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Quantization in Compressive Sensing: A Signal Processing Approach [PDF]

open access: goldIEEE Access, 2020
The influence of finite-length registers and the corresponding quantization effects on the reconstruction of sparse and approximately sparse signals from a reduced set of measurements is analyzed in this paper.
Isidora Stankovic   +4 more
doaj   +7 more sources

Reconsidering Linear Transmit Signal Processing in 1-Bit Quantized Multi-User MISO Systems [PDF]

open access: greenIEEE Transactions on Wireless Communications, 2018
In this contribution, we investigate a coarsely quantized Multi-User (MU)-Multiple Input Single Output (MISO) downlink communication system, where we assume 1-Bit Digital-to-Analog Converters (DACs) at the Base Station (BS) antennas.
De Candido, Oliver   +4 more
core   +8 more sources

Optimized Quantization in Distributed Graph Signal Processing [PDF]

open access: greenICASSP 2019 - 2019 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), 2019
Distributed graph signal processing methods require that the graph nodes communicate by exchanging messages. These messages have a finite precision in a realistic network, which may necessitate to implement quantization. Quantization, in turn, generates errors in the distributed processing tasks, compared to perfect settings.
Isabela Cunha Maia Nobre   +1 more
semanticscholar   +4 more sources

Optimization of Quantized Analog Signal Processing Using Genetic Algorithms and μ-Law [PDF]

open access: goldIEEE Open Journal of Circuits and Systems, 2022
Digital mismatch calibration for quantized analog (QA) signal processing is proposed for the first time. Since the proposed calibration mechanism does not require uniform QA slicer levels, non-uniform quantization can be applied to improve the system ...
Qingnan Yu   +2 more
doaj   +5 more sources

Quantization of VLSI digital signal processing systems [PDF]

open access: goldEURASIP Journal on Advances in Signal Processing, 2012
Digital systems have finite precision, which imposes a maximum bound on the accuracy of the results of the computed mathematical operations. The so-called quantization process, also wordlength optimization, aims at finding cost-efficient hardware architectures that comply with a given maximum accuracy loss. Floating-point arithmetic is commonly used to
Gabriel Caffarena   +4 more
semanticscholar   +5 more sources

WEIGHTLESS PROCESSING OF QUANTIZED SIGNAL LOAD

open access: diamondT-Comm, 2021
Optimal methods for processing input information signals often involve operations, implementation of which is extremely difficult and significantly increases the requirements for automated information processing systems. However, the use of various approaches to solving this problem has led to the appearance of synthesized methods for processing a ...
В. И. Филатов   +3 more
openalex   +2 more sources

Signal Processing of Coast-Ship Bistatic High-Frequency Surface-Wave Radar Using One-Bit Quantized Measurement [PDF]

open access: goldIEEE Access, 2020
The feasibility of one-bit quantization in coast-ship bistatic high-frequency surface-wave radar (HFSWR) is mainly discussed in order to enjoy the advantages of simplicity, low cost and low power consumption. Our theoretical derivation shows that the received signal after one-bit quantization can be expressed as a linear combination of infinite ...
Bingfan Liu, Baixiao Chen, Minglei Yang
openalex   +3 more sources

Noise reduction in speech signal processing by neural network and vector quantization [PDF]

open access: bronzeThe Journal of the Acoustical Society of America, 1988
The autocorrelation function provides basic data for speech signal processing by the linear prediction algorithm. The first step toward developing a practical speech signal processing technique is noise reduction in the autocorrelation function of speech corrupted by noise.
Kazuo Nakata, Akihiko Sugiura
openalex   +2 more sources

Quantized Approximate Signal Processing (Qasp): Towards Homomorphic Encryption for Audio [PDF]

open access: green
Audio and speech data are increasingly used in machine learning applications such as speech recognition, speaker identification, and mental health monitoring. However, the passive collection of this data by audio listening devices raises significant privacy concerns.
Tu Duyen Nguyen   +3 more
  +5 more sources

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