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Measurements of the energy gin in the modified circuit signal processing unit [PDF]

2016 13th International Conference on Modern Problems of Radio Engineering, Telecommunications and Computer Science (TCSET), 2016
In this paper represents measured the energy gain from the use of polyphase structures in the signal processing unit.
Juliy Boiko, Oleg Shynkaruk, Oleksander Eromenko   +2 more
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Application of entropy and energy measures of fuzziness to processing of ECG signal

Fuzzy Sets and Systems, 1998
The paper deals with the application of entropy and energy measure of fuzziness to processing of ECG signal. After formulation of the problem the notion of entropy measure of fuzziness is recalled. Next the idea of a fuzzy signal created from the original signal is proposed and the entropy measure of fuzziness determined for each sample is calculated ...
E. Czogala, J. Łęski
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System Level Power and Energy Modeling for Signal Processing Applications

2007 2nd International Design and Test Workshop, 2007
This article presents a new methodology of consumption and performance characterization of software's intellectual property (IPs) computing on DSPs. These IPs are generally submitted to various constraints especially the real time and energy. The proposed approach exploits parametric models representing the consumption's behavior of both DSP's ...
M. Abid, J. Ktari
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Energy-Efficient Mixed-Signal Circuits and Systems for Communication and Signal Processing

2022
In recent years, there has been an explosive increase in digital data generated globally, driven by various online applications such as video streaming, social media, shopping, gaming, etc. With the rise of deep learning and the Internet of Things (IoT), this trend is likely to be maintained for the foreseeable future.
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Comparison of energy-based endpoint detectors for speech signal processing

Proceedings of SOUTHEASTCON '96, 2002
Accurate endpoint detection is a necessary capability for the construction of speech databases from field recordings. We describe the implementation of two endpoint detection algorithms which use signal features based on energy and rate of zero crossings.
K. Bush, P. Kornman, L. Webster, A. Ganapathiraju, J. Trimble   +4 more
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Opportunities for statistical signal processing in high energy physics

IEEE/SP 13th Workshop on Statistical Signal Processing, 2005, 2005
Data processing in high energy physics experiments is a multi-tiered process in which raw detector signals are first processed locally into physics objects, and then collated into event records which can be scrutinized by a fast online trigger system.
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Energy-efficient soft error-tolerant digital signal processing

IEEE Transactions on Very Large Scale Integration (VLSI) Systems, 2006
In this paper, we present energy-efficient soft error-tolerant techniques for digital signal processing (DSP) systems. The proposed technique, referred to as algorithmic soft error-tolerance (ASET), employs low-complexity estimators of a main DSP block to achieve reliable operation in the presence of soft errors.
Byonghyo Shim, Naresh R. Shanbhag
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Multistability phenomenon in signal processing, energy harvesting, composite structures, and metamaterials: A review

Mechanical systems and signal processing, 2022
Shitong Fang, Shengxi Zhou, D. Yurchenko, Tao Yang, W. Liao   +4 more
semanticscholar   +1 more source

Entropy and Energy Measures of Fuzziness in ECG Signal Processing

2000
The paper deals with the application of entropy and energy measures of fuzziness to digital processing of ECG signal. After the introduction the notion of entropy measure of fuzziness is recalled and the idea of a fuzzy signal created from the original signal is proposed. Next the energy measures of fuzziness is recalled.
J. Leski, E. Czogala
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Energy-efficient soft error-tolerant digital signal processing

The Thrity-Seventh Asilomar Conference on Signals, Systems & Computers, 2003, 2004
In this paper, we present energy-efficient soft error (SE)-tolerant techniques for digital signal processing (DSP) systems. The proposed technique, referred to as algorithmic soft error-tolerance (ASET), employs an low-complexity estimator of a main DSP block to guarantee reliability in presence of soft errors either in the MDSP or the estimator.
Naresh R. Shanbhag, Byonghyo Shim, Seok-jun Lee   +2 more
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