NUST Institutions Library Catalogue Search for 'kw,wrdl: (su-br:"Signal processing, System analysis")'

Advances in 3G enhanced technologies for wireless communications

Boston, MA : Artech House, 2002 . xv, 350 p. : , Based on selected papers from the Fourth IEEE Workshop on Emerging Technologies in Circuits and Systems--Third Generation (3G) Mobile Technologies and Applications, held Nov. 29-Dec. 1, 2000, in Hong Kong. 24 cm.. 1580533027

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Handbook of Biomedical Instrumentation

By Khandpur. New York : | Maidenhead : McGraw-Hill Professional Publishing | McGraw-Hill Education [Distributor] 2015 9789339205430 | 933920543X (Trade Paper)

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Space-time coding /

By Vucetic, Branka.. West Sussex, England ; | Hoboken, NJ : Wiley, 2003 . xxvii, 302 p. : 25 cm.. 0470847573 (alk. paper)

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Biomedical Engineering (E-Book)

By Carlos Alexandre Barros de Mello. India In-Teh 2009 . x,658 P;

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Intelligent and Biosensors (E-Book)

By Vernon S. Somerset. India In-Teh 2010 . 396 p;

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Trends in commutative algebra /(E-BOOK)

Cambridge, UK ; | New York : Cambridge University Press, 2004 . x, 254 p. : 25 cm.. 0521831954 (hb)

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Real-Time Target Acquisition Test for Rehabilitation Using EMG /

By Ain ,Qurat Ul . . 109p. ; , This research investigates the use of electromyography (EMG) signals for real-time control in rehabilitation applications. Utilizing the Myo armband, we captured EMG signals corresponding to 12 distinct hand and finger movements. We compared the performance of two machine learning classifiers, Long Short-Term Memory (LSTM) networks and Vanilla Neural Networks (VNN), in accurately classifying these movements. LSTM networks demonstrated superior performance, achieving higher accuracy and robustness in signal classification compared to VNN. To address adaptability and reduce training time for new users, we employed transfer learning techniques. Our research also incorporated transfer learning techniques to enhance model performance, leveraging both a broad dataset collected from multiple subjects and a focused dataset from a single individual over an extended period. Our results show that transfer learning significantly improves the adaptability of the system, allowing for quicker and more efficient integration of new subjects into the model. The study further includes statistical analysis to validate the performance improvements, with paired t-tests and ANOVA confirming the significance of our findings. This work highlights the potential of LSTM networks and transfer learning in enhancing the usability and effectiveness of EMG-based control systems for rehabilitation, paving the way for more responsive and adaptable prosthetic devices. The integration of advanced machine learning techniques into EMG signal processing presents a promising avenue for future research and clinical applications. 30cm..

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