Brain-computer interfaces (BCIs) records brain activity using electroencephalogram (EEG) headsets in the form of EEG signals; these signals can be recorded, processed and classified into different hand movements, which used to control other IoT devices. Classification movements will one step closer applying algorithms real-life situations headsets. This paper uses feature extraction techniques ...

An attempt has been made to characterize Compound Nerve Action ~ o ~ ~ n t i a ~ s from normals and Hanseniasis patients using wavelet transforms. The work reported here involves classification of peripheral potentials recorded at the elbow of normals and Hanseniasis patients following electrical stimMla~ion of the median nerves. Employing the system developed by the first author, nerve conduct...

In this study; time series electromyography (EMG) data have been classified according to hand movements using wavelet analysis and deep learning. A pre-trained CNN (Convolitonal Neural Network-GoogLeNet) has used in the classification process performed with signal processing, by way results can be obtained continuous transform methods. The dataset taken from Machine Learning Repository at Unive...

The ability to detect anomalies in time series is considered highly valuable numerous application domains. sequential nature of objects responsible for an additional feature complexity, ultimately requiring specialized approaches order solve the task. Essential characteristics series, situated outside domain, are often difficult capture with state-of-the-art anomaly detection methods when no tr...

The landscape scaling relation challenges catchment ecological management; however, how the relations change among naturally and anthropogenically differentiated catchments is still unknown. In this study, approximately 1500 soil samples were determined; more than 800 households surveyed; pattern was investigated in 120 sub-catchments of a subtropical Chinese urbanizing agricultural catchment. ...

This article develops a wavelet decomposition of a stochastic process which parallels a time{localized Cram er (Fourier) spectral representation. We provide a time{scale instead of a time{frequency decomposition and, hence, instead of thinking as scale in terms of \inverse frequency" we start from genuine time{scale building blocks or \atoms". Using this class of locally stationary wavelet (LSW...

The trend of using deep learning techniques to classify arbitrary tasks has grown significantly in the last decade. Such background provide a stack non-linear functions solve that cannot be solved linear manner. Naturally, models can always almost any problem with right amount functional parameters. However, set preprocessing techniques, these might become much more accessible by negating need ...

Connected and automated vehicles (CAVs) involving massive advanced sensors electronic control units (ECUs) bring intelligentization to the transportation system conveniences human mobility. Unfortunately, these face security threats due complexity connectivity. Especially, existing in-vehicle network protocols (e.g., controller area network) lack consideration, which is vulnerable malicious att...

Because the health care field generates a large amount of data, we must employ modern ways to handle this data in order give effective outcomes and make successful decisions based on data. Heart diseases are major cause mortality worldwide, accounting for 1/3th all fatalities. Cardiovascular disease detection can be accomplished by disturbance cardiac signals, one which is known as phonocardiog...

Objectives: This review outlines the method ological fundamentals of the most frequently used non-parametric time-frequency analysis techniques in biomedicine and their main properties, as well as providing decision aids concerning their applications. Methods: The short-term Fourier transform (STFT), the Gabor transform (GT), the S-transform (ST), the continuous Morlet wavelet transform (CMWT),...