Abstract: A spectrum sensing method for cognitive radio wherein spectrum holes are detected in a wireless environment having spectrum scarcity. First, a cognitive radio user (CR) determines the power spectral density (PSD) of a wideband signal and detects subbands within the wideband using wavelet transforms (WT). WT coefficients are calculated by convolving the PSD with first derivatives of wavelet smoothing functions. The extrema of the WT coefficients demark frequency subband edges. Detecting subband edges becomes more robust against noise by median filtering the PSD before calculating WT coefficients, summing over WT coefficients with different scale factors, and suppressing WT coefficients below a noise threshold. After identifying subbands, the CR determines subband availability by measuring the subband power and signaling the power to a fusion center receiving power measurements from multiple cooperating CRs, and final decisions are based on data and decision fusion.

Abstract: A spectrum sensing method for cognitive radio to detect spectrum holes in an environment of bandwidth scarcity. The method comprises first receiving a wireless signal at a cognitive radio user, and then discovering the frequency edges of allocated frequency bands by using wavelet transform coefficients to detect discontinuities in the power spectral density of the received signal. After determining the allocated frequency bands, the method determines frequency band availability by detecting the in band energy using a bi-threshold energy detector, where the energy detector makes hard decisions and soft decisions. Finally, a fusion center combines hard and soft decisions collected from a cooperative spectrum sensing network of cognitive radio users and makes a final decision using a hybrid of data fusion and decision fusion to determine the final decision regarding spectrum availability.

Abstract: The system and method for detecting seizure activity combines signal traces from both an electroencephalogram (EEG) and an electrocardiogram (ECG) in order to detect and predict a seizure event in a patient. Determination of a seizure classification of the combination is based on Dempster-Shafer Theory (DST) to calculate a combined probability belief. Prior to combination, classification of the EEG and ECG data is performed by linear discriminant analysis (LDA) or naïve Bayesian classification to provide a seizure event classification or a non-seizure event classification.

Abstract: A spectrum sensing method for cognitive radio wherein spectrum holes are detected in a wireless environment having spectrum scarcity. First, a cognitive radio user (CR) determines the power spectral density (PSD) of a wideband signal and detects subbands within the wideband using wavelet transforms (WT). WT coefficients are calculated by convolving the PSD with first derivatives of wavelet smoothing functions. The extrema of the WT coefficients demark frequency subband edges. Detecting subband edges becomes more robust against noise by median filtering the PSD before calculating WT coefficients, summing over WT coefficients with different scale factors, and suppressing WT coefficients below a noise threshold. After identifying subbands, the CR determines subband availability by measuring the subband power and signaling the power to a fusion center receiving power measurements from multiple cooperating CRs, and final decisions are based on data and decision fusion.

Abstract: A spectrum sensing method for cognitive radio to detect spectrum holes in an environment of bandwidth scarcity. The method comprises first receiving a wireless signal at a cognitive radio user, and then discovering the frequency edges of allocated frequency bands by using wavelet transform coefficients to detect discontinuities in the power spectral density of the received signal. After determining the allocated frequency bands, the method determines frequency band availability by detecting the in band energy using a bi-threshold energy detector, where the energy detector makes hard decisions and soft decisions. Finally, a fusion center combines hard and soft decisions collected from a cooperative spectrum sensing network of cognitive radio users and makes a final decision using a hybrid of data fusion and decision fusion to determine the final decision regarding spectrum availability.