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

The article announces the Best Paper Awards for papers published in the periodical in areas of electric circuits, systems and signal processing including the Armen H. Zemanian Best Paper Award to Michał Tadeusiewicz and others; and the Sydney R. Parker Best Paper Award to Fatema Tuz Jhohura and others.

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]

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]

Several works report on overcoming the challenge of complex general-purpose signal processing algorithms that result in quick utilization of chip area resources (like FPGA in implementing system on chip or network on chip). However, with the advent of machine learning and deep learning algorithms, reducing this complexity and immense use of resources is a challenging task that can improve the system's performance in real time. One of the approaches is algorithm optimization, which consumes fewer resources and produces an equivalent performance. This approach may likely be accepted in less-sensitive applications, like voice or video processing. Recently, an optimized algorithm of correlation-less Wiener–Hopf-based adaptive channel equalizer is reported, in which the overall complexity of the system is reduced by employing a more compact way of weight optimization. This paper is the continuation of that work that reports the modification in the Steepest-Descent-based adaptive channel equalizer algorithm by keeping the autocorrelation matrix out of the algorithm. The proposed design is simulated and tested at equivalent spectral performance and produces comparable performance with fewer chip resources when translated on FPGA. The proposed design supports that algorithm-level optimization may be accepted extensively for the optimal hardware-based design of DSP systems. [ABSTRACT FROM AUTHOR]

The synchrosqueezing transform (SST) is an advanced post-processing method to sharpen the time–frequency representation (TFR). However, it still processes the signal in frequency domain. Therefore, it cannot effectively analyze signals whose energy is not well concentrated in frequency domain. The fractional S-transform (FrST) inherits the merits of the short-time fractional Fourier transform and the continuous wavelet transform, processing signals in fractional frequency domain. In this paper, a novel non-stationary signal processing tool, synchrosqueezing fractional S-transform (SSFrST) has been proposed, which combines the advantages of SST and FrST. First, we introduce a novel FrST and derive its fundamental properties. Second, based on the novel FrST, we propose SSFrST and discuss its reconstruction formula and implementation. It can not only improve time–frequency resolution, but also process signals in the time-fractional–frequency plane. Finally, we provide several applications to validate the effectiveness of our methods, including chirp signal parameters estimation, signal separation, filtering and noise separation. [ABSTRACT FROM AUTHOR]

As a combination of sound event detection and direction of arrival, the joint task of sound event localization and detection (SELD) is an emerging audio signal processing task and is applied in many areas widely. A popular convolutional recurrent neural network (CRNN)-based method uses convolution neural network (CNN) to extract high-level space features from manually designed features and utilizes recurrent neural network to model sequence context information. Some studies have shown that the normal CNN could not be robust in challenging acoustic environments such as overlapping, moving and discontinuous sources. To improve the performance of SELD in more complex acoustic scenes, parallel multi-attention enhancement (PMAE) is proposed as a convolution enhancement method to boost the representation ability of CNN in this paper. PMAE consists of attention feature enhancement (AFE) and parallel multi-attention (PMA) block. PMA, embedded into AFE, extracts boosting global–local features by efficient attention modules along with different dimensions. AFE, as a feature fusion strategy, fuses multi-scale enhanced features to improve feature representation. AFE shows great performance for overlapping sources. PMA adequately extracts characteristic information of different sound events and shows better performance on moving and discontinuous sources when it is combined with AFE. Based on such a framework, the SELD system becomes robust, while the target sources are moving and overlapping with unknown interference classes. The simulations show that proposed PMAE improves the performance enormously for SELD without other technologies, such as data augment and post-processing. [ABSTRACT FROM AUTHOR]

A multi-dimensional signal on graph has different features along different directions/dimensions. For example, image signal is isotropic along horizontal and vertical directions, and time-varying signal presents different correlation characters in the time and vertex domains. Nevertheless, the current graph signal processing concentrates on the single shift, which makes it difficult to differentiate the correlation features along different directions. In this paper, we define a multi-shift notion for the time-varying signals on graphs enabling us to separately analyze the multi-dimensional signal along different directions captured by diverse shifts. Furthermore, the multi-shift notion is leveraged to formulate the inpainting problem for time-varying signals on the strong product graph, which can be exploited to characterize three different kinds of elements interaction of time-varying signals by using three different kinds of shifts. The conjugate gradient descent algorithm is further deployed to solve the inpainting problem. Numerical experiments conducted on the synthetic signal and real-world data show the potentiality of the multi-shift representation and the effectiveness of the inpainting algorithm. [ABSTRACT FROM AUTHOR]

The digital media shared over the Internet is the backbone of the current generation of communication. Multimedia is becoming increasingly popular with no governing body for intellectual property rights, hence the need for protecting it efficiently while preserving the feature set of given data. Several watermarking methods have been proposed for the copyright protection of audio and speech signals yet the Recording Industry Association of America reported illegal downloading costs around $12.5 billion in the year 2021. Furthermore, the challenge to find the right balance between the three contrasting characteristics of watermarking; robustness, imperceptibility, and payload persists. This paper puts forward a new, hybrid, and purely data-driven way of watermarking the audio and speech signals using iterative filtering and singular value decomposition. The input audio is processed using iterative filtering to generate a band of intrinsic mode functions (IMFs) and a residue component. Depending upon the application, singular value decomposition is applied on residue or an IMF for insertion of bits of the watermark to minimize the effect of watermarking on the audio signal. The method focuses on the robustness of the audio signals against various signal processing attacks while providing a significant boost in imperceptibility and payload as well. Thus, providing a strikingly excellent balance between the characteristics of watermarking. With an average SNR of 38.06 db and normalized correlation close to unity, the proposed method validates its superiority compared to the state of the art. [ABSTRACT FROM AUTHOR]

Digital differentiators enable the computation of the derivative of a continuous-time signal at discrete time instances, and they are used in many signal processing applications. This paper derives a unified filter order estimate for digital differentiators that are realized with linear-phase finite-length impulse response filters and designed in the minimax sense. The estimate is useful at the high-level system design when assessing the implementation complexity and it enables fewer designs when finding the minimal filter order required to satisfy a prescribed tolerable approximation error. The proposed unified estimate covers both wideband and lowpass differentiators of integer degrees up to ten. Furthermore, degree-individual filter order estimates are derived which improve and extend previous results. The performance of both the unified and degree-individual order estimates is evaluated through simulation examples and compared with previous estimates. [ABSTRACT FROM AUTHOR]

Nowadays, many signal processing activities are related to the orthogonal properties of specific signals. However, only a few methods offer an analytical solution for generating orthogonal signals, especially when only one input to the generating system is available. These methods are often related to very specific applications and lack generalization. In this paper, the use of the Gram–Schmidt orthogonalization process combined with simple transformation operators is proposed as a new framework for generating orthogonal signals. The objective is to provide a rigorous, clear and simple procedure capable of deriving multiple orthogonal signals from a single input. Many examples are discussed to better illustrate the novelty of the method and the main results. [ABSTRACT FROM AUTHOR]

Single-window discrete Gabor transform with an adaptive window width selection algorithm (AWWS-DGT) and multi-window discrete Gabor transform (M-DGT) are the current mainstream analysis and processing methods for multi-component signals. However, the time–frequency accuracy in the time–frequency plane obtained by the two algorithms is not very high, and not all components of the signals that contain complex and very different time–frequency components can be displayed accurately. The linear combination window algorithm based on the ℓ 1 and ℓ 2 norm constraints of the transform coefficients can accurately display all the components of the signal, but the algorithm has poor anti-noise performance. In this paper, an algorithm for finding the best linear combination window under the sparse solution constraint of discrete Gabor transform coefficients is proposed. First, the optimal window number adaptive selection method proposed in this paper is used to select a set of window functions with different window widths, and the goal is to obtain the sparse solution of the transform coefficient. Through the gradient descent method, the best linear combination coefficient is obtained. Second, according to the best linear combination coefficient obtained, the selected window is combined into an analysis window function. Finally, the influence of the parameters on the combined window is further studied, and the results show that the number of windows has a great influence on the result and that the influence of the regularization parameter is negligible. Experiments on two simulated non-stationary signals and real electrocardiogram signals show that the proposed algorithm can display all components of the signals, the time–frequency accuracy is improved by 3% to 10%, and the anti-noise performance is more than 20% higher than that of the existing linear combination window algorithm. These advantages are very important for the analysis and processing of practical signals. [ABSTRACT FROM AUTHOR]

This paper presents a scheme to diagnose soft faults in switched-capacitor (SC) filters embedded in the PSoC1 processor from Infineon. The work addresses faults that cause reductions in the values of the filter capacitors due to degradations produced by electrical stress. The diagnosis scheme employs the step response of the pass band output of the filter under test. After simple signal processing steps, the test signal is delivered to a nearest neighbor (1NN) classifier that uses a similarity measure (dynamic time warping) to compare the incoming waveform with patterns stored in a dictionary. The signals in the dictionary are obtained from the filter's step response (using its transfer function) under fault-free and faulty conditions. The diagnosis characterization procedure consists of evaluations at different abstraction levels, including transfer function level simulations in MatLab (over a wide range of faulty conditions), SPICE level simulations, experimental fault injection, and on-chip signal measurements using the internal resources of the processor. All the simulations consider non-ideal effects like noise, jitter, thermal drift, capacitors mismatch, and inter-chip variation in offset voltages at a reasonable computational cost. The evaluations, performed at different abstraction levels, show an excellent performance of the method for diagnosing the addressed faults. [ABSTRACT FROM AUTHOR]

In recent years, researchers have considered quantum steganography and its various methods with the development and progress of research in computation theory and quantum signals processing. The destructive use of quantum steganography methods to establish illegal covert communications is rising, so it is essential to introduce ways to detect hidden data in a quantum medium. Accordingly, this paper presents a frequency-based universal audio steganalysis approach to detecting quantum steganography. First, based on the quantum Fourier transform, the characteristic of the quantum spectrum centroid (QSC) was computed, and its circuit network was implemented to extract feature vectors. The proposed method classifies quantum audio signals using a quantum machine learning approach called a quantum ensemble of quantum classifiers. This approach was implemented within the framework of the Deutsch–Jozsa algorithm, which uses the superposition property to create an ensemble of classifiers evaluated in parallel, significantly increasing the computational speed. The accuracy weight of the classifiers is adjusted based on the classifiers' performance in training data classification; finally, the measurement of the first n qubits of the Deutsch–Jozsa algorithm predicts whether the quantum audio signals belong to the stego or clean class. The idea stems from the classic ensemble methods that try to build more robust models by combining different classifiers. The results show that the proposed frequency domain steganalysis method with 95% accuracy performs better than the previous methods in the time domain. [ABSTRACT FROM AUTHOR]

Appropriate signal representation is the fundamental issue of concern in all signal processing-based applications. For instance, in the context of signal compression one would require the signal representation to be such that most of the information is confined in the smallest subspace with least number of coefficients. In classification scenario, the representation has to be such that it accentuates differential information amongst classes. In the context of applications dealing with signal decomposition or denoising, one would require the representation such that it separates the input into its independent components so that individual components, i.e. the signal and noise lie in separate spaces. In this paper, we propose signal representation scheme that can be regarded as generalised KLT for multi-class scenario. We introduce an approach that would find the differential information between two classes rather than modelling individual classes separately. These classes are viewed on a common frame of reference in which one class would have a constant variance, unlike the other class which would have unequal variance along its basis vectors which would capture the differential information of one class over the other. This, when mathematically formulated, leads to the solution of the Matrix Pencil equation. This is borne out by illustrative examples on the classification of the MNIST (Deng in IEEE Signal Process Mag 29(6):141–142, 2012) and Google Speech Command Dataset (Pete in Software Engineer, G.B.T. Google Speech Command Dataset. https://ai.googleblog.com/2017/08/launching-speech-commands-dataset.html, 2017). Its applicability for biomedical data like brain state transition detection has also been explored and recorded. [ABSTRACT FROM AUTHOR]

Owing to the advantages of low cost and easy implementation, the 1-bit sampling technique attracts more and more attention recently. In many signal processing areas, such as in the direction finding problem, the complete analytical 1-bit signal is required to provide the instantaneous phase information, rather than the real-valued 1-bit signal. However, how to generate the imaginary component of the 1-bit signal continues to be an open problem in the literature. To this end, in this paper the concept of 1-bit Hilbert transform is introduced. Moreover, we propose an algorithm to construct the analytical signal for the real-valued 1-bit signal based on the 1-bit compressive sensing method. Simulations show that the proposed method can obtain the analytical 1-bit signal with a reasonable accuracy. [ABSTRACT FROM AUTHOR]

The fractional Fourier transform (FrFT) is one of the generalizations of the Fourier transform (FT). This paper is centered on the compression of different forms of signal in FrFT domain in order to extract some properties of each one with a comparison between the FrFT and the usual FT. Also, our focus here will be on two qualitative uncertainty principles for the fractional Fourier transform: The Cowling–Price's theorem and the L p - L q version of Morgan's theorem for the FrFT. These two results estimate the decay of two fractional Fourier transforms F α (f) and F γ (f) , with γ - α ≠ n π , ∀ n ∈ Z , which allows us to deduce the usual uncertainty principles between a function f and its fractional Fourier transform F γ (f) . [ABSTRACT FROM AUTHOR]

Some notes are presented on the paper "Finite-Time Control of Uncertain Fractional-Order Positive Impulsive Switched Systems with Mode-Dependent Average Dwell Time " by Liu L. et al (Circuit Syst Signal Processing 37: 3739–3755, 2018). Fractional calculus has the memory property and this is relevant to the whole process from the initial value t 0 to the current time t. However, this important property is neglected in the above paper, and consequently, the main outcome is incorrect. A counter example is presented to verify our analysis. [ABSTRACT FROM AUTHOR]

CW signal is widely used in sonar, communication, biomedicine and other fields. But because of its low Doppler tolerance, many detectors for CW signal are limited in large Doppler. To solve this problem, this paper proposes a new detector based on the consistency of sub-array instantaneous frequency sequence. The idea of the detector is as follows: First, use split beamforming technology to process the received signals of array. Next, extract the instantaneous frequency sequences of the output signals of the left and right sub-array, respectively. After that, calculate the variance of the difference sequence of two instantaneous frequency sequences as the detection statistics. Then, achieve the purpose of detecting CW signal. Simulation results and the verification of practical measured data show that this detector can effectively detect CW signals, and the detection performance under large Doppler is significantly better than matched filter and variance-of-instantaneous-frequency detector. [ABSTRACT FROM AUTHOR]

Decentralized mobile user communications rely on sensing and signal processing that are aided by fusion centers. Device-to-device (D2D) communications form the backend network for facilitating mobile user communication through the appropriate signal sensing and channel selection. The sensing and processing of tightly coupled channels ensure seamless uninterrupted communications with fewer outages. An imbalance in channel allocation and selection increases the gap between communication networks and signal processing systems. Unattended channel allocation and selection result in delayed communications and additional power exploitation with less reliability. This paper introduces an intelligence-based recurrent learning (IRL) scheme for optimal channel allocation and selection for mobile users' D2D communication. The objective of this paper is to select a delay-controlled channel satisfying both the data rate and power control requirements in the channel allocation. The allocated channel is analyzed through a responsive linear system transformation for its power, data rate, and time constraints in a recurrent manner. The intelligent learning technique evaluates the consistency of the channel based on a recurrent analysis. The outcome of the analysis is the selection of an optimal channel from the allocated channels that satisfies the objective and channel policies. Synchronized channel allocation achieves power-controlled communications in a cooperative manner under controlled interference. The proposed IRL minimizes D2D communication delay, transmits the power requirement and outage, and improves throughput with better reliability. [ABSTRACT FROM AUTHOR]

The discrete Gabor transform (DGT) is an important and widely used time–frequency analysis tool in signal processing. In this paper, a sparse time–frequency representation (TFR) method based on the DGT is studied using mixed ℓ 1 / 2 – ℓ 2 norm regularization which can overcome the over-sparse problem and permit a better sparsity and stability. Because the width of the window function in the DGT directly affects the time–frequency resolution and concentration of the Gabor spectrum, an adaptive optimal window width (AOWW) selection algorithm based on information entropy theory can be applied to search for the optimal width in the DGT. In this paper, the traditional DGT is converted into a constraint optimization problem by minimizing a mixed ℓ 1 / 2 – ℓ 2 norm sparse constraint of the Gabor coefficients. The numerical experimental results show that the proposed sparsity-based TFR based on the conventional DGT is an effective and powerful tool for analyzing and processing nonstationary signals, obtaining a higher time–frequency concentration and sparsity of the Gabor TFR of a given signal. [ABSTRACT FROM AUTHOR]

This paper provides an overview of recent approaches to deep learning as applied to speech processing tasks, primarily for automatic speech recognition, but also text-to-speech and speaker, language and emotion recognition. The focus is on efficient methods, addressing issues of accuracy, computation, storage, and delay. The discussion puts the speech processing tasks in the broader context of pattern recognition, comparing with signals other than speech. It also compares machine learning with other recent methods of speech analysis, e.g., hidden Markov models. The paper emphasizes a thorough understanding of the choices made in analyzing and interpreting speech signals. It minimizes use of mathematics and is aimed at non-experts; the references provide needed detail for those interested. [ABSTRACT FROM AUTHOR]

Deep neural network (DNN), being an important member of machine learning family, has been employed to serve a wide range of applications in the area of signal and image processing like pattern recognition, speech recognition, language processing, image segmentation, etc. To this aim, this paper concentrates on the design of a narrow transition-band finite impulse response (FIR) filter with the aid of back-propagation-based deep learning approach. The proposed deep learning-based approach offers a unified design framework for a variety of FIR filters. Convergence behaviour of the proposed algorithm has been proved analytically in situations when weights between adjacent layers are updated continuously. Simulation results have shown the frequency response characteristics of several FIR filters with narrow transition-band, designed with the help of proposed approach. Advantage of our design strategy has also been established in terms of magnitude response over a number of state-of-the-art techniques of recent interest. Simulation results have shown noticeable improvement in terms of transition bandwidth when compared with few existing works. Designed filter is subsequently implemented on Altera's Cyclone IV field programmable gate array (FPGA) chip, and hardware efficiency of the suggested design has strongly been established by correlating its hardware cost with many of the state-of-the-art FIR filters. [ABSTRACT FROM AUTHOR]

Manifold separation technique plays an important role in array modeling and signal processing for arbitrary arrays. By utilizing this technique, the atomic norm minimization (ANM) methods can be extended to the nonlinear arrays for DOA estimation via the generalized line spectral estimation approach. However, such an approach results in a high computational burden for the large-scale arrays. In this paper, a low-dimensional semidefinite programming (SDP) implementation to the coarray manifold separation atomic norm minimization (CMS-ANM) is proposed for a class of nonlinear arrays based on the compressed super-resolution of positive sources. The theoretical guarantee of the proposed SDP implementation for the exact recovery is presented, and the CMS-ANM-based low-complexity DOA estimation method is developed. The simulation results validate the theoretical analysis and demonstrate the satisfying trade-off for the performance and complexity. [ABSTRACT FROM AUTHOR]

In this paper, we introduce a proper approximation of the amplitude response of a digital filter to the Nth power of a Dirichlet kernel, which allows for the construction of efficient decimation architectures. We present an explicit formula to compute how this approximation affects the worst-case aliasing attenuation in the resulting spectral shape with respect to the exact function, such that the user can identify if the proposed approach is convenient for any given specifications. Additionally, a couple of digital decimation architectures are derived from this approximation. These architectures possess the easy reconfiguration capability of the state-of-the-art Cascaded Integrator-Comb (CIC) exact solution while exhibiting a higher maximum frequency of operation and a reduced utilization of hardware resources without practically affecting power consumption. [ABSTRACT FROM AUTHOR]

This paper presents a new optimal second-order design of the infinite impulse response digital differentiator. This design manifests the L 1 -error fitness function's optimization using the multi-verse optimization algorithm. The optimizing variables are obtained from the direct wave-form-based transfer function. The acquired magnitude response approximates the ideal differentiator with the mean absolute magnitude error − 45.8842 dB. The designed optimal differentiator has also been compared with the existing designs to manifests its efficacy. [ABSTRACT FROM AUTHOR]

The Hilbert transformation for generating the analytic signal or signal envelope is widely used in modern communication receivers, in radar and sonar systems. It introduces a 90∘\documentclass[12pt]{minimal}\usepackage{amsmath}\usepackage{wasysym}\usepackage{amsfonts}\usepackage{amssymb}\usepackage{amsbsy}\usepackage{mathrsfs}\usepackage{upgreek}\setlength{\oddsidemargin}{-69pt}\begin{document}$$90^{\circ }$$\end{document} phase shift of the input signal. Since the impulse response of the ideal Hilbert transformer is non-causal, it must be approximated by an FIR or IIR filter. This paper shows results of novel algorithms for designing broadband digital IIR Hilbert transformers and its implementation. The designs employ Galerkin or collocation techniques. The transfer function of the Hilbert transformer is a rational polynomial of low order and exhibits approximately linear phase. The filters match the 90∘\documentclass[12pt]{minimal}\usepackage{amsmath}\usepackage{wasysym}\usepackage{amsfonts}\usepackage{amssymb}\usepackage{amsbsy}\usepackage{mathrsfs}\usepackage{upgreek}\setlength{\oddsidemargin}{-69pt}\begin{document}$$90^{\circ }$$\end{document} phase shift requirement of Hilbert transformers almost perfectly and exhibit approximately constant group delay in the passband. The achieved image rejection ratio is typically larger than 50 dB. The quantization of the filter coefficients is realized by a Canonical Signed Digit (CSD) representation, reducing the hardware resources compared with two’s complement. The resulting filters are multiplier-less, which is crucial for high-speed signal processing and low power consumption. The design techniques and the CSD representation are realized in a MATLAB toolbox. The filters were moreover implemented in VHDL and SystemC. Additionally, a MATLAB tool for automatically generating a VHDL package containing the filter parameters has been implemented. [ABSTRACT FROM AUTHOR]

Group delay processing of audio signals has been extensively studied in the last decade. In this paper, tonic estimation in Turkish makam music is addressed using modified group delay processing. Tonic is one of the stable pitches of the performance, which serves as the reference throughout the performance. The proposed methodology does not require metadata such as the melodic pitch values of the audio files for tonic estimation. Modified group delay functions (MODGD) computed from the flattened music spectrum of non-melodic segments are bin-wise summed to form a summary-modgdgram. The peak position in the tonic range of summary-modgdgram is mapped to the tonic pitch. Two corpora of the Turkish music tonic dataset, created by the CompMusic project group, are used to assess the system. The proposed methodology outperforms the baseline last note detection (LND) method, which uses pitch contour as a prerequisite for tonic estimation. The results show the potential of processing audio signals using the phase-based approach in tonic pitch estimation. [ABSTRACT FROM AUTHOR]

Digital signal processing allows a wide range of digital applications, including audio processing. Discrete wavelet transform (DWT) is one of the most efficient ways of time–frequency analysis of an audio signal. The Internet of Things (IoT) is a technology that has been growing in commercial automation applications with the availability of low-cost modules and microcontrollers. IoT projects that use DWT for digital audio processing can solve many problems involving audio signals. In this paper, a performance comparison of an algorithm implementing DWT on three commercially available IoT devices is presented. The results show that it is possible to process signals in the 256 Hz to 6.5 KHz range, opening possibilities for future work integrating the technologies described. [ABSTRACT FROM AUTHOR]

The reconstruction of time-varying signals on graphs is a prominent problem in graph signal processing community. By imposing the smoothness regularization over the time-vertex domain, the reconstruction problem can be formulated into an unconstrained optimization problem that minimizes the weighted sum of the data fidelity term and regularization term. In this paper, we propose an approximate Newton method to solve the problem in a distributed manner, which is applicable for spatially distributed systems consisting of agents with limited computation and communication capacity. The algorithm has low computational complexity while nearly maintains the fast convergence of the second-order methods, which is evidently better than the existing reconstruction algorithm based on the gradient descent method. The convergence of the proposed algorithm is explicitly proved. Numerical results verify the validity and fast convergence of the proposed algorithm. [ABSTRACT FROM AUTHOR]

A distributed actuator fault-tolerant strategy for fuzzy multi-agent systems is investigated in this paper. The proposed strategy utilizes an adaptive methodology in designing the consensus control. Compared with well-known consensus analysis, this paper devotes to the fault effect in analyzing multi-agent consensus problem. We conducted a Lyapunov stability analysis to unfold the advantages of proposed method in the distributed fault-tolerant control. Simulation example is provided to demonstrate the effectiveness of the theoretical results. [ABSTRACT FROM AUTHOR]

The concept of frequency interleaving (FI) for digital-to-analog converters (DACs) has recently experienced renewed interest, in order to overcome the DACs’ bandwidth limitations. In this concept, the output signals of multiple DACs are interleaved in the frequency domain in order to generate a combined output signal that exceeds both the sampling rate and the bandwidth of a single DAC. In this paper, a mathematical framework for FI-DACs with a large number of DACs is presented and the inherent interdependencies between sampling rate, the number of samples, LO frequencies, symbol rate and analog frequency responses are highlighted. A mathematical expression for the distribution of data samples among the DACs is presented, taking into account bandwidth limitations of the analog components as well as guard bands to mitigate cross talk. In order to balance the FI-DAC’s parameters a mathematical optimization approach is introduced and illustrated with examples. Both simulation and experimental results validate the FI-DAC concept, and an outlook on implementation issues completes this paper. [ABSTRACT FROM AUTHOR]

This paper proposes a robust asynchronous controller for continuous-time Markov jump linear systems (MJLSs). The asynchronous structure considers a case in which the Markov chain governing candidate controllers does not match the chain which manages the switching between system modes. This type of asynchronousy arises because the real-time, exact and precise detection of the system modes is not practical; therefore, the observed modes slightly differ with the actual modes. This paper models the asynchronousy through an additional, observed Markov chain which depends on the original Markov chain of the system according to uncertain transition probabilities. By this representation, the whole system is viewed as a non-homogeneous MJLS, and the sufficient stabilizability conditions as well as the controller gains are obtained through multiple mode-dependent Lyapunov functions. This approach leads to less conservative design results than the previous mode-independent schemes and proposes a much simple methodology to obtain the controller than the previous mode-dependent studies. For more generality, the proposed controller also takes the possible gain variations occurring in the implementation procedures into account and reports all the results in terms of linear matrix inequalities. Simulation results on a vertical takeoff and landing helicopter are presented and compared with the common mode-independent controller to illustrate the effectiveness of the developed method. [ABSTRACT FROM AUTHOR]

Noninvasive nature of Electrocardiogram (ECG) signal makes it widely accepted for cardiac diagnosis. During the process of data acquisition, ECG signal is generally corrupted by a number of noises. Further, during ambulatory monitoring and wireless recording, ECG signal gets corrupted by additive white Gaussian noise. Without affecting the morphological structure, denoising of ECG signal is essential for proper diagnosis. This paper presents an ECG denoising method based on an effective combination of non-local means (NLM) estimation and empirical mode decomposition (EMD). Earlier works have shown that the patch-based NLM approach is insufficient for denoising the under-averaged region near high-amplitude QRS complex. To address this issue, the denoised signal obtained by NLM is decomposed into intrinsic mode functions (IMFs) using EMD in this work. Next, thresholding of the IMFs is done using the instantaneous half period criterion and the soft-thresholding to obtain the final denoised output. Furthermore, the modified empirical mode decomposition (M-EMD) is used in the place of standard EMD to reduce the computational cost. Performance of the proposed method is tested on a number of ECG signals from the MIT-BIH database. The experimental results presented in this paper show that the aforementioned shortcoming of the NLM method is addressed to a large extent. Moreover, the proposed approach provides improved performance when compared to different state-of-the-art ECG denoising methods. [ABSTRACT FROM AUTHOR]

The binary-coded decimal (BCD) being the more accurate and human-readable representation with ease of conversion is prevailing in the computing and electronic communication. In this paper, a tree-structured parallel BCD addition algorithm is proposed with the reduced time complexity O(N(log2b)+(N-1)), where N = number of digits and b = number of bits in a digit. BCD adder is more effective with a lookup table (LUT)-based design, due to field programmable gate array (FPGA) technology’s enumerable benefits and applications. A size-minimal and depth-minimal LUT-based BCD adder circuit construction is the main contribution of this paper. The proposed parallel BCD adder gains a radical achievement compared to the existing best known LUT-based BCD adders. The proposed BCD adder is coded in VHDL and implemented in a Virtex-6 platform targeting XC6VLX75T Xilinx FPGA with a -3 speed grade by using ISE 13.1. The proposed BCD adder provides prominent better performance with 20.0% reduction in area and 41.32% reduction in delay for the post-layout simulation. Since the proposed circuit is improved in both area and delay parameter, it is 53.06% efficient in terms of area-delay product compared to the best known existing BCD adder, which is surely a significant achievement. Moreover, the proposed design consumes 34.28% less power in comparison with existing best known approach at a clock frequency of 200 MHz and a reference voltage of 5 V. [ABSTRACT FROM AUTHOR]

Challenges for optimal design of infinite impulse response digital filters include the high nonconvexity of design problem and inevitable stability constraints on the filters. To reduce the nonconvexity and tackle the stability constraints, a sequential partial optimization (SPO) algorithm was recently developed to divide the design problem into a sequence of subproblems, each updating only two second-order denominator factors. But the convergence of that algorithm is not guaranteed. By applying an incremental update with an optimized step length in each subproblem, this paper presents an improved SPO algorithm which is guaranteed to converge to a Karush-Kuhn-Tucker (not necessarily global) solution of the design problem. This paper also extends the SPO algorithm to a more general case where the number of denominator factors optimized in the subproblems can be any positive number smaller than half of the denominator order. Convergence performance of the algorithm is shown by the design of two example filters with typical specifications widely adopted in the literature. Comparisons with state-of-the-art methods demonstrate that the improved SPO algorithm obtains better filters than the competing methods in terms of the maximum magnitude of frequency-response error. [ABSTRACT FROM AUTHOR]

Edge detection is an important aspect of image processing to improve image edge quality. In the literature, there exist various edge detection techniques in spatial and frequency domains that use integer-order differentiation operators. In this paper, we have implemented feature and contrast enhancement of image using Riemann-Liouville fractional differential operator. Based on the direction of strong edge, we have evaluated edge components and carried out a performance analysis based on several well-known metrics. We have also improved the pixel contrast based on foreground and background gray level. Moreover, by theoretical and experimental results, it is observed that the proposed feature and contrast enhancement outperforms the existing methods under comparison. We have discussed that the edge components calculated using fractional derivative can be used for texture and contrast enhancement. This paper is based on fractional-order differentiation operation to detect edges with the help of the directional edge components across eight directions. The experimental comparison results are shown in tabular form and as qualitative texture results. The six experimental input images are used to analyze various performance metrics. The experiments show that for any grayscale image the proposed method outperforms classical edge detection operators. [ABSTRACT FROM AUTHOR]

This paper focuses on direction finding using a co-prime array from the view of a difference co-array. According to the corresponding relationship between the correlation lag and virtual element position of the difference co-array, from the correlation matrix of the co-prime array, the desired correlation units are extracted as single-snapshot data of the virtual co-array elements of the co-prime array and are then coherently accumulated into a pseudo-beam pattern. Because the difference co-array of a co-prime array consists of a group of contiguous virtual elements and multiple non-uniform virtual elements, this paper considers pseudo-beam-forming using only contiguous virtual elements, as in existing studies, and all of the virtual elements. Compared with the existing sub-beam multiplication method, pseudo-beam-forming reduces the negative effect from grating lobes and resolves more uncorrelated sources than the number of physical elements. Moreover, application of non-uniform virtual elements improves the resolvable source number, angle resolution and noise immunity, which are analyzed quantitatively based on the proposed distribution characteristic of virtual elements. Finally, to suppress side-lobe interference caused by the non-uniform virtual elements, we introduce and evaluate three coherence weighting factors, namely coherence factor (CF), phase coherence factor (PCF) and sign coherence factor (SCF), where CF is proved to be ineffective and SCF is optimal in suppression and computation performance. [ABSTRACT FROM AUTHOR]

The model-based signal decomposition algorithm is an important research direction in the field of digital signal processing, especially based on the amplitude modulation and frequency modulation (AMFM) model. In this paper, a signal decomposition algorithm based on multiple complex AMFM model is proposed to analyze multi-model data sets. Firstly, the analyzed signal is converted into the form of the analytic signal because of the simple representation of the AMFM model in the analytic domain. Then, the multi-model optimization equation of the analytic signal is realized by the estimated instantaneous frequency (IF) of each model, which can be estimated by time–frequency analysis (TFA). Finally, each model parameter of the optimization equation is solved by the partial differential equation and the alternating direction method of multipliers method (ADMM) to find the global optimal solution of the signal. In the optimization equation, we introduce the leakage factor to improve the extraction accuracy of the model; at the same time, we employ the cyclic iteration method to optimize the equation parameters to improve the convergence rate of the algorithm. Several examples of the simulated and real-life signals are provided to show that the proposed algorithm can accurately estimate the parameters of each model in the signal. [ABSTRACT FROM AUTHOR]

The complex kernel adaptive filter (CKAF) has been widely applied to the complex-valued nonlinear problem in signal processing and machine learning. However, most of the CKAF applications involve the complex kernel least mean square (CKLMS) algorithms, which work in a pure complex or complexified reproducing kernel Hilbert space (RKHS). In this paper, we propose the generalized complex kernel affine projection (GCKAP) algorithms in the widely linear complex-valued RKHS (WL-RKHS). The proposed algorithms have two main notable features. One is that they provide a complete solution for both circular and non-circular complex nonlinear problems and show many performance improvements over the CKAP algorithms. The other is that the GCKAP algorithms inherit the simplicity of the CKLMS algorithm while reducing its gradient noise and boosting its convergence. The second-order statistical characteristics of WL-RKHS have also been developed. An augmented Gram matrix consists of a standard Gram matrix and a pseudo-Gram matrix. This decomposition provides more underlying information when the real and imaginary parts of the signal are correlated and learning is independent. In addition, some online sparsification criteria are compared comprehensively in the GCKAP algorithms, including the novelty criterion, the coherence criterion, and the angle criterion. Finally, two nonlinear channel equalization experiments with non-circular complex inputs are presented to illustrate the performance improvements of the proposed algorithms. [ABSTRACT FROM AUTHOR]

Ultra-high-definition (UHD) video standards demand processing speed from 60 to 120 fps. These standards require relatively huge resources for providing such high processing speed. In this paper, an area-efficient and high-speed two-dimensional (2D) 2 × 2 and 3 × 3 block parallel scalable recursive convolution (BPSRC) architectures are proposed. The 2 × 2 and 3 × 3 BPSRC architectures are used to implement block parallel filters with small to large kernel sizes but limited to multiples of 2 and 3, respectively. For a block parallel convolution, the spatial window is partitioned into fixed size blocks for parallel processing of the block outputs. The algorithm proved effective with respect to area and computational time. The increase in kernel size does not affect the processing time but increases the hardware cost. However, the increase in hardware cost is considerably less when compared with conventional block parallel convolution (BPC). Overall, multiplier complexity is reduced by a factor of 4/9 and 9/16 for 3 × 3 and 2 × 2 BPSRC implementation of 2D finite impulse response (FIR) filters, respectively, over conventional BPC. A throughput of 1.55 Giga operations per second is achieved with 2 × 2 BPSRC, and that of 1.86 Giga operations per second is achieved with 3 × 3 BPSRC on Virtex 7 XC7VX485T FPGA. [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]

This work is part of the Psypocket project which aims to conceive an embedded system able to recognize the stress state of an individual based on physiological and behavioural modifications. In this paper, one of the physiological data, the electrocardiographic (ECG) signal, is focused on. The QRS complex is the most significant segment in this signal. By detecting its position, the heart rate can be learnt. In this paper, a field-programmable gate array (FPGA) architecture for QRS complex detection is proposed. The detection algorithm adopts the integer Haar transform for ECG signal filtering and a maximum finding strategy to detect the location of R peak of the QRS complex. The ECG data are originally recorded by double-precision decimal with the sampling frequencies of 2000 Hz. For the FPGA implementation, they should be converted to integers with rounding operation. To find the best multiplying factor for rounding, the comparison is performed in MATLAB. Besides, to reduce the computation load in FPGA, the feasibility of the reduction in the sampling frequency is tested in MATLAB. The FPGA Cyclone EP3C5F256C6 is used as the target chip, and all the components of the system are implemented in VHSIC hardware description language. The testing results show that the proposed FPGA architecture achieves a high detection accuracy (98.41%) and a good design efficiency in terms of silicon consumption and operation speed. The proposed architecture will be adopted as a core unit to make a FPGA system for stress recognition. [ABSTRACT FROM AUTHOR]

In this paper, we propose a model for empirical mode decomposition algorithm to represent nonlinear and nonstationary data. Two threshold operators (Signum and Relu) and the set of fundamental operators of a linear time invariant system (viz. delay, summer, and scalar multiplier) are used to completely characterize the proposed model. Models for finding number of zero crossings and number of local extrema of residual intrinsic mode function are also discussed. These representations are also based on the same block of elements, n-bit asynchronous up-counter and binary to decimal conversion. We obtain a closed-form expression for residual intrinsic mode functions to decompose the input signal using the proposed model. Performance of the proposed model is analyzed and discussed in terms of orthogonality index and percentage error in energy. Also, linear-in-the-parameter model for the two threshold operators is discussed in this paper. [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, we propose an $$l_0$$ -norm penalized shrinkage linear affine projection ( $$l_0$$ -SL-AP) algorithm and an $$l_0$$ -norm penalized shrinkage widely linear affine projection ( $$l_0$$ -SWL-AP) algorithm. The proposed algorithms provide variable step-size by minimizing the noise-free a posteriori error at each iteration and introduce an $$l_0$$ -norm constraint to the cost function. The $$l_0$$ -SWL-AP algorithm also exploits noncircular properties of the input signal. In contrast with conventional AP algorithms, the proposed algorithms increase the estimation accuracy for time-varying sparse system identification. A quantitative analysis of the convergence behavior for the $$l_0$$ -SWL-AP algorithm verifies the capabilities of the proposed algorithms. To reduce the complexity, we also introduce dichotomous coordinate descent (DCD) iterations to the proposed algorithms ( $$l_0$$ -SL-DCD-AP and $$l_0$$ -SWL-DCD-AP) in this paper. Simulations indicate that the $$l_0$$ -SL-AP and $$l_0$$ -SWL-AP algorithms provide faster convergence speed and lower steady-state misalignment than the previous APA-type algorithms. The $$l_0$$ -SL-DCD-AP and $$l_0$$ -SWL-DCD-AP algorithms perform similarly to their counterparts but with reduced complexity. [ABSTRACT FROM AUTHOR]

The extrapolation theorem is an essential and important theory in signal analysis. Since the offset linear canonical transform (OLCT) has proven to be an effective method in signal processing and optics, extrapolation theorems for continuous band-limited signals associated with the OLCT have been well studied. However, the extrapolation theorem for discrete signals in the OLCT domain remains unknown. In this short paper, by using the discrete generalized prolate spheroidal sequences (DGPSSs), the extrapolation theorem for discrete signals is presented. First, the definition of DGPSSs in the OLCT domain is proposed. Subsequently, the extrapolation theorem for OLCT discrete band-limited signals is obtained using the DGPSSs. In addition, a simplified computation of the extrapolation theorem for discrete band-limited signals in the OLCT domain is also obtained. Finally, simulation results are presented to show the utility and efficacy of the derived theorems. [ABSTRACT FROM AUTHOR]

Two-dimensional Wigner distribution (2D WD) and ambiguity function (2D AF) are important tools for time–frequency analysis and signal processing, especially in the analysis of 2D chirp signals. In this paper, based on the classical 2D WD and 2D AF, a new kind of 2D WD and 2D AF associated with 2D nonseparable linear canonical transform (2D NSLCT) are proposed, namely NSLCWD and NSLCAF. The definition obtained by this method not only has the advantages of 2D NSLCT, but also has the good characteristics of 2D WD (2D AF). The emergence of the new definition also broadens the development of 2D theory to a certain extent. Then, we derive a series of important properties related to the new definition and present theoretical proof. Moreover, we find that the original 2D WD and 2D AF exist as special cases of two new definitions. In addition, the relationship between the new definition and 2D NSLCT is also the focus of our discussion. Finally, the NSLCWD and NSLCAF are applied to detect different forms of 2D chirp signals. The results confirm that our new definitions, NSLCWD and NSLCAF, are useful and effective. [ABSTRACT FROM AUTHOR]