Department of Electrical, Computer, and Biomedical Engineering Faculty Publications Copyright (c) 2013 University of Rhode Island All rights reserved. http://digitalcommons.uri.edu/ele_facpubs Recent documents in Department of Electrical, Computer, and Biomedical Engineering Faculty Publications en-us Mon, 20 May 2013 09:05:12 PDT 3600 Securing Collaborative Spectrum Sensing against Untrustworthy Secondary Users in Cognitive Radio Networks http://digitalcommons.uri.edu/ele_facpubs/3 http://digitalcommons.uri.edu/ele_facpubs/3 Thu, 14 Mar 2013 11:00:19 PDT Cognitive radio is a revolutionary paradigm to migrate the spectrum scarcity problem in wireless networks. In cognitive radio networks, collaborative spectrum sensing is considered as an effective method to improve the performance of primary user detection. For current collaborative spectrum sensing schemes, secondary users are usually assumed to report their sensing information honestly. However, compromised nodes can send false sensing information to mislead the system. In this paper, we study the detection of untrustworthy secondary users in cognitive radio networks. We first analyze the case when there is only one compromised node in collaborative spectrum sensing schemes. Then we investigate the scenario that there are multiple compromised nodes. Defense schemes are proposed to detect malicious nodes according to their reporting histories.We calculate the suspicious level of all nodes based on their reports. The reports from nodes with high suspicious levels will be excluded in decision-making. Compared with existing defense methods, the proposed scheme can effectively differentiate malicious nodes and honest nodes. As a result, it can significantly improve the performance of collaborative sensing. For example, when there are 10 secondary users, with the primary user detection rate being equal to 0.99, one malicious user can make the false alarm rate (Pf ) increase to 72%. The proposed scheme can reduce it to 5%. Two malicious users can make Pf increase to 85% and the proposed scheme reduces it to 8%.

]]> Wenkai Wang et al. Study of Stability of Time-Domain Features for Electromyographic Pattern Recognition http://digitalcommons.uri.edu/ele_facpubs/2 http://digitalcommons.uri.edu/ele_facpubs/2 Wed, 05 Sep 2012 07:15:09 PDT Background: Significant progress has been made towards the clinical application of human-machine interfaces (HMIs) based on electromyographic (EMG) pattern recognition for various rehabilitation purposes. Making this technology practical and available to patients with motor deficits requires overcoming real-world challenges, such as physical and physiological changes, that result in variations in EMG signals and systems that are unreliable for long-term use. In this study, we aimed to address these challenges by (1) investigating the stability of time-domain EMG features during changes in the EMG signals and (2) identifying the feature sets that would provide the most robust EMG pattern recognition.

Methods: Variations in EMG signals were introduced during physical experiments. We identified three disturbances that commonly affect EMG signals: EMG electrode location shift, variation in muscle contraction effort, and muscle fatigue. The impact of these disturbances on individual features and combined feature sets was quantified by changes in classification performance. The robustness of feature sets was evaluated by a stability index developed in this study.

Results: Muscle fatigue had the smallest effect on the studied EMG features, while electrode location shift and varying effort level significantly reduced the classification accuracy for most of the features. Under these disturbances, the most stable EMG feature set with combination of four features produced at least 16.0% higher classification accuracy than the least stable set. EMG autoregression coefficients and cepstrum coefficients showed the most robust classification performance of all studied time-domain features.

Conclusions: Selecting appropriate EMG feature combinations can overcome the impact of the studied disturbances on EMG pattern classification to a certain extent; however, this simple solution is still inadequate. Stabilizing electrode contact locations and developing effective classifier training strategies are suggested to further improve the robustness of HMIs based on EMG pattern recognition.

]]> Dennis Tkach et al. A Case Study of Self-Checking Circuits Reliability http://digitalcommons.uri.edu/ele_facpubs/1 http://digitalcommons.uri.edu/ele_facpubs/1 Wed, 05 Sep 2012 07:15:08 PDT In this paper, we analyze the reliability of self-checking circuits. A case study is presented in which a fault-tolerant system with duplicated self-checking modules is compared to the TMR version. It is shown that a duplicated self-checking system has a much higher reliability than that of the TMR counterpart. More importantly, the reliability of the selfchecking system does not drop as sharply as that of the TMR version. We also demonstrate the trade-offs between hardware complexity and error handling capability of self-checking circuits. Alternative self-checking designs where some hardware redundancies are removed with the lost of fault-secure and/or self-testing properties are also studied.

]]> Jien-Chung Lo