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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 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]

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]

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 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]

This paper proposes an algorithm to obtain the sampling factors to model any frequency partitioning so that it is realized using low complexity rational decimated non-uniform filter banks (RDNUFBs). The proposed algorithm is employed to approximate the Bark scale to find a rational frequency partitioning that can be realized using RDNUFBs with less approximation error. The proposed Bark frequency partitioning is found to reduce the average deviation in centre frequency and bandwidth by 65.04% and 48.50%, respectively, and root-mean-square bandwidth deviation by 54.46% when compared to those of the existing perceptual wavelet approximation of Bark scale. In the first filter bank design approach discussed in the paper, a near-perfect reconstruction partially cosine modulated filter bank is employed to obtain the improved Bark frequency partitioning. In the second design approach, the hardware complexity of the PCM-based RDNUFB is reduced by deriving the channels with different sampling factors from the same prototype filter by the method of channel merging. There is a considerable reduction of 40.83% in the number of multipliers when merging of partially cosine modulated channels is employed. It is also found that both the proposed approaches can reduce the delay by 95.17% when compared to the existing rational tree approximation methods and hence, are suitable for real-time applications. [ABSTRACT FROM AUTHOR]

This paper studies global exponential stability (GES) of discrete-time (D-T) BAM neural networks (BAMNNs) affected by impulses and time-varying delays. An impulse-free D-T BAMNN with time-varying delays is first developed via the properties of M-matrices, and then the relation between the solutions of the original and new BAMNNs is established. From which, sufficient conditions for GES of the original BAMNN are derived by investigating GES conditions of the new BAMNN based on the counter-evidence and the nature of the nonsingular M-matrix. The results of illustrative examples show that the obtained GES criteria are effective. By comparing with previous results, this paper has the following three merits: (a) the obtained GES criteria are to check the positivity of eigenvalues of a constant matrix, which is easy to realize; (b) this method can be used for other D-T system models affected by impulses and time-varying delays; and (c) the numerical results show that our method is less conservative than ones in other literatures. [ABSTRACT FROM AUTHOR]

This paper presents a detailed design of a broadband power amplifier (PA) utilizing 0.18-μm CMOS technology, operating in the frequency range of 2–5 GHz. This ultra-wideband (UWB) power amplifier uses a cascode topology with current reuse to enhance gain over the desired frequency band, utilizing an inductive source degenerate design to achieve the best linearity and output power and gain while maintaining a wide bandwidth. The inductive source degenerate adopts bondwire and microstrip line design. The paper includes extensive analysis, simulation results, and measurements to verify the performance of the amplifier and demonstrate its suitability for broadband power amplifier applications. The measured results for the proposed UWB power amplifier show excellent performance, with an impressive third-order input intercept point (IIP3) ranging from 15 to 18.7 dBm over the frequency range of 2–5 GHz. The output third-order intercept point (OIP3) ranges from 33 to 35 dBm. Additionally, the gain flatness is specified as 12.5 ± 1.5 dB. The reverse isolation is greater than or equal to − 40 dBm. The PA has a P1dB output power (OP1dB) of 10.2 dBm at 3 GHz, 9.2 dBm at 4 GHz, and 9.2 dBm at 5 GHz, respectively. The PA has a P1dB power-added efficiency of 30.5% at 3 GHz, 23% at 4 GHz, and 22% at 5 GHz with a 50 Ω load termination. The power dissipation is 33.3 mW at a supply voltage of 1.8 V. In addition, the PA has an excellent small group delay variation of ± 65 ps. The chip area is 1.01 × 1.08 mm2, including the pads. The proposed UWB PA demonstrates exceptional linearity, efficiency, and group delay in the 2–5 GHz frequency band. [ABSTRACT FROM AUTHOR]

This paper investigates the sliding mode control (SMC) problem of discrete-time singular Markovian jump systems with time-varying delay under an event-triggered strategy (ETS). In the system we study, there are uncertainties in the system matrix, especially in the difference matrix. A state augmentation strategy is proposed due to uncertainties in the difference matrix. A suitable sliding mode surface (SMS) function is built, and the corresponding sliding mode dynamics (SMD) is derived based on ETS. Some sufficient conditions are presented to guarantee that SMD is H ∞ -admissible for all uncertainties. Furthermore, an SMC law under ETS is derived so that the system trajectories can be driven onto the prescribed SMS in finite time. Finally, the effectiveness of the approach in the paper is demonstrated by two simulated examples. [ABSTRACT FROM AUTHOR]

Under the condition of low signal-to-noise ratio, for the problem of insufficient speech feature extraction and speech enhancement effect of the traditional neural network, this paper is based on empirical mode decomposition (EMD), temporal convolutional network (TCN), and gated convolution recurrent neural network (GCRN), while combining with feature fusion module (FFM), the adaptive mean median-empirical mode decomposition-multilayer gated feature fusion module convolutional recurrent neural networks (ME-MGFCRNs) for speech enhancement modeling. The network model uses a split-frequency learning strategy to learn low-frequency features and high-frequency features, i.e., the TCN and MGFCRN networks are used to obtain low-frequency and high-frequency features, and FFM processes the two sets of features to achieve speech enhancement in the form of feature mapping. The model proposed in this paper performs ablation and comparison experiments on the dataset to evaluate the enhancement effect of speech using PESQ, FwSegSNR, and STOI metrics. The research shows that under different noise environments and SNR conditions, the model proposed in this paper improves compared with other baseline models, especially under the low SNR condition of − 5 dB, FwSegSNR and PESQ improve by more than 0.86 dB and 0.02 compared with other baseline models. [ABSTRACT FROM AUTHOR]

This paper studies the consensus problem of Multi-Agent Systems (MASs) with input-delay using the Model Predictive Control (MPC) approach. Due to challenges such as input delay and communication graph, the traditional MPC is not efficient for the considered systems. In this regard, a novel MPC is developed for MASs with input delay. The main advantage of this paper is to design distributed controllers based on minimizing given cost functions which result in the improvement of the transient responses. For this purpose, first, distributed observers are derived to estimate the leader's states, perfectly. Then, distributed controllers are achieved through the MPC approach. Finally, numerical and practical examples are simulated to affirm the efficiency and applicability of the presented scheme. [ABSTRACT FROM AUTHOR]

While the practical application of adaptive filters has indeed garnered substantial attention, two pressing issues persist that have a profound impact on their performance—system sparsity and the presence of contaminated Gaussian impulsive noise. In this research paper, we propose a novel approach to tackle both of these issues simultaneously by introducing the concept of a proportionate matrix. Specifically, we present a proportionate maximum total complex correntropy algorithm based on the errors-in-variables model. The paper presents a theoretical analysis of the steady-state weight error power under the influence of impulsive noise. Furthermore, it discusses the performance comparison in system identification and highlights the robustness of the proposed algorithm. To validate its effectiveness, a simulation involving stereophonic acoustic echo cancellation is conducted, and the results confirm the clear advantages of the proposed Proportionate Maximum Total Complex Correntropy algorithm. [ABSTRACT FROM AUTHOR]

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 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 this paper, we generalize the conditions for the exact or stable recovery of weighted k-sparse signals in weighted sparse phase retrieval in our previous work [11] from the weighted ℓ 1 minimization to the weighted ℓ q (0 < q ≤ 1) minimization in a broad sense. Specifically, we first present that the weighted null space property (WNSP) is a sufficient and necessary condition to guarantee the exact recovery of a weighted k-sparse signal from its noiseless phaseless measurements via the weighted ℓ q (0 < q ≤ 1) minimization in both the real and complex cases. In addition, we establish a general strong weighted restricted isometry property (SWRIP) condition for the stable recovery of a weighted k-sparse signal from its noisy phaseless measurements via the weighted ℓ q (0 < q ≤ 1) minimization in the real case. [ABSTRACT FROM AUTHOR]

Model order reduction technique provides an effective way to reduce computational complexity in large-scale circuit simulations. This paper proposes a new model order reduction method for delay circuit systems based on Hermite expansion technique. The presented method consists of three steps i.e., first the delay elements are approximated using the recursive relation of Hermite polynomials, then in the second step, the reduced order is estimated for the delay circuit system using a delay truncation in the Hermite domain and in the third step, a multi-order Arnoldi process is computed for obtaining the projection matrix. In the following, the reduced order delay circuit model is obtained by the projection matrix. Moment matching and passivity properties of the reduced circuit system are also analyzed. Two circuit examples with delay components are performed to verify the effectiveness of the proposed MOR approach. [ABSTRACT FROM AUTHOR]

In this paper, we propose a novel rearranged coprime array (RCA) configuration for direction-of-arrival (DOA) estimation, which achieves more degrees-of-freedom (DOF) than the existing RCA with the same number of sensors. It is known that RCA is formed by placing additional sensors to fill up the holes in the difference coarray of the thinned coprime array (TCA). Here, we propose a generalized rearranged coprime array (GRCA) configuration, which is obtained by strategically placing extra sensors to fill up the holes in the difference coarray of the generalized thinned coprime array (GTCA). As the GTCA achieves more DOF than the TCA by judiciously choosing its parameters, the proposed GRCA also attains a considerably large number of DOF than the original RCA. The required simulations are carried out to demonstrate the effectiveness and superiority of the proposed GRCA by comparing it with the existing coprime arrays. [ABSTRACT FROM AUTHOR]

In this paper, we develop a new algorithm for pattern synthesis of large linear arrays. In our algorithm, the large linear array is divided into multiple disjoint nonuniformly-spaced subarrays and is only weighted at subarray level. Unlike the existing methods, the subarray synthesis is implemented in two stages. Firstly, the optimization of subarray partition and subarray weights are performed by using the K-means approach. Based on the determined subarray partition and subarray weights, the subarray element positions are further optimized by the iterative convex programming method. Compared with the state-of-the-art methods, the proposed algorithm can synthesize lower peak sidelobe level and yield smaller pattern matching error. Numerical results demonstrate the effectiveness of the proposed algorithm. [ABSTRACT FROM AUTHOR]

This paper introduces and analyzes three innovative Linear Prediction (LP) models, consisting of two Fractional Order (FO) LP models and an integer LP model. The closed-form expressions for the proposed methods-namely, Two Parameter FO Linear Prediction (TP-FLP), FO Convex Linear Prediction (FCLP), and integer Convex Linear Prediction (CLP)-are derived for derivative order 0 ≤ α ≤ 2 . These derivations contribute to a more profound understanding of underlying mathematical principles of the proposed predictor. The foundation of our proposed methodologies is based on the presentation of the current signal sample as a linear combination of the FO derivative of two past samples, coupled with the utilization of the convex combination technique. An extensive set of experiments is conducted to compare the effectiveness of the proposed models with baseline methods involving two traditional LP models: First- and Second-order LP, along with the One Parameter FO Linear Prediction (OP-FLP) model. The performance evaluation encompasses a diverse range of signal types, including sinusoidal waves, damped sine waves, electroencephalogram (EEG), and speech signals. The simulation results demonstrate significant advancements achieved by the proposed models. Notably, the proposed FCLP exhibits a remarkable prediction gain of 24.74 dB, outperforming baseline methods such as OP-FLP, First- and Second-order LP models, that achieve prediction gains of 23.17, 23.15, and 10.14 dB, respectively. [ABSTRACT FROM AUTHOR]

This paper studies the problem of designing event-triggered state observers for a class of nonlinear time-delay fractional-order systems. The time delay in the system is assumed to be unknown and non-differentiable but bounded within a closed interval. A novel discrete-time event-triggered fractional-order observer is first designed to provide an estimation vector for the state vector of the considered systems. It is ensured that the estimation vector robustly estimates the state vector of the nonlinear time-delay fractional-order systems. Then, a delay-dependent sufficient condition in terms of a convex optimization problem for the existence of the observer is established based on some lemmas relating to the Caputo derivative estimation of quadratic functions. Finally, three numerical examples demonstrate the validity of the proposed approach. [ABSTRACT FROM AUTHOR]

For the output error (OE) models whose outputs are contaminated by colored process noises (i.e., correlated noises), this paper derives a new form of bias compensation recursive least squares (BCRLS) algorithm by means of the data filtering technology and the bias compensation principle. The basic idea is to firstly transform the OE model disturbed by colored process noise into a simple OE model with the white noise by adopting the data filtering technology at each recursive calculation, and then to calculate the bias compensation term, based on the new OE model with the bias-compensation technique. Finally, eliminate this bias term in the biased RLS parameter estimation of the OE model to be identified, thereby achieving its unbiased parameter estimation. Unlike the previous BCRLS algorithm, this algorithm can still achieve unbiased parameter estimation of OE systems in the presence of colored process noise without calculating complex noise correlation functions. The performance of the proposed algorithm is demonstrated through three digital simulation examples. [ABSTRACT FROM AUTHOR]

The performance of adaptive beamforming will deteriorate severely under small sample support, especially when the number of snapshots is smaller than the number of sensors. In this paper, we propose an effective algorithm for robust adaptive beamforming under small sample. Firstly, we utilize standard Guassian random matrices to construct projection matrices for dimension reduction of sample covariance matrix (SCM) and steering vector (SV). Subsequently, the dimensionality-reduced SCM and SV are used to obtain more accurate Capon power spectrum in the case of small sample. By integrating the corresponding Capon power spectrum over the angular sector without desired signal, the interference-plus-noise covariance matrix (INCM) is then reconstructed. Moreover, the SV of desired signal is estimated by solving a quadratic programming problem. Finally, the weight vector of the beamformer is calculated based on the reconstructed INCM and the estimated SV. Simulation results demonstrate the effectiveness and robustness of the proposed algorithm. [ABSTRACT FROM AUTHOR]

Machine learning-based direction-of-arrival (DOA) estimation methods can have a good predictive ability even in complex scenarios. However, their estimation performance in the face of unknown data is poor because they rely on the generalization ability of the algorithm itself. Therefore, this paper proposes a DOA estimation method based on the Light Gradient Boosting Machine (LightGBM) algorithm. Using the histogram algorithm, gradient-based one-sided sampling and exclusive feature bundling measures, the LightGBM algorithm can reduce the time to find the best segmentation point, reduce the amount of data and the number of features in the dataset, thus reducing the model training time, and achieve high prediction accuracy. Applying the LightGBM algorithm to the DOA estimation problem and using a large dataset for training can improve the estimation accuracy while reducing the training cost. Simulation and real experimental results verify the effectiveness of the method. [ABSTRACT FROM AUTHOR]

The design of a Ternary Logic Processor using CNTFETs (Carbon-Nanotube-Field-Effect-Transistor) is a challenging task, but it also has the potential to offer significant advantages over the traditional binary logic processors based on CMOS (Complementary-Metal-Oxide-Semiconductor) technology. This paper presents the design and implementation of a Ternary Logic Processor (TLP) using CNTFETs. The TLP is a single-cycle processor that operates on three-trit data. An Instruction Set Architecture (ISA) is defined, at first, for this TLP that consists of instructions of the Register type, Load-store type, Immediate type, and branch type. Based on the ISA, the architecture of the CNTFET-based TLP is proposed and the transistor level designs of the TLPs' fundamental blocks like the Ternary Instruction Fetch (TIF), Ternary Register File (TRF), Ternary Arithmetic and Logic Unit (TALU) and Ternary Data Memory (TDM) are presented. HSPICE simulations using a standard CNTFET model, are performed for the TLP and the TLPs' individual blocks and the performance parameters like the power consumption, propagation delay, and the number of CNTFETs required are calculated. In addition to this, the functionality of the processor is verified using a few of the standard programs. [ABSTRACT FROM AUTHOR]

The growing concern over high-profile violence has led to a widespread adoption of intelligent smart video surveillance systems in educational institutions. However, accurately identifying abnormal events in the video stream remains a complex task due to the absence of a definitive generic definition for abnormal actions, heavily reliant on contextual factors. In this paper, we propose a fog-assisted abnormal motion recognition system utilizing an ontology-based semantic algorithm to bolster security within university campuses under real-time surveillance. Our ontology-based semantic analysis is meticulously designed to characterize intricate spatio-temporal interactions and contextual relationships in the video scene, thereby significantly improving the sensitivity in distinguishing abnormal events based on their severity level. To ensure swift responses to specific abnormal events, the video streams captured through surveillance cameras are processed at the edge of the network. Prior to semantic analysis, two crucial steps—foreground object segmentation and object tracking—are executed to streamline the detection process. These steps involve segmenting the target foreground using a connected components labeling algorithm and tracking motion patterns based on velocity, direction, and acceleration, employing the Kalman filter. The rule-based reasoning of the ontology accurately defines abnormal conditions in the video scene, providing a clearer understanding of the decision-making process. Furthermore, we incorporate a context-aware refinement step aimed at enhancing detection accuracy. This step distinguishes abnormal events based on their severity and generates corresponding alerts. We conducted extensive experiments and evaluated the proposed system using various measures, demonstrating its potential in real-time video analysis. The results showcase an impressive prediction success ratio of 0.989, affirming the reliability and robustness of our system in detecting abnormal events. [ABSTRACT FROM AUTHOR]

One of the major study areas in VLSI system design is the creation of an area-efficient, high-speed adder. So this paper focused on an effective adder design as an Area Effective Novel Sum Generator (AENSG) to make it suitable for real-time applications. The major operations involved in this adder are Brent Kung addition, group kill and generate (KG) signal generation, Kill Generate Propagate (KGP) signal generation, and various sum generation categories. In the sum-generating block, AENSG is utilized to minimize the space by a reduction in the number of logic gates. Xilinx ISE 13.1 is used to synthesize and validate the suggested adder design. The 64-bit AENSG adder has 37.98%, 9.09% lesser number of logic gates and 34.81%, 28.40% lesser delay when compared with PPF/CSSA_4 and hybrid adder respectively. [ABSTRACT FROM AUTHOR]

This paper presents a novel multiple-input single-output current-mode shadow filter and oscillator using current-controlled current conveyors (CCCIIs). The CCCII current gains are used to set the filter's quality factor and natural frequency. The filter is resistorless with grounded capacitors, making it more suitable for integration. The filter offers low-pass, high-pass, band-pass, band-stop and all-pass transfer functions in a single topology by appropriately applying the input signals. To validate the functionality of the new topology, the proposed circuit is simulated by SPICE using bipolar transistors arrays AT&T ALA400-CBIC-R. [ABSTRACT FROM AUTHOR]

In this paper, a new non-uniform differential sample and hold circuit is proposed using low-distortion sampling switches for biomedical signal processing applications. The proposed design can be used in the biomedical low-frequency range with low-power consumption which makes the proposed design a good candidate for bio-signal sampling purposes. The body biasing technique is employed to reduce the distortion of the sampling switches. Moreover, to reduce the required sampled-data storage capacity, the signal slope rate detection is used to predict the input signal frequency and variations in order to adjust the proper sampling rate of the proposed non-uniform sampling circuit. To realize the proposed design, a capacitive digital-to-analog converter is used to sample the slope changes of signals at two sampling frequencies of 1000 and 100 Hz. The circuit simulation results using 0.18 μm CMOS technology parameters at 1 V supply voltage, indicate the saving in power consumption value up to 57.5% in comparison with other conventional designs. Additionally, the output signal of the proposed circuit can be reconstructed with a percentage root-mean-square difference factor of as low as 2.1%, a mean square error of 0.0025, and a signal-to-noise ratio value of 79.34 dB. [ABSTRACT FROM AUTHOR]

Recent advancements in deep learning-based speech enhancement models have extensively used attention mechanisms to achieve state-of-the-art methods by demonstrating their effectiveness. This paper proposes a novel time-frequency attention (TFA) for speech enhancement that includes a multi-scale subconvolutional U-Net (MSCUNet). The TFA extracts valuable channels, frequencies, and time information from the feature sets and improves speech intelligibility and quality. Channel attention is first performed in TFA to learn weights representing the channels' importance in the input feature set, followed by frequency and time attention mechanisms that are performed simultaneously, using learned weights, to capture both frequency and time attention. Additionally, a U-Net based multi-scale subconvolutional encoder-decoder model used different kernel sizes to extract local and contextual features from the noisy speech. The MSCUNet uses a feature calibration block acting as a gating network to control the information flow among the layers. This enables the scaled features to be weighted in order to retain speech and suppress the noise. Additionally, central layers are employed to exploit the interdependency among the past, current, and future frames to improve predictions. The experimental results show that the proposed TFAMSCUNet mode outperforms several state-of-the-art methods. [ABSTRACT FROM AUTHOR]

This article focuses on the fixed-time stability of a class of nonlinear systems with uncertain disturbances and time-varying delays. Different from other papers, the second-order sliding mode control algorithm (SOSMCA) is developed in this article to study the fixed-time stability of nonlinear systems for the first time. Additionally, the novel sliding mode surface (SMC) and second-order SMC are presented. And the stability and reachability of the sliding-mode dynamics under a faster settling time are demonstrated. Finally, the applicability and validity of the obtained SOSMCA are demonstrated by a simulation example with the F-404 aircraft engine model. [ABSTRACT FROM AUTHOR]

Traditional thresholding methods are widely used to extract objects of interest from image backgrounds in various practical applications. However, these methods often face challenges in complex scenes due to poor uniformity, noise, and low contrast. To overcome these limitations, this paper proposes a peak-weaken Otsu method (PWOTSU) that improves the segmentation performance of the Otsu method for automatically extracting objects in complex scenes. The proposed approach uses a set of cross parameters as weights for the Otsu criterion function to adaptively weaken the between-class variance at the peak of the histogram. This ensures that an appropriate threshold value is always obtained for images with different types of histogram distribution. The improved criterion function has the advantage of obtaining a more accurate threshold value without the need for additional parameters, making it easily applicable to various practical applications. Experimental results demonstrate that the proposed method effectively improves the segmentation accuracy and robustness compared to the standard Otsu method and its modifications, as evidenced by qualitative and quantitative evaluations. [ABSTRACT FROM AUTHOR]

Balancing imperceptibility, data payload and robustness is a crucial and unsolved issue in the area of digital watermarking. It has been found that the method based on statistical modeling can alleviate the contradiction among them. On the basis of this, we suggest a novel image watermark detector based on Weibull Mixtures-HMT model in this paper. The Otsu-Canny edge detection method is used to select the high entropy blocks in the target subband obtained by non-subsampled shearlet transform (NSST), and fast polar complex exponential transform (FPCET) is computed on the target blocks to obtain the NSST–FPCET magnitude domain. The digital watermark is inserted into the NSST–FPCET magnitude domain by a multiplicative method. In the statistical modeling step, the NSST–FPCET magnitudes are modeled by Weibull Mixtures-HMT model, where Weibull Mixture model describes the distribution characteristic of the NSST–FPCET magnitudes while the HMT model describes the dependency between the NSST–FPCET magnitudes. Parameters of the proposed model are estimated by an efficient variance reduced stochastic expectation maximization method. Using the locally most powerful test, we finally develop an image watermark detector based on Weibull Mixtures-HMT model. Massive experiments demonstrate that the designed scheme has an impressive performance in detecting the presence of the watermark, and it can achieve a better balance among the data payload, imperceptibility and robustness. [ABSTRACT FROM AUTHOR]

In this paper, a novel sliding mode preview control (SMPC) problem with H ∞ performance is investigated for a category of discrete-time singular Markovian jump systems (SMJSs). A novel augmented error system (AES) model is first developed for discrete-time SMJSs based on the analysis of preview information, and the problem of SMPC is reformulated as the stability problem of AES. Secondly, a novel mode-independent sliding surface function is established for AES such that the reachability of sliding mode surfaces (SMS) can always be achievable. Thirdly, sufficient conditions of the H ∞ admissible stability for sliding mode dynamics is derived, based on which a suitable SMPC law is designed to satisfy discrete-time reachability condition. Finally, simulation results have shown that the proposed SMPC law is superior to the control law without previewable information. [ABSTRACT FROM AUTHOR]

How to guarantee the confidentiality of sensitive data communicated over the Internet and restrict access to designated information is today's key security and protection concern in telemedicine. In this work, we suggest a reliable and blind medical image watermarking method that combines integer wavelet transform (IWT) and singular value decomposition to keep such information private. A major drawback of current IWT-based watermarking systems is their low embedding capacity. This paper suggests an IWT-based secure large capacity watermarking solution to overcome this specific drawback. The proposed technique effectively preserves a considerable quality of watermarked images, and the watermark is resistant to the most frequently used attacks in watermarking, according to experiment results on imperceptibility and robustness. [ABSTRACT FROM AUTHOR]

In this paper, the non-fragile distributed fault detection (FD) problem is discussed for delayed Markov jump systems over sensor networks under the weighted try-once-discard protocol, where the randomly switching nonlinearities and the randomly occurring faults are considered. Specifically, a non-fragile distributed FD filter is constructed, and the compact residual dynamic system is obtained in terms of augmentation technique. Subsequently, by applying the Lyapunov stability theory and Wirtinger inequality, sufficient conditions are derived to ensure that the nominal system corresponding to residual dynamic system is stochastically stable and stochastically strictly (Q 1 , Q 2 , Q 3) - γ dissipative. On this basis, the explicit expressions of FD filter matrices are obtained by means of the linear matrix inequality method. At last, a simulation is provided to demonstrate the effectiveness of the proposed distributed FD scheme. [ABSTRACT FROM AUTHOR]

SRAMs, the go-to choice for system-on-chips nowadays, suffer from high power consumption and unstable behavior in nanoscale CMOS technology. Consequently, we observe the emergence of troublesome short-channel effects (SCEs), necessitating the utilization of innovative nano-devices. Combining its attractive features, including reduced SCEs and leakage, the fin-shaped field-effect transistor (FinFET) shines as a skillful replacement for CMOS, skillfully tackling those injurious problems. This paper presents a near-threshold single-bitline 9T (SB9T) SRAM cell, with a primary focus on enhancing stability and energy efficiency. The SB9T SRAM cell integrates a read-assist scheme and a feedback-cutting technique to enhance read stability and writability. By conducting simulations utilizing a 7-nm FinFET technology and operating at a voltage of 0.5 V, the SB9T SRAM cell demonstrates remarkable performance advantages when compared to existing designs. Specifically, in comparison to the conventional 6T cell, the SB9T SRAM cell achieves an improvement of 1.86 × in read stability and 2.41 × in writability. Additionally, the SB9T SRAM cell minimizes power dissipation from leakage by at least 8.73% and enhances write energy efficiency by a minimum of 4.84%. It should be noted that although the SB9T SRAM cell consumes 1.03 × more read energy than the conventional 8T cell, it still surpasses alternative designs by reducing read energy consumption by at least 4.86%. [ABSTRACT FROM AUTHOR]

This paper investigates the exponential mixed H ∞ / L 2 - L ∞ state estimation for Markov switching memristive neural networks (MNNs) with time-varying delays. First, in addition to Markov switching parameters, the random variables that obeys Bernoulli distribution are also involved in the MNN model, and thus the considered system model is much more general. Second, the solution in Filippov's sense is used to transform the Markov switching MNNs into a stochastic system with interval parameters. Third, the reciprocally convex combination technique and an appropriate Lyapunov-Krasovskii functional containing multiple integrals are used to derive less conservative conditions ensuring the existence of the desired state estimators. Finally, a numerical example is provided to show the validity of the proposed method. [ABSTRACT FROM AUTHOR]

Epilepsy is a persistent health condition marked by unusual and highly synchronized electrical activity in the brain cells, resulting in recurring seizures. This paper proposes a novel real-time method to improve the detection of seizures using the spectral features of non-stationary electroencephalogram (EEG) signals. It is observed that the discrete wavelet transform (DWT)-based features do not consider the interrelationship among EEG signal components. This interrelationship has been well captured by the novel representation of EEG in the form of graph signals. Here, the spectral analysis of the graph signals is investigated by the graph-based Fourier transform (GFT). Then, GFT-based features have been selected and fed into different classifiers for analysis. The seizure detection rate in two publicly available EEG-based datasets, the University of Bonn (UB) and the Neurology Sleep Clinic New Delhi (NSC-ND), have been achieved with accuracy of 98.68% and 96.84%, respectively. The accuracy achieved is significantly better than the existing state-of-the-art techniques. This approach demonstrates the impact of utilizing the interrelationship among the EEG components, followed by enhanced feature selection based on GFT for the improved detection of seizures. [ABSTRACT FROM AUTHOR]

In order to lower the hardware requirements of digital systems and to reduce the amount of collected data, 1-Bit sampling technique has received much attention. Following the trend, in this paper we try to address a classic problem based on the 1-Bit sampling data—the problem of detection and estimation of periodic signals in White Gaussian Noise. To achieve the tasks, the 1-D convolutional neural networks (CNN) are used to recover the waveforms of the periodic signals from the 1-Bit measurements. Subsequently, the method of generalized likelihood ratio test (GLRT) is applied on the recovered waveforms to detect the periodic signals and to estimate their unknown parameters. The simulation results show that CNN can recover the waveforms of periodic signals with a reasonable accuracy, and the parameters of frequency, time delay, initial phase, and relative amplitude can be obtained. [ABSTRACT FROM AUTHOR]

The resistance distance r G (u , v) between two vertices u and v of a graph G is defined as the net effective resistance between them in the electric network constructed from G by replacing each edge with a unit resistor. The Kirchhoff index Kf(G) is defined as the sum of resistance distances between all pairs of vertices. Let L n m be a K n -chain network with m complete graphs. Then identifying the opposite lateral edges of L n m in an order way yields the K n -ring, denoted by C n m . In this paper, we first construct a new equivalent network transformation on complete graphs. Then utilize combinatorial and electrical network approaches, we give explicit formula for the resistance distances between any two vertices in L n m and C n m . Further, the closed-form formulas of the Kirchhoff index for L n m and C n m are also obtained. In addition, our results contain the main results of [Symmetry. 15(5) (2023) 1122] and [Phys. Scr. 98(4) (2023) 045222] as special cases. [ABSTRACT FROM AUTHOR]

In this paper, we propose a mixed-domain watermarking algorithm based on integer wavelet transform (IWT), discrete cosine transform (DCT) and multiple matrix decomposition. The combination of IWT–DCT avoids the rounding error of discrete wavelet transform (DWT) in the existing algorithm, so it has better performance than the combination of DWT–DCT. Singular value decomposition (SVD) based watermarking algorithm has a false positive problem (FPP), despite its strong robustness. To solve FPP, we propose a scheme based on two-stage encryption and multi-decomposition. This scheme also further improves the security of the algorithm. In addition, we double embed the watermark data into the host image, and introduce improved multi-objective whale optimization algorithm (MOWOA2) to optimize the embedding intensity of different frequency sub-bands. It not only avoids the problem of weight selection in the face of multiple fitness functions, but it strengthens the algorithm's resistance to various attacks. In comparison to other relevant algorithms, simulation findings demonstrate that the proposed approach has good security, invisibility, and robustness, particularly in resisting various clipping attacks and flipping assaults. [ABSTRACT FROM AUTHOR]

In passive localization, the time-difference-of-arrival (TDOA) measurement model is commonly used for source location estimation. Methods for TDOA-based estimation can be categorized into two main groups: closed-form algebraic solutions and iterative approaches. Algebraic solutions circumvent convergence issues and achieve global optima, but are usually sensitive to TDOA measurement inaccuracies. Iterative methods optimize the objective function through multiple iterations, including deterministic iterative methods that require an iterative initial value and stochastic optimization methods reliant on optimization algorithms. In this paper, a stochastic optimization algorithm named snake optimization (SO) is used to solve the TDOA localization problem and improved to meet the localization requirements. Initially, a chaotic system is utilized to generate three random sequences to establish the initial snake population. The search strategy in the exploration phase is subsequently improved to enhance the early-stage convergence speed of the algorithm. Moreover, an adaptive evolutionary stage threshold is introduced to adaptively handle various noise conditions and source locations in TDOA localization scenarios. Lastly, a snake oviposition strategy, inspired by genetic principles, is proposed. Simulations show that the improved SO algorithm can converge to the source position quickly and stably and has better positioning accuracy. [ABSTRACT FROM AUTHOR]

PGME-3/NGME-3 only consists of three MOSFETs and can be operated at 5 GHz. The minimum step time of the material implication circuit based on PGME-3 and NGME-3 is 70 ps. However, the negative/positive lobe of PGME-3/NMGE-3's i i - v i curve expands as the frequency of input v i increases. The tails of the PGME-3's negative lobe and NMGE-3's positive lobe get longer as the amplitude of v i gets bigger. This paper proposes a GHz-level memristor emulator consisting of one MOSFET and two diodes, i.e., PGME-1M2D and NGME-1M2D. The operating frequency of the proposed memristor emulator is high up to 10 GHz. The minimum step time of the material implication circuit based on PGME-1M2D and NGME-1M2D is 85 ps. The long tails of PGME-1M2D/NGME-1M2D's i i - v i curves do not appear when the amplitude of v i is 3 V. Additionally, the PGME-1M2D is verified with the discrete p-channel MOSFET of CD4007. [ABSTRACT FROM AUTHOR]

Deep learning has been applied to compressive sensing (CS) of images successfully in recent years. However, existing network-based methods are often trained as the black box, in which the lack of prior knowledge is often the bottleneck for further performance improvement. To overcome this drawback, this paper proposes a novel CS method using non-local prior which combines the interpretability of the traditional optimization methods with the speed of network-based methods, called NL-CS Net. We unroll each phase from iteration of the augmented Lagrangian method solving non-local and sparse regularized optimization problem by a network. NL-CS Net is composed of the up-sampling module and the recovery module. In the up-sampling module, we use learnable up-sampling matrix instead of a predefined one. In the recovery module, patch-wise non-local network is employed to capture long-range feature correspondences. Important parameters involved (e.g. sampling matrix, nonlinear transforms, shrinkage thresholds, step size, etc.) are learned end-to-end, rather than hand-crafted. Furthermore, to facilitate practical implementation, orthogonal and binary constraints on the sampling matrix are simultaneously adopted. Extensive experiments on natural images and magnetic resonance imaging demonstrate that the proposed method outperforms the state-of-the-art methods while maintaining great interpretability and speed. [ABSTRACT FROM AUTHOR]

This paper proposes a simple but effective method for constructing the state feedback controller to solve the exponential stabilization problem for a class of feedforward nonlinear systems subject to different time-delay states. The form of state feedback controller, which depends on a positive parameter to be determined, is firstly presented. And then, by considering the Lyapunov-Krasovskii functional with exponential term and taking the appropriate positive parameter, the state feedback controller is achieved to ensure the exponential stability of the closed-loop system. Finally, the proposed design scheme is utilized to the control design of resonant circuit system. [ABSTRACT FROM AUTHOR]