scholarly journals WiDMove - um sensor de movimento direcional baseado em perturbações do sinal eletromagnético de interfaces 802.11

2018 ◽  
Author(s):  
Bruno Soares da Silva ◽  
Gustavo Teodoro Laureano ◽  
Kleber Vieira Cardoso
Keyword(s):  
Principal Component Analysis ◽  
Support Vector Machine ◽  
Fourier Transform ◽  
Principal Component ◽  
Support Vector ◽  
Short Time Fourier Transform ◽  
Channel State ◽  
State Information ◽  
Leave One Out ◽  
Short Time

A detecção acurada de indivíduos em ambientes fechados demanda dispositivos de alto custo, enquanto dispositivos de baixo custo, além da baixa acurácia, oferecem poucas informações sobre os eventos monitorados. As perturbações que podem afetar o sinal eletromagnético utilizado por interfaces de rede 802.11 tornam esse tipo de dispositivo um sensor de baixo custo, amplamente disponível e com acurácia satisfatória para várias aplicações. Neste trabalho, apresentamos o WiDMove, uma proposta para detecção da entrada e saída de pessoas em ambientes fechados utilizando medidas de qualidade do canal oferecidas pelo padrão IEEE 802.11n, conhecidas como Channel State Information (CSI). Nossa proposta é baseada em técnicas de processamento de sinal e de aprendizado de máquina, as quais nos permitem extrair e classificar assinaturas de eventos usando as medidas CSI. Em testes de laboratório com interfaces 802.11 convencionais, coletamos medidas CSI influenciadas por 8 pessoas distintas e extraímos as assinaturas de entrada e saída utilizando, dentre outras técnicas, Principal Component Analysis (PCA) e Short-Time Fourier Transform (STFT). Treinamos um classificador do tipo Support Vector Machine (SVM) e o validamos com validação cruzada, utilizando as técnicas K-Fold e Leave-One-Out. Os testes demonstraram que o WiDMove pode atingir a uma acurácia média superior a 85%. 

scholarly journals Seizure Detection in Epileptic EEG Using Short-Time Fourier Transform and Support Vector Machine

2021 ◽  
Vol 17 (14) ◽  
pp. 65-78
Author(s):  
Achmad Rizal ◽  
Wahmisari Priharti ◽  
Sugondo Hadiyoso
Keyword(s):  
Support Vector Machine ◽  
Fourier Transform ◽  
Main Tool ◽  
Signal Spectrum ◽  
Time Range ◽  
Support Vector ◽  
Detection Accuracy ◽  
Short Time Fourier Transform ◽  
Time Frequency ◽  
Short Time

Epilepsy is the most common form of neurological disease. The electroencephalogram (EEG) is the main tool in the observation of epilepsy. The detection and prediction of seizures in EEG signals require multi-domain analysis, one of which is the time domain combined with other approaches for feature extraction. In this study, a method for detecting seizures in epileptic EEG is proposed using analysis of the distribution of the signal spectrum in the time range t. The EEG signal which includes normal, inter-ictal and ictal is transformed into the time-frequency domain using the Short-Time Fourier Transform (STFT). Simulations were carried out on varying window length, overlap and FFT points to find the highest detection accuracy. The frequency distribution and first-order statistics were then calculated as feature vectors for the classification process. A support vector machine was employed to evaluate the proposed method. The simulation results showed the highest accuracy of 92.3% using 25-20-512 STFT and quadratic SVM. The proposed method in this study is expected to be a basis for the detection and prediction of seizures in long-term EEG recordings or real-time EEG monitoring of epilepsy patients.

scholarly journals Lightweight Anomaly Detection Scheme Using Incremental Principal Component Analysis and Support Vector Machine

Sensors ◽  
2021 ◽  
Vol 21 (23) ◽  
pp. 8017
Author(s):  
Nurfazrina M. Zamry ◽  
Anazida Zainal ◽  
Murad A. Rassam ◽  
Eman H. Alkhammash ◽  
Fuad A. Ghaleb ◽  
...  
Keyword(s):  
Principal Component Analysis ◽  
Support Vector Machine ◽  
Sensor Networks ◽  
Computational Complexity ◽  
Anomaly Detection ◽  
Principal Component ◽  
Support Vector ◽  
Communication Overhead ◽  
Detection Scheme ◽  
Memory Utilization

Wireless Sensors Networks have been the focus of significant attention from research and development due to their applications of collecting data from various fields such as smart cities, power grids, transportation systems, medical sectors, military, and rural areas. Accurate and reliable measurements for insightful data analysis and decision-making are the ultimate goals of sensor networks for critical domains. However, the raw data collected by WSNs usually are not reliable and inaccurate due to the imperfect nature of WSNs. Identifying misbehaviours or anomalies in the network is important for providing reliable and secure functioning of the network. However, due to resource constraints, a lightweight detection scheme is a major design challenge in sensor networks. This paper aims at designing and developing a lightweight anomaly detection scheme to improve efficiency in terms of reducing the computational complexity and communication and improving memory utilization overhead while maintaining high accuracy. To achieve this aim, one-class learning and dimension reduction concepts were used in the design. The One-Class Support Vector Machine (OCSVM) with hyper-ellipsoid variance was used for anomaly detection due to its advantage in classifying unlabelled and multivariate data. Various One-Class Support Vector Machine formulations have been investigated and Centred-Ellipsoid has been adopted in this study due to its effectiveness. Centred-Ellipsoid is the most effective kernel among studies formulations. To decrease the computational complexity and improve memory utilization, the dimensions of the data were reduced using the Candid Covariance-Free Incremental Principal Component Analysis (CCIPCA) algorithm. Extensive experiments were conducted to evaluate the proposed lightweight anomaly detection scheme. Results in terms of detection accuracy, memory utilization, computational complexity, and communication overhead show that the proposed scheme is effective and efficient compared few existing schemes evaluated. The proposed anomaly detection scheme achieved the accuracy higher than 98%, with (𝑛𝑑) memory utilization and no communication overhead.

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