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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]

The measurement matrices play a crucial role in compressed sensing, directly impacting the performance of signal sampling and reconstruction. As one of the primary construction methods for measurement matrices, designing structured measurement matrices is a challenging problem. In practical sampling, the measurement matrices often have strong coherence. Therefore, it is significant to design structured measurement matrices with superior reconstruction performance at low sampling, although the coherence is strong. In this paper, by introducing a special Gram matrix and merging it with the deterministic Fourier matrix, we construct a kind of measurement matrices with superior signal recovery performance under strong coherence. Furthermore, utilizing Katz' character sum estimation allows us to establish an upper bound on the coherence of the constructed matrices. All experimental results demonstrate that the performance of the proposed matrices outperform that of Fourier matrices and Gaussian random matrices. Consequently, the proposed matrices hold significant application in sparse signal processing. [ABSTRACT FROM AUTHOR]

Emotional speech has some dependency on language or within a language itself, there are certain variations due to accents. The presence of accents degrades the performance of the speech emotion recognition (SER) system. A pre-trained accent identification system (AID) could effectively capture the characteristics of accent variations in emotional speech which is an important factor to develop a more reliable SER system. In this work, we investigate the dependencies between accent identification and emotion recognition to enhance the performance of SER. This paper proposes a novel transfer learning-based approach utilizing accent identification knowledge for SER. In the proposed method, the deep neural network (DNN) is used to model the accent identification system, which uses statistical aggregation functions (mean, std, median, etc.,) of spectral subband centroid (SSC) features and Mel-frequency discrete wavelet coefficients (MFDWC). To build the SER, the deep convolutional recurrent autoencoder produces the attention-based latent representation, and the acoustic features are extracted by the openSMILE toolkit. A separate DNN model is used to learn the mapping between attention features and acoustic features for SER. In addition, the a priori knowledge of accent can lead the SER to effect the improvement which is possible through transfer learning (TL). The performance of the proposed method is assessed using the accented emotional speech utterances of the Crema-D dataset and also compared with state-of-the-art techniques. The experimental results show that transferring AID learning improves the recognition rate of the SER and results in around 8% relative improvement in accuracy as compared to the existing SER techniques. [ABSTRACT FROM AUTHOR]

This paper discusses the event-triggered set-membership state estimation problem for discrete delayed linear systems over sensor networks, where noises are unknown but bounded. For the purpose of saving limited communication resources between sensor nodes, a novel adaptive dynamic event-triggered mechanism is applied to reduce the frequency of data transmission. Based on the topological structure of sensor networks and the event-triggered condition, a distributed set-membership state estimator is designed to ensure that the states and estimation errors are restricted within the corresponding zonotopes. The appropriate gain matrices of the estimator are selected to minimize the F-radius of the zonotopes, where the augmented estimation error is located at every instant. In order to avoid the huge computational load caused during the estimation iteration process, the reduction operator is introduced to find a compromise between the conservative of the zonotopic estimation results and the complexity of operation. Finally, a numerical example is offered to demonstrate the effectiveness of the proposed event-triggered zonotopic set-membership state estimation algorithm. [ABSTRACT FROM AUTHOR]

This paper is aiming at the fault estimation issue within finite time domain for a type of discrete-time stochastic nonlinear systems. The considered sensor network is affected by randomly occurring sensor saturation and missing measurements. A sensor model is established based on Bernoulli distributions of two known probabilities, which can describe the two types of random incomplete information in a unified framework. Additionally, in an effort to alleviate communication load and maintain data security, the torus-event-based strategy is employed to schedule output data in network control systems. On this basis, a suitable estimator is developed, and sufficient conditions for a fault estimator with reduced conservatism are presented. These conditions guarantee that the dynamic error of the designed estimator meets predetermined performance requirements, and the corresponding fault estimator gains are designed accordingly. Finally, simulation examples are used to testify that the considered estimation algorithm can maintain good estimation performance under torus-event-based mechanisms in complex networks. [ABSTRACT FROM AUTHOR]

In this paper, a newfangled technique for edge detection that combines the Khalil conformable derivative and the Hessian matrix is developed and experimentally validated. The following main aspects are considered: (i) to attenuate image noise a Gaussian kernel inspired by the Khalil conformable derivative was developed. (ii) The spatial derivatives of the image gray level are calculated via the Khalil derivative, which is suitable for maintaining object contours and texture information even in low-contrast and resolution images. (iii) The conformable Hessian matrix is obtained from these image derivatives, generating continuous, thicker, and brighter edges. Our operator is compared with some existing techniques. Simulation results on different test images confirm a greater robustness to noise by our operator as well as a better visual quality of the edge maps obtained. This statement is validated through a comparative analysis based on peak signal-to-noise ratio and edge-strength-similarity-based image quality. In addition, it is applied in medical image processing and analysis tasks such as mammogram images, computed tomography scans, and magnetic resonance imaging for identification tasks of middle cerebral artery aneurysms, calcifications and breast cancer, proliferative Diabetic Retinopathy, and cerebral arteriovenous malformations. According to our findings, the conformable operator allows better visual identification of the structure of these conditions, improving the accuracy of clinical diagnosis and its subsequent monitoring. [ABSTRACT FROM AUTHOR]

This paper considers the deep-learning-aided compressed video sensing problem. To this end, a deep generative adversarial network has been proposed to provide an approximation of the non-reference frame using its corresponding reference frame. The tests confirm the superiority of this scheme over the conventional methods used earlier. Furthermore, two scenarios have been suggested for deep compressed video sensing and recovery. In the first scenario, the difference between the non-reference frame and its approximation obtained from the pre-trained network is compressively sampled and transmitted to the receiver where the proposed residual reconstruction network is adopted to reconstruct the signal. The second scenario utilizes a pre-trained network followed by an augmented layer to approximate the non-reference frames. In the transmitter, the parameters of the augmented layer are trained for the current non-reference block. Instead of transmitting the samples of the block, the parameters of its trained augmented layer are sent to the receiver where the reconstruction is done using the same pre-trained network. The performances of the proposed scenarios demonstrate their objective and subjective superiority over the state-of-the-art algorithms in both the reconstruction quality and run time. [ABSTRACT FROM AUTHOR]

In this paper, we present a novel approach for the reconstruction of sparse graph signals using a distributed adaptive thresholding recursive least squares algorithm. Our proposed scheme leverages signal measurements across multiple time steps to estimate the underlying graph signal. To achieve this, we define a cost function that combines a weighted least squares term and an L 0 norm. The cost function is then minimized using a surrogate minimization scheme and the recursive least squares technique. The resulting algorithm is an adaptive thresholding recursive least squares scheme that effectively recovers sparse graph signals. Additionally, we apply a consensus-based method to enable distributed recovery, and derive recursive updating relations to speed up the algorithm. Our approach offers a significant novelty in the field of graph signal recovery, providing an efficient and accurate solution for reconstructing sparse graph signals. The numerical simulations show the superiority of the proposed algorithm over the other state-of-the-art algorithms from the point of view of the convergence rate and the mean square deviation (MSD) criterion. [ABSTRACT FROM AUTHOR]

In this paper, two floating memcapacitor emulator circuits, one based on a second-generation current conveyor (CCII) and the other on an operational transconductance amplifier (OTA), are proposed. The first floating memcapacitor contains a single CCII, one multiplier, and four transistors, while the second one comprises a single OTA, one multiplier, and only two transistors. External transistors are operated as electronically controllable grounded capacitors and resistors. Therefore, the inverse memcapacitances of the circuits are adjusted electronically by applying appropriate bias voltages. Both emulator circuits can be operated to have incremental or decremental characteristics. The analyses of frequency response, electronic adjustability, temperature behavior, and non-volatile behavior of the circuits, as well as Monte Carlo analysis, are performed. Furthermore, an adaptive learning circuit is utilized to demonstrate the applicability of the proposed memcapacitor. Additionally, the CCII-based emulator circuit is constructed on a printed circuit board using discrete circuit elements and tested experimentally. The non-volatility feature of the circuit was tested for both incremental and decremental memcapacitors. The simulation and experimental results agree with the expected results. [ABSTRACT FROM AUTHOR]

This paper proposes an Interpolated Discrete-Time Fourier Transform (LIpDTFT) estimator for complex-valued noisy sinewave frequency based on the Linearization of the DTFT module behavior around the spectrum peak. It belongs to a class of LIpDTFT estimators that compensate the poor Discrete Fourier Transform (DFT) frequency resolution by interpolating two DTFT samples located very close to an initial frequency estimate. As compared with other DTFT-based frequency estimators recently proposed in the literature, the proposed LIpDTFT estimator ensures a smaller interpolation error and a lower processing effort. Moreover, if the rectangular window is adopted, it almost attains the unbiased Cramér-Rao Lower Bound (CRLB) even if a very small number of samples is analyzed. The accuracies ensured in different operating conditions by the proposed frequency estimator and other state-of-the-art DTFT-based algorithms are compared to each other through computer simulations. [ABSTRACT FROM AUTHOR]

Constrained least mean square (CLMS) algorithm is the most popular constrained adaptive filtering algorithm due to its simple structure and easy implementation. However, its convergence slows down when the input signal is colored. To address this issue, this paper firstly introduces the normalized subband adaptive filter (NSAF) into the constrained filtering problem and derives a constrained NSAF (CNSAF) algorithm using the Lagrange multiplier method. Benefiting from the good decorrelation capability of the NSAF, the proposed CNSAF algorithm significantly improves the convergence performance of the CLMS algorithm under colored inputs. Then, the mean and mean-square stability of the CNSAF algorithm is analyzed, and the theoretical models to characterize the transient and steady-state mean square deviation (MSD) behaviors of the CNSAF algorithm are derived utilizing the Kronecker product property and vectorization method. Further to extend the CNSAF algorithm to the problem of sparse system identification, a sparse version of the CNSAF algorithm (S-CNSAF) is derived. Finally, the validity of the derived theoretical MSD prediction models and the superiority of the proposed algorithms are confirmed by extensive computer simulations on system identification with colored inputs. [ABSTRACT FROM AUTHOR]

Pseudo-random number generators are predominantly utilised as entropy sources in encryption and seeding processes, however, they are deterministic by nature. This paper presents the analysis and design methodology of an on-chip quantum noise source that may suitably be used as an entropy source in true random number generators. Quantum tunnelling is achieved through the strong biasing of a MOS structure to generate gate-referred shot noise. Structures are fabricated in a commercial 40 nm process and the sampled noise is tested against the NIST SP800-90B and SP800-22 test suites, showing a maximal entropy of 0.985 at a bias current of 1500 µA with a consistent pass rate across all NIST tests. [ABSTRACT FROM AUTHOR]

This paper proposes a novel one-shot voice conversion (VC) method called DS-ESR-StyleGAN-VC, which encompasses several innovations to address the challenges faced by StarGAN-VC. Firstly, we adopt ESR network in the generator to extract deep features, effectively solving the problem of semantic content corruption in StarGAN-VC. Secondly, we leverage the advantages of the dense weighted normalized shortcut employed by DSNet, which circumvents the performance degradation and gradient disappearance caused by the increasing convolutional layers. The DSNet network is integrated into the middle of the encoder and decoder of generator to further extract the spliced features, further enhancing VC quality. Thirdly, we remove the classifier module in StarGAN-VC and use a style encoder to extract speaker style features in order to improve speaker similarity. Moreover, our proposed method can naturally support one-shot VC. Experiments show that our proposed method consistently outperforms the competitive StarGAN-VC in terms of semantic content completeness, naturalness and speaker similarity under the many-to-many setting; our proposed method is superior to the competitive StarGAN-ZSVC in terms of naturalness under the one-shot setting. [ABSTRACT FROM AUTHOR]

Frequency estimation of sinusoid is an important research topic in the field of statistical signal processing. Efficient frequency estimation is of great significance for applications with high real-time requirements. In this paper, two efficient frequency estimators of complex sinusoid by using autocorrelation and discrete time Fourier transform (DTFT) are presented in additive white noise background. Firstly, autocorrelation algorithm is adopted to obtain the coarse frequency estimate. Then, three DTFT samples with arbitrary intervals located near the coarse estimate are used to get the fine frequency estimate. In order to evaluate the calculation complexity and estimation accuracy, the proposed estimators are compared with the competing algorithms. The computational complexity of the presented estimators is much lower than that of the competitive estimators. Simulation results show that the mean square errors of the presented estimators are close to the Cramer–Rao lower bound when the signal-to-noise ratio varies in a large range. [ABSTRACT FROM AUTHOR]

This paper introduces a highly efficient and low-power inverse class-F voltage doubler (VD) designed for radio frequency (RF) energy harvesting systems. Specifically tailored for mid-band 5G technology, the VD is designed for operation within the 1240–1300 MHz satellite band. The innovative design employs an inverse class-F architecture, incorporating a λ / 8 short-ended transmission line (TL) connected to the diode anode and a ( λ / 12 ) open-ended transmission line linked to the input of the voltage doubler. This configuration aims to reshape voltage and current waveforms, effectively reducing losses and series resistance in the diode. Dual-coupled transmission lines (CTLs) are utilized to provide passive voltage boosting at low-input power levels. The suggested voltage doubler is implemented using RO4003C substrate material with a dielectric relative permittivity ( ε r ) of 3.38 and a thickness of 0.81 mm. Measured results demonstrate a minimum input return loss of − 29.3 dB at 1.25 GHz, operating seamlessly within a frequency band from 1.18 to 1.32 GHz. The measured conversion efficiency is 45.2% at an input power (Pin) of − 4dBm. Furthermore, the peak RF–DC efficiency reaches 50% at an input power of 0dBm. Simulated results predict a remarkable conversion efficiency of 60% and 68.7% at − 4dBm and 0 dBm, respectively. In addition to its exceptional performance, the suggested voltage doubler exhibits an experimental DC output voltage of 0.53 V at P in = - 10 dBm and a saturated DC voltage of 3.4 V at an input power of 10dBm under a load terminal resistance of 8 KΩ. Finally, the dimensions of the proposed voltage doubler are 25.3 × 10.5 mm 2 . [ABSTRACT FROM AUTHOR]

In this paper, we propose a hybrid network structure to achieve more discriminant feature representations for speaker recognition, termed Resformer, local frame-level features are extracted by convolution operation, and global segment-level features are extracted by transformer, which the information learned from the frame-level features is used to guide the segment-level feature learning, to take advantage of convolutional operations and self-attention mechanisms for enhanced representation learning. Resformer uses an interactive approach to combine local features and global representations at varying resolutions. With a concurrent structure, Resformer ensures that both local features and global representations are preserved to the fullest extent possible. Extensive experiments are performed on VoxCeleb, a public SV dataset, and the findings indicate that Resformer outperforms existing deep embedding architectures by a margin of at least 10–37% on the test set. Our proposed approaches are shown to achieve significant improvement over prior methods through the ablation experiments. [ABSTRACT FROM AUTHOR]

Ternary logic has received substantial attention over binary logic due to diminished delay, reduced power consumption, and minimized area requirements. Carbon Nanotube Field Effect Transistors (CNTFETs) are mostly preferred for the implementation of ternary logic because of their ability to achieve multi-threshold transistors. This paper presents an energy-efficient CNTFET-based 3-trit Wallace tree multiplier design using a novel 4-input ternary adder (called 4-ITA), full-adder, and half-adder. We have proposed half-adder, full-adder, and 1-trit multipliers using a new 3:1 multiplexer module (called MUX-2) that significantly enhances energy efficiency within our ternary multiplier design. This multiplexer, leveraging high and standard threshold voltage devices, greatly optimizes our ternary multiplier's power consumption and computational speed. Furthermore, we reduced power dissipation by minimizing switching transitions within the signal generation circuits of both MUX-2 and the arithmetic modules. All the arithmetic designs were designed using the 45 nm CNTFET technology. Results prove that the 3-trit multiplier achieved 40% lesser power consumption and a 51% power delay product improvement compared to the best literature design. [ABSTRACT FROM AUTHOR]

In recent years, vector-to-vector mapping-based raw waveform biosignal enhancement methods have gained significant attention in remote health monitoring system. In this paper, a novel end-to-end convolutional encoder–decoder (CED) model with stacked gated recurrent unit (SGRU) is proposed to learn sequential information of biosignal for signal enhancement. The proposed model CED-SGRU employs convolutional neural network to capture the spatial features and SGRU to capture temporal distributions of the biosignal layer by layer which increases the robustness of the proposed model. This work applies mean absolute error as a loss function for CED-SGRU-based vector-to-vector model. The proposed method has been evaluated on three foremost required biosignals, namely electrocardiogram, photoplethysmography and heart rate signals for diagnosing cardiovascular diseases. Experimental result shows outstanding denoising capability of the proposed CED-SGRU model on three biosignals which yields significantly higher reconstruction signal-to-noise ratio value and lower average absolute error, root mean square error and percent root mean square difference values when compared with state-of-the-art-methods. Moreover, the simple architecture of SGRU lowers the complexity of the model; thereby, reducing the inference time for denoising and restoring compressed biosignal tasks is fairly compared with baseline models, namely recurrent neural network model and long short-term memory model. [ABSTRACT FROM AUTHOR]

The real-time and accurate recognition of premature ventricular contractions (PVC) in dynamic 12-lead ECGs poses a clinical challenge due to noise and variability. The accurate location of the QRS complex is crucial for efficient PVC heartbeat recognition. This study proposes a robust PVC recognition approach, combining a self-adaptive multi-detector fusion model for R-peak detection and a multi-parameter squeeze–excitation ResNet-based heartbeat classifier. The detection results of multiple detectors are weighted with coefficients, and decision fusion is performed through adaptive threshold comparison. Tested on the INCART arrhythmia and 2018 China Physiological Signal Challenge databases, the R-peak detection results exhibit that our proposed fusion model outperforms majority, mean, and median voting strategies, with sensitivity improvements of 0.33%, 0.78%, and 0.41% for INCART dataset and 0.28%, 0.61%, and 0.34% for 2018 Physiological Signal dataset. In addition, our model is also superior to the best single annotator used in this paper. Evaluation of the multi-parameter SE-ResNet classifier reveals increased F1 scores of PVC heartbeat recognition by 4.33%, 3.44%, 5.04%, 12.41%, and 1.56% using INCART dataset compared to CNN, Inception, MLP, AlexNet, and LSTM, respectively, and 2.51%, 1.8%, 2.32%, 12.54%, and 2.27% using 2018 Physiological Signal dataset. Finally, the Se and Sp metrics also show improvement on the two datasets using the SE-ResNet. [ABSTRACT FROM AUTHOR]

The secure transmission of medical images and related digital patient records on untrusted channels has recently become a focus in healthcare industries. Data hiding and encryption are important tools for this goal. This paper proposes a data-hiding method for medical images in the context of invisibility, robustness, security and low time cost. A dual watermarking is introduced to accomplish chaos-based encryption to ensure medical images' copyright protection and content security. First, a local binary pattern based on neighbouring pixels is used to compute an optimal value, called an embedding factor, for embedding both marks. Second, the host medical image is marked using the lifting wavelet transform, the lower–upper (LU) decomposition and singular value decomposition with an embedding factor to protect ownership. Last, the marked image is encrypted by using a 3D-chaotic map. The method is tested on two standard datasets, which is convenient for medical applications. Our experimental results and performance analysis demonstrate that the proposed scheme produces a peak signal-to-noise ratio (PSNR) and NC wat 1 / NC wat 2 of 54.82 dB and 0.9916/0.9928, respectively. Furthermore, the key space analysis of our encryption technique is greater than 2 100 , which provides a high protection degree against brute-force attacks. The proposed scheme achieves better robustness, imperceptibility, and security performance than previous schemes. Our method can reduce time costs by 98% (91% on average), which results in lower time costs than other conventional encryption techniques. [ABSTRACT FROM AUTHOR]

Based on the criterion of minimum error entropy, this paper proposes a novel conjugate gradient algorithm, called MEE-CG. This algorithm has robust performance under non-Gaussian interference. Theoretical analysis and experimental results demonstrate that the proposed algorithm displays more robust performance than the conventional conjugate gradient methods on the basis of the mean square error and the maximum correntropy criterion. Compared with the stochastic gradient minimum error entropy algorithm and the recursive minimum error entropy algorithm, the proposed algorithm provides a trade-off between computational complexity and convergence speed. [ABSTRACT FROM AUTHOR]

One of the most relevant tasks of high-dimensional estimation is the computation of the parameters covariance matrix. This matrix offers valuable insights into the uncertainties associated with the estimation process. In the context of adaptive filtering, the weight error covariance matrix is a key parameter that determines how the filter adapts to input statistics and noise levels. Additional statistical information about the weight error vector allows one to depict a more precise description of the stochastic coupling between the adaptive weights. This paper concentrates on an in-depth study of the least mean squares asymptotic weight error covariance matrix, employing the exact expectation analysis. Through this examination, a key conclusion emerges: The symmetries commonly engendered by traditional analyses are not present in the actual covariance matrix. Instead, these symmetries are artifacts of the widespread assumption of independence. In summary, the advanced analysis performed in this work reveals a significantly more nuanced learning behavior exhibited by the least mean squares algorithm, challenging the conventional understanding put forth by traditional approaches. The theoretical predictions are confirmed by extensive simulations. [ABSTRACT FROM AUTHOR]

The digital revolution places great emphasis on digital media watermarking due to the increased vulnerability of multimedia content to unauthorized alterations. Recently, in the digital boom in the technology of hiding data, research has been tending to perform watermarking with numerous architectures of deep learning, which has explored a variety of problems since its inception. Several watermarking approaches based on deep learning have been proposed, and they have proven their efficiency compared to traditional methods. This paper summarizes recent developments in conventional and deep learning image and video watermarking techniques. It shows that although there are many conventional techniques focused on video watermarking, there are yet to be any deep learning models focusing on this area; however, for image watermarking, different deep learning-based techniques where efficiency in invisibility and robustness depends on the used network architecture are observed. This study has been concluded by discussing possible research directions in deep learning-based video watermarking. [ABSTRACT FROM AUTHOR]

This paper presents a high-resolution 18-bit SAR ADC with a high 10-bit capacitor DAC and a low 8-bit resistor DAC. The total required number of the unit capacitors is decreased to 512. Foreground digital calibration based on capacitive recombination is introduced to improve linearity. Preamplifiers and output offset storage(OOS) enhance the noise and offset performance of the comparator. As a result, the design under 180 nm process achieves a signal-to-noise and distortion ratio(SNDR) of 105.5dB and a spurious-free dynamic range (SFDR) of 116.3dB under 1 MS/s sampling rate with a single channel. The effective number of bits (ENOB) can reach 17.23 bits with a Nyquist-rate input while consuming 46 mW from a 5 V supply. The resultant Schreier and Walden figures of merit (FoM) are 178.92 dB and 295.34 fJ/conversion-step, respectively. The proposed SAR ADC occupies an actual area of 3850 μ m by 2810 μ m. [ABSTRACT FROM AUTHOR]

Super-resolution is an ill-posed inverse problem. It needs a self-similarity regularization term in its formulation to solve the issue. Researchers have studied self-similarity in natural images thoroughly and extended the results to medical data. This paper investigates the self-similarity property using a general affine model and the SSIM, PSNR, matrix rank, and mutual information measures. We study variations of self-similarity with image modality, inter/intra-resolution, tissue types, and acquisition phase. Based on our experiments, super-resolution algorithms that use self-similarity property achieve better results in CT images than MR data. Some organs, including the liver, have a stronger self-similarity compared to the brain and lung. The extension of our study prepares a comprehensive guide map to employ self-similarity properties in medical image processing algorithms. [ABSTRACT FROM AUTHOR]

Gaussian filter (GF) is a commonly used linear filter in signal and image noise reduction. However, its limitation is that it cannot adapt parameters to deal with non-stationary noise that varies over time. To address this problem and improve the filtering effectiveness of GF in the face of non-stationary non-Gaussian (NSNG) noise, this paper proposes a new approach called adaptive Gaussian filter based on improved complete ensemble empirical mode decomposition (ICEEMDAN-AGF). The ICEEMDAN-AGF firstly uses the fusion information of the dispersion entropy (DE) and the power spectral entropy (PSE) to divide the intrinsic mode functions (IMFs) into two groups. One group is called guiding IMFs, which contains the high-frequency components of the NSNG noise, and the other group is called hybrid IMFs, which contains the low-frequency components of the NSNG noise and all the noise-free signals. Next, a method called multi-resolution local similarity (MRLS) is proposed to identify the mixed modes presented in the guiding IMFs. Then, the variance of the guiding IMFs is used to adjust the window width w and kernel parameter σ of GF. Finally, the adaptive Gaussian filter (AGF) obtained above is used to filter the hybrid IMFs. The experiments shows that ICEEMDAN-AGF performs better than other conventional algorithms on known signals. [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]

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]

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]

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 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]

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]

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 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 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]

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]

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]

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 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]

Gabor phase retrieval is the problem of recovering a signal from the absolute values of Gabor transform, which has turned into a very active research area. In the paper, we define a generalized Paley–Wiener space, which is obtained by applying the special affine Fourier transform to L p ([ - B , B ]) (2 ≤ p ≤ ∞) and provide the uniqueness of the Gabor phase retrieval in the generalized Paley–Wiener space. When the window function ψ is the inverse special affine Fourier transform of ϕ = 2 1 4 e - π x 2 , we show that every signal in the generalized Paley–Wiener space can be uniquely recovered, up to a global sign, from its the magnitudes of Gabor measurement sampled on a rectangular lattice. Recently, Grohs, Liehr and Wellershoff studied the Gabor phase retrieval problems in the conventional Paley–Wiener space. The paper extends their results to the generalized Paley–Wiener space. Since the generalized Paley–Wiener space includes a broader class of signals, our results expand the applied range of the Gabor phase retrieval. [ABSTRACT FROM AUTHOR]

This research paper presents a circuit-based phase noise model of a cross-coupled LC tank oscillator. The effect of the Oscillator's noise is one of the most critical items in designing modern RF communication systems. The noise factor necessitates a more thorough comprehension of design tradeoffs in RF VCO IC implementations. This research paper introduces a new analytical derivation to produce a well-bounded third-order polynomial equation accurately representing the LC oscillator's phase noise behaviour. The third-order polynomial equation includes circuit parameters such as capacitance, inductance and quality factors to understand the phase noise characteristics. The proposed method determines the amplitude and phase noise and forms a link with the circuit operating point, and phase noise is produced as a direct result of the circuit parameters. The advantage of the proposed resulting equation is that it avoids the need for complex simulations to identify the noise characteristics in such a process. The proposed equation was validated by comparing the tank circuit simulation results. The simulation results verify the applicability of the third-order polynomial equation representing the noise characteristics of the LC oscillator. [ABSTRACT FROM AUTHOR]