A Review on Automatic Signal Classification Techniques for Software Defined Radios

2019 ◽  
Author(s):  
Ghunsar Manisha ◽  
Marriwala Nikhil
Keyword(s):  
Signal Classification ◽  
Classification Techniques ◽  
Software Defined Radios

scholarly journals Automated Authorship Attribution Using Advanced Signal Classification Techniques

PLoS ONE ◽  
2013 ◽  
Vol 8 (2) ◽  
pp. e54998 ◽  
Author(s):  
Maryam Ebrahimpour ◽  
Tālis J. Putniņš ◽  
Matthew J. Berryman ◽  
Andrew Allison ◽  
Brian W.-H. Ng ◽  
...  
Keyword(s):  
Signal Classification ◽  
Authorship Attribution ◽  
Classification Techniques

scholarly journals Investigation of EEG Signal Classification Techniques for Brain Computer Interface

2020 ◽  
Vol 9 (3) ◽  
pp. 3266-3274
Keyword(s):  
Feature Extraction ◽  
Classification Accuracy ◽  
Brain Computer Interface ◽  
Signal Classification ◽  
Computer Interface ◽  
Support Vector ◽  
Eeg Signals ◽  
Classification Techniques ◽  
Feature Extraction And Selection ◽  
Bci System

Brain-computer interface (BCI) has emerged as a popular research domain in recent years. The use of electroencephalography (EEG) signals for motor imagery (MI) based BCI has gained widespread attention. The first step in its implementation is to fetch EEG signals from scalp of human subject. The preprocessing of EEG signals is done before applying feature extraction, selection and classification techniques as main steps of signal processing. In preprocessing stage, artifacts are removed from raw brain signals before these are input to next stage of feature extraction. Subsequently classifier algorithms are used to classify selected features into intended MI tasks. The major challenge in a BCI systems is to improve classification accuracy of a BCI system. In this paper, an approach based on Support Vector Machine (SVM), is proposed for signal classification to improve accuracy of the BCI system. The parameters of kernel are varied to attain improvement in classification accuracy. Independent component analysis (ICA) technique is used for preprocessing and filter bank common spatial pattern (FBCSP) for feature extraction and selection. The proposed approach is evaluated on data set 2a of BCI Competition IV by using 5-fold crossvalidation procedure. Results show that it performs better in terms of classification accuracy, as compared to other methods reported in literature.

scholarly journals Support Vector Machine-Based EMG Signal Classification Techniques: A Review

2019 ◽  
Vol 9 (20) ◽  
pp. 4402 ◽  
Author(s):  
Diana C. Toledo-Pérez ◽  
Juvenal Rodríguez-Reséndiz ◽  
Roberto A. Gómez-Loenzo ◽  
J. C. Jauregui-Correa
Keyword(s):  
Support Vector Machine ◽  
Signal Processing ◽  
Support Vector Machines ◽  
Feature Vector ◽  
Signal Classification ◽  
Support Vector ◽  
Classification Techniques ◽  
Emg Signal ◽  
Vector Machines ◽  
Electromyographic Signals

This paper gives an overview of the different research works related to electromyographic signals (EMG) classification based on Support Vector Machines (SVM). The article summarizes the techniques used to make the classification in each reference. Furthermore, it includes the obtained accuracy, the number of signals or channels used, the way the authors made the feature vector, and the type of kernels used. Hence, this article also includes a compilation about the bands used to filter signals, the number of signals recommended, the most commonly used sampling frequencies, and certain features that can create the characteristics of the vector. This research gathers articles related to different kinds of SVM-based classification and other tools for signal processing in the field.

Advancement of Criminal Profiling Classification Techniques Using a Multi-Method Scaling Framework

2012 ◽  
Author(s):  
Alasdair Matthew Goodwill ◽  
Skye Stephens ◽  
Sandra Oziel ◽  
Nicola Bowes
Keyword(s):  
Criminal Profiling ◽  
Classification Techniques
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