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One of the pandemics that have caused many deaths is the Coronavirus disease 2019 (COVID-19). It first appeared in late 2019, and many deaths are increasing day by day until now. Therefore, the early diagnosis of COVID-19 has become a salient issue. Additionally, the current diagnosis methods have several demerits, and a new investigation is required to enhance the diagnosis performance. In this paper, a set of phases are performed, such as collecting data, filtering and augmenting images, extracting features, and classifying ECG images. The data were obtained from two publicly available ECG image datasets, and one of them contained COVID ECG reports. A set of preprocessing methods are applied to the ECG images, and data augmentation is performed to balance the ECG images based on the classes. A deep learning approach based on a convolutional neural network (CNN) is performed for feature extraction. Four different pre-trained models are applied, such as Vgg16, Vgg19, ResNet-101, and Xception. Moreover, an ensemble of Xception and the temporary convolutional network (TCN), which is named ECGConvnet, is proposed. Finally, the results obtained from the former models are fed to four main classifiers. These classifiers are softmax, random forest (RF), multilayer perception (MLP), and support vector machine (SVM). The former classifiers are used to evaluate the diagnosis ability of the proposed methods. The classification scenario is based on fivefold cross-validation. Seven experiments are presented to evaluate the performance of the ECGConvnet. Three of them are multi-class, and the remaining are binary class diagnosing. Six out of seven experiments diagnose COVID-19 patients. The aforementioned experimental results indicated that ECGConvnet has the highest performance over other pre-trained models, and the SVM classifier showed higher accuracy in comparison with the other classifiers. The resulting accuracies from ECGConvnet based on SVM are (99.74%, 98.6%, 99.1% on the multi-class diagnosis tasks) and (99.8% on one of the binary-class diagnoses, while the remaining achieved 100%). It is possible to develop an automatic diagnosis system for COVID based on deep learning using ECG data. [ABSTRACT FROM AUTHOR]

This paper proposes a digital morphological filtering method based on a novel structuring element (SE) formulated using fractional Fourier transform (FrFT) and cross-convolution of window functions. The highlighting feature of this newly formulated filter is its flexibility in adding an adaptive nature to classical morphological filtering. Until now, every method listed in the literature makes a convenient assumption that noises corrupting ECG signal are Gaussian and model the filter around this assumption. Addressing this shortcoming, the first-of-its-kind filter adopts the α -stable distribution model (of which Gaussian distribution is a special case) of noise to better replicate the real-time noises interfering with ECG signals. The designed filter can suppress the noise and adapt to the changes in ECG signal morphology for better reconstruction. The proposed filter is tested on MIT-BIH Arrhythmia Database. The simulation results show improved performance in several quantitative metrics, demonstrating the superiority of our suggested method over the currently used state-of-the-art techniques. [ABSTRACT FROM AUTHOR]

In this paper, a quasi-time-dependent (QTD) H ∞ filtering approach to discrete-time two-dimensional switched systems is presented, by applying the mode-dependent persistent dwell-time (MPDT) switching method. The Fornasini–Marchesini local state-space model is used to describe the interested system. Compared with the dwell-time and the average dwell-time switchings which are often used in the literature, a MPDT switching, which is a more general class of switching form, is studied in this paper. The objective is to design a full-order filter that ensures the filtering error system exponentially stable with a guaranteed H ∞ noise attenuation performance. By constructing a QTD switched Lyapunov-like function, the sufficient conditions for the existence of the filter are established by using the MPDT switching. The time-independent filter is actually a special case of QTD filter studied in this paper, which implies the developed results in this paper are more general with less conservativeness. Finally, two numerical examples are showed to validate the effectiveness and potential of the developed filter design method. [ABSTRACT FROM AUTHOR]

This work describes the log-MAP (BCJR) algorithm implementation of a close to capacity dirty paper coding CODEC. The CODEC consists of eight deep pipeline processors. It decodes blocks of 975 bits in 26.9 ms using less than 9.7% of low-cost FPGA (and no DSP blocks). Two pipelines, for alpha and beta, calculate the values of gamma (of the BCJR) to reduce the storage requirements. The final log-likelihood ratio (LLR) is calculated together with alpha, reusing intermediate results. The number of bits used by the different signals of the processor is easily configurable. It was set to six bits to the channel measure signals and eight bits to log of probability signals like alpha, beta, and others. The CODEC clock was 100 MHz. The achieved bit rate is 36.2 Kbps per CODEC, but multiple CODECs can be fit into a single chip. The CODEC is 3.49 dB from the channel capacity. [ABSTRACT FROM AUTHOR]

The article announces the recipients of the Best Paper Awards for 2022 in the journal Circuits, Systems & Signal Processing. The awards were established in 2013 and nominations were invited from Associate Editors and readers of the journal. A Selection Committee was formed to choose the best papers in different categories. The recipients of the Armen H. Zemanian Best Paper Award for Circuits and Systems are Sudip Ghosh, Yuvam Bateja, Joshua Roy Palathinkal, and Hafizur Rahaman. The recipients of the Sydney R. Parker Best Paper Award for Signal Processing are N. Ramesh Babu, K. Sanjay, and P. Balasubraminam. The paper by Babu et al. was also chosen for the M.N.S. Swamy Best Paper Award. Each awardee will receive a certificate and cash prize. The Editor-in-Chief congratulates the winners and thanks the nominators and Selection Committee for their contributions. [Extracted from the article]

This paper studies the problem of finite-time stabilization for singular Markov jump systems (SMJSs) with time-varying delays and generally uncertain transition rates. First, a suitable Lyapunov–Krasovskii functional is constructed. And the criterion of the finite-time stability for singular Markov jump systems is analyzed. We adopt a better design approach for the matrix P ¯ i satisfying the equality constraints, which is much less conservative. Then, a state feedback controller design method based on linear matrix inequalities (LMIs) is presented to ensure that the closed-loop system is finite-time stable. Finally, simulation examples are given to illustrate the results' correctness and validity of this paper. [ABSTRACT FROM AUTHOR]

Several real-life problems that involve a time delay are modeled using fractional time-delay systems. However, most studies related to these systems assume that the delay is already known, which is not the case in practical scenarios where the delay is often uncertain or unknown. To address this issue, this paper proposes an algebraic and robust method to estimate the input delay for a class of fractional time-delay systems in a noisy environment, by applying a cross-convolution approach. Besides, a filtering methodology is incorporated with the proposed approach to enhance its efficacy. In addition, this paper presents novel theories on convolution in the field of fractional calculus. Finally, the performance of the proposed approach is demonstrated by numerical simulations. [ABSTRACT FROM AUTHOR]

Developing an automatic speaker verification (ASV) system for children's speech presents significant challenges. One major obstacle is the scarcity of domain-specific data. This issue is exacerbated when dealing with short speech utterances, a relatively unexplored area in children's ASV. Voice biometric systems struggle during enrollment and verification phase, when faced with inadequate speech data, both in volume as well as in duration. To address data scarcity, this paper explores various in-domain and out-of-domain data augmentation techniques. Out-of-domain data from adult speakers, which have distinct acoustic attributes from children, are modified using techniques like voice-conversion, prosody and formant modification to make them acoustically similar to children's speech. In-domain data augmentation involves perturbing the speed of children's speech. This combined data augmentation approach not only increases training data volume but also captures missing target attributes, resulting in a significant 43.91% reduction in equal error rate (EER) compared to the baseline system. Additionally, the paper addresses the challenge of preserving higher-frequency components in children's speech. It achieves this by concatenating conventional Mel-frequency cepstral coefficients (MFCC) with Inverse-Mel-frequency cepstral coefficient (IMFCC) features at the frame level. The low canonical correlation between MFCC and IMFCC feature vectors motivates this fusion. The feature concatenation approach, when combined with proposed data augmentation, results in an appreciable reduction of 48.51% in the overall EER, demonstrating its effectiveness in improving the performance of children's ASV system. [ABSTRACT FROM AUTHOR]

Analytic signals constitute a class of signals that are widely applied in time–frequency analysis such as extracting instantaneous frequency (IF) or phase derivative in the characterization of ultrashort laser pulse. The purpose of this paper is to investigate the phase retrieval (PR) problem for analytic signals in C N by short-time Fourier transform (STFT) measurements since such measurements enjoy some nice structures. Since generic analytic signals are not sparse in the time domain, the existing PR results for sparse (in time domain) signals do not apply to analytic signals. In this paper, the windows are required to be bandlimited such that they usually have the full support length N and allow us to get much better resolutions on low frequencies. By exploiting the structure of the STFT associated with bandlimited windows for analytic signals, we prove that the STFT-based phase retrieval (STFT-PR for short) of generic analytic signals can be achieved by their (3 ⌊ N 2 ⌋ + 1) measurements, where ⌊ x ⌋ denotes the largest integer that is not larger than x. Since the generic analytic signals are (⌊ N 2 ⌋ + 1) -sparse in the Fourier domain, such a number of measurements is lower than 4 N + O (1) and O (k 3) which are required in the literature for STFT-PR of all signals and of k 2 -sparse (in the Fourier domain) signals in C N 2 , respectively. Moreover, we also prove that if the length N is even and the windows are also analytic, then the number of measurements can be reduced to (3 N 2 - 1) . As an application of this, we get that the instantaneous frequency (IF) of a generic analytic signal can be exactly recovered from the STFT measurements. [ABSTRACT FROM AUTHOR]

This paper studies the stability criterion and controller design for time-delay switched systems with input saturation. The main contributions of this paper are as follows: (1) Based on constructing the Lyapunov–Krasovskii functional (LKF) with the triple integral term and making full use of the delay lower bound information, the sufficient conditions for the exponential stability of the system are given. (2) A state feedback controller is designed for the input-saturated system. (3) The symmetric delay rate problem is considered to accurately define the derivative of LKF, which reduces the conservatism of the system. By reducing conservatism, that is, the time-delay upper bound is raised, allowing for a wider range of time-delay signals. Finally, the effectiveness of the proposed method is verified by the numerical examples. [ABSTRACT FROM AUTHOR]

The task of developing an automatic speaker verification (ASV) system for children is extremely challenging due to unavailability of sufficiently large and free speech corpora from child speakers. On the other hand, hundreds of hours of speech data from adult speakers are freely available. Therefore, majority of the works on speaker verification reported in the literature deal predominantly with adults' speech, while only a few works dealing with children's speech have been published. The challenges in developing a robust ASV system for child speakers are further exacerbated when we use short utterances which is largely unexplored in the case of children's speech. Therefore, in this paper, we have focused on children's speaker verification using short utterances. To deal with data scarcity, several out-of-domain data augmentation techniques have been utilized. Since the out-of-domain data used in this study is from adult speakers which is acoustically very different from children's speech, we have resorted to techniques like prosody modification, formant modification, and voice conversion in order to render it acoustically similar to children's speech prior to augmentation. This helps in not only increasing the amount of training data, but also in effectively capturing the missing target attributes relevant to children's speech. A staggering relative improvement of 33.57% in equal error rate with respect to the baseline system trained solely on child dataset speaks volume of the effectiveness of the proposed data augmentation technique in this paper. Further to that, we have also proposed frame-level concatenation of Mel-frequency cepstral coefficients (MFCC) with frequency-domain linear prediction coefficients, in order to simultaneously model the spectral as well as temporal envelopes. The proposed idea of frame-level concatenation is expected to further enhance the discrimination among the speakers. This novel approach, when combined with data augmentation, helps in further improving the performance of the speaker verification system. The experimental results support our claims, wherein we have achieved an overall relative reduction of 38.04 % for equal error rate. [ABSTRACT FROM AUTHOR]

With the rapid development of information transfer technology and the influence of complex electromagnetic environment, the signal components in communication systems are becoming more and more complex, and the spectrum congestion is further aggravated, which seriously affects the performance of communication systems. Single-channel blind source separation technology is an effective solution, but the existing algorithms are deficient in terms of estimation accuracy, time delay and computational complexity. Based on the powerful feature extraction capability and timing signal processing capability of deep learning, this paper proposes an end-to-end time-domain blind separation convolutional network structure, including three parts: encoder, separation module and decoder. The attention mechanism module is also introduced into the encoding layer of the network to enhance the deep feature extraction ability of the network, highlight the influence of important timing features of communication signals, further improve the separation performance and enhance the practicality of the network. The experimental results show that the algorithm has superior performance and can effectively separate the mixed communication signal of various modulation types, and it can also extract own-side communication signals from high-power masked signals. Compared with the existing algorithms, the network in this paper has high separation accuracy and low computational complexity, which breaks the bottleneck of traditional single-channel blind separation algorithms in complex communication electromagnetic environment. [ABSTRACT FROM AUTHOR]

This paper investigates the design of the event-triggered controller for positive Takagi–Sugeno (T–S) fuzzy systems with input constraints. To begin with, considering the characteristics of positive systems, the 1-norm event-triggered mechanism is adopted. Then, by using the linear co-positive Lyapunov function, the positivity and stability of positive T–S fuzzy systems under the event-triggered controller are discussed, and sufficient conditions for the existence of the bounded controller are developed. Furthermore, the occurrence of the Zeno-free phenomenon is illustrated. Finally, a simulation is performed to demonstrate the effectiveness of the results in this paper. [ABSTRACT FROM AUTHOR]

In this paper we have proposed a novel robust method of estimation of the unknown parameters of a fundamental frequency and its harmonics model. Although the least squares estimators (LSEs) or the periodogram type estimators are the most efficient estimators, it is well known that they are not robust. In presence of outliers the LSEs are known to be not efficient. In presence of outliers, robust estimators like least absolute deviation estimators (LADEs) or Huber's M-estimators (HMEs) may be used. But implementation of the LADEs or HMEs are quite challenging, particularly if the number of component is large. Finding initial guesses in the higher dimensions is always a non-trivial issue. Moreover, theoretical properties of the robust estimators can be established under stronger assumptions than what are needed for the LSEs. In this paper we have proposed novel weighted least squares estimators (WLSEs) which are more robust compared to the LSEs or periodogram estimators in presence of outliers. The proposed WLSEs can be implemented very conveniently in practice. It involves in solving only one non-linear equation. We have established the theoretical properties of the proposed WLSEs. Extensive simulations suggest that in presence of outliers, the WLSEs behave better than the LSEs, periodogram estimators, LADEs and HMEs. The performance of the WLSEs depend on the weight function, and we have discussed how to choose the weight function. We have analyzed one synthetic data set to show how the proposed method can be used in practice. [ABSTRACT FROM AUTHOR]

The complex domain adaptive filtering algorithms has shown excellent performance in the field of signal processing. Among them, the well-known complex least mean square (CLMS) algorithm has been widely used in practical projects. However, the CLMS algorithm only considers the case where the output signal is disturbed by noise, ignores the input signal may also be interfered by noise. Moreover, the signal is likely to be disturbed by impulsive noise in practice. Based on the above problems, the complex total least mean M-estimation (CTLMM) adaptive algorithm based on the M-estimation function is put forward in this paper. The algorithm can well handle the case where both input and output signals are subject to complex domain noise interference, and it also shows robustness with impulsive noise. This paper also analyzes the local stability of the CTLMM algorithm and discuss the theoretical steady-state error of the algorithm. Finally, simulation results verify the advantages of CTLMM algorithm and the rightness of the theoretical analysis. [ABSTRACT FROM AUTHOR]

This article considers the problem of finite-time command-filtered control for switched nonlinear systems with input quantization and output constraints. The unmeasurable state is estimated by designing a switched state observer. During the design process, to overcome the chattering problem effectively, the hysteresis quantization is designed as two bounded nonlinear functions. Furthermore, in order to restrict the output to an expected range, the barrier Lyapunov function approach is introduced. The "explosion of complexity" and the error compensation problems in the backstepping design are solved by using a finite-time command-filtered approach. A first-order Levant differentiator is used instead of the general command filter in this paper, which can not only filter the intermediate signals accurately to get the differential signals, but also ensure the finite-time stability of the filter. Stability of the closed-loop system in the sense of semi-global practical finite-time stability (SGPFS) is proved by exploring a multiple Lyapunov functions approach. Finally, a simulation example is provided to verify the validity of the presented control method. [ABSTRACT FROM AUTHOR]

A call for nominations for these awards was sent out to all the Associate Editors inviting them to nominate those papers published in 2020 or 2021 in CSSP, which should be considered for these awards. This call was also posted on the CSSP Website inviting nominations from CSSP readers. During 2013, Birkhäuser (Springer) instituted three Best Paper Awards for papers published in Circuits, Systems and Signal Processing (see the announcement in the October 2013 issue of CSSP). [Extracted from the article]

This paper proposes a carbon nanotube FET (CNFET)-based ultra-low-power dual- V DD ternary half adder (HA) circuit. The proposed design utilizes both the available ternary power supply voltages ( V DD & V DD / 2 ) and prevents direct path between the power supplies and ground, thus significantly reducing the power dissipation as compared to the conventional designs. The performance of the proposed CNFET dual- V DD HA has been compared with the same circuit implemented with 45 nm MOSFETs and also with other CNFET-based state-of-the-art HA designs proposed in the literature. The proposed HA consumes merely 86 nW of power which is significantly lesser (66–90% lower) than the power required by other ternary HA designs, and also exhibits 69–91% lower delays. The overall PDP of the proposed HA circuit is merely 4–11% of the PDP of corresponding CMOS ternary HA and other benchmarked CNFET HA designs. [ABSTRACT FROM AUTHOR]

In the past decade, the augmented complex-valued least mean square adaptive filtering algorithm based on the minimum mean square error criterion and widely-linear model has attracted much attentions due to its simplicity and applicability to non-circular signals. However, the system noise with impulse characteristics will dramatically affect the convergence of the augmented complex-valued least mean square algorithm. Recently, a robust widely-linear complex-valued least mean M-estimate algorithm has been proposed, which significantly improves the robustness of the augmented complex-valued least mean square algorithm to impulsive noise. But it should be noted that the widely-linear complex-valued least mean M-estimate algorithm with fixed step size faces the trade-off between convergence speed and steady-state accuracy. To overcome this drawback, a variable step size widely-linear complex-valued least mean M-estimate algorithm is presented in this paper, in which the optimal step size at each iteration is obtained by maximizing the difference between the mean square deviations of the WL-CLMM algorithm at adjacent moments. In order to reveal the statistical convergence behavior of the proposed variable-step-size widely-linear complex-valued least mean M-estimate algorithm to better guide the parameter selection in practical applications. Theoretical transient and steady-state mean-square deviation convergence behaviors of the variable-step-size widely-linear complex-valued least mean M-estimate algorithm are analyzed and the corresponding mean-square deviation expressions are also derived. Computer simulations on system identification and stereophonic acoustic echo cancellation in impulsive noise environments confirm the validity of the analysis results and the performance improvement from the variable step size strategy. [ABSTRACT FROM AUTHOR]

Lag consensus is an essential dynamic behavior in nature and practical systems, which means that the trajectory of followers lags behind the leader owing to a track time-delay. In this paper, the lag consensus problem under event-triggered control is investigated for leader-following multi-agent systems with nonlinear dynamics. A novel static event-triggered control strategy based on combined measurement is presented to avoid continuous information interaction between agents by introducing a track time-delay. It should be emphasized that this control protocol is fully distributed, that is, the global information in the network is not involved. In addition, for each agent, the information transmission and the updates of control protocol only arise at its own triggering instants. Based on stability theory, a sufficient condition is derived for leader-following multi-agent systems with nonlinear dynamics to achieve lag consensus. Moreover, it is exhibited that Zeno behavior is excluded by proving the existence of a positive lower bound for the triggering interval. Finally, the correctness of theoretical results is verified via numerical simulations. [ABSTRACT FROM AUTHOR]

This article investigates the asymptotic stability of fractional-order (FO) systems with uncertainty and time-varying delay based on the sliding mode control (SMC) method. First, based on the SMC method, a suitable integral type fractional-order sliding mode surface (FOSMS) is designed and the dynamic equations of FO systems under SMC are obtained. Second, by inequality techniques, the condition for asymptotic stability of the FO system has been mathematically established. Then, a novel adaptive SMC law is introduced, which can make sure the accessibility of sliding mode surfaces (SMS). Finally, the feasibility of the results obtained in this paper is verified through a simulation. [ABSTRACT FROM AUTHOR]

In this paper, we propose a novel method for 3D data cube denoising, where the 3D data cube is corrupted by noise with spatially varying noise levels. We perform 3D dual tree complex wavelet transform (DTCWT) to the 3D data cube, and then conduct wavelet-based thresholding based on three scales of dependency in wavelet coefficients. Instead of using the global noise level, we estimate the noise levels locally, which improve the denoising results substantially. We conduct inverse DTCWT to obtain the noise reduced data cubes. Experiments demonstrate that our proposed method outperforms block matching and 3D filtering, video block matching and 3D filtering, 2D bivariate shrinkage, and 3D bivariate shrinkage significantly for noise reduction of 3D data cubes. [ABSTRACT FROM AUTHOR]

Approximate multipliers are widely used in image processing and multimedia signal processing applications for the reduction in area, computation time, and power consumption. 4:2 compressors play a key role in multipliers for efficient addition of partial product bits. In this paper, an inexact 4:2 compressor is proposed, where a major portion of the logic associated with the generation of Carry and Sum are shared to reduce the logic/circuit complexity. During the design time overestimates are balanced with the underestimates so as to reduce the total error distance. The proposed compressor is used for the addition of partial products of the Baugh-Wooley multiplier. A novel algorithm is proposed for the placement of inexact 4:2 compressors in the partial product bit array, based on the minimum peak signal-to-noise ratio requirement of the application. The approximate multipliers thus obtained are used for edge detection using Sobel operator. It is observed from the Pareto analysis that the proposed design offers significant saving in area, computation time, and power consumption over the design with nearly the same error performance. Besides, the proposed design is more error tolerant compared with the design with nearly the same area-delay trade-off and power-delay trade-off. Moreover, the proposed inexact 4:2 compressor-based Baugh-Wooley multiplier is found to offer better accuracy-area trade-off compared to the state-of-the-art 4:2 compressors in convolutional neural network-based classification. [ABSTRACT FROM AUTHOR]

This paper designs a decentralized sliding mode preview repetitive control (SMPRC) for interconnected nonlinear systems. First, an augmented error system, which is composed of the derivative equation of the available future reference dynamics, the output of the modified repetitive controller and the tracking error dynamics, is constructed. Next, an integral switching surface is designed to verify the robust asymptotic stability of the considered system subject to nonlinearity. Then, the preview repetitive-control (PRC) law design problem is transformed into the stability problem of the augmented system considering the nominal conditions. Thereafter, a sliding mode control (SMC) law cooperates with the PRC law with the purpose to ensure the robustness of the interconnected systems in the presence of nonlinearity. Further, the required stability conditions are derived on the basis of a Lyapunov analysis, linear matrix inequalities (LMI) and the singular-value-decomposition (SVD) technique. Finally, the numerical simulation results demonstrate the effectiveness of the proposed method. [ABSTRACT FROM AUTHOR]

In this paper, we propose a novel convex combination algorithm based on the distributed diffusion strategy, utilizing the least mean square (LMS) approach to enhance the performance of sensor networks. By enabling communication among nodes, the algorithm achieves decentralization and improves the robustness of the network. To address the limitations of slow convergence speed and large static error associated with fixed step size errors, we introduce a convex combination strategy that integrates two filters with variable step sizes. The LMS algorithm with the convex combination variable step size assigns a higher weight to the filter with a larger step size when the error is significant, ensuring rapid convergence. Conversely, the convex combination small step size filter is assigned a higher weight when the error is small, reducing the error in the stable state. The convergence behavior of the proposed algorithm is analyzed through theoretical analysis, and its complexity is compared with that of the distributed diffusion LMS algorithm through extensive experimental simulations. The results demonstrate that the combination of the LMS algorithm with the distributed diffusion strategy offers advantages in challenging external environments, including improved convergence speed and reduced stability error. This study makes significant contributions to the existing research field by introducing a novel convex combination algorithm that addresses the shortcomings of fixed step size errors and expands the range of available algorithms. By leveraging the distributed diffusion strategy, our approach enhances the robustness of sensor networks and achieves improved performance. The findings of this study provide valuable insights for researchers working on decentralized algorithms and highlight the potential of combining the LMS algorithm with the distributed diffusion strategy in challenging environmental conditions. [ABSTRACT FROM AUTHOR]

In this article, we focus on the application of fractional differential calculus in image processing, specifically in the context of image decoding techniques. The article presents different methods of image decoding based on Riemann–Liouville fractional integration and compares their performance with established methods in the denoising of noisy images. Here we create new fractional masks by using the Middle Point approach method, the Grumwald–Letnikov scheme, the Toufik–Riemann scheme, and the Euler method with the help of the Riemann–Liouville fractional integral operator. The results demonstrate that the suggested algorithms outperform the compared techniques. The paper also suggests that the concepts presented can be applied to other definitions of fractional calculus. The future research goal is to determine the optimal fractional order for a given set of algorithms. [ABSTRACT FROM AUTHOR]

The controlled autoregressive autoregressive moving average (CARARMA) models are of popularity to describe the evolution characteristics of dynamical systems. To overcome the identification obstacle resulting from colored noises, this paper studies the identification of the CARARMA models by forming an intermediate correlated noise model. In order to realize the real-time prediction function of the models, the on-line identification scheme is developed by constructing the dynamical objective functions based on the real-time sampled observations. Firstly, a rolling optimization cost function is built based on the observation at a single sampling instant to catch the modal information at a single time point and a generalized extended stochastic gradient (GESG) algorithm is proposed through the stochastic gradient optimization. Secondly, a rolling window cost function is built in accordance with the dynamical batch observations within data window by extending the proposed GESG algorithm and the multi-innovation generalized extended stochastic gradient algorithm is derived. Thirdly, from the perspective of theoretical analysis, the convergence proof of the proposed algorithm is provided based on the stochastic martingale convergence theory. Finally, the simulation analysis and comparison studies are provided to show the performance of the proposed algorithms. [ABSTRACT FROM AUTHOR]

This paper studies a class of input-saturated nonlinear systems with Lipschitz condition. We combine the fully actuated system approach with anti-windup control to design control system. The proposed control method has the advantages of both fully actuated system approach and anti-windup control. The main advantages are: (1) establishing a fully actuated system approach control framework based on anti-windup control. The controller of the nonlinear system can be written out immediately when a fully actuated system (FAS) model or a group of FAS models of the system is derived, simplifying the steps of control system analysis and design; (2) many actual systems are FAS models, and there is no need to convert the system into a state space model; (3) the anti-windup compensator works only when the actuator saturation occurs to ensure the stability of the control system. In fact, most of the time, the system operates in an unsaturated state, which means that in most cases, the system performance is not affected. A numerical simulation example is given to illustrate the effectiveness of the proposed method. [ABSTRACT FROM AUTHOR]

In this paper, we present an underdetermined estimation algorithm for two-dimensional (2-D) direction of arrival (DOA) based on three-parallel co-prime arrays. By exploiting the correlation among the received signals in both the time and spatial domains, a conjugate augmented spatial–temporal virtual array is constructed, which significantly increases the degrees of freedom (DOF) and expands the array aperture, effectively filling the holes within the physical array. By vectorizing the covariance matrix obtained from the spatial–temporal virtual array, we can transform the 2-D DOA estimation problem into a one-dimensional (1-D) sparse recovery problem. Thereafter, the excellent gridless methods: sparse and parametric approach (SPA) and the matrix pencil and pairing (MAPP), are adopted to efficiently resolve the 1-D sparse recovery problem, enabling the automatic pairing of the 2-D angles. And simulation results are provided to validate the enhanced accuracy of the proposed algorithm in comparison with other existing methods. [ABSTRACT FROM AUTHOR]

This paper introduces a novel algorithm for optimizing the coefficients of the digital filters used in incremental delta-sigma analog-to-digital converters (IDSC). This algorithm is modified from constrained linear least squares (LS) to improve the signal-to-noise-and-distortion ratio (SNDR) of IDSC and minimize the oversampling rate (OSR) of the modulator, which enhances system speed and reduces power consumption. In the case of the same SNDR, the first-order IDSC with the proposed filter can reduce the OSR of the modulator by 50%, at least compared to the IDSC with the conventional filter. The second-order IDSC with the proposed filter can reach a higher SNDR of 106 dB with an OSR of 64. Considering the nonlinearity of the integrator, the SNDR of IDSC with the proposed filter is also 10 dB greater than that of the conventional filters and 3dB greater than that of the IDSC using the near-optimal algorithm filter. The experimental results indicate that the proposed filter possesses an excellent figure of merit of 0.028 pJ / conv . [ABSTRACT FROM AUTHOR]

In this paper, we introduce the salp swarm optimization algorithm (SSOA)-based novel model reduction algorithm for simplifying commensurate and incommensurate fractional-order systems. In the case of commensurate fractional-order systems (CFOSs), the presented method converts it into a non-fractional-order system first. Then, we implement the SSOA with the stability equations to find a non-fractional-order approximant. Finally, the non-fractional-order approximant is transformed to determine the proposed reduced-order CFOS. It is also demonstrated using numerical examples that some existing methods fail to achieve the stability claim. On the other hand, the stability of the proposed fractional-order approximants is ascertained by incorporating stability equations along with SSOA. Further, the proposed method is extended for the model reduction in incommensurate and unstable fractional-order systems. In this case, the Oustaloup approximation is used along with the SSOA and stability equations to obtain the proposed non-fractional-order approximation. The simulation results corroborate the performance of the suggested method in both cases and establish its transcendency. The obtained results showcase the efficacy of optimization in simplification of fractional-order systems. The work also highlights the importance of order reduction in practical engineering and scientific applications of fractional-order systems. [ABSTRACT FROM AUTHOR]

Image enhancement is a vital process that serves as a tool for improving the quality of a lot of real-life applications. Fractional calculus can be utilized in enhancing images using fractional order kernels, adding more controllability to the system, due to the flexible choice of the fractional order parameter, which adds extra degrees of freedom. The proposed system merges two fractional order kernels which helps in image enhancement techniques, and the contribution of this work is based on the study of how to optimize this process. The optimization of the two fractional kernels was done using the neural network optimization algorithm (NNA) to utilize the best order for the two kernels. In this paper, three fractional kernels are studied to highlight the performance of image enhancement using fractional kernels against different metrics. Furthermore, three different combinations of two kernels are combined and studied to enhance the metrics score by utilizing two different fractional orders for each kernel. Various optimization algorithms are used to obtain the optimum fractional order for both single and combined kernels. Using the constrained NNA, the evaluation metrics of the image enhancement show a 33% increase in measure of enhancement metric (EME), 21% increase in contrast, and 4% increase in average gradient compared to the best-achieved metrics by the literature while keeping the similarity metric above 0.75. [ABSTRACT FROM AUTHOR]

The task of developing an automatic speaker verification (ASV) system for children's speech is extremely challenging due to the dearth of domain-specific data. The challenges are further exacerbated in the case of short utterances of speech, a relatively unexplored domain in the case of children's ASV. Voice-based biometric systems require an adequate amount of speech data for enrollment and verification; otherwise, the performance considerably degrades. It is for this reason that the trade-off between convenience and security is gruelling to maintain in practical scenarios. In this paper, we have focused on data paucity and preservation of the higher-frequency contents in order to enhance the performance of a short utterance-based children's speaker verification system. To deal with data scarcity, an out-of-domain data augmentation approach has been proposed. Since the out-of-domain data used are from adult speakers which are acoustically very different from children's speech, we have made use of techniques like prosody modification, formant modification, and voice conversion in order to render it acoustically similar to children's speech prior to augmentation. This helps in not only increasing the amount of training data but also in effectively capturing the missing target attributes which helps in boosting the verification. Further to that, we have resorted to concatenation of the classical Mel-frequency cepstral coefficients (MFCC) features with the Gamma-tone frequency cepstral coefficient (GTF-CC) or with the Inverse Gamma-tone frequency cepstral coefficient (IGTF-CC) features. The feature concatenation of MFCC and IGTF-CC is employed with the sole intention of effectively modeling the human auditory system along with the preservation of higher-frequency contents in the children's speech data. This feature concatenation approach, when combined with data augmentation, helps in further improvement in the verification performance. The experimental results testify our claims, wherein we have achieved an overall relative reduction of 38.5 % for equal error rate. [ABSTRACT FROM AUTHOR]

This paper introduces an innovative electrocardiogram (ECG ) denoising technique based on stationary wavelet transform ( S W T) and wavelet/total variation (WATV). In this technique, we also use two different artificial neural networks ( ANNs ) to determine two ideal thresholds, t h r 1 and t h r 2 . The latter is used for the soft thresholding of a noisy details coefficient, c d b 2 , to obtain a denoised coefficient, c d d 2 . The threshold t h r 1 is used for the soft thresholding of a noisy details coefficient, c d b 1 , yielding a denoised coefficient, c d d 1. The coefficient c d b 1 and a noisy approximation coefficient, c a b 1 , are obtained by applying SWT to the noisy ECG signal. The coefficient c d b 2 and another noisy approximation coefficient, c a b 2 , are obtained by applying SWT to c a b 1 . In this proposed ECG denoising system, we also apply a WATV-based denoising technique to c a b 2 to obtain a denoised approximation coefficient, c a d 2 . This WATV-based denoising technique requires the estimation of the level of the noise corrupting the clean ECG signal. This noise is additive Gaussian white noise (AGWN) and its level is denoted as σ , which is estimated from c d b 1 . After that, the inverse of SWT ( S W T - 1 ) is applied to c d d 2 and c a d 2 to obtain a denoised approximation coefficient, c a d 1 . Subsequently, we apply S W T - 1 to c d d 1 and c a d 1 to finally obtain the denoised ECG signal. The performance of this proposed ECG denoising technique is proven by the results obtained after computing the signal-to-noise ratio ( SNR ), the peak SNR ( PSNR ), the mean square error ( MSE ), the mean absolute error ( MAE ) and the cross-correlation ( CC ). [ABSTRACT FROM AUTHOR]

The ever-increasing use of artificial intelligence (AI) to optimize embedding a watermark or find the optimal location in the host image for inserting a watermark might solve some problems in this field. However, the main problem, which is finding the optimum trade-off point among several watermarking criteria, has still not been investigated by researchers in this field, especially for speech signals. This paper aims to find the best trade-off among the watermarking requirements such as capacity, inaudibility, and robustness by applying an AI model. Moreover, a novel watermarking technique is proposed by modification of the probability density function (PDF) of the linear predictive (LP) residual and wavelet detail coefficient. For this method, a mathematical model is developed based on applying higher-order statistics for embedding and extracting the watermark. Sinh-arcsinh is used to shape the skewness and kurtosis of normal distribution for the LP residual or the wavelet high-frequency sub-bands, respectively, based on the watermark bits. Experimental results will show that although LP residual is not robust and it shows random behavior for modeling its PDF, the wavelet high-frequency band is quite robust and it can model the PDF of the wavelet. Moreover, it is demonstrated that AI has the capability to compromise among the watermarking criteria. Conclusions are drawn based on theoretical (maximum likelihood) and AI (machine learning) approaches, which confirm the effectiveness of the proposed model. Finally, in conclusion, several potential areas are discussed for further exploration. [ABSTRACT FROM AUTHOR]

Ternary circuits are promising due to their lower interconnect complexity, storage requirement, and lesser pin count than binary circuits. The adder is one of the most important building blocks of a digital processor. This paper proposes carbon nanotube field effect transistor (CNTFET)-based 'exact' and 'approximate' ternary full adders (TFA). The CNTFET is attractive for realizing multi-valued logic (MVL)/ternary circuits because its threshold voltage can be changed by altering the diameter of its carbon nanotube (CNT). The exact ternary adders are realized using unary functions and multiplexers. The circuit size of only the 'Sum' block of a TFA is pruned in approximate ternary full adders by varying degrees for achieving superior performance in terms of transistor count, delay, and power consumption compared to the 'exact' TFAs. This performance gain in approximate TFAs is obtained at the cost of accuracy in some of the 'Sum' outputs. Circuits are simulated with HSPICE using a 32 nm CNTFET technology node at various supply voltages and temperatures. The proposed exact adders exhibit lower power consumption and transistor count, higher robustness, and lower power delay product (PDP) and energy delay product (EDP) than existing ternary adders. The improvement in PDP of the proposed exact TFAs varies from 20 to 96% compared to existing TFAs. The performance of the exact TFAs is then further improved by approximating its 'Sum' block in approximate TFAs. Image blending is used to demonstrate the effectiveness of approximate ternary adders in error-tolerant applications. [ABSTRACT FROM AUTHOR]

With the development of deep learning techniques, the field of direction of arrival (DOA) estimation has also made significant progress. However, the accuracy of DOA estimation using end-to-end neural networks (NNs) heavily relies on the classification step of the networks, which necessitates the use of large and representative datasets. Additionally, conventional speech presence probability (SPP) estimation methods based on the ideal ratio mask (IRM) may misclassify time-frequency (T-F) bins dominated by non-speech and noise, which hinders the accurate extraction of directional information. To improve the robustness of existing DOA estimation algorithms, this paper proposes a DOA estimation method with T-F bin selection. In terms of output, instead of using IRM-based SPP, our proposed approach focuses on the a posteriori SPP, a deliberate choice aimed at circumventing potential confusion. For input optimization, we construct features that encompass spatial, temporal, and directional information concurrently, and these are coupled with a frequency bin-wise recurrent neural network (RNN) model to attain precise multi-channel SPP estimation. Subsequently, these SPP estimates are utilized to extract local information for DOA estimation. Moreover, the cascaded structure ensures that the model has the ability to complete out-of-label tasks, effectively reducing the dataset requirements by training only a subset of direction information to achieve omnidirectional DOA estimation. Besides, this contributes to the algorithm's ability to eliminate its reliance on the step size, setting it apart from other end-to-end methods. Simulation results validate that the proposed method achieves higher accuracy and lower error compared to both NN-based end-to-end approaches and traditional full-band approaches under various conditions of reverberation and signal-to-noise ratio. [ABSTRACT FROM AUTHOR]

In this paper, we devise an effective algorithm for direction-of-arrival (DOA) estimation using the cross-dipole array via one-bit quantization. Firstly, we theoretically prove that after one-bit quantization, two circular complex Gaussian random processes with different variance still satisfy the arcsine law and calculate the normalized covariance matrix of the cross-dipole array output based on this property. Subsequently, we reconstruct the noise-free normalized covariance matrix by subtracting the normalized noise matrix. Finally, we apply the TLS-ESPRIT algorithm to the reconstructed noise-free normalized covariance matrix to extract the DOAs of sources. Numerical results demonstrate the validity of the proposed algorithm. [ABSTRACT FROM AUTHOR]

This paper deals with the fault detection observer (FDO) design for discrete-time switched singular systems (SSSs) under persistent dwell time (PDT) switching. Compared to the widely used dwell time and average dwell time switching in the literature, PDT switching is more general due to its covering such two switchings as special cases. The PDT switching is firstly employed to establish the admissibility criterion and the H ∞ and H - observer synthesis conditions for SSSs. Also, an efficient H - / H ∞ FDO design algorithm is proposed. First, the admissibility analysis of the observing error system is addressed by incorporating the PDT technique into the multiple Lyapunov function method, and an admissibility criterion in the form of linear matrix inequality is established. Based on this, two FDO synthesis conditions are then developed to guarantee that the generated residual signal achieves prescribed H ∞ and H - performance with regard to disturbances and faults, respectively. The FDO should be designed such that the effects of faults and disturbances on the residual signal are maximized and minimized, respectively. To this end, the FDO design is expressed as a multi-objective optimization problem, and the FDO gains are characterized in terms of the solution of the multi-objective optimization problem. Moreover, a suitable trade-off between the robustness to disturbances and the sensitivity to faults is obtained based on a corresponding proposed algorithm. Finally, one illustrative example is given to show the validity of the developed method. [ABSTRACT FROM AUTHOR]

This paper presents a novel approach for automated dysarthria detection and severity assessment using a variable short-time Fourier transform layered convolutional neural networks (CNN) model. Dysarthria is a speech disorder characterized by difficulties in articulation, resulting in unclear speech. The model is evaluated on two datasets, TORGO and UA-Speech, consisting of individuals with dysarthria and healthy controls. Various variations of the CNN's first layer, including spectrogram, log spectrogram, and pre-emphasis filtering (PEF) with and without learnables, are investigated. Notably, the PEF with 5 learnables achieves the highest accuracy in detecting dysarthria and assessing its severity. The study highlights the significance of dataset size, with UA-Speech dataset showing superior performance due to its larger size, enabling better capture of dysarthria severity variations. This research contributes to the advancement of objective dysarthria assessment, aiding in early diagnosis and personalized treatment for individuals with speech disorders. [ABSTRACT FROM AUTHOR]

State estimation is an essential issue in modern industry, which is used in a wide range of fields. Measurements corrupted by outliers have become an inevitable phenomenon leading to a degradation in the performance of estimators. To mitigate the negative impact of measurement outliers, the development of estimators with outlier suppression capabilities is crucial. In this paper, we propose a simple yet effective outlier suppression technique for discrete-time linear systems in the framework of moving horizon estimation (MHE) combined with the event-triggered mechanism. This is a novel attempt to integrate event-triggered outlier detection and correction mechanisms with the MHE approach. Specifically, we propose two event-based outlier detection methods that can effectively identify measurement outliers. Subsequently, two outlier correction techniques are designed according to the different detection schemes. Lastly, two MHE algorithms are proposed based on the corrected measurements. The proposed algorithms are applied to a target tracking simulation and compared with existing advanced outlier-robust estimators, demonstrating the effectiveness and superiority of the proposed algorithms. [ABSTRACT FROM AUTHOR]

This paper investigates the finite-time and fixed-time bipartite consensus problem of multiple Euler–Lagrange systems (MELSs) under directed interaction topologies, considering external disturbances. Two novel distributed hierarchical control algorithms are developed to address the challenging problems. Specifically, a new distributed estimator is designed to estimate the states of agents in finite/fixed-time. Moreover, based on the obtained estimations, the finite/fixed-time controller is presented to accomplish the bipartite consensus of the MELSs in the local control layer. By utilizing Lyapunov stability and the nearest neighbor-interactions theory, sufficient conditions for achieving the designed performance of the MELSs are derived. Simulation examples are provided to illustrate the effectiveness of the proposed algorithms. [ABSTRACT FROM AUTHOR]

In this paper, a radio frequency energy harvesting system with a wide dynamic range rectifier is presented. This rectifier has two feedback and feedforward structures. These paths keep the rectifier's power conversion efficiency (PCE) high at different input powers and thus create a high dynamic range (DR). This rectifier also has better sensitivity. Advances in the rectifier contribute to more satisfactory results for the final system. The circuit is simulated with 180 nm TSMC CMOS technology at a frequency of 900 MHz. Also, a π-type input impedance matching network circuit is used. This circuit is matched at Pin = − 19.5 dBm and f = 900 MHz. In addition, an off-chip balun is utilized to convert the received single-ended signal to the differential one. The achieved PCE and DR are 86.03% and 9.76 dB, respectively, with a sensitivity of − 19.32 at 1 V output voltage. Furthermore, the overall circuit results indicate a PCE of 76.13%, a DR of 6.3 dB, and a sensitivity of − 18.75 dBm. [ABSTRACT FROM AUTHOR]

In this paper, fixed-time stability (FxTS) of time-varying (TV) hybrid systems with time-delay (TD) is investigated. Starting with TV nonlinear systems with TD, some sufficient conditions are given to verify the FxTS of systems by using TV Lyapunov–Krasovskii functional and integral technique. Then, the obtained results are extended to the case of hybrid systems, several FxTS conditions are provided by using the multiple TV Lyapunov–Krasovskii functional and the function-dependent minimum dwell time technique. Comparing with the existing results, the obtained results can provide the more accurate estimation of the convergence time of systems, and can be applied to a wider class of systems. Finally, two numerical examples are given to illustrate the theoretical results. [ABSTRACT FROM AUTHOR]

High leakage power consumption has become one of the main concerns of data security protection with CMOS device scaling. Spintronic technology is one of the efficient solutions to control circuit leakage power consumption by benefiting from its non-volatility property. Spintronic devices such as magnetic tunnel junctions (MTJs) are also compatible with CMOS transistors and suitable for designing hybrid MTJ/CMOS circuits. Hybrid MTJ/CMOS circuits can be used in many applications, such as logic-in-memory (LiM) and high-security structures. These circuits demonstrate enhanced data security compared to CMOS-based circuits by leveraging their non-volatile feature, making information leakage and data access more challenging. However, these circuits are not completely immune to attacks, and information can be revealed by potent power analysis attacks such as differential power analysis (DPA) and correlation power analysis (CPA). This paper investigates the resistance to side-channel attacks, especially DPA and CPA, for the magnetoresistive RAM (MRAM) and hybrid MTJ/CMOS AND, OR, XOR gates, hybrid MTJ/CMOS full-adder, and multiplexer circuits. Comprehensive post-layout simulation results using 40 nm TSMC CMOS technology, Monte-Carlo results, and values of normalized energy deviation (NED), normalized standard deviation (NSD)), and normalized power deviation (NPD) of PCSA-based MRAM indicate that the power consumption pattern remains constant in circuits with symmetrical structures in the reading phase even in the presence of inevitable process variation, so these circuits are resilient to power attacks and are competent for use in high-security applications such as hardware implementation of encryption algorithms. [ABSTRACT FROM AUTHOR]

This paper investigates the stability of complex-valued neural networks (CVNNs) with semi-Markovian jump (sMJ) and generally uncertain transition rates. Each transition rate may be totally unknown or its estimate is determined. Firstly, two improved reciprocally convex inequalities (RCIs) and three less conservative integral inequalities are generalized to the complex-valued domain. Secondly, the existence and uniqueness of the addressed networks are proposed by the complex-valued homeomorphism theorem. Thirdly, by constructing a Lyapunov–Krasovskii functional (LKF), delay-dependent robust stability criteria of the CVNNs are obtained by utilizing the improved complex-valued RCIs and integral inequalities. Finally, simulations are presented to demonstrate the effectivenes and practicality of the established method. [ABSTRACT FROM AUTHOR]

This paper considers the finite-time H ∞ dynamic output feedback control for a class of one-sided Lipschitz nonlinear rectangular descriptor Markov jump systems (DMJSs). The differential matrix E ∈ R m × n is not subject to any constraints, i.e., it includes two cases of m ≥ n and m ≤ n . For making the closed-loop system as square DMJSs, the rectangular dynamic output feedback controller is proposed. Firstly, the sufficient conditions are given to guarantee that the augmented closed-loop systems are singular stochastic H ∞ finite-time bounded (SS H ∞ FTB) and have a unique solution simultaneously by adopting a mode-dependent Lyapunov functional and implicit function theorem. Then a novel and rigorous strict linear matrix inequality (LMI) sufficient condition for the existence of a rectangular dynamic output feedback controller is given based on the certain matrix decoupling techniques, and the controller is obtained. Ultimately, numerical examples are provided in order to substantiate the soundness of the results. [ABSTRACT FROM AUTHOR]

In this paper, design of infinite impulse response lowpass differentiators that can be realized by parallel connection of two allpass filters whose orders differ by two, where one of the allpass branches is pure delay, is considered. As adopted structure of proposed lowpass differentiators allows formulation of their magnitude and phase responses as functions of allpass filter phase response, a set of nonlinear equations in unknown allpass filter coefficients is derived and iteratively solved in a way that magnitude response approximates the ideal one in weighted Chebyshev sense, in both passband and stopband. On the other hand, passband phase response linearity is shown to be related to the maximum of the magnitude response, which can be directly controlled by an additional design parameter. Design examples reveal that proposed infinite impulse response lowpass differentiators of low order can have very low relative passband magnitude errors and nearly linear phases, while results of comparison with existing lowpass differentiators show that proposed differentiators usually require fewer multiplications. [ABSTRACT FROM AUTHOR]

A novel source enumeration method based on criterion of searching for the best-matched preset sparse dictionary is presented in this paper, which is applicable under the condition of both white and colored noise. In this method, different source numbers are mapped with different sparse dictionaries innovatively in advance, which are constructed by utilizing the K-SVD algorithm and the training data under the same SNR. Then, in the source enumeration stage, all the preset dictionaries are employed to sparsely encode the observed signal through the orthogonal matching pursuit (OMP) algorithm, and the best-matched one which corresponds to the correct source number leads to the least information loss after sparse encoding. Due to the difficulty of directly evaluating the information loss through each error signal which is the difference between the observed signal and the sparse reconstructed signal, the energy norm for the singular values, which is calculated after performing singular value decomposition (SVD) on error signal, is proposed to reflect the information loss indirectly. Simultaneously, the source number corresponding to the best-matched preset sparse dictionary is the final estimated result. The experimental results show the superiority of our proposed method compared to other advanced methods. [ABSTRACT FROM AUTHOR]

Speech presence probability (SPP) and gain functions such as Wiener filter or MMSE estimators require an estimate of the a-priori signal-to-noise ratio (SNR). However, the estimation of the a-priori SNR is computationally involved and sensitive to noise variations. This paper proposes to approximate the SPP and the overall gain function of a speech enhancement system by using sigmoid functions to reduce the need of estimating the a-prior SNR. By applying an approximation via the sigmoid functions it is shown that only the a-posteriori estimate of SNR is needed, resulting in a low complexity system. The sigmoid function is designed with an optimization algorithm to optimize its parameters with respect to speech quality measures. The optimization algorithm is based on the idea that the solution obtained for a given problem should move towards the best solution and avoid the worst solution. The proposed algorithm requires minimal control parameters and does not require any algorithm specific parameters. Simulation results show that the proposed sigmoid functions achieve good results in terms of speech quality measures when compared with existing methods while providing significantly lower complexity for implementation. [ABSTRACT FROM AUTHOR]