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Advances in adaptive signal processing: totally adaptive systems
Annual Reviews in Control, 1998Abstract The design of traditional adaptive systems in signal processing applications has been strongly influenced by the need to provide an efficient implementation. At high bandwidths this has tended to be an ASIC (Application Specific Integrated Circuit). This, together with the need to make adaptation as reliable as possible has led to the use of
Colin Cowan +4 more
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Adaptive Systems for Signal Process
2000The chapter describes an important class of the nonlinear adaptive system commonly known as artificial neural networks or just simply neural networks. A neural network is a massively parallel distributed processor that has a natural propensity for storing experiential knowledge and making it available for use.
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Self-adaptive software for signal processing
Communications of the ACM, 1998Digital signal processing (DSP) systems are widely used in communication, medical, sonar, radar, equipment health monitoring and many other applications. Frequently, the signal processing system has to meet real-time requirements and provide very large throughput.
Janos Sztipanovits +2 more
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1997 IEEE Ultrasonics Symposium Proceedings. An International Symposium (Cat. No.97CH36118), 2002
We describe a novel modular learning strategy for detection of a target signal of interest in a nonstationary environment, which is motivated by the information preservation rule. The strategy makes no assumptions on the environment. It incorporates three functional blocks: time-frequency analysis, feature extractions, and pattern classification, the ...
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We describe a novel modular learning strategy for detection of a target signal of interest in a nonstationary environment, which is motivated by the information preservation rule. The strategy makes no assumptions on the environment. It incorporates three functional blocks: time-frequency analysis, feature extractions, and pattern classification, the ...
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An Adaptive Signal-Processing Approach to Online Adaptive Tutoring
2011Conventional intelligent or adaptive tutoring online systems rely on domain-specific models of learner behavior based on rules, deep domain knowledge, and other resource-intensive methods. We have developed and studied a domain-independent methodology of adaptive tutoring based on domain-independent signal-processing approaches that obviate the need ...
Bryan Bergeron, Andrew Cline
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Knowledge-based adaptive signal processing
ICASSP '87. IEEE International Conference on Acoustics, Speech, and Signal Processing, 2005In this paper, the use of Artificial Intelligence (A.I.) techniques for improving the performance of adaptive signal processing algorithms is considered. Three potential application areas are identified: (1) The selection and adaptation of secondary control parameters and underlying models, (2) intelligent search methods for adaptation algorithms, and (
Yu Hen Hu, Ali Hussein Abdallah
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2015
Adaptive frequency band (AFB) and ultra-wideband (UWB) systems require either rapidly changing or very high sampling rates. Conventional analog-to-digital devices are nonadaptive and have limited dynamic range. We investigate AFB and UWB sampling via a basis projection method.
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Adaptive frequency band (AFB) and ultra-wideband (UWB) systems require either rapidly changing or very high sampling rates. Conventional analog-to-digital devices are nonadaptive and have limited dynamic range. We investigate AFB and UWB sampling via a basis projection method.
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Exploiting Nonlinearity in Adaptive Signal Processing
2007Quantitative performance criteria for the analysis of machine learning architectures and algorithms have been long established. However, the qualitative performance criteria, e.g., nonlinearity assessment, are still emerging. To that end, we employ some recent developments in signal characterisation and derive criteria for the assessment of the changes
Phebe Vayanos +3 more
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Adaptive Signal Processing Techniques for Chaotic Systems
The Digital Signal Processing workshop, 1993The issue of modeling chaotic systems is addressed. Present methods for treating chaotic dynamics are based on state space reconstruction through delay embedding. These approaches are computationally intensive and are adversely affected by noise in the experimental time series.
Fawad Rauf, Hassan M. Ahmed
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