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The reaction of detection and classification of signals in low Signal-to-Noise Ratio (SNR) regimes poses significant challenges in the physical layer design of cognitive radio networks. This paper considers a cognitive radio consisting of one Primary User (PU) and K Secondary Users (SUs), where the main objective for an arbitrary SU is to recognize the PU's signal from other secondary users' signals,...
In this paper, we propose a novel algorithm for automatic modulation classification of single carrier digital modulations widely used in High Frequency (HF) band that serves both military and civilian applications. Specifically, the proposed algorithm addresses the classification of 2FSK, 4FSK, 8FSK, 2PSK, 4PSK, 8PSK, 16QAM, 32QAM, 64QAM, and OQPSK using two features: Dissimilarity in constellation...
Tangible Acoustic Interfaces (TAIs) are innovative acoustic Human-Machine Interaction devices. Exploiting a number of contact sensors distributed on a surface, the vibrational signal generated from the interaction between the surface and an object moved by the user is acquired and analyzed to recognize what the user is doing on the device. The usage of vibrational sensors naturally opens the way also...
An active paradigm was employed to produce reliable and prominent target response in an auditory brain computer interface (BCI), in which subject's voluntary recognition of the property of a target human voice enhances the discriminability between target and non-target EEG response. Furthermore, to adaptively decide the optimal number of trials being averaged for SVM classification, a statistical...
To realize brain computer interface, a recording electroencephalogram (EEG) and determining whether or not P300 is evoked by the presented stimulus have become increasingly important. Using the machine learning method for this classification is effective, but constructing feature vectors with all data points might result in very high-dimensional data. Because such redundant features are undesirable...
Two effective ECG beat classification methods based on signal decomposition were compared in terms of effective feature selection and noise tolerance. The HOS-DWT-FFBNN method associated with the linear correlation based filter (LCBF) provides imposing capability to select the more representative features than the IC reordering method OWSL associated with the ICA-SVM method. Both methods are insensitive...
Distinction of the type of modulated signals is very important in cognitive radio system. In this paper, a novel approach to signal classification is proposed for cognitive radio. Combining the spectral cyclostationary features, embed SVM into the framework of HMM to construct a hybrid HMM/SVM classifier for signal recognition. The simulation results show that the high performance and robustness of...
With the rapid development of the communication technology, the communication environment becomes more and more complicated these years. Many signal modulation types are used simultaneously in digital TV communication systems. Therefore, a need arises for modulation classification that can automatically detect the incoming modulation type. In this paper, we propose a new approach for modulation classification,...
In this study, heartbeat time series are classified using support vector machines (SVMs). Statistical methods and signal analysis techniques are used to extract features from the signals. The SVM classifier is favorably compared to other neural network-based classification approaches by performing leave-one-out cross validation. The performance of the SVM with respect to other state-of-the-art classifiers...
The performance of cognitive radio is sensitive to the accuracy of signal classification. The proposed method can increase the accuracy of existing methods on the certain degree at SNR=0 dB and below. In simulation, we classify five types of signals which are AM, BPSK, FSK, MSK and QPSK. The experiments show that above 99.9% received signals are correctly classified at SNR=-12 dB and above.
In this study, we discuss the use of support vector machine (SVM) learning to classify heart rate signals. Each signal is represented by an attribute vector containing a set of statistical measures for the respective signal. At first, the SVM classifier is trained by data (attribute vectors) with known ground truth. Then, the classifier learnt parameters can be used for the categorization of new signals...
In this study, we discuss the use of support vector machine (SVM) learning to classify heart rate signals. Each signal is represented by an attribute vector containing a set of statistical measures for the respective signal. At first, the SVM classifier is trained by data (attribute vectors) with known ground truth. Then, the classifier learnt parameters can be used for the categorization of new signals...
In this study we have investigated the classification of old myocardial infarction through the analysis of 192 lead body surface potential maps (BSPM). Following an analysis of the most prominent features based on a signal to noise ratio ranking criterion the top 6 features were selected. These features were subsequently used as inputs to a series of supervised classification models in the form of...
This paper deals with automatic modulation classification of communication signals. A new scheme of automatic modulation classification using wavelet analysis and wavelet support vector machine (WSVM) is proposed. Further, a new way of training for wavelet features is carried out to adapt to signals which are non-stable and varied in a wide range of signal-to-noise rates (SNR). Through such training,...
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