Circuits, Systems and Signal Processing最新文献

In correlation-based processing, sparse arrays offer the capacity to resolve a greater number of uncorrelated sources than physical sensors due to the considerable breadth of their difference coarrays, originating from variations in the locations of elements. Consequently, there is significant interest in devising sparse arrays with sizable difference coarrays and expanding the analysis to encompass additional array characteristics like symmetry, resilience, and cost-effective engineering. We present a scalable and systematic methodology for designing large sparse arrays. Considering several attributes and factors, we can address Fractal arrays that were used for low-side lobe antenna array designing and have very low degrees of freedom; hence, sparsity is introduced to design a hole-free difference coarray which not only increases the number of degrees of freedom in fractal arrays but also aids in better beamforming applications and enhanced DoA results due to regularization in coarrays. We develop an innovative sparse fractal array to enhance the accuracy of DoA estimation for predicting a maximum number of uncorrelated sources with a minimum possible actual sensors. First, the 1D sparse fractal array is constructed and then it is extended to a 2D sparse fractal array for both azimuth and elevation angle estimation. Comprehensive robustness analysis is conducted on the proposed sparse fractal array, encompassing one-dimensional (1D) and two-dimensional (2D) configurations, in response to sensor failures. RMSE analysis shows that the proposed 1D and 2D arrays possess the minimum error when used for direction estimation.

The intelligibility of speech is a primary component to assess the severity level of Dysarthria, a speech disorder, which is caused not only due to weakness in vocal motor muscles but also difficulty in controlling its movements. Prior information about the severity of Dysarthria, aids the therapist during the rehabilitation process. This paper introduces a novel hybrid architecture comprising Gaussian Mixture Model and Neural Network (GMM-NN) for categorizing Dysarthric severity into four classes based on speech intelligibility. Mel Frequency Cepstral Coefficients (MFCC) extracted from the segmented speech signals are used to train the classifier. The proposed model produced a 1.9% improvement in accuracy when compared to the baseline Gaussian Mixture Model (GMM). The Gaussian Mixture Model Deep Neural Network (GMM-DNN) and Gaussian Mixture Model Feed Forward Neural Network (GMM-FFNN) architectures showed an accuracy of 96.7% and 96.42% with F1 scores of 0.9649, 0.9604 respectively.

Adjusting the sensitivity-weighting matrix, which is a key parameter affecting the filtering accuracy in the desensitized Kalman filter (DKF), is still an open problem. To address this issue, a new adaptive fast DKF (AFDKF) algorithm and adaptive fast desensitized extended Kalman filter (AFDEKF) have been proposed. The fast filters have an adaptive factor that enables them to adjust the sensitivity-weighting matrix based on the orthogonality principle of measurement residuals. This adaptive factor is calculated by using the corresponding process and measurement information. Then, a new desensitized cost function with an adaptive factor is designed. An analytical gain is obtained by minimizing this cost function to reduce computation cost. The performance of the AFDKF and AFDEKF algorithms are demonstrated using two numerical examples.

Human emotions are easy to identify through facial expressions, body movements, and gestures. Speech carries a lot of emotional cues including variations in pitch, tone, intensity, and rhythm. In recent years, the increasing demand for human–computer interaction has spurred the development of speech recognition methods. Traditional Speech emotion detection methods are less effective in recognizing emotions, considering features like pitch, intensity, and spectral characteristics. To address these issues, this paper proposed a novel method named Dual Kernel Support Vector based Levy Dung Beetle (DKSV-LDB) Algorithm to accurately identify emotions like happiness, anger, sadness, etc. from speech patterns. In this study, the model is designed by combining a Dual Kernel Support Vector Machine (SVM) method with a Dung beetle Optimization algorithm, enriched by the Levy Flight strategy. This work conducted experiments in the datasets namely the CREMA-D, TESS, and EMO-DB (German). The performance evaluation measures such as accuracy, precision, recall, F-measure, and specificity are utilized for the evaluation of the proposed DKSV-LDB method and these results are compared with existing methods. The DKSV-LDB method achieved accuracy, precision, recall, F-measure, and specificity of 98.57%, 97.91%, 97.86%, 97.84%, and 97.78%. The experimental results depict the performance of the developed DKSV-LDB technique for speech emotion identification.

This study delves into fixed-time and predefined-time bipartite consensus tracking (BCT) of second-order multi-agent systems (MASs), where cooperative and competitive behaviors coexist, while accounting for bounded disturbances. Based on the sliding mode control method, the fixed-time and predefined-time control protocols are proposed to ensure the achievement of fixed-time and prescribed-time BCT for the MASs, which can effectively eliminate singularity and chattering. Leveraging Lyapunov stability, we establish a set of adequate conditions to achieve fixed-time and predefined-time BCT for second-order MASs. Furthermore, we present numerical simulation results to substantiate the theoretical conclusions.

This paper solves the finite-time extended dissipative fault estimate problem for discrete-time Markov jump neural networks based on an event-triggered approach in fully/partially known transition probability cases. Firstly, the systems are expanded into new systems treating sensor faults as states. Based on the proposed event-triggered scheme and an intermediate variable, an event-triggered intermediate observer is designed to estimate states, faults of actuator and sensor, and the intermediate variable, simultaneously. Next, the finite-time stability of error systems with extended dissipativity is analyzed, and the observer gains are shown in fully/partially known transition probability case, respectively, whose existence conditions are given. Finally, an example is given to illustrate the feasibility of the proposed scheme.

This paper addresses data privacy concerns and the need for a low computational framework in health sciences education. Introducing hybrid parametric splines as a novel approach in ECG signal modeling, the study explores three cases: parametric cubic spline, parametric quartic spline, and a hybrid approach combining both methods with second-order continuity. Accuracy is assessed using power spectrum analysis, root mean square error (RMSE), percent root mean square difference (PRD), Cross correlation, and Dynamic Time Warping (DTW) validated measures. Additionally, statistical analysis, including the Bland-Altman scatter plot, supports the hybrid approach. The hybrid approach achieves a harmonious blend of smoothness, heightened flexibility, and increased accuracy while ensuring computational simplicity. Efficient utilization of fewer data points optimizes storage and processing. Capable of generating diverse ECG signals, it allows flexibility in creating various scenarios. The hybrid approach demonstrates superior accuracy, as evidenced by the obtained RMSE, PRD, Cross correlation, and DTW values of 0.0410, 15.76, 0.98, and 0.49, respectively.Contributing to the advancement of ECG modeling, these findings provide enhanced visualization, analysis, and educational demonstrations in health science education, particularly in cardiovascular physiology. This research offers valuable insights for improving education in cardiovascular health sciences through the application of ECG modeling. By adopting the hybrid approach, educators and researchers can enhance their understanding and teaching of cardiovascular health, ultimately leading to improved education and advancements in the field.

The underwater imaging environment is very different from land, and some common land image enhancement methods are often not applicable to the underwater environment. This paper proposes a two-step underwater image enhancement method. White balance is a commonly used color correction method. In underwater environments, the traditional white balance method has certain limitations and results in severe color bias. This is caused by the faster attenuation of red light in underwater environments. We develop a new white balance method based on the assumption of the gray world method. A red correction module is embedded in the method, which is more suitable for underwater environments. For contrast correction, we design an illuminance correction method based on the Retinex model. The method significantly reduces the computational burden compared to traditional methods, while enhancing the brightness and contrast of the images. In addition, most of the current underwater image enhancement methods deal with color and contrast issues separately. However, these two factors influence each other, and processing them separately may lead to suboptimal results. Therefore, we investigate the relationship between color and contrast and propose a trade-off method. Our method integrates color and contrast within a histogram framework, achieving a balanced enhancement of both aspects. To avoid chance, we utilized four datasets, each containing 800 randomly selected images for metric testing. On the five non-referential metrics, three firsts and two seconds were ranked. Our method ranked second on two referenced metrics. Superior results were also achieved in runtime comparisons. Finally, we further demonstrate the superiority of our method through detailed demonstrations and ablation experiments.

The widely linear complex-valued least mean square (WL-CNLMS) algorithm is extensively used for processing complex-valued signals, but it exists performance compromise between convergence rate and steady-state misadjustment. In response to this problem, we incorporate the idea of switching step-size (SSS), that is, selecting an optimal step-size at each iteration by comparing the mean-square deviation trends of the WL-CNLMS algorithm with pre-set different step-sizes and then proposing the SSS based WL-NLMS algorithm. Meanwhile, to keep the robustness of the algorithm in the impulsive noise environment, a robust variant of it is proposed by utilizing the modified Huber function instead of the quadratic function. Through extensive simulations in the contexts of system identification and beamforming, we have verified the effectiveness of the proposed algorithms.

It is essential for meeting the stringent real-time demands encountered in actual production scenarios. Employing the low computational complexity of recursive algorithms, some new schemes are developed for the parameter estimation of a class of time-varying systems. The temporal evolution of parameters is characterized through the autoregressive process, facilitating the construction of the identification model with regard to the autoregressive coefficients. Based on the computational efficiency of the gradient search, a parametric autoregression-based stochastic gradient algorithm is derived with an appropriate step size, achieving a compromise between the steepest descent and convergence rate. In order to address the limitation of the low estimation accuracy in gradient algorithms, a parametric autoregression-based multi-innovation stochastic gradient algorithm is explored by making use of the favorable information for corrections. The simulation results are given to demonstrate the effectiveness of the proposed algorithms. Therefore, for a class of time-varying systems whose parameters become the further insight through the autoregressive process, the proposed gradient methods can obtain the parameter estimates faster and more accurately while ensuring the real-time performance of time-varying systems.