Cooperative Wideband Spectrum Sensing Based on Joint Sparsity

COOPERATIVE WIDEBAND SPECTRUM SENSING BASED ON JOINT SPARSITY By Ghazaleh Jowkar, Master of Science A thesis submitted in partial fulfillment of the requirements for the degree of Master of Science at Virginia Commonwealth University Virginia Commonwealth University 2017 Major Director: Dr. Ruixin Niu, Associate Professor of Department of Electrical and Computer Engineering In this thesis, the problem of wideband spectrum sensing in cognitive radio (CR) networks using sub-Nyquist sampling and sparse signal processing techniques is investigated. To mitigate multi-path fading, it is assumed that a group of spatially dispersed SUs collaborate for wideband spectrum sensing, to determine whether or not a channel is occupied by a primary user (PU). Due to the underutilization of the spectrum by the PUs, the spectrum matrix has only a small number of non-zero rows. In existing state-of-the-art approaches, the spectrum sensing problem was solved using the low-rank matrix completion technique involving matrix nuclear-norm minimization. Motivated by the fact that the spectrum matrix is not only low-rank, but also sparse, a spectrum sensing approach is proposed based on minimizing a mixed-norm of the spectrum matrix instead of low-rank matrix completion to promote the joint sparsity among the column vectors of the spectrum matrix. Simulation results are obtained, which demonstrate that the proposed mixed-norm minimization approach outperforms the low-rank matrix completion based approach, in terms of the PU detection performance. Further we used mixed-norm minimization model in multi time frame detection. Simulation results shows that increasing the number of time frames will increase the detection performance, however, by increasing the number of time frames after a number of times the performance decrease dramatically

Electromyography (EMG) Based Finger Movement Detection

Thesis (Master's)--University of Washington, 2020One fundamental component of much modern human-machine interaction (HCI) devices is Myoelectric control systems which is a system that receives the Electromyography(EMG) signal originated from muscle movement. Much research has focused on determining the best general structure of the control system for a given application where the same element choices are used for all subjects. However, due to the nature of the signal and human body, the best structure may be subject-specific. The primary aim of this research can be categorized into two major areas. 1)Movement extraction and movement duration detection from a recorded set of moves. 2) Subject-specific selection of classification system elements (i.e., feature set, classifier, window characteristics, dimensionality reduction method) for individual finger movement detection. In this study, we focus on individual finger movements, therefore, two movement sets for each finger were tested: one where each finger is closed for half a second and open for half a second and the other with the duration of one second closed finger one-second open finger. Myoelectric data were collected from the forearm muscles of 27 years old female subject using a single channel Epidermal Electric System (EES). We performed three sets of tests on our subject; giving us three data sets to study and develop a model for. These data were first got prepossessed for movement extraction then used to train and test a series of classification systems, each consist of a different combination of system element choices. We introduce a novel model for movement extraction from an unfiltered EMG signal and achieve an average accuracy of 88% for our five class finger movement classification. Additionally, we show the effect of Principal Component Analysis on Classification, as well as multi-layer classification of EMG signals