proteção convencionais não sejam sensibilizados. Nos últimos anos, as faltas de alta impedância vêm sendo tema de trabalhos na literatura, sendo que a maioria deles define métodos para detecção dessas faltas. Além da detecção, a localização de faltas é importante para que a equipe de manutenção possa efetuar o reparo com mais rapidez e reduzir o número de consumidores impactados com a interrupção no fornecimento de energia. Por esta razão, este trabalho propõe uma metodologia para localização de faltas de alta impedância monofásicas em sistemas de distribuição utilizando medições RMS sincronizadas através da minimização de erros das medições. Essa minimização consiste na resolução de um problema de programação não-linear inteira mista. O método é avaliado para sistemas de distribuição desequilibrados, com incertezas nas cargas e erros nas medições. A metodologia proposta apresentou bons resultados ao estimar o local da falta, mas verificou-se que a precisão dos resultados está atrelada aos erros acrescentados nos dados. Os sistemas IEEE 34 barras e IEEE 123 barras foram utilizados nos testes.CAPES - Coordenação de Aperfeiçoamento de Pessoal de Nível Superio

Using Quality Measures for Multilevel Speaker Recognition

The use of quality information for multilevel speaker recognition systems is addressed in this contribution. From a definition of what constitutes a quality measure, two applications are proposed at different phases of the recognition process: scoring and multi-level fusion stages. The traditional likelihood scoring stage is further developed providing guidelines for the practical application of the proposed ideas. Conventional user-independent multilevel Support Vector Machine (SVM) score fusion is also adapted for the inclusion of quality information in the fusion process. In particular, quality measures meeting three different goodness criteria: SNR, F0 deviations and the ITUP.563 objective speech quality assessment are used in the speaker recognition process. Experiments carried out in the Switchboard-I database assess the benefits of the proposed quality-guided recognition approach for both the score computation and score fusion stages