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The template matching method has been widely utilized in the defect detection of wafer surfaces. However, the traditional matching approaches are limited by illumination, noise, and deformation, which cannot meet the requirements of accuracy and robustness. In this paper, a novel multiple targets localization method, named Normalized Cross-correlation Adaptive Variable Step-Size Dynamic Template (NCC-AVSSDT) matching, is proposed to improve the accuracy and efficiency of image localization, which combines the advantages of NCC and AVSSDT. The AVSSDT method is utilized to dynamically adjust the scanning step size based on the NCC matching coefficients. This approach optimizes the scanning process, accelerating convergence toward the optimal matching position. Experimental results verify the accuracy and robustness of the proposed method under different conditions, especially when dealing with rotational variations and variations in noise textures. Therefore, NCC-AVSSDT can be used to perform multiple targets localization of chip image in nearly real-time. Three experiment types were used for comprehensive evaluations, including multiple targets, noise, and rotation angles. Experimental results show that NCC-AVSSDT is much better than the sequential similarity detection algorithm and mean absolute deviation methods in terms of multiple targets (0.667 vs 0.811 s, 0.832 s) and success rate (100% vs 35%, 20%). [ABSTRACT FROM AUTHOR]

This paper presents a novel approach—an efficient closed-form approximation iterative fitting algorithm based on laser absorption spectra. Through this closed-form approximation iterative fitting, key parameters such as peak value, spectral line width, and normalized signal area serve as indicators for iteration completion, improving the speed without compromising accuracy. Furthermore, it employs the spectral signal of n cycles as a window for further processing, minimizing external interference. The results show that the proposed method averages 9.75 iterations, while the Levenberg–Marquardt fitting method averages 60.17 iterations. The average iteration time for the proposed method is 588.83 ms, a substantial 81.7% reduction compared to the 3210.5 ms required by the Levenberg–Marquardt fitting. These results decisively demonstrate the efficacy of the proposed method in reducing iteration time and enhancing measurement precision. [ABSTRACT FROM AUTHOR]

After the service provider temporarily selects the required edge nodes based on social and storage capabilities, application execution causes the edge nodes to cache part of the application data. Therefore, offloading part of the application computing tasks to the selected edge nodes can effectively improve application execution performance. However, in cases where the resources of user's IoT devices are insufficient, tasks can be further offloaded to traditional edge servers or even to the cloud to maximize application execution efficiency. In this paper, the entire uninstall utility is modeled as a weighted sum of task completion time and energy consumption. Under the premise of considering users' preferences for completion time and energy consumption, a game-based uninstallation algorithm is proposed. The algorithm performs uninstallation by optimizing the uninstallation decision. Based on user preferences, the total system overhead is relatively small. The subsequent simulation experiments show that the algorithm can reduce system overhead on the basis of satisfying user preferences and has relatively good adaptability. [ABSTRACT FROM AUTHOR]

Steganography is widely recognized as an effective method for protecting information via digital media. This paper presents an innovative image steganography algorithm incorporating image compression, chaotic maps, and the least significant bit. The process begins with the compression of a confidential medical image using Huffman encoding. The compressed image then undergoes shuffling, facilitated by the chaotic logistic map. The bits from the shuffled image are discreetly embedded into randomly selected pixels of the cover image, guided by the chaotic piecewise smooth map. The resulting stego image is generated. Statistical analyses are applied to both the cover and stego images for evaluation. The proposed algorithm is compared against state-of-the-art algorithms, and the results demonstrate its superiority over existing methods. [ABSTRACT FROM AUTHOR]

Eddy current thermography (ECT) is a non-invasive testing method that combines electromagnetic induction and infrared thermography to identify flaws in materials that conduct electricity. However, ECT faces difficulties in accurately locating and classifying defects owing to its low signal-to-noise ratio and complex defect patterns. In this paper, we propose a new method that integrates an improved faster region-convolutional neural network (R-CNN) and an adaptive Canny algorithm to enhance the defect detection performance of ECT. An improved faster R-CNN is a deep neural network that can automatically detect and locate multiple defects in a single ECT image, whereas the adaptive Canny algorithm is an edge detection technique that can identify defect boundaries. The proposed method was tested using a dataset of ECT images with different types of defects. The results demonstrated that our method achieved better accuracy, precision, and speed than existing methods. [ABSTRACT FROM AUTHOR]

An improved version of the mayfly algorithm called the golden annealing crossover-mutation mayfly algorithm (GSASMA) is proposed to address the low convergence efficiency and insufficient search capability of existing mayfly algorithms. First, the speed of individual mayflies is optimized using a simulated annealing algorithm to improve the update rate. The position of individuals is improved using the golden sine algorithm. Second, the impact of using different crossover and mutation methods in the algorithm is compared, and the optimal strategy is selected from the algorithm. To evaluate the performance of the algorithm, simulation experiments were carried out for 10 different test functions, and the results were compared with those of existing algorithms. The simulation results show that the algorithm developed in this paper converges faster and the solutions obtained are closer to the global optimum. Finally, GSASMA was used to optimize a support vector machine (SVM) that was used to identify the P300 signal for five subjects. The experimental results show that the SVM optimized by the algorithm proposed in this paper has higher recognition accuracy than an extreme learning machine. [ABSTRACT FROM AUTHOR]

A DC model for silicon carbide (SiC) metal–oxide–semiconductor field effect transistors (MOSFETs) is proposed in this paper using a hybrid modeling method based on the artificial neural network and artificial bee colony (ABC) algorithm. A multi-layer perceptron neural network using the Levenberg–Marquardt (LM) method is applied to model SiC MOSFETs based on the data provided by the datasheet. The search strategy of artificial bees is improved based on the standard ABC, which enhances the search ability of the standard ABC. In view of the sensitivity of the LM method to the initial value, the improved ABC algorithm is adopted to help the neural network find initial weights and biases, which improves the accuracy of the modeling results. Comparing the modeling results with the I–V curves in the datasheet, the accuracy of the DC model is verified under different temperatures. In addition, the small signal parameters gm and gd that are not exposed in the training process also fit well with the datasheet, which fully demonstrates the feasibility of this hybrid modeling method. [ABSTRACT FROM AUTHOR]

Recently, full-waveform inversion (FWI) has become a promising tool for ultrasound computed tomography (USCT). However, as a computationally intensive technique, FWI suffers from computational burden, especially in conventional time-domain full-waveform inversion (TDFWI). On the contrary, frequency-domain full-waveform inversion (FDFWI) provides a relatively high computational efficiency as the propagation of discrete frequencies is much cheaper than full time-domain modeling. FDFWI has already been applied in soft tissue imaging, such as breast, but for the musculoskeletal model with high impedance contrast between hard and soft tissues, there is still a lack of an effective source estimation method. In this paper, a water-referenced data calibration method is proposed to address the source estimation challenge in the presence of bones, which achieves consistency between the measured and simulated data before the FDFWI procedure. To avoid the cycle-skipping local minimum effect and facilitate the algorithm convergence, a starting frequency criterion for musculoskeletal FDFWI is further proposed. The feasibility of the proposed method is demonstrated by numerical studies on retrieving the anatomies of the leg models and different musculoskeletal lesions. The study extends the advanced FDFWI method to the musculoskeletal system and provides an alternative solution for musculoskeletal USCT imaging with high computational efficiency., (© 2023 Acoustical Society of America.)

This document is an erratum for a publication titled "Path planning of water surface garbage cleaning robot based on improved immune particle swarm algorithm" in the journal AIP Advances. The erratum acknowledges several errors in the original publication, including missing names of components in a figure, a mistake in the placement of another figure, and a correction in a table. The authors assure readers that these corrections do not significantly affect the overall findings and conclusions of the paper. [Extracted from the article]

The feature graph (Q-H graph) is the best way to intuitively and quantitatively reflect all features of the ventilation network. In this paper, an optimized adaptive genetic algorithm is proposed to solve the problem that rectangular blocks are cut in the process of drawing a Q-H diagram of a three-dimensional ventilation network. The algorithm adopts binary coding based on node sorting and mixed genetic coding based on integer coding. The formulas for calculating the adaptive crossover rate and mutation rate are designed, which can effectively generate new individuals and get rid of the search for local optimal values, ensuring the global optimal solution. Matlab was used to test the optimization effect of the Q-H graph; the results show that, for a complex ventilation network, the improved adaptive genetic algorithm can make the Q-H graph significantly reduce the number of rectangular pieces, which is divided, and make the Q-H graph have better effect to draw clearly and intuitively. [ABSTRACT FROM AUTHOR]

The establishment of a resistor network model has become a sharp edge to solve complex scientific problems. In this paper, we introduce Chebyshev polynomials to express the potential formula of the hammock resistor network and improve the general solution of the hammock resistor network. Moreover, through the change in different parameters, special potential formulas are proposed and displayed in 3D dynamic view. A fast algorithm of the calculating potential is given by using the matrix equation model, discrete cosine transform-II, and the fast matrix-vector multiplication. Finally, we show the advantages of our improved potential formula and fast algorithm by the calculation efficiency of the three methods. The modified potential formula and the presented fast algorithm provide a new tool for the field of science and engineering. [ABSTRACT FROM AUTHOR]

Direction-of-arrival (DOA) estimation algorithms are crucial in localizing acoustic sources. Traditional localization methods rely on block-level processing to extract the directional information from multiple measurements processed together. However, these methods assume that DOA remains constant throughout the block, which may not be true in practical scenarios. Also, the performance of localization methods is limited when the true parameters do not lie on the parameter search grid. In this paper, two trajectory models are proposed, namely the polynomial and harmonic trajectory models, to capture the DOA dynamics. To estimate trajectory parameters, two gridless algorithms are adopted: (i) Sliding Frank–Wolfe (SFW), which solves the Beurling LASSO problem, and (ii) Newtonized orthogonal matching pursuit (NOMP), which is improved over orthogonal matching pursuit (OMP) using cyclic refinement. Furthermore, our analysis is extended to include multi-frequency processing. The proposed models and algorithms are validated using both simulated and real-world data. The results indicate that the proposed trajectory localization algorithms exhibit improved performance compared to grid-based methods in terms of resolution, robustness to noise, and computational efficiency. [ABSTRACT FROM AUTHOR]

In this paper, we propose a dual projection generalized sidelobe canceller (DPGSC) based on mixed subspace (MS) for ultrasound imaging, which aims to improve the speckle signal-noise-ratio (sSNR) and decrease the dark-region artifacts. A mixed signal subspace based on the correlation between the desired steering vector and the eigenvectors is constructed to further optimize the desired steering vector and the final weight vector. The simulated and experimental results show that the proposed method can greatly improve the speckle uniformity. In the geabr_0 experiment, the standard deviation of background and sSNR of MS-DPGSC can be improved by 48.07% and 58.49% more than those of eigenspace-based generalized sidelobe canceller (ESGSC). Furthermore, for a hyperechoic target, the maximal improvement of contrast ratio is 95.29%. In terms of anechoic cyst, the contrast-to-noise ratio of MS-DPGSC is increased by 123.08% than that of ESGSC. The rat mammary tumor experimental data show that the proposed method has better comprehensive imaging effect than traditional generalized sidelobe cancellers and ESGSCs.

For maneuvering target tracking, the interactive multiple model (IMM) algorithm and its variants have shown good performance, among which the variable structure IMM (VSIMM) algorithm is the most widely studied one. The approximation degree of the algorithm and the matching degree of the model subset will affect the performance of the VSIMM. In addition, considering that smoothing can provide better estimation of the target state, a new adaptive VSIMM filtering and smoothing (AVSIMMFS) algorithm is proposed in this paper. First, an accurate model of the VSIMM algorithm is established, in which the IMM is run in parallel with and independently from the different model subsets, and the state estimation of the model subset with the highest probability is selected as the final estimation result. Then, an adaptive VSIMM (AVSIMM) algorithm was designed based on the VSIMM algorithm. The adaptation is reflected in the construction of a new model subset from the original model subsets, which improves the matching degree between the model subset and the actual maneuvering model of the target. Finally, by smoothing the filtering data of the AVSIMM algorithm, the AVSIMMFS algorithm is obtained. Because of the combination of forward filtering and backward smoothing, the target tracking accuracy is further improved. Simulation results show that the tracking performance of the AVSIMMFS algorithm is better than that of other methods. [ABSTRACT FROM AUTHOR]

In this paper, a method based on Bayesian inference is proposed to conjointly estimate the following two fields of thermophysical parameters. The first is a thermal characteristic time directly linked to the thermal diffusivity and the thickness, whereas the second is the Biot number, which is directly linked to the heat loss and thermal conductivity. This method is robust to noise and leads to very good estimations of the parameters with an algorithm that is very fast and less time consuming than a classical minimization method. At the end of the study, a setup and a methodology are also presented to estimate the average value of the thermal conductivity of an unknown material. [ABSTRACT FROM AUTHOR]

One of the main fields of application of personal sound zones (PSZs) is the car industry. PSZ systems are usually designed assuming that the acoustic environment is not changing. However, due to variability in the car cabin's acoustic conditions, it is necessary to design a system capable of adapting to these variations. This paper introduces the use of the filtered-x least-mean-squares algorithm to adapt a PSZ system in a car cabin when the seat positions change. The article focuses on four aspects. First, the effect of the initial coefficients is investigated to help with the convergence of the adaptive algorithm. Second, a study is made on the definition of the desired pressure and its effect on the reproduction. Third, the system's sensitivity to external noise is also evaluated. Last, the possibility to reduce the number of microphones for the adaptive system is considered. The method's performance is evaluated using a two headrest system installed in a car cabin and when moving the seats to different positions. It is shown that the system is capable of maintaining the performance in the different seat positions. [ABSTRACT FROM AUTHOR]

Of growing concern for the security of many nations are numerous incidents of lost or stolen radioactive sources or materials. The detection of and search for these abnormal radioactive sources plays an important role in monitoring nuclear safety and disposal of nuclear waste. In this paper, a method for autonomously searching for radioactive sources in a flat open rectangular-shaped field through mobile platforms was proposed. In this method, by using the possibility particle filter, the search for radioactive sources was realized according to a series of radiation information measured by the mobile platform carrying a Geiger–Müller counter. According to the inverse square law and the radiation counting governed by Poisson distribution, a radioactive source localization model was constructed. Then, a mobile platform controlled by an information entropy strategy constantly moved within the search area and detected radiation at specific points. The possibility filter algorithm, implemented via the sequential Monte Carlo method, is used to update posterior probability distributions of the source parameters. The performance of the proposed search algorithm, including a comparison with a standard particle filter algorithm, is studied by simulations. The simulation experiment proves that the possibility particle filter algorithm has good robustness. The successful application of the experimental dataset collected in the simulations verifies the measurement model and theoretical consideration. [ABSTRACT FROM AUTHOR]

In order to improve the stability of the second-order magnetic gradient tensor data under magnetic interference, a stability optimization algorithm based on the improved central difference method is proposed in this paper, and a new measuring device is designed according to the new algorithm. In the simulation, the root mean square error (RMSE) of the old and new methods under different noise conditions is studied, and the results show that the proposed method is more stable. In the experiment, the measurement was carried out in a site with complex magnetic interference, and the positioning results were analyzed through the RMSE. The RMSE of the positioning results obtained by the traditional method and the proposed method was (3.3782, 1.3482, 0.3337) and (0.3988, 0.0070, 0.0510), respectively. The simulation and the experiment showed the superiority of the proposed method. [ABSTRACT FROM AUTHOR]

Given the lack of a standardized evaluation system for the infrared jamming effectiveness of false targets, this paper first uses a co-saliency detection model to extract the main parts of the true and false targets. Then the perceptual similarity algorithm is improved by combining the operational requirements of false targets in the infrared band. Finally, a background-independent evaluation model for infrared jamming effectiveness of false targets is constructed. The experimental results show that the model can quantitatively reflect the infrared jamming effectiveness of a single false target and distinguish the infrared jamming effectiveness of different types of false targets. In addition, the model has stronger robustness than traditional evaluation models. [ABSTRACT FROM AUTHOR]

An adaptive variable step-size algorithm is proposed in this paper to address the impact of the real-time acoustic feedback and the real-time secondary path identification on the overall noise reduction performance of an active noise control system. An automated adjustment weight factor is introduced in the algorithm to accelerate the convergence of the acoustic feedback path as well as the secondary path identification, and to prevent possible system divergence. It is shown in this study that the proposed algorithm can resolve the trade-off between a fast convergence and a low misalignment of the virtual and the actual control paths typically found in conventional algorithms. An optimized control structure is also proposed in the study by enabling an adaptive gain adjustment based on the output of the auxiliary filter to enhance the practicality of the control system. The effectiveness of the algorithm is tested using two simulated multi-component signals and a broadband noise signal, and the results confirm that the proposed algorithm can achieve a good noise reduction with only a few iterations. [ABSTRACT FROM AUTHOR]

Recent studies on diffusion adaptation for distributed active noise control (DANC) systems have attracted significant research interest due to their balance between computational burden and stability compared to conventional centralized and decentralized adaptation schemes. The conventional multitask diffusion FxLMS algorithm assumes that the converged solutions of all control filters are consistent to each other, which is unrealistic in practice hence results in inferior performance in noise reduction. An augmented diffusion FxLMS algorithm has been proposed to overcome this problem, which adopts a neighborhood-wide adaptation and node-based combination approach to mitigate the bias in the converged solution of the multitask diffusion algorithms. However, the improvement comes at the expense of a higher computational burden and communication cost. All existing DANC systems, including the multitask and augmented diffusion algorithms, assume one-way communication between nodes. By contrast, this paper proposes a bidirectional communication scheme for the augmented diffusion algorithm to further reduce the memory requirement, computational burden, and communication cost. Simulation results in the free field and with measured room impulse responses both demonstrate that the proposed augmented diffusion algorithm with bidirectional communication can achieve a faster convergence speed than that based on one-way communication with a lower memory, computation, and communication burden. [ABSTRACT FROM AUTHOR]

In an active headrest system virtual sensing tends to transfer the spatial zone of quiet from the residual error microphone to the ear canal. An attempt has been made in this paper to develop an auxiliary filter based virtual sensing scheme integrated with the filtered-x least mean square/fourth algorithm for an active headrest. The performance of the proposed method has been evaluated experimentally using periodic and band limited white noise. Improved noise control performance has been observed for both periodic and broadband noise. The effect of causality constraint of the performance of the algorithm has also been tested. [ABSTRACT FROM AUTHOR]

There is growing interest in research on the non-modal voice quality, creaky voice; however, its identification often relies on time-consuming manual annotation, leading to a recent focus on automatic creak detection methods. Various automatic methods have been proposed, which rely on varying types and combinations of acoustic cues for creak detection. In this paper, we compare the performance of three automatic tools, the AntiMode method, the Creak Detector algorithm, and the Roughness algorithm, against manual annotation of creak using data from 80 Australian English speakers. We explore the possibility that tools used in combination may yield more accurate creak detection than individual tools used alone. Based on method comparisons, we present options for researchers, including an "out-of-the-box" approach, which supports combining automatic tools, and propose additional steps to further improve creak detection. We found restricting analysis to sonorant segments significantly improves automatic creak detection, and tools performed consistently better on female speech than male speech. Findings support previous work showing detection may be optimised by performing a creak probability threshold sweep on a subset of data prior to applying the Creak Detector algorithm on new datasets. Results provide promising solutions for advancing efficient large-scale research on creaky voice. [ABSTRACT FROM AUTHOR]

Magnetic resonance (MR) imaging is becoming an established tool in capturing articulatory and physiological motion of the structures and muscles throughout the vocal tract and enabling visual and quantitative assessment of real-time speech activities. Although motion capture speed has been regularly improved by the continual developments in high-speed MR technology, quantitative analysis of multi-subject group data remains challenging due to variations in speaking rate and imaging time among different subjects. In this paper, a workflow of post-processing methods that matches different MR image datasets within a study group is proposed. Each subject's recorded audio waveform during speech is used to extract temporal domain information and generate temporal alignment mappings from their matching pattern. The corresponding image data are resampled by deformable registration and interpolation of the deformation fields, achieving inter-subject temporal alignment between image sequences. A four-dimensional dynamic MR speech atlas is constructed using aligned volumes from four human subjects. Similarity tests between subject and target domains using the squared error, cross correlation, and mutual information measures all show an overall score increase after spatiotemporal alignment. The amount of image variability in atlas construction is reduced, indicating a quality increase in the multi-subject data for groupwise quantitative analysis.

At present, microseismic technology is a widely used method for monitoring the rock burst phenomenon during the construction of deep-buried tunnels. The rock fracture in the tunnel will generate seismic waves. The seismic wave has strong randomness and low energy and is usually mixed with environmental noise, which is called the microseismic signal. The microseismic signal received by the geophone will contain other noises. So, there is an urgent need for an algorithm that can quickly decompose the mixed signal. To solve this problem, this paper proposes a blind source wavelet algorithm to extract the rock fracture signal. First, the signal is preprocessed, and the mixed signal matrix is established. Second, the blind source decomposition algorithm is used to process the signal, and the effective signal is reconstructed. Finally, the wavelet algorithm is used to further remove the noise to enhance the microseismic signal. The proposed method is compared with the wavelet decomposition method and the empirical mode decomposition (EMD) method through the laboratory simulation data and the actual signal of Baihetan Hydropower Station. It is concluded that the proposed algorithm can effectively decompose and remove the noise in the mixed signal, and the decomposition accuracy is better than the wavelet decomposition method and the EMD method. The proposed algorithm has certain practical significance for the identification of microseismic signals of rock fracture and for rock burst early warning. [ABSTRACT FROM AUTHOR]

Packet loss concealment (PLC) aims to mitigate speech impairments caused by packet losses so as to improve speech perceptual quality. This paper proposes an end-to-end PLC algorithm with a time-frequency hybrid generative adversarial network, which incorporates a dilated residual convolution and the integration of a time-domain discriminator and frequency-domain discriminator into a convolutional encoder-decoder architecture. The dilated residual convolution is employed to aggregate the short-term and long-term context information of lost speech frames through two network receptive fields with different dilation rates, and the integrated time-frequency discriminators are proposed to learn multi-resolution time-frequency features from correctly received speech frames with both time-domain waveform and frequency-domain complex spectrums. Both causal and noncausal strategies are proposed for the packet-loss problem, which can effectively reduce the transitional distortion caused by lost speech frames with a significantly reduced number of training parameters and computational complexity. The experimental results show that the proposed method can achieve better performance in terms of three objective measurements, including the signal-to-noise ratio, perceptual evaluation of speech quality, and short-time objective intelligibility. The results of the subjective listening test further confirm a better performance in the speech perceptual quality.

We train an object detector built from convolutional neural networks to count interference fringes in elliptical antinode regions in frames of high-speed video recordings of transient oscillations in Caribbean steelpan drums, illuminated by electronic speckle pattern interferometry (ESPI). The annotations provided by our model aim to contribute to the understanding of time-dependent behavior in such drums by tracking the development of sympathetic vibration modes. The system is trained on a dataset of crowdsourced human-annotated images obtained from the Zooniverse Steelpan Vibrations Project. Due to the small number of human-annotated images and the ambiguity of the annotation task, we also evaluate the model on a large corpus of synthetic images whereby the properties have been matched to the real images by style transfer using a Generative Adversarial Network. Applying the model to thousands of unlabeled video frames, we measure oscillations consistent with audio recordings of these drum strikes. One unanticipated result is that sympathetic oscillations of higher-octave notes significantly precede the rise in sound intensity of the corresponding second harmonic tones; the mechanism responsible for this remains unidentified. This paper primarily concerns the development of the predictive model; further exploration of the steelpan images and deeper physical insights await its further application.

This review presents a high-level overview of the uses of machine learning (ML) to address several challenges in spatial auditory display research, primarily using head-related transfer functions. This survey also reviews and compares several categories of ML techniques and their application to virtual auditory reality research. This work addresses the use of ML techniques such as dimensionality reduction, unsupervised learning, supervised learning, reinforcement learning, and deep learning algorithms. The paper concludes with a discussion of the usage of ML algorithms to address specific spatial auditory display research challenges. [ABSTRACT FROM AUTHOR]

This paper proposes the use of gated feedback gated recurrent unit network (GFGRU), a learning-based sparse estimation algorithm, for multiple source localization in the direct arrival zone of the deep ocean. The GFGRU, trained on sound field replicas of a single source generated by an acoustic propagation model, is used to estimate the ranges and depths of multiple sources without knowing the number of sources. The performance of GFGRU is compared to the Bartlett processor, feedforward neural network (FNN), and sparse Bayesian Learning (SBL) algorithm. Simulations indicate that GFGRU behaves similarly to SBL and offers modest localization performance improvement over the Bartlett and FNN in the presence of array tilt mismatch. The results of real data from the South China Sea also verify the robustness of the proposed GFGRU using a 105 m-aperture vertical array in the deep ocean. [ABSTRACT FROM AUTHOR]

The present paper proposes a generic design-optimization procedure for the concomitant tuning of bending and torsional modes of bars used in mallet percussion instruments. The undercut model uses a series of discontinuous cuts aimed to facilitate the manufacturing process. Compared to one-dimensional beam models, the use of three-dimensional (3-D) finite element modeling not only allows for the calculation of torsional modes but also provides an increased accuracy in the prediction of modal frequencies, an important aspect when dealing with the demanding tuning tolerances required in a musical context. A global optimization problem is formulated and solved using a surrogate function algorithm, which enables fast computations even with the expensive function evaluations associated with 3-D finite element models. Modal identification of experimental bars demonstrates the potential of the proposed procedure, leading to bars with demanding tuning ratios (six target frequencies) at absolute tuning deviations typically below 15 cents. Measurements of the radiated sound from the experimental bars illustrate the benefits of the improved designs compared to those without torsional tuning. The proposed framework, aside from dealing with the comprehensive tuning of percussive musical bars, also accounts for important practical considerations regarding efficient optimization, modeling accuracy, and manufacturing complexity. [ABSTRACT FROM AUTHOR]

Aerodynamic parameters play a decisive role in the ballistic characteristics of the projectile. How to accurately obtain the aerodynamic parameters of the projectile is an important task in the development process of the projectile. In order to further improve the identification accuracy of the projectile drag coefficient, this paper generates huge ballistic data through numerical simulation and uses the extreme learning method to identify the ballistic drag coefficient under three kinds of noise conditions. The method avoids the iterative updating process of weights and thresholds by randomly generating the input weights and threshold values of hidden layer neurons and overcomes the problem of long identification time of the traditional back propagation (BP) neural network algorithm. Based on the least squares principle, the Moore–Penrose generalized inverse matrix of the hidden layer output matrix was solved to determine the optimal output weight of the network, and then, the projectile drag coefficient was accurately identified. Comparing the extreme learning method with the traditional BP neural network method, the results show that the proposed method has higher identification accuracy and faster convergence speed and can effectively identify the projectile drag coefficient, which can meet the practical needs of engineering. [ABSTRACT FROM AUTHOR]

Contact imaging based on MeV energy laser accelerated protons is studied in this paper. First, we show that both external structures and the internal organs of ants can be distinguished with micrometer spatial resolution by proton radiography. Then, we systematically study several specific influence factors and their coupled effects on the spatial resolution of proton radiography, i.e., the accumulated shot number, the proton irradiation dose, the different types of detectors, and the incident proton energy, using two specially designed resolution calibration targets. Under our experimental conditions with MeV protons, the best resolution obtained with the radiochromic film is 20 μm, and that with the solid-state nuclear-track detector (CR39) is 10 μm. In the end, we propose an image deblurring algorithm that uses deconvolution to eliminate the blurring caused by the nonzero spatial extent of the source and multiple Coulomb scattering. This work studies the comprehensive factors of laser accelerated proton radiography with resolution calibration targets and presents incremental additions to previous work on proton radiography. [ABSTRACT FROM AUTHOR]

This study contemplates the Finite Element Method (FEM), a well-known numerical method, to find numerical approximations of the Convection–Diffusion–Reaction (CDR) equation. We concentrate on analyzing the convergence and stability of the nonlinear parabolic partial equations. The method is generally applied without truncating the nonlinear terms and avoiding restrictive assumptions. Regular and irregular geometrical shapes are the key objective of this research paper. This study also focuses on the accuracy and acceptance of the FEM method by utilizing dissipation error, dispersion error, and total error analysis. The results are portrayed both graphically and in a tabular form, which virtually ensures the method's validity and the algorithm's efficiency to sustain the accuracy, simplicity, and applicability for solving nonlinear CDR equations. The proposed technique may also be applied for solving any nonlinear reaction–diffusion equations. [ABSTRACT FROM AUTHOR]

To solve the problems of noise coverage defect and low contrast between the defect and the background of ZrO2 ceramic bearing balls, a surface defect extraction algorithm based on shearlet transform image enhancement for ZrO2 ceramic bearing balls is proposed. According to the shape characteristics of ceramic bearing balls, the surface defect image acquisition platform is built to collect and analyze surface defect images. Gaussian filtering weakens the scatter-particle noise in the image, and the threshold corrects the coefficient generated by the shearlet transform. After shearlet transform, the relatively low-frequency and high-frequency parts appear. The low-frequency part reflects the edge information of defects, and the high-frequency part reflects the edge and texture information of defects. Thus, the integrity of the defect is ensured, and an enhanced surface defect image is obtained. The gray histogram of the enhanced image is observed. The optimal threshold is selected by the histogram threshold segmentation method, and the process of defects being completely extracted from the background is realized. Experimental results showed that the extraction rates of pits, scratches, and cracks in ZrO2 ceramic bearing balls' surface images are 95.00%, 92.50%, and 92.50%, respectively. [ABSTRACT FROM AUTHOR]

A study proposed an improved war strategy algorithm called the chaos genetic manifestation war strategy algorithm (CGM-WSO) to extract parameters of solar cell model. The CGM-WSO algorithm incorporates three improvement measures. First, it introduces the logistic–tent chaos map to enable random perturbation during initialization. Second, it utilizes the crossover and mutation operators of the genetic algorithm to enhance the algorithm's ability to escape local optima. Finally, the Lambert W function is employed to obtain explicit analytic expressions of single and double diode models. The performance of the CGM-WSO algorithm was validated using several cell's models, and the results demonstrated significantly improved accuracy, reliability, and convergence speed compared to other algorithms. [ABSTRACT FROM AUTHOR]

In this study, we introduce a novel implementation of Shor's algorithm specifically designed for the Graphics Processing Unit (GPU) acceleration framework. Our focus lies on achieving efficient execution of the modular multiplication circuit through GPU simulation. To seamlessly integrate our design into the PyQPanda library framework, we made necessary modifications, making a deliberate trade-off by sacrificing a small number of quantum resources to leverage the advantages of GPU acceleration. Subsequently, we conducted simulations and rigorously validated the functionality of our circuit using the PyQPanda library, resulting in a significant speedup compared to a central processing unit-only mode. [ABSTRACT FROM AUTHOR]

The improved particle filter (PF) based on the geometric center and likelihood estimation is proposed in order to solve the problem of particle dilution and degradation. In the resampling stage, the geometric center is used to resample the particles. The particles are filtered according to the distance between the particles and the geometric center, and then the particles are resampled. The resampled particles are composed of newborn particles and non-resampled particles. The former can help alleviate the degradation problem, while the latter can keep the diversity of the particle set. In order to ensure effectiveness of the PF, the positioning error threshold of the particle filter is introduced. In the phase of particle weighting calculation, in view of the problem of low accuracy and divergence of PF state estimation caused by non-stationary and non-Gaussian noise, it adopts non-Gaussian noise parameter estimation based on likelihood to approximately estimate the measurement noise instead of the Gaussian density function. The proposed model is applied to particle weight calculation to avoid particle degradation caused by Gaussian density function approximation. The simulation results show that, after the improved algorithm, the root-mean-square error is reduced to 0.085, the variance is reduced to 0.014, and the running time is shortened by 14.8% compared with the polynomial resampling algorithm, which can effectively alleviate particle degradation and dilution in the traditional PF algorithm, and the positioning accuracy is also improved. [ABSTRACT FROM AUTHOR]

Epidermal electrophysiological monitoring has garnered significant attention for its potential in medical diagnosis and healthcare, particularly in continuous signal recording. However, simultaneously satisfying skin compliance, mechanical properties, environmental adaptation, and biocompatibility to avoid signal attenuation and motion artifacts is challenging, and accurate physiological feature extraction necessitates effective signal-processing algorithms. This review presents the latest advancements in smart electrodes for epidermal electrophysiological monitoring, focusing on materials, structures, and algorithms. First, smart materials incorporating self-adhesion, self-healing, and self-sensing functions offer promising solutions for long-term monitoring. Second, smart meso-structures, together with micro/nanostructures endowed the electrodes with self-adaption and multifunctionality. Third, intelligent algorithms give smart electrodes a "soul," facilitating faster and more-accurate identification of required information via automatic processing of collected electrical signals. Finally, the existing challenges and future opportunities for developing smart electrodes are discussed. Recognized as a crucial direction for next-generation epidermal electrodes, intelligence holds the potential for extensive, effective, and transformative applications in the future. HIGHLIGHTS: • Three key aspects of smart epidermal electrophysiological monitoring electrodes are reviewed comprehensively, i.e., materials, structures, and algorithms. • Smart materials and structures enable adaptive and long-term monitoring, but improvements are require regarding stability and adaptation. • Intelligent algorithms can self-process signals, but challenges are encountered regarding generalization and interpretability. [ABSTRACT FROM AUTHOR]

In marine magnetic detection, in order to improve the accuracy of the measurement data of the field, to consider the impact of the storm interference environment, and to design a weakly driven USV tracking control system, it is necessary to drive the USV in a complex marine environment to determine the accuracy and stability of tracking. First, a 3-degrees of freedom mathematical model is built and decoupled by virtue of coordinate transformation to rely only on the absolute geographic velocity. Second, the variable forward-looking distance integral line-of-sight method is used to design the guidance law and stabilize the course tracking error. Moreover, sliding mode control is adopted to design the heading and velocity virtual control law and stabilize the heading angle and longitudinal velocity errors. Third, an adaptive observer is designed by introducing tracking errors to estimate the interference. The algorithm effectively improves the accuracy and reduces the oscillation during convergence. In the end, theoretical analysis and simulation experiments are carried out to confirm whether the proposed method is effective and advanced. [ABSTRACT FROM AUTHOR]

Indoor mobile robots are widely used in modern industry. Traditional motion control methods for robots suffer from discontinuous path curvature, low planning efficiency, and insufficient verification of theoretical algorithms. Therefore, a motion control system for an intelligent indoor robot was designed. By optimizing the radar map detecting and positioning, path planning, and chassis motion control, the performance of the system has been improved. First, a map of the warehouse environment is established, and the number of resampling particles interval is set for the Gmapping building process to improve the efficiency of map construction. Second, an improved A* algorithm is proposed, which converts the path solution with obstacles between two points into the path solution without obstacles between multiple points based on the Rapidly expanding Random Trees and Jump Point Search algorithms and further improves the pathfinding speed and efficiency of the A* algorithm by screening the necessary expansion nodes. The Dynamic Window Approach (DWA) algorithm based on the dynamic window is used to smooth the path, and the target velocity is reasonably assigned according to the kinematic model of the robot to ensure the smooth motion of the chassis. By establishing raster map models of different sizes, the traditional and improved A* pathfinding algorithms are compared and validated. The results illustrate that the improved pathfinding algorithm reduces the computing time by 67% and increases the pathfinding speed by 47% compared with the A* algorithm. Compared with the traditional method, the speed and effect are greatly improved, and the motion control system can meet the requirements of autonomous operation of mobile robots in indoor storage. [ABSTRACT FROM AUTHOR]

Textbook treatments of classical mechanics typically assume that the Lagrangian is nonsingular; that is, the matrix of second derivatives of the Lagrangian with respect to the velocities is invertible. This assumption ensures that (i) Lagrange's equations can be solved for the accelerations as functions of coordinates and velocities, and (ii) the definitions of the conjugate momenta can be inverted to solve for the velocities as functions of coordinates and momenta. This assumption, however, is unnecessarily restrictive—there are interesting classical dynamical systems with singular Lagrangians. The algorithm for analyzing such systems was developed by Dirac and Bergmann in the 1950s. After a brief review of the Dirac–Bergmann algorithm, several examples are presented using familiar components: point masses connected by massless springs, rods, cords, and pulleys. [ABSTRACT FROM AUTHOR]

We present a new method, the solving fractional order dynamical systems using reservoir computing (RC-FODS) algorithm, for solving fractional order nonlinear dynamical systems using deep learning. The method is shown to have advantages over traditional methods, such as less calculation time and higher accuracy. This study also compares the RC-FODS algorithm with the traditional recurrent neural network and echo state network algorithms and finds that it has a higher accuracy and shorter computation time. The accuracy of the method is validated using the largest Lyapunov exponent, and the study also analyzes the advantages and disadvantages of different deep learning models. Our study concludes that the RC-FODS algorithm is a promising method for solving fractional order nonlinear dynamical systems with a high accuracy and low error rate. [ABSTRACT FROM AUTHOR]

Reliable fundamental frequency (f0) extraction algorithms are crucial in many fields of speech research. The current bulk of studies testing the robustness of different algorithms have focused on healthy speech and/or measurements of sustained vowels. Few studies have tested f0 estimations in the context of pathological speech, and even fewer on continuous speech. The present study evaluated 12 available pitch detection algorithms on a corpus of read speech by 24 speakers (8 healthy speakers, 8 speakers with Parkinson's disease, and 8 with head and neck cancer). Two fusion methods' algorithms have been tested: one based on the median of algorithms and one based on the fusion between the best algorithm for voicing detection and the algorithm that generates the most accurate f0 estimations on voiced parts. Our results show that time-domain algorithms, like REAPER, are best for voicing detection while deep neural network algorithms, like FCN- f0, yield better accuracy for the f0 values on voiced parts. The combination of REAPER and FCN- f0 yields the best ratio performance/implementation complexity, since it generates less than 4% errors on voicing detection and less than 5% of gross errors in the estimation of the f0 values for all speaker groups. [ABSTRACT FROM AUTHOR]

Classical beamforming algorithms are based on rectilinear propagation of signals that are corrupted by additive noise. However, sound waves propagating in the atmosphere are refracted by temperature and wind gradients and distorted by turbulence and scattering from the ground. These effects can reduce the performance of acoustic systems. To investigate these effects, a simulation capability is used to propagate signals from elevated sources to an array of microphones near the ground. Detection algorithms based on four standard beamforming algorithms, implemented with either no model mismatch error or model mismatch errors generated by atmospheric refraction, are tested. The results are evaluated using the area under the receiver operating characteristic curve statistic. The analysis shows no significant improvement in detection performance when refraction effects are included in the beamforming algorithms over a wide range of frequencies. Any small performance improvements achieved by incorporating refraction effects into the beamforming algorithms are overwhelmed by the negative effects of atmospheric turbulence and rough surface scattering. For the selected scenario, incorporating refraction effects into the beamformers is over-engineering. These results demonstrate some of the limitations of developing new signal processing algorithms or array designs for atmospheric acoustic systems. [ABSTRACT FROM AUTHOR]

Ray tracing is a simple and efficient three-dimensional method which reduces the problem of infrasound propagation to a series of one-dimensional cases along acoustical rays. However, in relatively frequent cases, infrasound stations are located in geometrical shadow zones, where only diffracted waves are recorded. The corresponding arrivals cannot be predicted by ray theory. To simulate infrasound propagation in these zones, the ray tracing method is generalized to complex ray theory. The source, media, and ground parameters are all considered as complex numbers. For applications with realistic atmospheric data, including stratified temperature and wind as well as the range dependency of atmospheric profiles, an efficient algorithm determining complex eigenrays in the shadow zones is presented. It is illustrated by a two-dimensional case of a point source. [ABSTRACT FROM AUTHOR]

Near-field acoustical holography (NAH) is a useful tool for sound field reconstruction and sound source identification. In NAH, a basis model is first selected to represent the physical sound field, and then a near-field measurement is made with a microphone array. Next, the parameters in the selected model can be estimated based on the measurements by using an inverse approach, resulting in the sound field near the source being reconstructed so that the sound source location can be identified. But, in addition to being able to reconstruct the near-field of a source, the far-field can also be predicted with the identified sound source model. A significant amount of work has been performed to study the near-field reconstruction capability of different NAH algorithms, but there has been a limited number of publications in which the far-field prediction accuracy, based on the near-field measurement constructed model, has been considered. In the present experimental work, two multi-transducer loudspeakers were placed side-by-side to create a multi-component sound source, and two sets of measurements were conducted: an intensity probe scanned the sound field generated by the loudspeakers in both the near-field (0.05 m) and far-field (0.48 m) such that the true near- and far-field intensity spatial distributions and total sound power could be identified. Then, based on the near-field pressure measurements, four acoustical holography algorithms, statistically optimized near-field acoustical holography, wideband acoustical holography, l1-norm minimization, and a hybrid compressive sampling method were used to predict the near- and far-field sound intensity distributions. The near- and far-field prediction results were compared with the direct measurement, and the sound field reconstruction accuracy was studied. It was found that all of the algorithms were able to reconstruct the near-field well when the near-field measurements were used to construct the model. It was found that with the abovementioned models, far-field reconstructions could correctly predict the spatial sound field distribution, but in all of the cases, the total sound power was underestimated. [ABSTRACT FROM AUTHOR]

Memristor-based computer architectures are becoming more attractive as a possible choice of hardware for the implementation of neural networks. However, at present, memristor technologies are susceptible to a variety of failure modes, a serious concern in any application where regular access to the hardware may not be expected or even possible. In this study, we investigate whether certain training algorithms may be more resilient to particular hardware failure modes and, therefore, more suitable for use in those applications. We implement two training algorithms—a local update scheme and a genetic algorithm—in a simulated memristor crossbar and compare their ability to train for a simple image classification task as an increasing number of memristors fail to adjust their conductance. We demonstrate that there is a clear distinction between the two algorithms in several measures of the rate of failure to train. [ABSTRACT FROM AUTHOR]

Multichannel auralizations based on spatial room impulse responses often employ sample-wise assignment of an omnidirectional response to form loudspeaker responses. This leads to sparse impulse responses in each reproduction loudspeaker and the auralization of transient signals can sound rough. Based on this observation, we conducted a listening test to examine the general phenomenon of roughness due to spatial assignment. First, participants assessed the roughness of both Gaussian noise and velvet noise, assigned sample-wise to up to 36 loudspeakers by two algorithms. The first algorithm assigns channels merely by selecting random indices, while the second one constrains the time between two peaks on each channel. The results show that roughness already occurs when few channels are used and that the assignment algorithm influences it. In a second experiment, virtualizations of the test were used to examine the factors contributing to increased roughness. We systematically show the effect of spatial assignment on noise and conclude that besides time-differences, level-differences caused by head-shadowing are the principal cause for the perceived roughness. The results have significance in spatial room impulse response rendering and spatial reverberator design. [ABSTRACT FROM AUTHOR]

Active noise control algorithms aimed at reducing Gaussian noise have been studied. However, the performance of these algorithms can deteriorate if impulsive noise or other non-Gaussian noise such as piling noise exists. To solve this problem, an algorithm named the filtered-x affine projection sign algorithm with a post-adaptive filter is proposed to reduce impulsive noise. The proposed algorithm is based on the filtered-x affine projection sign algorithm and the post-adaptive filter structure. In addition, an algorithm with a variable step size is proposed to improve the performance of the filtered-x affine projection sign algorithm with a post-adaptive filter by using a convex combination strategy. The fixed step size in the convex combination is replaced with a linear function of the ratio between the estimated error signal and the estimated desired signal. The detailed derivation processes of the proposed algorithms are given. The computational complexities are analyzed. Numerical simulations are carried out to demonstrate the performance of the proposed algorithms. The results illustrate that the proposed algorithms can effectively reduce impulsive noise. At the same time, the algorithm with a variable step size has better convergence performance than the filtered-x affine projection sign algorithm with a post-adaptive filter. [ABSTRACT FROM AUTHOR]

Automated generation of reasonable atomic-level interface models, for example, at a grain boundary, is generally computationally intensive partly because of the three degrees of freedom in a rigid-body translation (RBT) of one side of the interface against the other. We propose an algorithm to obtain reasonable interface models using as few first-principles calculations as possible. The valence charge densities of two surface slabs constituting the interface are calculated using first-principles calculations. The surface charge densities are filtered with an exponential function using a parameter λ to obtain the reaction front. Models where the overlap of filtered charge densities between the two slabs takes a local maximum are adopted as initial models with desirable RBTs, which are then relaxed using first-principles calculations to obtain a reasonable interface model. The proposed algorithm successfully generated reasonable initial models for three out of three orientations in 75% of homointerfaces of body-centered cubic, face-centered cubic, and hexagonal close-packed non-magnetic elementary metals. For the Al {001} Σ5 twist grain boundary, the present algorithm also reproduced γ-surface features of RBTs showing correct displacement shift complete lattice periodicity. Further modifications and improvements to this method are expected to accelerate automated interface model generation from a previously unexplored approach. [ABSTRACT FROM AUTHOR]