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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]

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]

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]

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]

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]

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]

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]

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]

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]

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]

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]

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]

Of the many topics generally taught in undergraduate or graduate optics courses, the propagation of light through crystals with electrical anisotropy is one of the most difficult topics for students and for teachers who do not work in this field. In particular, the mathematics and equations are complex vectorial equations, and this complicates the visualization of the implications of the theory. Also, the standard textbooks and papers on the subject have led to some misconceptions on this problem by presenting results only for specific cases of the theory in graphical form. For example, it is a general belief that after refraction of an incident wave on the crystal, the polarization state of the two waves propagating inside the crystal, given by their displacement vectors D, are always orthogonal to each other. However, this is not the case, as explained and illustrated for uniaxial crystals by Alemán-Castañeda and Rosete-Aguilar [J. Opt. Soc. Am. A 33(4), 677–682 (2016)]. The purpose of this paper is to help students and teachers visualize the solution to Maxwell's equations in electrical anisotropic crystals. An algorithm has been written in mathematica so the reader can use it to visualize the normal surface in uniaxial and biaxial crystals. This algorithm can be used as the basis of computational projects for students to better understand the equations involved, or as a visualization tool for teachers in class. [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]

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]

This paper studies the sparsity prior to compressed video reconstruction algorithms. An effective non-convex 3DTPV regularization (0 < p < 1) is proposed for sparsity promotion. Based on the augmented Lagrangian reconstruction algorithm, this paper analyzes and compares three non-convex proximity operators for the ℓp-norm function, and numerous simulation results confirmed that the 3DTPV regularization can gain higher video reconstruction quality than the existing convex regularization and is more competitive than the existing video reconstruction algorithms. [ABSTRACT FROM AUTHOR]

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]

As a non-destructive testing technology, γ-photon imaging technology has a good application prospect in the industrial field. However, due to the γ-photon scattering problem caused by high-density metal and the rapid industrial detection requirement, γ-photon imaging technology has not been effectively applied in industrial detection. Thus, this paper proposed a compensatory single scatter simulation (C-SSS) and combined it with the 3D ordered subset expectation maximization (3D-OSEM) image reconstruction algorithm for optimization to solve the above problems. To verify the effectiveness of the C-SSS algorithm, this paper compared C-SSS with the single scatter simulation by two types of phantoms and the inner cavity detection of industrial components. The comparison results proved that C-SSS can effectively improve the measurement accuracy and reduce the scanning time. In addition, after the algorithm code was optimized and accelerated by the graphic processing unit, the execution time of C-SSS was ∼4 min and a single iteration of 3D-OSEM took ∼3 min, basically satisfying the requirements of rapid industrial detection. Simulation and experiment results show the potential of the proposed algorithm in industrial detection. [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]

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]

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]

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. [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]

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]

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]

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]

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]

The timbre of marimba and other idiophone bars can sometimes be polluted by untuned torsional modes, leading to substandard instruments or rejected materials. Makers have complained of problems with these untuned modes over a specific range of notes. Marimba, vibraphone, and similar idiophone bars are tuned by carving one side of the bar to bring up to three flexural modes into harmonic relationships. Torsional and other mode types are commonly left untuned. The relative frequency of these untuned modes with respect to the fundamental mode will vary along the keyboard. This paper investigates tuning both torsional and flexural modes simultaneously. This tuning is achieved using sophisticated carved geometries, and without employing concentrated masses or additional materials. Bars are modeled using three-dimensional finite elements. Geometry is defined by a large number of input parameters. Algorithms are implemented to identify bar modes automatically, eliminating the need for human intervention. Tuning is performed via a Newton-Raphson approach using the Moore-Penrose generalized matrix inverse to solve systems of tuning equations. This method is found to be effective at finding satisfactory bar geometries in proximity to initial conditions. Numerous example marimba and vibraphone bar models are provided, representing both typical and atypical modal tuning ratios. [ABSTRACT FROM AUTHOR]

To ensure the speed, recall, and precision of the algorithm to solve the algorithm's measurement failure due to the problem of high underdetermination and the deviation of some outgoing lines from accessible nodes, the ideal resistance method is proposed to diagnose the corrosion of the grounding grid. The information extraction algorithm, a two-way heuristic network resistance diagnosis method, is added. A non-linear multi-objective optimization model of the deviation between the measured potential and the real potential is established. This paper puts forward that the grounding network's fault diagnosis is analogous to a neural network's training process, which makes full use of the excellent robustness and rapidity of a neural network training method. Simultaneously, the algorithm adds the retraining method combined with the field excavation in training and proves its feasibility through the Grobner basis codimension theory in algebraic geometry. Through result verification, it is found that the new algorithm can avoid the inversion failure caused by the algorithm failure due to the lack of accessible nodes and the abnormal part of input data at the same time as a fast solution. [ABSTRACT FROM AUTHOR]

Compressive beamforming has been successfully applied to direction-of-arrival estimation with sensor arrays. The results demonstrated that this technique achieves superior performance when compared with traditional high-resolution beamforming methods. The existing compressive beamforming methods use classical iterative optimization algorithms in their compressive sensing theories. However, the computational complexity of the existing compressive beamforming methods tend to be excessively high, which has limited the use of compressive beamforming in applications with limited computing resources. To address this issue, this paper proposes a fast compressive beamforming method which combines the shift-invariance of the array beam patterns with a fast iterative shrinkage-thresholding algorithm. The evaluation shows that the proposed fast compressive beamforming method successfully reduces the number of floating-point operations by 3 orders of magnitude when compared with the existing methods. In addition, both the simulations and experiments demonstrate that the resolution limit for discerning closely spaced sources of the introduced fast method is comparable to those of the existing compressive beamforming methods, which use classical iterative optimization algorithms. [ABSTRACT FROM AUTHOR]

Ultrasonic array imaging is widely used to provide high quality defect detection and characterization. However, the current imaging techniques are poor at detecting and characterizing defects near a surface facing the array, as the signal scattered from the defect and the strong reflection from the planar backwall will overlap in both time and frequency domains, masking the presence of the defect. To address this problem, this paper explores imaging algorithms and relevant methods to eliminate the strong artefacts caused by the backwall reflection. The half-skip total focusing method (HSTFM), the factorization method (FM) and the time domain sampling method (TDSM) are chosen as the imaging algorithms used in this paper. Then, three methods, referred to as full matrix capture (FMC) subtraction, weighting function filtering, and the truncation method, are developed to eliminate or filter the effects caused by the strong backwall reflection. These methods can be applied easily with few tuning parameters or little prior knowledge. The performances of the proposed imaging techniques are validated in both simulation and experiments, and the results show the effectiveness of the developed methods to eliminate the artefacts caused by the backwall reflections when imaging near backwall defects. [ABSTRACT FROM AUTHOR]

Quantitative measurement of small forces and small displacement using holographic optical tweezers (HOTs) is finding increasing applications due to the features of non-contact and high accuracy manipulation. Although hologram optimization algorithms have been widely reported, the holographic optical trapping performance relying on the algorithms has not been studied systematically. In this paper, we investigated the force measuring the performance of various types of HOTs generated with six different hologram algorithms (GSW, GAA, GS, SR, S, and RM). To do this, we built up a HOT instrument and compared the light fields' intensity distribution, trap stiffness, efficiency, and calculation time of multi-point trap arrays generated by six hologram algorithms with this setup. Our work will provide a better understanding of the performance of different hologram algorithms in HOTs. [ABSTRACT FROM AUTHOR]

The phase-shifting based active noise control (ANC) algorithm without secondary path modeling is appealing due to its simple implementation and high computational efficiency. However, the algorithm suffers from the influence of an implicit circular convolution caused by directly applying the most appropriate phase shift to the reference signal in the frequency domain. The implicit circular convolution results in a poor linear relationship between the phase-shifted reference signal and the error signal, which considerably degrades the convergence performance of the ANC system. In this paper, two methods are proposed to deal with this problem. In the first method, the circular wraparound artifacts are removed by the time-domain constraining operation. In the second method, a noncausal phase-shifting filter is used to regularize the phase of the reference signal. Compared with the standard frequency-domain implementation, the two proposed algorithms both eliminate the influence caused by the wraparound artifacts of the circular convolution and improve the linear relationship between the phase-shifted reference signal and the error signal, leading to a higher level of noise attenuation with a tolerable increase in computational complexity. Simulation results validate the better noise attenuation ability of the proposed algorithms. [ABSTRACT FROM AUTHOR]

This paper addresses trade-offs in adaptive feedback cancellation (AFC) for hearing aids. Aggressive AFC for improved added stable gain (ASG) reduces speech quality. In this paper, the hearing-aid speech quality index (HASQI) is used to investigate AFC performance before the system becomes unstable. It is demonstrated that for a desired speech quality, multiple AFC algorithms can be evaluated for their ASG and computational efficiency. An example is presented with HASQI=0.8, baseline AFC, and two advanced approaches. For the advanced AFCs, ASG gains of 4 and 7 dB were obtained at additional computational complexity of 8% and 11%, respectively. [ABSTRACT FROM AUTHOR]

The active headrest is an important application of active noise control (ANC) where the occupant gets a quiet zone during use. However, its performance degrades due to head movement, which is an important issue. Unlike conventional active headrest algorithm, in the present paper, the two error microphones are attached to a head attachment such as a hat or band so that both error microphones can move along with the head, keeping the relative distance of ears and the microphones intact. The two secondary loudspeakers are kept near to the head, facing the ears. The performance of the active headrest was studied through real-time experimentation considering head movement. The innovation in this paper is that the ANC algorithm uses signals of moving error microphones to tune the controller without re-estimating the secondary path transfer functions. Since, the phase error of secondary path estimates at different head positions remains within ±90°, the secondary paths estimated once, when the head is positioned at the center, can be used for other head positions. This is established through real-time experiments using dSpace 1104 ACE system (dSpace, Germany). [ABSTRACT FROM AUTHOR]

The development of the computer network makes image files transportation via network become more and more convenient. This paper is concerned with the image encryption algorithm design based on the chaotic S-box mechanism. This paper proposes an Image Encryption algorithm involving both chaotic dynamic S-boxes and DNA sequence operation(IESDNA). The contribution of this paper is three folded: Firstly, we design an external 256-bit key to get large key space; secondly, we design a chaotic system to scramble image pixels; thirdly, DNA sequence operations are adopted to diffuse the scrambled image pixels. Experimental results show that our proposed image encryption algorithm can meet multiple cryptographic criteria and obtain good image encryption effect. [ABSTRACT FROM AUTHOR]

In this paper, the sparse direct adaptive equalization based on the recently developed proportionate recursive least squares (PRLS) adaptive filtering algorithm is investigated for multiple-input multiple-output (MIMO) underwater acoustic (UWA) communications. First, performance analysis is made for the PRLS, and simulation results show its gain over a standard recursive least squares algorithm under sparse systems. The fast implementation of the PRLS, named the proportionate stable fast transversal filters (PSFTF), is revisited to implement a direct adaptive decision-feedback equalizer which outperforms the existing PSFTF direct adaptive linear equalizer. The PSFTF direct adaptive equalizers (DAEs) are then compared with the selective zero-attracting stable fast transversal filter DAEs (SZA-SFTF-DAEs) enabled by the SZA-SFTF adaptive filtering algorithm. The SZA-SFTF algorithm is designed with the zero-attracting sparsity-promoting principle, which is in parallel to the proportionate updating principle used to design the PSFTF algorithm. Experimental results of an at-sea MIMO UWA communication trial show that PSFTF-DAEs outperform the SZA-SFTF-DAEs. [ABSTRACT FROM AUTHOR]

This paper proposes an effective image encryption algorithm based on the transformational prospective synchronization of a fractional-order hyperchaotic system. Compared with other chaos-based algorithms, fractional orders and synchronization precision are added as secret keys. It is shown that fractional-order, in comparison with its integer counterpart, has bigger complexity and larger key-space. Numerical simulation test results and security analyses demonstrate good performance of the proposed algorithm by encrypting the color image, gray medical image, and binary image. Furthermore, it is found that the synchronization precision accounted for in the decryption process has a significant effect on the decryption resolution. [ABSTRACT FROM AUTHOR]

As a complex system with multiple variables, nonlinearity, and strong coupling, the BLDCM (Brushless Direct Current Motor) has many problems, such as bad parameter tuning, poor adaptability, low control accuracy, and weak anti-interference ability by using the double closed loop traditional PI (Proportional Integral) control algorithm. In order to obtain good control performance, a fuzzy parameter adaptive PI algorithm based on speed loop was designed by combining fuzzy control with traditional PI control. This paper analyzes the mathematical model and operating characteristics of the BLDCM and designs a fuzzy system that takes the deviation e and deviation change rate ec of the reference speed and feedback speed as input and takes the corresponding PI adjustment parameters as output. The step response of the BLDCM at different reference speeds is analyzed. The variable speed response with the initial speed of 4000 r/min under different control algorithms and the changes in the three-phase current, back electromotive force, and electromagnetic torque in this state are compared. The results show that the designed fuzzy parameter adaptive PI algorithm based on the speed loop can make the motor have a faster response time, a smaller overshoot, and a steady-state error when the motor achieves the stable operation. The proposed algorithm also has better control effect, robustness, and stable operation under variable speed conditions. [ABSTRACT FROM AUTHOR]

Superdirectivity can yield large directivity using a small array aperture, but it suffers from high sensitivity to random errors. In a previously proposed model, robust Nth-order superdirectivity was obtained by combining low-order superdirectivity sub-solutions with orders from 0 to N. However, the orders N were exclusively positive integers, which may lead to an over-improved robustness with an excessively degraded directivity. This paper presents an arbitrary-order superdirectivity model of circular sensor arrays, which can achieve a more flexible compromise between directivity and robustness than the previous model. A coefficient η is weighted at the (N + 1)th-order entry of the optimal weighting vector and the new weighting vector will consist of the 0th- to Nth-order and the weighted (N + 1)th-order entries. The beampattern, the directivity factor, and the error sensitivity function can then be expressed as functions of the coefficient η. Moreover, broadband superdirective beampatterns with a desired directivity factor or error sensitivity function can be readily synthesized using the closed-form solutions derived from the arbitrary-order superdirectivity model. Simulations and experimental results show that the proposed model can easily synthesize superdirective beampatterns with arbitrary orders, and it is more flexible than the previous integer-order model. It is also demonstrated that the performance of the proposed method is similar to that of the optimization-based method, but it uses a simple closed-form weighting vector rather than a numerical algorithm. [ABSTRACT FROM AUTHOR]

The subdivision scheme is used to illustrate smooth curves and surfaces. It is an algorithmic technique which takes a coarse polygon as an input and produces a refined polygon as an output. In this paper, a 10-point interpolating subdivision scheme is used to develop a numerical algorithm for the solution of fourth order nonlinear singularly perturbed boundary value problems (NSPBVPs). The studies of convergence and approximation order of the numerical algorithm are also presented. The solution of NSPBVPs is presented to see the efficiency of the algorithm. [ABSTRACT FROM AUTHOR]

Stochastic Brownian dynamics is an extremely powerful way to simulate the polymer dynamics in solutions and melts. Mathematically, these models are described by stochastic differential equations. The most challenging problems are the Monte Carlo algorithm, which simulates the motion of a large number of model particles and hence requires an enormous amount of computer time. It is therefore necessary to develop an efficient numerical method in operational emergency response applications. In this paper, we give an improved multilevel Monte Carlo (improved-MLMC) method based on equilibrium control variables at each level to calculate the propagation of polymers. The improved-MLMC method can be shown to result in asymptotically optimal random errors and reduce total cost when compared to the standard Monte Carlo and MLMC methods. Finally, the effect of the Wi number (dimensionless parameter) on the total cost of the presented MLMC method is also discussed in detail. [ABSTRACT FROM AUTHOR]

Doppler radar-based fast heart rate (HR) extraction has great application potential in stress and emotion recognition, anxiety treatments, etc. However, fast extraction of HR is still a great challenge for the vital sign detection applying the traditional discrete Fourier transform (DFT) method based on a continuous-wave (CW) Doppler radar sensor. When applying traditional DFT to the baseband signal analysis, the spectrum resolution will become insufficient if the time window is less than 10 s. In this paper, an interpolated DFT algorithm based on a poly-item cosine window is introduced to achieve fast extraction of HR with only 3 s data length. In addition, the leakage and grid effect phenomenon is presented to show the defects caused by the traditional DFT method. To verify the effectiveness of the proposed method, simulations are performed and experiments are executed using a 10-GHz CW Doppler radar sensor platform. Compared with the traditional DFT method, the interpolated DFT method reduces the average HR error from 8.48% to 1.87% based on the Hanning window and from 8.48% to 1.45% based on the rectangular window. [ABSTRACT FROM AUTHOR]

Compressive spherical beamforming (CSB) with spherical microphone arrays enjoys high spatial resolution and strong sidelobes suppression at medium to high frequencies. In this paper, the adaptive reweighting homotopy (ARH) algorithm is adapted to CSB, and the proposed method is abbreviated as ARH-CSB, which bypasses a priori signal-to-noise ratio (SNR) estimation. ARH-CSB iteratively solves the CSB model by determining the support set and adaptively searching for weights while estimating the source strength. Simulations and experiments demonstrate that ARH-CSB can effectively identify acoustic sources. Furthermore, compared to CSB solved by ℓ 1 -norm minimization algorithm and iterative reweighted ℓ 1 -norm minimization algorithm, ARH-CSB does not depend on a priori SNR and has better adaptability to low SNR, stronger robustness, higher weak sources quantification accuracy, and faster computation speed. [ABSTRACT FROM AUTHOR]

In the process of block compressed sensing (CS) applied to the rolling bearing fault signal, the reconstruction accuracy of the signal is low due to the large difference in sparsity between blocks and the unreasonable components of reconstruction support set, which affects the overall reconstruction effect of the signal. To improve the signal reconstruction results, forward and backward stagewise orthogonal matching pursuit (FBStOMP) based on the adaptive block method is proposed. First, to equalize the sparsity of each block signal, the fault signal is divided into blocks according to the adaptive block length, which is obtained by the short-time autocorrelation algorithm. Then, the K-singular value decomposition algorithm is used to train the sparse dictionary to obtain a better sparse effect. Finally, the FBStOMP algorithm is proposed. The atom backtracking and screening process is added in the reconstruction process to improve the possibility that all the effective atoms can be selected into the support set. The experimental analysis of the simulation signal and bearing fault signal show that, compared with the traditional CS reconstruction algorithm, the adaptive block-FBStOMP algorithm proposed in the paper can effectively improve the reconstruction accuracy of the bearing fault signal. [ABSTRACT FROM AUTHOR]