NUST Institutions Library Catalogue Search for 'kw,wrdl: (su-br:"Real-time data processing")'

Quantum communications and cryptography

Boca Raton, FL : Taylor & Francis, 2006 . 232 p. : 24 cm.. 0-8493-3684-8

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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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Advances in Embedded Computer Vision \

By Kisačanin, Branislav.. 9781849967761

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Lecture Notes in Computer Science /

By Jung.H, Albrecht .A. Berlin Springer-Verlag 1987 . 205p .: 3540180990

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Real-Time Design Patterns :robust scalable architecture for Real-time systems -(E-BOOK)

By Douglass, Bruce Powel.. Boston, MA : Addison-Wesley, 2003 . xx, 500 p. : 24 cm. +. 0201699567 (alk. paper)

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Perception of Emotion in Human-Robot Interaction /

By Zia, Muhammad Faisal. . 59p. , Perception of emotion is an intuitive replication of a person’s internal state without the need for verbal communication. Visual emotion recognition has been broadly studied and several end-toend deep neural networks (DNNs)-based and Machine learning-based models have been proposed but they lack the ability to be implemented in low-specification devices like robots, and vehicles. The drawbacks of conventional handcrafted feature-based Facial Emotion Recognition (FER) methods are eliminated by DNNs-based FER approaches. In spite of that, Deep Neural Network based FER techniques suffer from high processing costs and exorbitant memory requirements, their application is constrained in fields like Human-Robot Interaction (HRI) and HumanComputer Interaction (HCI) and relies on hardware requirements. In aforementioned study, we presented a computationally inexpensive and robust FER system for the perception of six basic emotions (i.e., disgust, surprise, fear, anger, happy, and sad) that is capable of running on embedded devices with constrained specifications. In the first step after pre-processing input images, geometric features are extracted from detected facial landmarks, considering the facial spatial position among influential landmarks. The extracted features are given as input to trainthe SVM classifier. Our proposed FER system was trained and evaluated experimentally using two databases, Karolinska Directed Emotional Faces (KDEF) and Extended Cohn-Kanade (CK+) database. Fusion of KDEF and CK+ datasets at the training level were also employed in order to generalize the FER system’s response to the variations of ethnicity, race, national and provincial backgrounds. The results show that our proposed FER system is optimized for real-time embedded applications with constrained specifications and yields an accuracy of 96.8%, 86.7% and 86.4% for CK+, KDEF and fusion of CK+ and KDEF databases respectively. As a part of our future research objectives, the developed system will make a robotic agent capable of perceiving emotion and interacting naturally without the need for additional hardware during HRI. 30cm.

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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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