Results 211 to 220 of about 12,316 (267)
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Optimum nonlinear filters for quantized inputs
IEEE Transactions on Information Theory, 1961Optimum least-square filters belonging to Zadeh's nonlinear class \mathcal{n}_1 are considered. Attention is restricted to those systems whose present output is influenced only by a portion of the past input. The input signal consists of a message and noise, both of which are stationary random processes.
F. Tung, R. J. Schwarz
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Optimal dynamic quantizer and input in quantized feedback control system
IECON 2016 - 42nd Annual Conference of the IEEE Industrial Electronics Society, 2016In networked control systems, the data needs to be quantized for transmission over a limited bandwidth communication channel. We consider a dynamic quantizer in a quantized feedback control system. In this system, the quantizer parameters and input are optimized on-line by using the model predictive control to achieve the optimal control performance ...
Atsuki Tokunaga +2 more
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Adaptive Control with Quantized Inputs Processed by Lipschitz Logarithmic Quantizer
International Journal of Control, Automation and Systems, 2021In this paper, the design of adaptive control for a class of control systems with quantized inputs is investigated. According to the stability analysis result of adaptive control design, the quantized errors act as a great potential unstable risk for a class of sophisticated multi-channel system with quantized inputs.
Guanghao Zhang +3 more
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Optimal Control of Quantized Input Systems
2002In this paper we consider the problem of optimal control (specifically, minimum-time steering) for systems with quantized inputs. In particular, we propose a new approach to the solution of the optimal control problem for an important class of nonlinear systems, i.e. chained-form systems.
PANCANTI S. +3 more
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Minimum mean square error quantizers with uncorrelated input and quantization noise
ISCAS 2001. The 2001 IEEE International Symposium on Circuits and Systems (Cat. No.01CH37196), 2002Scalar quantizers with the minimum mean square error are designed in the case where the input signal and the quantization noise are uncorrelated. The probability density function (pdf) of the stochastic signal is assumed to be known. This design is then generalized to vector quantizers in the case where the expected value of the inner product between ...
Are Hjørungnes, Tapio Saramäki
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On Infimum Quantization Density for Multiple-input Systems
Proceedings of the 44th IEEE Conference on Decision and Control, 2006This paper deals with quadratic stabilization of discrete-time linear time-invariant systems, when the control is based on a static (or memoryless) quantized measurement of the state. A measure of quantization density is utilized in accordance with previous definitions in the literature.
Hernan Haimovich, María M. Seron
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Finding the capacity of a quantized binary-input DMC
2012 IEEE International Symposium on Information Theory Proceedings, 2012Consider a binary-input, M-output discrete memoryless channel (DMC) where the outputs are quantized to K levels, with K < M. The subject of this paper is the maximization of mutual information between the input and quantizer output, over both the input distribution and channel quantizer.
Brian M. Kurkoski, Hideki Yagi
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Stabilization of LTI Systems with Quantized State - Quantized Input Static Feedback
2003This paper is concerned with the stabilizability problem for discrete-time linear systems subject to a uniform quantization of the control set and to a regular state quantization, both fixed a priori. As it is well known, for quantized systems only weak (practical) stability properties can be achieved.
PICASSO B., BICCHI, ANTONIO
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SNR-Adaptive Input Quantization for Turbo Decoding
2012 IEEE 75th Vehicular Technology Conference (VTC Spring), 2012This paper presents how to control the input bit-width of a turbo decoder according to the signal-to-noise ratio (SNR) adaptively. It is crucial to minimize the input bit-width while maintaining the error-correcting performance. Several quantization schemes have been presented to properly decide the input resolution of turbo decoding, but they all ...
Injae Yoo, In-Cheol Park
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Averaging and oversampling correlator receiver with input quantization
Digital Signal Processing, 2006Correlation is a fundamental technique to detect signals buried in noise. However, due to noise, the fluctuations of the output signal may be significant and the detection of the desired signal can then fail. It is known that a simple one-bit correlator receiver can give nearly identical performance to the full precision correlator in determining time ...
Rouvaen, Jean-Michel +3 more
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