Classification of EMG Signals Using PCA and FFT
Journal of Medical Systems
A survey of trust and reputation systems for online service provision
Decision Support Systems
Benchmarking GPUs to tune dense linear algebra
Proceedings of the 2008 ACM/IEEE conference on Supercomputing
Proceedings of the 2009 ACM symposium on Applied Computing
Securing rating aggregation systems using statistical detectors and trust
IEEE Transactions on Information Forensics and Security - Special issue on electronic voting
Information theoretic framework of trust modeling and evaluation for ad hoc networks
IEEE Journal on Selected Areas in Communications
Discrete-time Markov Model for Wireless Link Burstiness Simulations
Wireless Personal Communications: An International Journal
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This paper presents a design and implementation of a cyber-physical system (CPS) for neurally controlled artificial legs. The key to the new CPS system is the neural-machine interface (NMI) that uses an embedded computer to collect and interpret electromyographic (EMG) signals from a physical system that is a leg amputee. A new deciphering algorithm, composed of an EMG pattern classifier and finite state machine (FSM), was developed to identify the user's intended lower limb movements. To deal with environmental uncertainty, a trust management mechanism was designed to handle unexpected sensor failures and signal disturbances. Integrating the neural deciphering algorithm with the trust management mechanism resulted in a highly accurate and reliable software system for neural control of artificial legs. The software was then embedded in a newly designed hardware platform based on an embedded microcontroller and a graphic processing unit (GPU) to form a complete NMI for real time testing. Our preliminary experiment on a human subject demonstrated the feasibility of our designed real-time neural-machine interface for artificial legs.