Abstract:
A series of short time stochastic resonance (SR) phenomena, realized in a bistable receiver, can be utilized to convey train of information represented by frequency-shift keying (FSK) signals. It is demonstrated that the SR regions of the input noise intensity are adjacent for input periodic signals that differ in frequency appropriately. This establishes the possibility of decomposing M-ary FSK signals in bistable receivers. Furthermore, the mechanism of the M-ary FSK signal detection via short time SR effects is explicated in terms of the receiver response speed. The short time SR phenomenon might be of interest for neuronal information processing in non-stationary noisy environments, regardless of the short timescale or the frequency jitter of stimulus.

Abstract:
This paper presents a thorough evaluation of a bistable system versus a matched filter in detecting bipolar pulse signals. The detectability of the bistable system can be optimized by adding noise, i.e. the stochastic resonance (SR) phenomenon. This SR effect is also demonstrated by approximate statistical detection theory of the bistable system and corresponding numerical simulations. Furthermore, the performance comparison results between the bistable system and the matched filter show that (a) the bistable system is more robust than the matched filter in detecting signals with disturbed pulse rates, and (b) the bistable system approaches the performance of the matched filter in detecting unknown arrival times of received signals, with an especially better computational efficiency. These significant results verify the potential applicability of the bistable system in signal detection field.

Abstract:
The origins of Fisher information are in its use as a performance measure for parametric estimation. We augment this and show that the Fisher information can characterize the performance in several other significant signal processing operations. For processing of a weak signal in additive white noise, we demonstrate that the Fisher information determines (i) the maximum output signal-to-noise ratio for a periodic signal; (ii) the optimum asymptotic efficacy for signal detection; (iii) the best cross-correlation coefficient for signal transmission; and (iv) the minimum mean square error of an unbiased estimator. This unifying picture, via inequalities on the Fisher information, is used to establish conditions where improvement by noise through stochastic resonance is feasible or not.

Abstract:
We analyze signal detection with nonlinear test statistics in the presence of colored noise. In the limits of small signal and weak noise correlation, the optimal test statistic and its performance are derived under general conditions, especially concerning the type of noise. We also analyze, for a threshold nonlinearity–a key component of a neural model, the conditions for noise-enhanced performance, establishing that colored noise is superior to white noise for detection. For a parallel array of nonlinear elements, approximating neurons, we demonstrate even broader conditions allowing noise-enhanced detection, via a form of suprathreshold stochastic resonance.

Abstract:
We report the regions where a signal-to-noise ratio (SNR) gain exceeding unity exists in a parallel uncoupled array of identical bistable systems, for both subthreshold and suprathreshold sinusoids buried in broadband Gaussian white input noise. Due to independent noise in each element of the parallel array, the SNR gain of the collective array response approaches its local maximum exhibiting a stochastic resonant behavior. Moreover, the local maximum SNR gain, at a non-zero optimal array noise intensity, increases as the array size rises. This leads to the conclusion of the global maximum SNR gain being obtained by an infinite array. We suggest that the performance of infinite arrays can be closely approached by an array of two bistable oscillators operating in different noisy conditions, which indicates a simple but effective realization of arrays for improving the SNR gain. For a given input SNR, the optimization of maximum SNR gains is touched upon in infinite arrays by tuning both array noise levels and an array parameter. The nonlinear collective phenomenon of SNR gain amplification in parallel uncoupled dynamical arrays, i.e. array stochastic resonance, together with the possibility of the SNR gain exceeding unity, represent a promising application in array signal processing.

Abstract:
For a known weak signal in additive white noise, the asymptotic performance of a locally optimum processor (LOP) is shown to be given by the Fisher information (FI) of a standardized even probability density function (PDF) of noise in three cases: (i) the maximum signal-to-noise ratio (SNR) gain for a periodic signal; (ii) the optimal asymptotic relative efficiency (ARE) for signal detection; (iii) the best cross-correlation gain (CG) for signal transmission. The minimal FI is unity, corresponding to a Gaussian PDF, whereas the FI is certainly larger than unity for any non-Gaussian PDFs. In the sense of a realizable LOP, it is found that the dichotomous noise PDF possesses an infinite FI for known weak signals perfectly processed by the corresponding LOP. The significance of FI lies in that it provides a upper bound for the performance of locally optimum processing.

Abstract:
In order to examine the intensity of surfi-cial sediment resuspension in Lake Taihu, a large shallow lake in eastern China, suspended sediment concentrations (SSCs) were measured on the basis of analysis of water samples collected using an in-novative multi-level water sampler. The results show that under calm weather conditions, the SSC is rela-tively homogenous through the entire water column. However, when strong winds occur, the SSC in the bottom layer is 1-2 orders of magnitude greater than in the surface layer; thus, in this case, the amount of total suspended matter in the water column cannot be estimated using the SSC values of the surface layer alone. Furthermore, the depth of disturbance, or the thickness of the sediment layer that is set in mo-tion by wind-wave induced currents, is of the order of 100 mm.

Abstract:
In order to examine the intensity of surficial sediment resuspension in Lake Taihu, a large shallow lake in eastern China, suspended sediment concentrations (SSCs) were measured on the basis of analysis of water samples collected using an innovative multi-level water sampler. The results show that under calm weather conditions, the SSC is relatively homogenous through the entire water column. However, when strong winds occur, the SSC in the bottom layer is 1–2 orders of magnitude greater than in the surface layer; thus, in this case, the amount of total suspended matter in the water column cannot be estimated using the SSC values of the surface layer alone. Furthermore, the depth of disturbance, or the thickness of the sediment layer that is set in motion by wind-wave induced currents, is of the order of 100 mm.

Abstract:
Blasting accomplishes the supplemental fragmentation of the natural coal rock with such discontinuity as joints, cracks, etc.. The effect of the original tectonic character on blasting result is taken into account. The concept of fractal dimension ratio and fractal dimension evaluation measurement of coal rock blasting is presented. The principal component analysis is introduced to identify the relationship among parameters, fractal dimension and fractal dimension ratio. In the case of one free surface, similar fractal dimension and its ratio of the fragmentation of coal rock blasting embody the whole distribution of the blasting cracks, while similar fractal dimension and its ratio of the surface of coal rock blasting represent the exterior cracks distribution. The proposed method is proved to get great economical benefit in the application in Datong Mining Bureau.

Abstract:
This paper studies the output-input signal-to-noise ratio (SNR) gain of an uncoupled parallel array of static, yet arbitrary, nonlinear elements for transmitting a weak periodic signal in additive white noise. In the small-signal limit, an explicit expression for the SNR gain is derived. It serves to prove that the SNR gain is always a monotonically increasing function of the array size for any given nonlinearity and noisy environment. It also determines the SNR gain maximized by the locally optimal nonlinearity as the upper bound of the SNR gain achieved by an array of static nonlinear elements. With locally optimal nonlinearity, it is demonstrated that stochastic resonance cannot occur, i.e. adding internal noise into the array never improves the SNR gain. However, in an array of suboptimal but easily implemented threshold nonlinearities, we show the feasibility of situations where stochastic resonance occurs, and also the possibility of the SNR gain exceeding unity for a wide range of input noise distributions.