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

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

In this paper, an improved high-speed adaptive filter is proposed and implemented using a field-programmable gate array platform. Specifically, a new filter structure combining systolic and convex architectures has been analyzed and compared with conventional filter architectures. The new filter structure efficiently removes power line interference noise from electrocardiogram signals at high convergence speeds. The systolic architecture is used to improve the convergence speed of the filter, and the convex architecture is used in combination to improve the signal-to-noise ratio of the filter. One fast filter was designed using the retimed delay recursive least square algorithm (recursive least square filter design with systolic architecture), and it was combined with one slow filter designed using the least mean square algorithm based on convex combination architecture. The proposed filter architectures are assessed for electrocardiogram noise cancellation, obtained from the MIT-BIH database, and the performance is compared with various filter structures in terms of the signal-to-noise ratio, convergence speed, learning behaviors, and complexity. The results show an improvement in signal-to-noise ratio of 24.2% and an increase in convergence speed of 50% when compared with conventional filter structures. [ABSTRACT FROM AUTHOR]

In this paper, we propose a tensor-based subspace algorithm for the two-dimensional direction of arrival (2D-DOA) estimation using a sparse array equipped with electromagnetic vector sensors (EMVS). Our approach capitalizes on the multidimensional characteristics of the collected data by arranging its covariance into a fourth-order tensor. Through the application of higher-order singular value decomposition, we improve signal subspace estimation compared to existing methods. To further enhance our algorithm, we integrate spatial rotation invariance techniques and vector cross-product methods. This combination enables automatic angle estimation without the need for pairing and without compromising aperture loss. Our proposed algorithm exhibits superior estimation performance, particularly in challenging scenarios characterized by low signal-to-noise ratios and limited snapshot availability. To validate the effectiveness and enhancements of our approach, we conduct numerical simulation experiments. [ABSTRACT FROM AUTHOR]

To solve the problem that the least mean square (LMS) algorithm cannot balance the convergence rate and steady-state mean square error (MSE) well and require excessive manual adjustment of parameters, this paper proposes a parameter-free variable step-size LMS algorithm based on the hyperbolic tangent function. First, the algorithm uses the hyperbolic tangent function to establish a nonlinear relationship between the error and the step size. Based on this, the average value of the error signal is used to update the step size. Further, the accumulated error value is multiplied by its average value and added to the input signal energy. Then, the algorithm is normalized by using this value to prevent the algorithm divergence due to the sudden increase in the signal power. The convergence proof of the proposed algorithm is provided in this paper. The simulation results indicate that the proposed algorithm can automatically adjust the parameters. The convergence rate and the steady-state MSE are better than those of other algorithms for both high and low signal-to-noise ratios. [ABSTRACT FROM AUTHOR]

The instantaneous frequency (IF) provides important characteristic information for analyzing signals. To improve the accuracy of IF estimation, time–frequency (TF) analysis with high resolution and high aggregation is necessary. A new IF estimation method is proposed in this paper, which is referred to as ridge path regrouping based on an adaptive short-time Fourier high-order synchrosqueezing transform (AFSST). In the TF analysis of the non-stationary signal, we optimize the window function of the short-time Fourier transform to be adaptive to signal characteristics, and then perform the high-order synchrosqueezing transform. Based on the high-resolution time–frequency distribution (TFD), the frequency ridge of the signal is extracted, and the ridge of the modal aliasing at the intersection is discarded and reorganized according to the fundamentals. Experiments on multicomponent nonstationary signals were performed, and the results show that AFSST has satisfactory TF aggregation and TF resolution. IF estimation experiment based on AFSST, the results indicate that the proposed method can estimate the IF accurately even under a low signal-to-noise ratio (SNR). [ABSTRACT FROM AUTHOR]

In this paper, a novel algorithm is presented for estimating three-dimensional (3-D) parameters of near-field (NF) sources with a centro-symmetric cross array, where the sub-array x and the sub-array y are uniform linear arrays equipped with the same number of array elements. By exploiting the time-delay-based spatial correlation of the received signal, the proper array elements are selected, respectively, from the two sub-arrays to define two sets of variables, and then two time-delay-based vectors are constructed. Further, a series of delay lags are uniformly sampled to form pseudo snapshots so as to obtain two corresponding time delay matrices, which facilitates the formation of the parallel factor (PARAFAC) model in time domain. Finally, trilinear alternating least squares decomposition is utilized to jointly estimate the two-dimensional (2-D) direction-of arrival and range parameters of NF sources from the PARAFAC model. In the case of low signal-to-noise ratio and small snapshots conditions, the estimation performance in the 3-D parameters of the proposed algorithm is superior to that of the subspace-based algorithm. In addition, the proposed algorithm only involves simple parameter pairing, which requires no eigenvalue decomposition and spectral peak searching. [ABSTRACT FROM AUTHOR]

Blind image deblurring is a challenging problem, which aims to estimate the blur kernel and recover the clear image from the given blurry image. A large number of image priors have been proposed to tackle this problem. Inspired by the fact that the blurring operation increases the ratio of dark channel to local maximum gradient, a weighted dark channel (WDC) prior is presented in this paper for blind image deblurring. It is shown that the WDC is more discriminative than the dark channel. The model is constructed by applying L1 norm to the WDC term and incorporating it into the traditional deblurring framework. The alternating optimization strategy is adopted together with the half-quadratic splitting method and the fast iterative shrinkage-thresholding algorithm (FISTA) to deal with the presented model, and the maximum-minimum filter is used to improve computational efficiency. Extensive experiments are conducted on the frequently used synthetic datasets and real images, and peak signal to noise ratios (PSNR), error ratio, structural similarity (SSIM) and so on are adopted to appraise our method and some other latest methods. Qualitative and quantitative results show that our method outperforms the state-of-the-art methods. [ABSTRACT FROM AUTHOR]

A joint empirical mode decomposition and optimal frequency band estimation approach is proposed to reduce the low-frequency noise adaptively. In each level of the decomposition, this paper considers the residue as the approximation of the low-frequency noise while the sum of the intrinsic mode functions is considered as the approximation of the denoised signal. Conventionally, the signal-to-noise ratio (SNR) of the denoised signal is used as a criterion for determining the optimal level of the decomposition. In particular, the level of the decomposition which achieves the highest SNR is defined as the optimal level of the decomposition. However, as the clean signal is unknown, it is difficult to find the optimal level of decomposition. To address this issue, the cutoff frequency of the residue in each level of the decomposition is estimated via an optimization problem. Here, the total energy of the difference between the energy spectrum of the residue and that of its corresponding matched filter is minimized subject to a constraint on the energy of the matched filter being equal to that of the residue. After finding the solution of the optimization problem for each level of the decomposition, the index corresponding to the global minimum of the absolute difference of the optimal cutoff frequency between two consecutive levels of the decomposition is defined as the optimal level of the decomposition. Since the optimal level of the decomposition is found automatically, our proposed method avoids the inappropriate selection of the parameter. Besides, the computer numerical simulation results show the superior performance yielded by our proposed method over the existing approaches on various test signals at different noise levels. [ABSTRACT FROM AUTHOR]

In this paper, we investigate the optimal joint time-vertex graph filter design, which aims to denoise time-varying graph signals. We first propose to design the optimal joint time-vertex ordinary Fourier (O-JTV-OF) graph filter. Specifically, we formulate the O-JTV-OF graph filter design problem based on the Cartesian product and further obtain the closed-form expression of the O-JTV-OF graph filter coefficients by solving the Wiener–Hopf equation on the time-vertex graph. Then, we extend the O-JTV-OF graph filter design to the optimal joint time-vertex fractional Fourier (O-JTV-FF) graph filter design by leveraging the graph fractional shift operator and the graph fractional Fourier transform, which can provide a more suitable space to separate the signal and noise. Our numerical results on three real-world datasets show that the proposed O-JTV-OF and O-JTV-FF graph filter design methods lead to higher signal-to-noise ratio (SNR) results than the traditional optimal static ordinary Fourier (OSOF) and the optimal static fractional Fourier (OSFF) graph filter design methods. Moreover, the proposed O-JTV-FF graph filter design method performs better than the proposed O-JTV-OF graph filter design method in terms of SNR of reconstructed signals. Compared to a linear filter, i.e., the joint Tikhonov regularization-based method, our methods also provide better performance overall, especially performing significantly better in a low-input SNR. [ABSTRACT FROM AUTHOR]

Noncircular (NC) signals constitute a significant class of signals that are frequently employed in communication systems. In the presence of extremely impulsive noises, determining the direction of these signals can present significant challenges. When the noise tail becomes heavier or the signal-to-noise ratio decreases, existing methods yield poor results. Recently, the authors have developed an empirical characteristic function (ECF)-based MUSIC algorithm. This method is called ECF-MUSIC. Despite its robustness and high precision, it only works with circular signals. This is a direct result of the covariance matrix of the ECF-MUSIC method containing a nonzero relation matrix. In this paper, a novel ECF-based MUSIC is developed to exploit the ECF in DOA estimation of NC signals. Using the CF of the output of the extended array, a new covariance-like matrix is introduced. Then, an analysis of eigen-decomposition is performed to develop a new MUSIC algorithm. Due to the unique properties of ECF, such as its ability to preserve information, this method yields precise results, particularly in the presence of heavy tail noise. Additionally, it doubles the maximum number of detectable sources. The Monte Carlo simulations demonstrate the excellent performance of the method, NC-ECF-MUSIC. This method outperforms existing methods in terms of estimation error and resolution, as demonstrated by these results. [ABSTRACT FROM AUTHOR]

In an uncontrolled scenario like hospitals, medical research centres, laboratories, forensics, the voice recordings are normally affected with babble noise. Most of the speech enhancement (SE) methods are limited by the colour noise due to non-consideration of phase aspect. Hence, the objective of the work is to depict a more efficient and compact SE method through the comparative study of conventional SE methods taking into account of both continuous speech and sustained vowels. This approach is supported with supervised voice activity detection (VAD). The study includes an experimental investigation of time and transform-based seven conventional methods. This approach consists of pre-processing, SE evaluation block and supervised VAD section. The speech segments are efficiently detected by VAD using bio-inspired Mel frequency cepstral coefficients and nonlinear features with support vector machine. This supervised VAD helps in reliable estimation of SNR-based parameters. The performance of each of the methods is evaluated with continuous NOIZEUS corpus at varying signal-to-noise ratio (SNR) levels, laryngeal pathology-based data from Saarbruecken voice database and voice disorder data collected from cancer hospitals. Experimental results show that the performance of the Wiener filtering and geometrical-based SE methods is found to be better comparatively, as far as continuous and sustained vowels are concerned with babble noise. The paper also reveals the significant involvement of nonlinear features in VAD, as the nonlinear dynamical methods are capable of analysing irregular behaviours of vocal cords. [ABSTRACT FROM AUTHOR]

Finite rate of innovation (FRI) schemes have been proposed to reconstruct a class of discrete-time signals having small number of nonzero coefficients (sparse signals) from a limited number of observations. However, these reconstruction schemes achieve optimal performance up to a certain signal-to-noise ratio (SNR) and breakdown for smaller SNR values. Moreover, these are not universal as they are aware of the number of nonzero coefficients (a.k.a. L0 norm) for reconstruction of the signal. In this paper, we propose a novel FRI reconstruction scheme based on error decrease detector criterion to extend the current scheme to a universal one which enables reconstructing signals with an unknown number of nonzero coefficients. With noiseless conditions, we show that the proposed FRI scheme achieves perfect reconstruction of the original signal. And also, computer simulations for the noisy case are presented where the proposed scheme shows improvements over the traditional FRI scheme in the breakdown SNR. Further, an application of the proposed universal FRI scheme on reconstruction of magnetic resonance images and QRS complexes is demonstrated. [ABSTRACT FROM AUTHOR]

This paper describes the advantages of tunnel field-effect transistor (TFET) to design a 4-bit flash analog to digital converter (ADC). In this work, the TFET used to design a 4-bit flash ADC is a III–V TFET. The 4-bit flash ADC is designed using TFET because TFETs are a promising candidate for low power applications. Low power ADCs are always in the demand for electronics systems, and using TFETs to design them makes them more efficient and functional. TFET-based reported circuits are designed to perform at low supply voltage and at high frequency considering the requirements for flash ADC. The primary motive of this study is to design a 4-bit flash ADC which can give a better signal-to-noise ratio, signal-to-noise and distortion ratio (SNDR), integral nonlinearity, differential nonlinearity, total harmonics distortion and low power consumption. To achieve the aim, the sub-circuits of 4-bit flash ADC such as comparator, a level shifter, and multiplexers are fully designed by III–V TFET. The designed flash ADC is operating at the supply voltage of 1 V and at 1 GHz frequency, shows the promising results for figures of merit (FoMs) and is found close to the ideal FoMs. [ABSTRACT FROM AUTHOR]

This paper considers the orthogonal observation matrix design of deterministic compressive sampling (CS). An observation matrix called the weighted truncated Hadamard-modulated wideband converter (WITH-MWC) is deterministically designed based on the truncated Hadamard matrix. This matrix can meet the restricted isometry property (RIP) condition with overwhelming probability by randomly or strategically extracting from a standard Hadamard matrix. Most compressed sampling systems are highly sensitive to noise. To reduce the adverse effects of noise interference, partial specific matrix elements are weighted according to the sparse characteristics of multiband signals, and the recovery probability is provably better than that of the original system and other deterministic observation matrices, especially in the low signal-to-noise ratio scenario. Compared to the random Gaussian and random Bernoulli matrices, WITH-MWC is much easier to implement in hardware. The simulations verify the above analysis. [ABSTRACT FROM AUTHOR]

In adaptive filters, variable step-size-based algorithms have demonstrated better performance than conventional least mean square (LMS) and normalized LMS (NLMS) algorithms in terms of convergence speed, mean square error (MSE), and signal-to-noise ratio (SNR) improvement. Recently, adaptive filters have been recommended to be implemented on field programmable gate array (FPGA) devices due to their flexibility and high speed. Thus, recent research has focused on not only the performance measures of the algorithm but also on the required area, operating frequency, and power consumed to evaluate the proposed design after implementation. This paper first demonstrates the superiority of the regularized square root absolute error LMS (R-SRAE-LMS) for acoustic noise cancellation compared to other variable step size algorithms through a comparative study. Furthermore, transient and steady state analyses are discussed for the R-SRAE-LMS algorithm. Then, a detailed design of the R-SRAE-LMS adaptive filter is proposed in this paper. The design is divided into a forward path and two feedback paths. The device utilization, operating frequency and power consumption are also presented after a complete FPGA implementation process. The results show that R-SRAE-LMS has a high and stable SNR improvement curve compared to that of other acoustic noise cancellation algorithms. Moreover, the proposed design has remarkable implementation results compared to those of other variable step size adaptive filter designs. The output signal of the implemented proposed filter design attains performance measures very close to those of the fixed-point case. [ABSTRACT FROM AUTHOR]

In this paper, a level-crossing analog-to-digital converter (LC-ADC) with the adaptive resolution is proposed which utilizes a signal-dependent sampling mechanism to adjust the quantization levels. Using a feedback network, composed of a charge-pump and a comparator, the periodicity of the samples is continuously evaluated. The quantization window is then dynamically configured according to the signal activity to prevent excessive sampling of the input signal, especially for the parts with relatively high slew rates. As a result, by regulating the number of samples, the design of the ADC main building blocks is relaxed regarding the speed and power requirements leading to higher power efficiency. Implemented in a 0.18 µm standard CMOS process, the proposed LC-ADC occupies ~ 0.0041 mm2 of silicon area and consumes ~ 18 nW from 1 V supply voltage. Assuming a 1 kHz full-scale input sinusoidal signal, it achieves an average signal-to-noise and distortion ratio of ~ 43 dB and an effective number of bits of ~ 6.8 bits. [ABSTRACT FROM AUTHOR]

This paper proposes a robust signal-to-noise ratio (SNR) estimation technique from continuous speech based on the non-negative frequency-weighted energy operator using the envelope of derivative of the speech signal. The proposed SNR evaluation method is implemented in two phases. The first phase of SNR estimation consists of speech region identification by glottal activity detection (GAD) along with two pre-processing and post-processing methods. Speech enhancement with background subtraction is the pre-processing and obstruent detection with duration modification is the post-processing modules to GAD, which improves the speech and non-speech region detection performance. The next stage comprises estimation of SNR from instantaneous energy contour obtained from the envelope of the derivative of the speech signal, which is based on non-negative frequency-weighted energy estimation. The ratio of instantaneous energy of speech region to non-speech region is considered as the estimated segmental SNR. The final SNR is calculated by averaging segmental SNR and compared with true SNR to get the estimation efficiency of the proposed method. The performance of the proposed method for SNR estimation from speech sample is evaluated using TIMIT, NOIZEUS, and TESDHE acoustic speech corpus. The TIMIT and NOIZEUS databases are used to evaluate the performance for a different type of noise and TESDHE database is used for analysis under a different mode of data. The result of our study shows that the proposed system provides accurate SNR estimation across different types of noise and different modes of signal for various signal levels and outperforms the other methods under observation. [ABSTRACT FROM AUTHOR]

The Gibbs phenomenon describes oscillations of small or large amplitudes that occur, when a signal with steep gradients or noise components is approximated. Such interruptions can degrade the quality of desired signal. Reduction of such oscillations is an essential task to extract vital information from the desired signal. This paper, therefore, presents the Chebyshev spectral method (CSM) that is combined with two novel concepts to reduce the influence of oscillatory structures. The first notion uses a thresholding approach to estimate true expansion coefficients in a noisy environment, while the second concept introduces a new smoothing function. The basic framework of the proposed concept is to introduce an additional threshold procedure into pre-existing Chebyshev collocation method to handle the fluctuations of noise interferences. Moreover, the CSM is the global-behaviour approximation based on the points of an entire domain, which allows for high-order convergence to be recovered. The method is implemented for sharp gradient-contained function and to a signal that has been distorted by noise. Through computational experiments, efficiency of the proposed method is verified graphically and numerically. Signal-to-noise ratio of 37.4810 dB is achieved with corresponding mean square error about 1.79e−04. The percentage root-mean-square difference (PRD) and maximum error are obtained as 1.3402% and 0.0399, respectively. [ABSTRACT FROM AUTHOR]

This paper proposes a high-speed deterministic digital technique to calibrate the errors due to capacitance mismatch and finite op-amp gain. Unlike other calibration techniques, this technique requires neither forcing the inputs of the intermediate stages being calibrated to exact voltages, nor reducing the gains of each stage to avoid saturation of output digital codes. A 1.5 bits/stage, 10 stages, 9 bits pipeline ADC with the three most significant stages calibrated is demonstrated in this paper. For a 10% mismatch in capacitances in the 3 MSB stages of the pipelined ADC, the calibration technique improved SNDR by more than 20 dB and SFDR by around 27 dB. The technique can also be slightly modified to calibrate algorithmic ADCs. For a 7% mismatch in capacitances of the algorithmic ADC, the proposed calibration technique improved SNDR by 18 dB and SFDR by around 28 dB. [ABSTRACT FROM AUTHOR]

Video is extensively used in everyday applications and services by heterogeneous users. High-definition video information corresponds to high-data-rate representation. Video coding scalability has an effect on bitrate requirement and peak signal-to-noise ratio (PSNR). However, the spatial information (SI) and temporal information (TI), that are inherent properties of the video, need to be maintained for the full-high-definition videos while performing scalably encoding using high-efficiency video coding (HEVC) standard. In this paper, we have established an empirical relationship of SI, TI, PSNR, and bitrate with quantization parameter (QP). Based on this study, parametric models have been derived that achieve correlation accuracy of at least 99.18 % . An optimization algorithm has been developed to find the best QP to achieve the highest PSNR and the least bitrate while maintaining SI and TI during the scalable transcoding of the video. Furthermore, the algorithmic architecture calculates the optimized QP that meets the heterogeneous user-end specification of minimum PSNR and maximum bitrate. The proposed architecture consumes low power and energy of 147 microwatts and 0.186 picojoules, respectively, and therefore, it can be integrated in an HEVC encoder for portable HEVC-compliant consumer electronic products. [ABSTRACT FROM AUTHOR]

Improving the visibility of hazy images is desirable for robot navigation, security surveillance, and other computer vision applications. The presence of fog significantly damages the quality of the captured image, which does not only affect the reliability of the surveillance system but also produce potential danger. Therefore, developing as well as implementing a simple and efficient image de-hazing algorithm is essential. The reconfigurable computing devices like Field Programmable Gate Array and Digital Signal Processing (DSP) processors are used to implement these image processing applications. Several strategies are available for configuring these reconfigurable devices. In this paper, two approaches for hardware implementation of image de-hazing algorithm are presented. The pixel wise and gray image-based de-hazing algorithm is proposed in this paper. The key advantage of this proposed method is to estimate accurate transmission map. It eliminates the computationally complex step of refine transmission map as well as halos & artifacts in the recovered image and achieves faster execution without noticeable degradation of the quality of the de-hazed image. The proposed method is initially verified in MATLAB and compared with the existing four state-of-art methods. This algorithm is implemented on two different hardware platforms, i.e., DSP Processor (TMS320C6748) with floating pointing operations and Zynq-706 fixed-point operations. The performance comparison of hardware architectures is made with respect to Average Contrast of the Output Image, Mean Square Error, Peak Signal to Noise Ratio, Percentage of Haze Improvement and Structural Similarity Index (SSIM). The results obtained show that Zynq-706-based hardware implementation processing speed is 1.33 times faster when compared to DSP processor-based implementation for an image dimensions of 256 × 256 . [ABSTRACT FROM AUTHOR]

Electrocardiogram (ECG) is a critical type of biological signal that brings significant data about the patients. The morphological structure of ECG signals is usually distorted by the recording and transmission processes. As a result of this distortion, the proper diagnosis of diseases related to the cardiac system is getting affected. In this paper, a new FPGA design of variable step-size variable tap length delayed error normalized least mean square (VSS-VT-DENLMS) noise removal algorithm is proposed that modifies the weight update equation of DENLMS algorithm by varying the step sizes and the tap lengths simultaneously to find better trade-off among the fast convergence and error tracking. Also, the adaptive filter structure of the proposed VSS-DENLMS is developed by considering both systolic and folding structure with compressor-based booth multiplier for improving the performance in terms of speed and area. The proposed filter design is validated by considering different ECG signals from MIT-BIH Arrhythmia database, and the filtered outputs are investigated using certain performance measures including signal-to-noise ratio (SNR), mean square error (MSE), root-mean-square error (RMSE) and hardware complexity in terms of area and delay. The simulation results illustrate that the proposed filter overtakes existing filters and minimizes hardware complexities, which proves the suitability of this approach on real-time applications of ECG signals. [ABSTRACT FROM AUTHOR]

Radio frequency distinct native attribute (RF-DNA) technology is very important in distinguishing RF devices. This paper presents a new method for distinguishing RF devices by combining transfer learning and long short-term memory (TL-LSTM). The main purpose of this paper is to identify which RF device sent the unknown RF signals. The data were collected from almost the same eight RF devices produced in 2011, 2014 or 2016. These RF devices emitted unintended signals at 2.4G bandwidth with frequency shift keying. The proposed method first used late production RF devices in 2011 or 2014 as source domain and transferred the trained model to target domain produced in 2016 and then used neural network LSTM model to identify the RF signals. The proposed method is advantageous because it does not require a huge amount of sampling data, and this technique is better than traditional strategies to select optimal features in the multi-domain feature space. The results reveal that the proposed method TL-LSTM can solve the problem of small sample training very well. [ABSTRACT FROM AUTHOR]

This paper presents a two-dimensional (2-D) eigenfilter technique to design 2-D perfect reconstruction (PR) filterbanks with directional vanishing moment (DVM). In this paper, we first introduce the DVM constraint for 2-D directional filterbank design. The proposed DVM constraint is imposed in 2-D eigenfilter formulation to obtain maximally flat 2-D low-pass analysis filter. Then, we use this designed analysis filter in 2-D time-domain formulation of PR condition to obtain complementary low-pass synthesis filters. The PR and DVM constraints are imposed to obtain 2-D low-pass synthesis filter. It is shown that the proposed 2-D filters satisfy the PR criteria with prescribed DVM. The performance of 2-D PR filters is evaluated in image denoising application. The performance of designed filterbank is compared with well-known existing methods in terms of peak signal-to-noise ratio to validate the results. [ABSTRACT FROM AUTHOR]

Detection and recovery of noise corrupted signals using adaptive filters are becoming popular due to their application in several fields, including communication and biomedical. Adaptive filters broadly utilize the LMS algorithm since it has low computational complexity and is robust in behavior. However, it fails to achieve a faster speed of convergence and minimum steady-state MSE simultaneously. Therefore, the objective is to estimate the noise-free signal faster with improved accuracy and obtain a lower steady-state MSE with a higher convergence speed. This paper introduces a multi-stage adaptive filtering model wherein the noisy signal is analyzed through a cascade of stages. The proposed signal de-noising scheme utilizes the automatic selection of stages to be cascaded such that the steady-state MSE is minimum. Further, to enhance convergence speed, the LMS adaptive filter's step-size is automatically adjusted at each stage. Hence, by controlling the number of stages to be cascaded automatically and utilizing a different step-size for each stage, we obtain a high convergence speed and minimum steady-state MSE. The proposed filter structure is tested for signal de-noising application to assess its performance concerning MSE, Signal-to-Noise Ratio (SNR), Average Noise Reduction (ANR), and convergence speed. The results attained have shown that the proposed filter structure provides remarkable performance improvement at different input noise levels. Further, the proposed Improved Variable-Stage (IVS) cascaded adaptive filter model employs LMS adaptive algorithm, hence offering a cost-effective and straightforward hardware implementation of ANC. [ABSTRACT FROM AUTHOR]

The evaluation of mathematical functions is a critical task in several hardware designs, and piecewise polynomial approximation (PPA) is one of the main techniques widely used for function evaluation. This technique employs uniform and non-uniform segmentation for splitting the function and approaching each function segment via polynomial approximation, where lookup tables are needed to store the polynomial coefficients. Today, several hardware-based PPA implementations have address decoder units with a considerable amount of hardware complexity. To face this problem, this paper proposes a new methodology that searches the optimal function segments, polynomial coefficients, and their representation in fixed-point format when the design is constrained to a decoder word-length. Thus, a second-order PPA evaluation architecture based on the Horner's rule and a simplified address decoder unit with reduced complexity are provided. The proposed methodology uses non-uniform segmentation relying on a linear combination of power-of-two, which results in a reduction of the number of segments and consequently in the hardware complexity of the address decoder unit. The figures of merit employed by the auto-tuning (self-adapting) segmentation process are the first-order derivative (slope) of the function and the signal to quantization noise ratio quality signal metric. Experimental results show a hardware reduction of the decoder design (number of segments, polynomial coefficient ROMs) when is compared with state-of-the-art proposals. [ABSTRACT FROM AUTHOR]

In this paper, a novel method is proposed to determine noisy blocks of an image. Three different threshold values for noisy/clean classification of the blocks of any image are determined by applying the common vector approach to the reference data set consisting of the clean samples of that image. The noise addressed in this paper is Gaussian noise with zero mean. By making a block-based noisy/clean classification of any image, it is possible to expose only the noisy blocks to the denoising process, rather than the entire image to denoising. When the first threshold value (Threshold 1) is considered for all peak signal-to-noise ratio (PSNR) values, more than 94% classification results are obtained for all 8 × 8 block-sized test images except images with 28–31 dB PSNR and more than 98.8% classification results are obtained for all 12 × 12 and 16 × 16 block-sized test images except images with 29-31 dB PSNR. Finally, popular image denoising algorithms are applied to the noisy images for comparison. It is observed that the PSNR values of noisy images are appreciably increased after the process. [ABSTRACT FROM AUTHOR]

Electrocardiogram (ECG) has an important reference value in the study of basic cardiac function and pathology. However, ECG signals are easily corrupted by various noises in the acquisition and transmission. Hence, the noise removal in ECG signals is helpful to the diagnosis and analysis. In this paper, both the local sample correlations and nonlocal sample correlations of the ECG signals are utilized to remove noise. According to the local sample correlations, the penalized least squares method is used to remove the noise in the segment. According to the similarity of nonlocal samples, the average of similar segments in different periods can be regarded as a reference estimate for the current signal segment. The effectiveness of the algorithm is verified by adding simulated noises in the MIT-BIH Arrhythmia Database. Compared with some existing methods quantitatively, the signal quality (the improvement in the signal-to-noise ratio, mean square error, and percentage root mean square difference) has been significantly improved. Finally, the proposed method combined with the baseline correction algorithm is applied to raw signals with real noises in ECG-ID Database and gets a favorable visual effect. [ABSTRACT FROM AUTHOR]

In this paper, we propose a computationally faster yet conceptually simple methodology to estimate the parameters of a two-dimensional (2-D) sinusoidal model in the presence of additive white noise. We develop the large sample properties like consistency and asymptotic normality of these low-complexity estimators, and they are observed to be theoretically as efficient as the ordinary least squares estimators. To assess the numerical performance, we conduct extensive simulation studies. The results indicate that the proposed estimators can successfully replace the least squares estimators for sample size as small as 20 × 20 and for signal-to-noise ratio (SNR) as small as 12 dB. [ABSTRACT FROM AUTHOR]

In this paper, we propose a new technique of Electrocardiogram (ECG ) denoising based on Lifting Wavelet Transform (LWT ) and Total Variation Minimization (TVM ). The first step of this technique consists in applying the LWT to the noisy ECG signal for obtaining three wavelet sub-bands, c D 1 , c D 2 and c A 2 . The coefficients c D 1 and c D 2 are details coefficients, denoised by soft thresholding for obtaining two denoised coefficients, c D d 1 and c D d 2 . The coefficient c A 2 is an approximation coefficient, denoised by TVM based denoising method for obtaining a denoised coefficient c A d 2 . The last step of this technique consists in applying the inverse of LWT to c D d 1 , c D d 2 and c A d 2 for obtaining the denoised ECG signal. The proposed technique evaluation is by comparing it with three other denoising approaches discussed in the extant in literature. The TVM-based approach is the first, the second is 1 - D double-density complex Discrete Wavelet Transform denoising method, and the third is the ECG denoising technique based on Non-local Means. These four mentioned techniques are applied to various ECG signals taken from MIT - BIH database. Those signals are degraded by an additive White Gaussian Noise at different values of Signal to Noise Ratio ( SNR i dB ). The results obtained from the calculation of SNR and the Mean Square Error, infer that the proposed technique outperforms the other denoising approaches selected for the comparative evaluation. [ABSTRACT FROM AUTHOR]

In order to generate volumetric underwater acoustic images, planar arrays are often used to collect the signals coming from a 3-D scene in underwater environment. These acquired signals are commonly processed using the 2-D conventional beamforming method. The 2-D conventional beamforming is robust and the most commonly used method. However, the 2-D conventional beamforming suffers from fat beams and high-level sidelobes. High-resolution beamforming, based on the inverse of the signal covariance matrix, such as minimum-variance distortionless response, produces narrow beamwidth and low sidelobe levels but is sensitive to signal mismatch and requires many snapshots of data. In order to overcome the above-mentioned limitations, this paper applies a 2-D deconvolution algorithm to the 2-D conventional beamforming result of planar array in order to improve the beamwidth and suppress the high-level sidelobes. The 2-D deconvolved beam power produces narrow beams, and low sidelobe levels without compromising the robustness of 2-D conventional beamforming. The 2-D deconvolution algorithm provides higher output signal-to-noise ratio than the 2-D conventional beamforming for isotropic noise. The vector extrapolation method is improved to make it applicable on two-dimensional data. The detailed implementations of the original 2-D deconvolved beamforming and the efficient 2-D deconvolved beamforming are presented. Simulations and experiments are conducted to validate the performance of the proposed method. [ABSTRACT FROM AUTHOR]

In this paper, a two-channel critically sampled spline graph filter bank (SGFB) for an arbitrary undirected graph with spectral domain sampling is proposed. The filters in the analysis section satisfy perfect reconstruction condition, and the synthesis section is implemented with low computational complexity. The proposed SGFB maintains the spectrum of the original signal in multi-level decomposition and then suppresses noise efficiently. It is worth noting that the proposed filter design is performed once for the desired filter shape in the spectral domain with desired cutoff frequency. Since the optimization problem is solved once, it reduces the complexity for large graph significantly. Through numerical analyses, we validate the efficacy of the proposed SGFB using multi-level decomposition of a graph signal and noise suppression in terms of signal-to-noise ratio (SNR) improvement. [ABSTRACT FROM AUTHOR]

Digital video technology has been increasingly needed in various fields, such as telecommunications, entertainment, medicine. Therefore, video compression is required. Motion estimation methods help in improving video compression efficiency by effectively removing the temporal redundancy between successive frames. Several block-based motion estimation (BME) algorithms are being suggested to reduce the coding process's computational complexity. This paper proposes a new rapid hybrid (BME) algorithm established on the primary search point prediction and advance ending search point strategies. It combines rough adaptive search and effective local search. The coarse search introduces a new motion vector (MV) prediction technique that utilizes the macro-blocks (MBs) Spatio-temporal correlations to optimize the traditional adaptive-rood-pattern search algorithm (ARPS) and speeding up the whole process without affecting the accuracy. In the accurate local search, the cross-formed search pattern using a one-step search (OSS) block matching algorithm is employed, to estimate the actual (MV) with less computation time and further speed up the search efficiency. Exhaustive experiments are performed to demonstrate the present algorithm's performance over the benchmark schemes concerning specific assessment criteria for results, including the peak signal-to-noise ratio (PSNR), computational complexity and computational gain. The results show that the proposed algorithm is efficient and reliable; it can always give better performance over diamond search (DS) and (ARPS). The conducted test shows an increased performance of search speed while preserving the visual quality of the motion-compensated images, and it achieves 59.76–88.03 speed improvement over (DS) and 20.98–72.06 over (ARPS) for different video sequences. Besides, the suggested method (ARP-OSS) provides the best result compared to (DS) and (ARPS) in terms of time complexity for analyzing all the video samples. [ABSTRACT FROM AUTHOR]

In order to enhance the robustness of image compression sensing system and reduce the mutual coherence between measurement matrix and sparse basis, this paper proposes an image measurement matrix optimization algorithm based on posterior information. Based on the traditional measurement matrix optimization model, the proposed algorithm considers the image reconstruction error from the OMP algorithm and uses it as a regular term. Matrix F-norm expansion and singular value decomposition are used to reduce the computational complexity and ensure the convergence of algorithm. Besides, the gradient matrix method is used to iteratively solve the measurement matrix. The proposed measurement matrix optimization model makes full use of the reconstruction error information of the image itself, not only improves the robustness of image compression sensing system, but also reduces the mutual coherence between the measurement matrix and the sparse basis. Experiments results show that compared with the state-of-the-art measurement matrix optimization algorithms, the proposed algorithm can reduce the average correlation coefficient more effectively, and the peak signal-to-noise ratio of the image can be increased by up to 1.2 dB. [ABSTRACT FROM AUTHOR]

Modulated wideband converter is a multi-branch compressed sampling structure in which each branch aliases the sparse input spectrum into a narrow band by mixing a pseudo-random local oscillator (LO) bit sequence. Based on the low-pass sampling sequences of the narrow bands and the sensing matrix which defines the weights of aliasing, the input signal can be recovered by compressed sensing reconstructing algorithms. Because the distortion in the hardware implementation is ignored, the calculated sensing matrix, which is obtained by directly expanding the Fourier values of the LO sequences, cannot lead a satisfactory signal reconstruction. To solve this problem, this paper proposed the concept of relative sensing matrix and considered these distortions as a part of the sensing matrix. To measure the relative sensing matrix, this paper also designs a triggering-based measurement system excited by wideband modulated signals. The system is synchronized by the trigger which controls the restart of input signals, LO sequences, and ADCs. Compared with the classical sinusoidal signals measurement system, the wideband modulated signals are more accessible to be synchronized by the trigger for their identifiable envelope header. A dual-carrier 5 MHz signal is injected to the receiver at a signal noise ratio of 37 dB, with the measured sensing matrix; the two baseband signals are reconstructed with the normalized mean-squared error (NMSE) reaching − 21.05 dB and − 19.72 dB, respectively. This result is much better than the baseband signals reconstructed by the calculated sensing matrix which only have the NMSE of − 7.47 dB and − 6.70 dB. [ABSTRACT FROM AUTHOR]

The paper presents a new model of noise cancellation using cascading of cascaded LMS adaptive filters. The model has a combination of ‘2N+1’ LMS filters for N-stage of adaptive noise cancellation. First LMS filter works as a basic noise canceller, next two work as 1st stage of noise canceller using a cascaded form of LMS filters known as LMS Block-1, and all others have the same arrangement as LMS Block-1 known as LMS Block-2, LMS Block-3, …, LMS Block-N. The LMS Block has a two-stage noise reduction of the additive noise or interference. LMS Block-1 is cascaded noise canceller, determines and reduces noise again after reduction in noise from 1st LMS filter. The analysis and simulation model gives the responses of noise cancellation like error signal, output signal and signal-to-noise ratio with respect to step sizes, filter lengths and initial weight of filters. This paper also shows the simulation of cascade-cascade LMS adaptive noise cancellation for two stages (N=2) and gives the higher signal-to-noise ratio at Nth stage with respect to previous stages. It is the next novel point of this paper that no other elements are present in the cascade-cascade LMS adaptive noise cancellation rather than LMS filters as noise canceller. [ABSTRACT FROM AUTHOR]

The phase interferometer is an effective direction finding (DF) method. It is widely utilized in various electronic reconnaissance systems due to its advantages of fast operation and high precision. In a wideband system, the combination of long and short baselines, or even multi-level baselines, is applied in an interferometer to solve the contradiction between accuracy and phase ambiguity for DF. In this paper, a novel analysis method is proposed to obtain the probability of successfully solving ambiguity based on mathematical statistics. According to the length ratio between short and long baselines, the joint density function of phase errors can be derived. Then, the probability can be achieved under different signal-to-noise ratios (SNRs) by integrating the joint density function in a specific interval. Furthermore, the formula of phase measurement error is adjusted by the least square method to improve computational accuracy in low SNR during the process. Under different baseline configurations, the strategy can provide the theoretical probability of successfully solving ambiguity, thus guiding the baseline design for obtaining a maximum probability without impacting the specified DF accuracy. Simulation results show that the mathematical model is efficient in some complex cases. [ABSTRACT FROM AUTHOR]

This paper presents a novel spike detection algorithm in nonstationary signals using a time–frequency (t–f) approach. The proposed algorithm exploits the direction of signal energy in the t–f domain to detect spikes in the presence of high-frequency nonstationary signals even at low signal-to-noise ratio. The performance of the proposed approach is evaluated using synthetic nonstationary signals, synthesized signals mimicking electroencephalogram (EEG) signals, manually selected segments of speech signals, and manually selected segments of real EEG signals. The statistical measures, such as hit rate and precision, are used to demonstrate that the proposed algorithm performs better than other widely used algorithms, such as the smoothed nonlinear energy detector. [ABSTRACT FROM AUTHOR]

A signal processing method of coherent Doppler lidar (CDL) for wind measurement is proposed in this paper, which combines the H-infinity control and coherent integration. This method can improve the signal-to-noise ratio and reduce the interference; the measured wind speed will be more exact. In this paper, the basic theory of signal processing method is proposed. That includes the main idea of how to combine the two different methods and the detailed information of this two methods. Then, the simulation by MATLAB is done, and the results are given to confirm the practicality of the new idea. The wind speed deviation before and after doing the signal processing is simulated and compared. It confirms that the accuracy has improvement by using this method mentioned in this paper. [ABSTRACT FROM AUTHOR]

The impact of low-power interferences on the performance of global positioning system receivers is ignored in most literature. Low-power interferences degrade the positioning accuracy as well as disrupting the performance of the receiver in some situations. Since the resolution of short-time Fourier transform (STFT)-based jamming mitigation methods is insufficient for both low- and high-power interference mitigation, this paper proposes an interference mitigation method which employs two windows to increase time-frequency (t-f) plane resolution. In this paper, we also propose a new technique to select near-optimum windows in order to increase resolution of the t-f representation. The proposed technique results in significant increase in signal-to-interference-plus-noise power ratio improvement, the number of acquired satellites, and the positioning accuracy as well as considerable decrease in mean square error of the retrieved signal compared to standard STFT method. [ABSTRACT FROM AUTHOR]

In this paper, higher-order spectral analysis is applied to infant cry signals for classification of normal infant cries from pathological infant cries. From the family of higher-order spectra, bispectrum is considered for the proposed task. Bispectrum is the Fourier transform of the third-order cumulant function. To extract features from the bispectrum, application of higher-order singular value decomposition theorem is proposed. Experimental results show the average classification accuracy of $${82.44} \pm {4.03}{ \%}$$ and Matthew's correlation coefficient (MCC) of 0.62 with proposed bispectrum features. In all of the experiments reported in this paper, support vector machine with radial basis function kernel is used as the pattern classifier. Performance of the proposed features is also compared with the state-of-the-art methods such as linear frequency cepstral coefficients, Mel frequency cepstral coefficients, perceptual linear prediction coefficients, linear prediction coefficients, linear prediction cepstral coefficients and perceptual linear prediction cepstral coefficients, and is found to be better than that given by these feature sets. The proposed bispectrum-based features are shown to be robust under signal degradation or noisy conditions at various SNR levels. Performance in the presence of noise is compared with the state-of-the-art spectral feature sets using MCC scores. In addition, effectiveness of cryunit segmentation in normal and pathological infant cry classification task is reported. [ABSTRACT FROM AUTHOR]

This paper is concerned with the design of optimal $$H_\infty $$ static-state feedback controller for an uncertain 2-D discrete system described by the Fornasini-Marchesini second model. The parametric uncertainties are assumed to be norm bounded. The main contribution of the paper lies in the application of the bounded real lemma derived using a more generalized asymmetric Lyapunov matrix. Based on the derived bounded real lemma, a linear matrix inequality (LMI)-based sufficient condition for the existence of $$H_\infty $$ static-state feedback controller is established. Furthermore, convex optimization problem with LMI constraints is formulated to design the optimal $$H_\infty $$ static-state feedback controller which not only guarantees the asymptotic stability of the closed-loop system but also achieves optimized noise attenuation level $$\gamma $$ for all admissible parametric uncertainties. Finally, with the help of various examples, it is demonstrated that the optimized noise attenuation level $$\gamma $$ is better achievable by using asymmetric Lyapunov matrix than using symmetric Lyapunov Matrix. [ABSTRACT FROM AUTHOR]

In this paper, a new method is proposed for image denoising inspired by the bidimensional empirical mode decomposition algorithm and the diffusion-based filtering. In the bidimensional empirical mode decomposition technique, the noisy image is decomposed into its respective intrinsic mode functions and the high-frequency and the low-frequency noises are removed with the help of the proposed diffusion filtering method using its parameters such as connectivity, conductance function, gradient threshold and the number of iterations. The image is reconstructed with the help of these denoised intrinsic mode functions. Performance of the proposed technique is evaluated in terms of the peak signal-to-noise ratio, the structural similarity index, the mean square error and is then compared with the already existing techniques on the image denoising. From the experimentation and results obtained, it is clear that the bidimensional empirical mode decomposition method using the diffusion filtering algorithm is efficient for the denoising of the images both qualitatively and quantitatively. [ABSTRACT FROM AUTHOR]

In this paper, we propose a new family of algorithms for underdetermined blind identification based on the second generating function (SGF). For the real case, the proposed algorithm first takes advantage of the partial derivatives of the SGF of the observation signals to construct a tensor and then reconstructs this tensor by grouping the parallel factors. The reconstructed tensor, which expands the length of several dimensions of the original tensor, transforms the underdetermined case into an overdertermined or determined case when (M 2 + M) / 2 ≥ N , where M is the number of sensors and N is the number of sources. Thus, obtaining the estimation of the mixing matrix by decomposing the reconstructed tensor results in the performance improvement. The analysis of the uniqueness of tensor decomposition and the complex analysis show that the proposed algorithm relaxes the limitation of the maximal number of sources and owns lower computational complexity order compared with the conventional ALESCAF algorithm. We also extend the proposed algorithm to the complex case by complex-to-real transformation. Simulation results verify the effectiveness of the proposed algorithms and show that the proposed algorithms are preferred when the signal-to-noise ratio is higher than 5 dB. [ABSTRACT FROM AUTHOR]

The problem of joint angle and frequency estimation is considered in this paper. An algorithm using spatial–temporal nested sampling and one-bit quantization is developed. It is shown that under moderate condition when the signal-to-noise ratio is low enough, the defined one-bit correlation matrix has linear relationship with the unquantized one, and therefore, subspace-based algorithms can be applied to this one-bit correlation matrix directly. The enhanced identification performance, offered by the nested sampling, can also be provided when one-bit quantization is used. Simulation results are provided to verify the performance of the presented algorithm. [ABSTRACT FROM AUTHOR]

Image segmentation is a method of subdividing an image into numerous meaningful regions or objects, which makes the image more informative and easy to analyze. Thresholding-based approaches are extensively employed for segmenting the image due to their low computational cost and are easy implementation. However, histogram-based thresholding schemes suffer from high variation that leads to abnormalities and sharp specifics. In this paper, we propose a technique for multilevel color image segmentation through variational mode decomposition (VMD) and Kapur's entropy. Initially, the VMD is employed in order to decompose the histogram into corresponding submodes of analysis and attributes extraction, which leads to the removal of the unfavorable effects. Then, Kapur's entropy is incorporated in order to generate accurate and optimal thresholds for segmentation. For the performance evaluation of the presented VMD–Kapur algorithm, various qualitative metrics have been used such as probability rand index, mean-square error, peak signal-to-noise ratio, variation of information, structural similarity index, dice error, feature similarity index, entropy, Jaccard/Tanimoto error, and normalized absolute error. The experimental results show that the proposed technique produces best-segmented images compared to Kapur's, Tsallis, Masi, and fuzzy entropies. [ABSTRACT FROM AUTHOR]

This paper focuses on the problem of the DOA estimation of coherent signals for the acoustic vector-sensor arrays (AVSAs) in the presence of the isotropic ambient noise. We propose a high-resolution DOA estimation method based on the acoustic intensity principle and the sparse representation technique. First, two cross-covariance matrices are constructed by employing the acoustic pressure and particle velocity components of the AVSA, which eliminates the isotropic noise. Then, in order to fully explore the DOA information of the particle velocity components, an augmented matrix is formed based on the two cross-covariance matrices. We observe an interesting fact that the left singular vector corresponding to the maximum singular value of the augmented cross-covariance matrix is the linear combination of all the signal steering vectors. Based on this fact, a high-resolution DOA estimation algorithm is developed via sparsely representing the left singular vector. This method does not require the prior knowledge of the noise variance or the number of signals to construct the sparse representation model. Simulation and experimental results demonstrate the proposed method outperforms the MUSIC method based on the forward/backward spatial smoothing and some existing sparse representation methods in estimation accuracy and angular resolution, especially in the cases of a low signal-to-noise ratio and/or coherent signals with small angular separations. [ABSTRACT FROM AUTHOR]

Reversible data hiding in encrypted images (RDHEI) is a widely used technique for privacy protection, which has been developed in many applications that require high confidentiality, authentication and integrity. Proposed RDHEI methods do not allow high embedding rate while ensuring losslessly recover the original image. Moreover, embedding computational complexity and embedding rate are not satisfactory when using public key encryption-based methods. This paper proposes a new fully reversible method based on Paillier homomorphic cryptosystem and pixel prediction. We use gradient adjust prediction to obtain redundant space in the original image and embed secret information with homomorphic operation after encryption. Experimental results show that our scheme can achieve high capacity (near 0.95 bpp) and can losslessly recover the original images with peak signal to noise ratio of +∞ and structural similarity of 1. Compared with proposed reversible data hiding algorithms, our scheme has a significant improvement in reconstructing quality, safety and capacity. [ABSTRACT FROM AUTHOR]

Image deblurring is a fundamental problem in imaging field which often needs to recover the important structure of images. This paper addresses the image deblurring problem by considering an image as a combination of its cartoon (the piecewise smooth part of the image) and texture (the oscillation part of the image) components. To recover both of these parts, we propose the use of coupled analysis-based sparse representations to regularize the cartoon structure and the texture part of the image. We apply anisotropic total variation with a quadratic term to enhance the edges existing in the cartoon part. Furthermore, we develop a multivariable Bregman optimization method to solve the proposed image restoration model by combining the alternating minimization method and the split Bregman iteration. The experiments show that the proposed algorithm not only performs well for image decomposition, but also outperforms the previously established methods in terms of the visual residual error, the structure similarity index and the peak signal-to-noise ratio for image deblurring. [ABSTRACT FROM AUTHOR]