Detection of weak signals using adaptive stochastic resonance. An aperiodic stochastic resonance signal processor for communication systems based on bistable dynamic mechanism. An effect of noise in a signalprocessing device, especially a very small amount of noise that is deliberately induced, in which the noise sporadically. An approach for enhanced medical image processing this paper presents a novel application of the stochastic resonance effect in medical image processing. Stochastic resonance in carbon nanotubes that detect subthreshold signals nano letters. Interplay between detection strategies and stochastic resonance. Furthermore, we establish the fisher information condition for stochastic resonance sr that has been studied for. Stochastic resonance as a tool for signal processing. Adaptive stochastic resonance for unknown and variable. In this study, the if intermediate frequency digital signal with low snr signalnoise ratio is selected as the research object, and the measuring function based on svd singular.
The excitable fitzhughnagumo fhn neuron model has been discussed for exploring the functional role of noise in neural coding of sensory information. Stochastic resonance, random processes, inference methods, time series analysis. Stochastic resonance is applied in a large number of fields. Jan 28, 2019 stochastic resonance can help enhancing detection and processing of a weak signal blurred by the many sources of uncertainties and perturbations. Stochastic resonance sr is a phenomenon where a signal that is normally too weak to be detected by a sensor, can be boosted by adding white noise to the signal, which contains a wide spectrum of frequencies. The method determines the stochastic resonance noise probability density function in nonlinear processing applications that is added to the observed data for optimal detection with no increase in probability of false alarm. Stochastic resonance is a phenomenon that occurs in a threshold measurement system e.
Stochastic resonance sr is a phenomenon in which noise can be employed to increase the performance of a system. Recently, concepts of stochastic resonance have been utilized in numerous fields including sensory biology e. This essential reference is intended for researchers, professional engineers, and scientists working in statistical signal processing and its applications in various fields such as humanistic intelligence, stochastic resonance, financial markets, optimization, pattern recognition, signal detection, speech processing, and sensor fusion. Stochastic resonance can help improve signal detection.
We investigate this effect for the novel case of spatial signals or images. Weak signal detection is an essential stage in many signal processingbased machine fault detection methods because the acquired machine signals are always corrupted by heavy background noise. Furthermore, we establish the fisher information condition for stochastic resonance sr that has been studied for improving system performance over several decades. In the field of digital signal processing, duan and abbott 18 explored the detectability of the sr bistable receiver for. Sr occurs when a noisy signal x has noise of a certain power. Mmse approximation for sparse coding algorithms using. Shown is the sr effect for the subthreshold signal on 1. We consider that such a signal detection is realized. Stochastic resonance 1,2,3 is often defined as a noiseinduced rise and then fall, for higher noise intensities of the signal tonoise ratio snr of a weak narrowband signal in a nonlinear.
However, stochastic resonance sr can utilize the noise to extract a weak characteristic signal. Dark and lowcontrast image enhancement using dynamic. Theory of the stochastic resonance effect in signal detection. A thorough evaluation of stochastic resonance with tuning system parameters in bistable systems is presented as a nonlinear signal processor. Organization stochastic resonancebased image enhancement. It is a challenging task to detect the weak character signal in the noisy background. Furthermore, sufficient conditions are established to determine the improvability of a fixed detector using sr. Pdf stochastic resonance and related topics researchgate. The concept of stochastic resonance in linear systems, optimal performance is obtained in the absence of noise.
Effects of colored noise on multifrequency signal processing via stochastic resonance with tuning system parameters. Michels, fellow, ieee abstractthis paper develops the mathematical framework to. Stochastic resonance is a network of artists devoted to experimentation with new forms of communication, resulting from the collaboration between different audiovisualcreative, digital and electronic languages, in order to produce a deeper and more perceptive work thanks to the mixture of genres and different sensory contributions. Hannes risken the fokkerplanck equation, 2nd edition, springer, 1989.
Stochastic resonance and sensory information processing. Osa adaptive monostable stochastic resonance for processing. For example, it has been experimentally observed to improve broadband encoding in the cricket cercal system see related story, page 3. Manzanares and salvador mafe received 27th january 2010, accepted 16th march 2010 first published as an advance article on the web 20th may 2010 doi. On the other hand, we can improve the signal processing method. To catch symptoms of machine failure as early as possible, one of the most important strategies is to apply more progressive techniques during signal processing. In part i of this paper ldquotheory of the stochastic resonance effect in signal detection.
This fact may seem at odds with almost a century of effort in signal processing to. Vibration analysis has been widely applied to diagnose bearing faults. May 29, 2009 the term stochastic resonance was first used in the context of noiseenhanced signal processing in 1980 by roberto benzi, at the 1980 nato international school of climatology, as a name for the mechanism suggested to be behind the periodic behavior of the earths ice ages,17. The physical significance of fisher information is that it provides a unified bound for characterizing the performance for locally optimal processing. Read stochastic resonance as a tool for signal processing. Enhancement of weaksignal response based on stochastic resonance in carbon nanotube fieldeffect. Here, the dct coefficients of the watermarked image were viewed as the input weak signal and as noise in the watermark detection process image processing using stochastic resonance 2006.
Fourier component makes sr appealing for signal detection. However, the principles of biological amplications are far from understood. Stochastic resonance from suprathreshold stochastic resonance to stochastic signal quantization stochastic resonance occurs when random noise provides a signal processing bene. Stochastic resonance sr is a phenomenon where a signal that is normally too weak to be. Stochastic resonance has also been demonstrated in complex systems of biological transducers and neural signal pathways.
Introduction i n signal processing, often times we have access to a corrupted signal and we wish to estimate its clean version. Part ifixed detectors,rdquo ieee transactions on signal processing, vol. Pdf effects of colored noise on multifrequency signal. First, a discrete model of a bistable system that can demonstrate sr is researched, and the stability condition for controlling the selection. Stochastic resonance can help enhancing detection and processing of a weak signal blurred by the many sources of uncertainties and perturbations. Adaptive parametertuning stochastic resonance based on svd. Stochastic resonance sr is a nonlinear effect whereby a system is able to improve, via noise addition, the detectability of a signal in noise. Stochastic resonance sr is a nonlinear phenomenon in which the weak signal can be enhanced with the assistance of proper noise. Engineering signal processing based on bistable stochastic. Us7668699b2 optimized stochastic resonance method for. The stochastic resonance sr method has been wildly adopted recently because it can not only reduce the noise, but also enhance the weak feature information simultaneously. With a theoretical model involving a threshold nonlinearity we describe a mechanism whereby the transmission of an image can be improved by the addition of noise. Weak signal detection is an essential stage in many signal processing based machine fault detection methods because the acquired machine signals are always corrupted by heavy background noise. Oct 21, 2011 stochastic resonance like enhancements of the response of a noisy system have also been established when the signal possesses a complex spectrum as is the case in many real situations multiperiodic signals, aperiodic signals with a finite bandwidth around a preferred frequency.
In conventional dsrbased techniques, the performance of a system can be. Parametertuning stochastic resonance can effectively use noise to enhance signal energy, whereas its system parameters are hard to select, and how to combine it with more practical signals needs to be researched. The nonlinear effect of noiseenhanced signal transmission by means of stochastic resonance in optics is studied. Osa stochastic resonance and noiseenhanced transmission of. This process includes a wide variety of problems, such as. Enhancement of noisy signals by stochastic resonance. However, the traditional bistable model for sr is not perfect. Reliable signal processing using parallel arrays of non. This paper analyzes the stochastic resonance sr effect under the condition that the. Analogtodigital conversion and signal processing employing noise abstract. However, the faulty signal acquired from the bearing is usually weak or. In conventional dsrbased techniques, the performance of a system can be improved by addition of external noise. May 26, 2017 adaptive stochastic resonance for unknown and variable input signals. Stochastic resonance 1,2,3 is often defined as a noiseinduced rise and then fall, for higher noise intensities of the signaltonoise ratio snr of a weak narrowband signal in a nonlinear.
Weak signal detection using pso and stochastic resonance. Stochastic resonance and coincidence detection in single. Read engineering signal processing based on bistable stochastic resonance, mechanical systems and signal processing on deepdyve, the largest online rental service for scholarly research with thousands of academic publications available at your fingertips. Pdf stochastic resonance with tuning system parameters. Stochastic resonance has emerged as a significant statistical phenomenon where the presence of noise is beneficial for signal and information processing in both manmade and natural systems 111. In selfadaptive signal detection systems based on stochastic resonance, the optimum noise level is continuously adjusted via a feedback loop, so that the system response in terms of information throughput remains optimal, even if the properties of the input signal change. This paper presents a method based on stochastic resonance sr to detect weak fault signal.
Stochastic resonance definition of stochastic resonance. Osa stochastic resonance and noiseenhanced transmission. Theory of the stochastic resonance effect in signal. The frequencies in the white noise corresponding to the original signals frequencies will resonate with each other, amplifying the original signal while not amplifying the rest of the.
Stochastic resonance sr can be used to help detect weak signals because of its ability to enhance periodic and aperiodic signals. Sr has been demonstrated with different types of systems and signals where in each case, an appropriate detectability measure is shown improvable at the output of the stochastic resonator when noise is. The term stochastic resonance was first used in the context of noiseenhanced signal processing in 1980 by roberto benzi, at the 1980 nato international school of climatology, as a name for the mechanism suggested to be behind the periodic behavior of the earths ice ages,17. Finally, an illustrative example is presented where performance. The present invention has radar, sonar, signal processing. The noisy signal xt has 0 mean gaussian white noise. A novel technique based on dynamic stochastic resonance dsr in discrete cosine transform dct domain has been proposed in this paper for the enhancement of dark as well as lowcontrast images. Stochastic resonance sr occurs when noise improves a system performance measure such as a spectral. Stochastic resonance in neurobiology david lyttle may 2008 abstract stochastic resonance is a nonlinear phenomenon in which the activity of a dynamical system becomes more closely correlated with a periodic input signal in the presence of an optimal level of noise. Stochastic resonance with colored noise for neural signal. Author links open overlay panel xiaole liu a b houguang liu a.
Stochastic resonance and the benefit of noise in nonlinear systems. Stochastic resonance has been usedaccording to the isi web. Apparatus and method for improving the detection of signals obscured by noise using stochastic resonance noise. Traditional processing methods attempt to eliminate background noise, which damages the absorption spectrum characteristics. This contributes to the identification of the unknown weak periodic weather signal. This paper reports a monostable stochastic resonance msr model for processing an uv no absorption spectrum. Adaptive monostable stochastic resonance for processing uv. Aug 20, 2009 to catch symptoms of machine failure as early as possible, one of the most important strategies is to apply more progressive techniques during signal processing. Stochastic resonance and adaptive function approximation noise can sometimes enhance a signal as well as corrupt it. Wangeffects of multiscale noise tuning on stochastic resonance for weak signal detection. Index termssparse coding, stochastic resonance, basis pursuit, orthogonal matching pursuit, mmse estimation i.
Reliable signal processing using parallel arrays of nonidentical nanostructures and stochastic resonance javier cervera, jose a. Stochastic resonance is said to be observed when increases in levels of unpredictable fluctuationse. Stochastic resonance sensory neurobiology wikipedia. In the field of digital signal processing, duan and abbott 18 explored the detectability of the sr bistable receiver for detecting binary modulated signals. The form of the optimal noise pdf is determined and the optimal stochastic resonance noise pdf which renders the maximum p d without increasing p fa is derived. This counterintuitive effect relies on system nonlinearities and on some parameter ranges being suboptimal. Such noiseenhanced signal processing at the nanolevel promises applications to signal detection in wideband communication systems and biological and artificial neural networks. Stochastic resonance sr is a phenomenon where noise can be used to enhance a signal. Introduction the stochastic resonance sr phenomenon, first used to explain glacial periods 1, consists in the counterintuitive increase of the signal tonoise ratio in a system periodically forced at a frequency much lower than the typical deterministic internal fre quencies, when the temperature and hence the noise is increased.
Part ifixed detectors hao chen, student member, ieee, pramod k. All four combinations of input voltage values produced a clear sr response in both mutual information bottom red curve and inputoutput correlation top green curve just as with additive white gaussian noise. For this processing principle the term adaptive stochastic resonance. Frontiers the promise of stochastic resonance in falls. Adaptive stochastic resonance for unknown and variable input. Adaptive stochastic resonance for unknown and variable input signals. Fisher information as a metric of locally optimal processing. Stochastic resonance of analog and digital signals stochastic resonance sr is a phenomenon where noise can be used to enhance a signal.
Improving the bearing fault diagnosis efficiency by the adaptive stochastic resonance in a new nonlinear system. Many aspects have been hotly debated by scientists for nearly 30 years, with one of the main. Finally, an illustrative example is presented where performance comparisons are made between. Improving the bearing fault diagnosis efficiency by the. Adaptive parametertuning stochastic resonance based on. We demonstrate that a realistic neuron model expressed by the hodgkinhuxley equations shows a stochastic resonance phenomenon, by computing crosscorrelation between input and output spike timing when the neuron receives both aperiodic signal input of spike packets and background random noise of both excitatory and inhibitory spikes.
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