Signal Enhancement Performance Research Articles

Gaussian bistable cascade double feedback stochastic resonance weak signal enhancement detection

Stochastic resonance (SR), as a nonlinear weak signal enhancement detection method, has found extensive applications in fields like fault diagnosis and biological information processing. However, further research has revealed that single stochastic resonance systems often fail to meet the requirements of practical engineering applications, leading to a growing interest in multi-system collaborative stochastic resonance. This article first constructs a cascaded SR system based on the Gaussian bistable model, and introduces the idea of feedback into the cascaded system. A Gaussian bistable cascaded dual feedback SR system is proposed to improve the output signal enhancement performance of the system. This system effectively utilizes the memory characteristics inherent in the output feedback and enables multi-level particle transitions and energy transfers in noisy input signals, thereby reducing noise and enhancing the extraction of weak signal features. Furthermore, the BSO optimization algorithm was utilized to optimize the system parameters of the model, resulting in an adaptive Gaussian bistable cascaded double feedback stochastic resonance (GBCDFSR) system. This system is applied to the processing of noisy periodic signals across various noise types. Simulation experiments and engineering applications show that the proposed model has stronger signal detection capabilities.

Weak abnormal acoustic signal enhancement and recognition using squeeze-and-excitation attention based denoising convolutional neural network during high-dam flood discharging

Acoustic signals (particularly cavitation acoustic signals) generated during the flood discharge of high dams are highly sensitive to various abnormal situations, whereas weak abnormal signal recognition under strong discharge-noise interference is extremely challenging. Based on the prototype and model experiments, the related abnormal acoustic signals and discharge noise were recorded to construct datasets. Subsequently, using the framework of the deep neural network (DNN) speech enhancement method, a squeeze-and-excitation attention based denoising convolutional neural network (DnCNN) based method for weak abnormal acoustic signal enhancement and recognition was proposed and verified using two case studies of cavitation acoustic signal enhancement and multicategory acoustic signal enhancement and recognition. Compared with the DnCNN method and traditional signal processing methods (such as wavelet, empirical mode decomposition, least mean square, and recursive least square), the proposed method achieved excellent signal enhancement performance after training based on limited prior knowledge of signal and noise. It also demonstrated good generalization ability and robustness in multicategory tasks, which significantly improved the abnormal signal recognition accuracy. This study provides technical support for the practical application of acoustic monitoring based on DNN for safety during the flood discharge of high dams.

Optimization of cotton SERS substrates based on different weave structures for surface trace detection

Cotton fabrics have been used to fabricate Surface-enhanced Raman spectroscopy (SERS) substrates due to their excellent properties such as flexibility, hygroscopicity, and large-scale production. The morphology of the SERS substrate has a great influence on the deposition of noble metal nanoparticles and affects the Raman-enhanced signal. Although numerous research reports have been conducted on SERS substrates made from cotton, there has been limited exploration on the impact of the weave structure of cotton fabric on SERS performance. Herein, the cotton SERS substrates were prepared by depositing silver nanoparticles (Ag NPs) on cotton fabrics with different weave structures by a simple vacuum-thermal evaporation method. The influence of substrate morphology on the deposition arrangement of Ag NPs and the enhancement of SERS performance was explored. The analysis findings demonstrate that the cotton SERS substrate displays exceptional Raman signal enhancement performance. Thereinto, the satin SERS substrate exhibited a stronger Raman signal due to its smoother surface favoring the deposition of Ag NPs. The limit of detection for p-aminothiophenol (PATP) was established at a low of 10−8 M, with an RSD value of 9.96%. Moreover, the satin SERS substrate demonstrated an enhancement factor of 5.18 × 105. In addition, it has excellent mechanical flexibility and good hygroscopicity, and thiram on the apple surface can be detected by simple wiping. Cotton SERS substrates show great potential in food safety analysis and monitoring. More importantly, this study provides assistance for the selection of SERS substrates for traditional cotton fabrics, and further promotes the broadening of the application fields of smart cotton fabrics.

A signal enhancement method based on the reverberation statistical information

This paper proposes a reverberation suppression algorithm utilizing fractional lower-order moments based on statistical properties. As fractional lower-order moments can only be applied on symmetric α-stable random variables, the energy redistribution method is used, so reverberation signals can obtain the characteristic exponents of the symmetric α-stable distribution. To evaluate the proposed algorithm, an experiment involving simulated linear frequency modulation reverberation with the proposed method and comparison methods is performed and discussed. Moreover, an experiment is conducted using reverberation as measured from the lake. The results show that the proposed algorithm can achieve better reverberation suppression and signal enhancement performance compared with other methods.

Research and application of stochastic resonance in quad-stable potential system

To solve the problem of low weak signal enhancement performance in the quad-stable system, a new quad-stable potential stochastic resonance (QSR) is proposed. Firstly, under the condition of adiabatic approximation theory, the stationary probability distribution (SPD), the mean first passage time (MFPT), the work (W), and the power spectrum amplification factor (SAF) are derived, and the impacts of system parameters on them are also extensively analyzed. Secondly, numerical simulations are performed to compare QSR with the classical Tri-stable stochastic resonance (CTSR) by using the genetic algorithm (GA) and the fourth-order Runge–Kutta algorithm. It shows that the signal-to-noise ratio (SNR) and mean signal-to-noise increase (MSNRI) of QSR are higher than CTSR, which indicates that QSR has superior noise immunity than CTSR. Finally, the two systems are applied in the detection of real bearing faults. The experimental results show that QSR is superior to CTSR, which provides a better theoretical significance and reference value for practical engineering application.

Rapid Dynamic Determination of Cetirizine Dihydrochloride in Urine Using Surface Enhanced Raman Scattering with Silver Colloids

ABSTRACTA novel method for the dynamic determination of cetirizine dihydrochloride in urine using surface enhanced Raman scattering (SERS) has been developed. Comparison of SERS activities between gold and silver colloid was made based on the analysis of the transmission electron micrographs and the SERS spectra, revealing that silver colloid is much more efficient on the signal enhancement performance. The primary sites of the adsorption on the nanoparticle surface were represented by the feature peaks of piperazine at ca. 1050 cm−1 and phenyl rings at ca. 1630 cm−1, respectively. The best signal response was extracted at pH 7 at the Cl−:Ag+ molar ratio of 2:1. The matrix effect was eliminated by subtracting the spectral contribution of the blank urine from the sample spectra. Sample preparation procedures were minimized. Quantification could be simply accomplished on the predicted versus actual concentration curve, within the Raman shift range from 500 to 2500 cm−1. Neither modeling nor complicate calculation was required. The method was shown to be specific and applicable for drug urine concentration monitoring in situ.

Superparamagnetic Iron Oxide Nanoparticles Stabilized with Multidentate Block Copolymers for Optimal Vascular Contrast in T1-Weighted Magnetic Resonance Imaging

Ultra-small superparamagnetic iron oxide nanoparticles (USPIOs) have been used as vascular contrast agents in magnetic resonance imaging (MRI), mainly for their capacity to generate negative contrast. To use USPIOs as positive contrast agents, it is necessary to achieve increased colloidal stability and signal-enhancement performance. Their molecular coatings must be carefully chosen so the vascular blood-pool contrast agents must lead to long blood turnover times. However, to avoid long-term toxicological effects, they must also be cleared rapidly through the urinary or the gastrointestinal pathways. In this context, highly stable USPIOs showing “positive” contrast in MRI and optimal clearance rates, call for the development of robust biocompatible molecular coatings. In the present study, USPIOs were stabilized with multidentate block copolymer (MDBC), using a one-pot polyol synthesis method in the presence of MDBC. Two types of MDBC having pendant COOH groups in the anchoring block were developed: a po...

Mildly reduced graphene oxide-Ag nanoparticle hybrid films for surface-enhanced Raman scattering.

Large-area mildly reduced graphene oxide (MR-GO) monolayer films were self-assembled on SiO2/Si surfaces via an amidation reaction strategy. With the MR-GO as templates, MR-GO-Ag nanoparticle (MR-GO-Ag NP) hybrid films were synthesized by immersing the MR-GO monolayer into a silver salt solution with sodium citrate as a reducing agent under UV illumination. SEM image indicated that Ag NPs with small interparticle gap are uniformly distributed on the MR-GO monolayer. Raman spectra demonstrated that the MR-GO monolayer beneath the Ag NPs can effectively quench the fluorescence signal emitted from the Ag films and dye molecules under laser excitation, resulting in a chemical enhancement (CM). The Ag NPs with narrow gap provided numerous hot spots, which are closely related with electromagnetic mechanism (EM), and were believed to remarkably enhance the Raman signal of the molecules. Due to the co-contribution of the CM and EM effects as well as the coordination mechanism between the MR-GO and Ag NPs, the MR-GO-Ag NP hybrid films showed more excellent Raman signal enhancement performance than that of either Ag films or MR-GO monolayer alone. This will further enrich the application of surface-enhanced Raman scattering in molecule detection.

On Microphone-Array Beamforming From a MIMO Acoustic Signal Processing Perspective

<para xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> Although many microphone-array beamforming algorithms have been developed over the past few decades, most such algorithms so far can only offer limited performance in practical acoustic environments. The reason behind this has not been fully understood and further research on this matter is indispensable. In this paper, we treat a microphone array as a multiple-input multiple-output (MIMO) system and study its signal-enhancement performance. Our major contribution is fourfold. First, we develop a general framework for analyzing performance of beamforming algorithms based on the acoustic MIMO channel impulse responses. Second, we study the bounds for the length of the beamforming filter, which in turn shows the performance bounds of beamforming in terms of speech dereverberation and interference suppression. Third, we address the connection between beamforming and the multiple-input/output inverse theorem (MINT). Finally, we discuss the intrinsic relationships among different classical beamforming techniques and explain, from the channel condition perspective, what the prerequisites are for those techniques to work. </para>

Fast amplitude-modulated pulse trains with frequency sweep (SW-FAM) in static NMR of half-integer spin quadrupolar nuclei

In solid-state NMR of quadrupolar nuclei with half-integer spin I, fast amplitude-modulated (FAM) pulse trains have been utilised to enhance the intensity of the central-transition signal, by transferring spin population from the satellite transitions. In this paper, the signal-enhancement performance of the recently introduced SW-FAM pulse train with swept modulation frequency [T. Bräuniger, K. Ramaswamy, P.K. Madhu, Enhancement of the central-transition signal in static and magic-angle-spinning NMR of quadrupolar nuclei by frequency-swept fast amplitude-modulated pulses, Chem. Phys. Lett. 383 (2004) 403–410] is explored in more detail for static spectra. It is shown that by sweeping the modulation frequencies linearly over the pulse pairs (SW(1/τ)-FAM), the shape of the frequency distribution is improved in comparison to the original pulse scheme (SW(τ)-FAM). For static spectra of 27Al (I=5/2), better signal-enhancement performance is found for the SW(1/τ)-FAM sequence, as demonstrated both by experiments and numerical simulations.

Audio signal enhancement performance of an RLS algorithm over long time intervals

In government communities recursive least squares (RLS) algorithms are not used because of the belief that they are unstable and thus cannot be incorporated as a problem solving tool. As a result the least mean squares (LMS) algorithms, time domain being most popular because of their ease of implementation, are used to suppress audio interference. The intent of this paper is to illustrate that the RLS can be used for interference suppression over long periods of time and has superior performance over the LMS algorithms.