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We propose an In-Memory Computing (IMC) scheme to implement Fast Fourier Transform (FFT) for noise signal processing using flash memory. By taking advantage of the algorithms of complex number calculation and butterfly operation in FFT, the analyses of low-frequency noise (LFN) and random telegraphy noise (RTN) signals using flash-based IMC were demonstrated successfully, showing the frequency properties of 1/ ${f}$ and 1/ ${f}$ 2, respectively, and matching well with the Matlab numerical calculation. An energy efficiency of 2.7 TOPS/W has been achieved with a dynamic power of 27.7 mW. Our proposed IMC scheme provides an efficient way to enable FFT using a hardware solution and is extendable for other important applications such as image processing and voice recognition. [ABSTRACT FROM AUTHOR]

Reduced biquaternion is a four dimensional hyper–complex number which is commutative algebra and an extension of complex. Due to this property, the corresponding fast algorithm is designed in time frequency analysis which can better fit the convolution theorem than the non-commutative quaternion. In addition, the reduced biquaternion can be interpreted as a single point in 2-dimensional hyperbolic space with its algebraic structure which has more capacity and variable ability than the Euclidean space. However, the algebra properties of the reduced biquaternion have not yet been well explored in the deep convolutional neural network. In this paper, we develop a new deep network structure, namely reduced biquaternion valued convolutional neural network (RQV-CNN). The proposed RQV-CNN includes basic modules of reduced biquaternion convolution layer and fully connection layer. Extensive experiments on color image classification and color image denoising are conducted to evaluate the promising performance of the RQV-CNN framework. The results show that RQV-CNN outperforms the real-valued CNN (RV-CNN), complex-valued CNN (CV-CNN), and quaternion-valued CNN (QV-CNN) with same structures. The source code can be found at https://github.com/tasteimage/RQVCNN. [ABSTRACT FROM AUTHOR]

The determination of complex elastic, piezoelectric, and dielectric coefficients of piezoelectric ceramics is important for precision engineering devices. Here, a novel method for determining the optimal material coefficients is presented. This method minimizes the average relative error in the values of conductance, susceptance, resistance, and reactance obtained from the 1-D model in the IEEE Standard (ANSI/IEEE Std 176-1987) and the experimental measurements of the first and second radial modes. Poisson’s ratio is assumed to be a complex number in addition to the elastic, piezoelectric, and dielectric coefficients in the present method. The global minimum of the average relative error is found by searching the minimum among all local minima of the average relative error, which are obtained with the Levenberg–Marquardt modification of Newton’s method from randomly chosen initial conditions. The optimal material coefficients of an APC 850 disk and an APC 855 disk are calculated with this method. The uncertainties in the optimal material coefficients are evaluated by calculating the minimum average relative error when the real or imaginary part of each coefficient is prescribed. [ABSTRACT FROM AUTHOR]

Falcon is one of the promising digital-signature algorithms in NIST’s ongoing Post-Quantum Cryptography (PQC) standardization finalist. Computational efficiency regarding software and hardware is also the main criteria for PQC standardization. In this paper, we present an efficient Falcon software implementation on ARMv8 environment. Until now, most of the software optimization on PQC algorithms have been conducted on 32-bit ARM (Cortex-M4) and typical CPUs (Intel and AMD CPUs). However, ARMv8 including Cortex-A30, 50, and 70 series have been widely used for various IoT (Internet of Things) applications, Edge computing devices, and OBUs (On Board Units) in autonomous driving cars. For optimizing the performance of Falcon, we take full advantage of NEON engine which is a kind of parallel processing unit in ARMv8 MCU. The main computation in Falcon belongs to polynomial multiplications in Complex number domain and Integer domain. Typically, FFT (Fast Fourier Transformation)-based multiplication method and NTT (Number Theoriteic Transform)-based multiplication method have been widely used for efficient polynomial multiplications in Complex number domain and Integer domain, respectively. Thus, in order to enhance the overall performance of Falcon, we improve the FFT-based multiplication method and NTT-based multiplication method by utilizing NEON engine in ARMv8. Specifically, we parallelize the overall process (FFT/NTT transformation, pointwise multiplication, and inverse FFT/NTT transformation) of FFT-based polynomial multiplication method and NTT-based polynomial multiplication method with strategically utilizing the NEON engine and vector instructions. Furthermore, we minimize the number of redundant memory accesses during FFT/NTT-based polynomial multiplication by making the most of available registers in NEON engine. Through the proposed parallel FFT/NTT-based polynomial multiplications, the proposed Falcon software provides 15.1% (resp. 18.1%), 16.5% (resp. 17.1%), and 65.4% (resp. 69.4%) of performance improvement in keypair generation, signing, and verification at security level 1 (resp. 5) compared with the reference Falcon implementation submitted to the final round of NIST PQC competition. Furthermore, as far as we know, this is the first optimized implementation of Falcon on ARMv8 environment.

This research centers on the application of the discrete-time Taylor–Fourier transform (DTTFT) algorithmic implementation for phasor estimation on a field-programmable gate array board. The system employs a finite impulse response structure of a digital Taylor–Fourier filter to extract amplitude and phase information. The hardware description utilizes a multiply accumulator architecture with only forty embedded 9-bit multiplier elements, achieving an 18-bit input–output resolution. Performance assessment involves signal analysis through FPGA-in-the-loop simulation in MATLAB/Simulink. Findings demonstrate that the DTTFT-based phasor estimator can be effectively characterized using VHDL code and implemented on an Intel D2-115 board. [ABSTRACT FROM AUTHOR]

The detection of metal particles in oil is of great significance to the fault diagnosis of mechanical equipment. Real domain detection methods cannot simultaneously identify the sizes and materials of particles. Because a metal particle in an alternating magnetic field induces a complex domain signal whose strength relates to the particle properties, this article introduces a double lock-in amplifier circuit (DLAC) into a triple-coil inductive particle sensor to detect the complex domain signal under noise interference conditions. Furthermore, various sizes of red copper, brass, aluminum, iron, and type 403 steel particles are chosen as the experimental materials. For the first time, the complex domain signals induced by the five kinds of particles are detected. Hence, in the complex plane, a method is proposed to identify the size and material of a particle based on the amplitude and phase of these signals. With the high sensitivity and strong antinoise ability of the DLAC, this system can detect 25- $\mu \text{m}$ iron particles and 70- $\mu \text{m}$ copper particles. This study presents a new strategy for the online detection of particles in oil. [ABSTRACT FROM AUTHOR]

In coherent optics transmission systems, the digital signal processor (DSP) application-specific integrated circuit (ASIC) is the most power-hungry part of the optical transceiver. Already in the edge of transistor technology, to achieve the power budget, we must look for opportunities to further optimize the designs. This letter explores a dynamic duty cycle for reducing the consumption in the pipeline of such DSP ASICs. We exploit the characteristics of estimator algorithms to introduce a dynamic duty cycle, reducing the mean consumption of designs originally constrained only for worst-case scenarios. We present the methodology to implement duty cycle control using the carrier frequency offset estimator (CFE) algorithm as case study, achieving in simulation level from 22% to 74% power consumption reduction in this algorithm, varying on-chip operation conditions. [ABSTRACT FROM AUTHOR]

Noisy intermediate-scale quantum (NISQ) computers face significant reliability challenges because they are vulnerable to quantum noise, which severely limits their fidelity in quantum applications. In particular, deep circuits with a large number of quantum gates are susceptible to errors as they are more likely to lose their states in a deep circuit. In this paper, we propose a quantum circuit for square root operation that generates correct results on NISQ devices. The square root operation is used in many applications such as complex number computations, computer graphics, etc. While there have been limited studies on quantum square root circuits, none of them can be implemented on NISQ devices. In this work, we simplify the structure of an exact quantum square root circuit and reduce the number of quantum gates. The proposed design reduces the complexity of the square root circuit while maintaining the same level of precision in outputs. In addition, we exploit approximate computing to simplify the circuit further and run it on a real quantum computer. Approximate computing is used in classical computers to enhance the power and/or performance in exchange for accuracy. We exploit approximate computing for a different purpose, which is reducing the depth as well as the number of quantum gates in the square root circuit. To validate the effectiveness of our approach, we conduct experiments on an IBM quantum computer, where our circuit produces meaningful results. Furthermore, we present examples of error-resilient applications to demonstrate the validity of our approximate circuit.

The growing number of data privacy breaches and associated financial losses have driven the demand for private database queries. Clients typically submit queries that involve both search and computation operations, such as counting students under a certain age or calculating the BMI of employees above a specific age. Existing protocols often face limitations due to reliance on specific-purpose encryption schemes or multiple communication rounds between clients and servers. In this work, we present a unified framework utilizing fully homomorphic encryption techniques to efficiently and privately process queries with search and computation operations. Our contributions include a homomorphic encryption-based private comparison algorithm, called the layered comparison algorithm, which achieves a 2.6-6.6X performance improvement compared to algorithms from prior work; a fast Fourier transform-based preprocessing method enabling accurate large integer arithmetic operations in the encrypted domain; and a scalable database encoding method. Evaluation results demonstrate the practicality of our system, as it processes an aggregated query for a 1k-row encrypted database in approximately 4.53 seconds. [ABSTRACT FROM AUTHOR]

Sarcasm, sentiment, and emotion are three typical kinds of spontaneous affective responses of humans to external events and they are tightly intertwined with each other. Such events may be expressed in multiple modalities (e.g., linguistic, visual and acoustic), e.g., multi-modal conversations. Joint analysis of humans’ multi-modal sarcasm, sentiment, and emotion is an important yet challenging topic, as it is a complex cognitive process involving both cross-modality interaction and cross-affection correlation. From the probability theory perspective, cross-affection correlation also means that the judgments on sarcasm, sentiment, and emotion are incompatible. However, this exposed phenomenon cannot be sufficiently modelled by classical probability theory due to its assumption of compatibility. Neither do the existing approaches take it into consideration. In view of the recent success of quantum probability (QP) in modeling human cognition, particularly contextual incompatible decision making, we take the first step towards introducing QP into joint multi-modal sarcasm, sentiment, and emotion analysis. Specifically, we propose a QUantum probabIlity driven multi-modal sarcasm, sEntiment and emoTion analysis framework, termed QUIET. Extensive experiments on two datasets and the results show that the effectiveness and advantages of QUIET in comparison with a wide range of the state-of-the-art baselines. We also show the great potential of QP in multi-affect analysis. [ABSTRACT FROM AUTHOR]

Radio frequency interference (RFI) has become a serious problem for synthetic aperture radar (SAR) systems. In recent years, numerous methods have emerged for the suppression of RFI. These methods often rely on assumptions regarding the narrowband characteristics and low-rank nature of radar echoes or interference signals. However, these assumptions may not hold true when dealing with complex RFI. To mitigate complex interference in dual-polarization (dual-pol) SAR systems, a proposed method is the utilization of a filtering algorithm based on sidelobe cancellation (SLC) technology. This method leverages the discrepancies in gain and coherence between observed targets and RFI signals in the co-polarization (co-pol) and cross-polarization (cross-pol) channels. It suppresses interference through SLC filtering on a range line by range line basis in the focused image domain, serving as a post-processing step. The method is capable of tackling multiple complex interferences optimally under the least mean square criterion, such as narrowband, wideband, and noise-modulated pulsed interferences. Both simulation and real data experiments show that the method can remove RFI artifacts and effectively recover the desired SAR images. [ABSTRACT FROM AUTHOR]

Ship target recognition is a vital task in synthetic aperture radar (SAR) imaging applications. Although convolutional neural networks have been successfully employed for SAR image target recognition, surpassing traditional algorithms, most existing research concentrates on the amplitude domain and neglects the essential phase information. Furthermore, several complex-valued neural networks utilize average pooling to achieve full complex values, resulting in suboptimal performance. To address these concerns, this letter introduces a complex-valued convolutional neural network (CVGG-Net) specifically designed for SAR image ship recognition. CVGG-Net effectively leverages both the amplitude and phase information in complex-valued SAR data. Additionally, this study examines the impact of various widely used complex activation functions on network performance and presents a novel complex max-pooling (CMaxPool) method, called Complex Area Max-Pooling. Experimental results from two measured SAR datasets demonstrate that the proposed algorithm outperforms conventional real-valued convolutional neural networks. The proposed framework is validated on several SAR datasets. [ABSTRACT FROM AUTHOR]

In inverse synthetic aperture radar (ISAR) imaging of maneuvering targets, traditional high-order phase compensation methods are usually limited by the heavy computational burden of parameter estimation, making it hard to apply in real-time cases. In this letter, an efficient ISAR motion compensation method is proposed based on fast parameter estimation. A novel analytic expression of the image entropy is derived with the help of a compensation matrix based on sinc function interpolation, which can eliminate the high-order phase term. Hence, the parameter estimation is transformed into an optimization problem where the gradient descent algorithm (GDA) can be adopted to accelerate the computation speed. Compared with other recently proposed methods, our method is superior in high robustness and low computing cost. Experiments with simulated and real data have verified that without affecting the image quality, the computing time of our method can be reduced to about 1/4 of the traditional search algorithm. [ABSTRACT FROM AUTHOR]

Moving targets always defocus and shift outside the scene in video synthetic aperture radar (video SAR) image sequences. However, the shadows of moving targets are immune to these issues and can reveal the true position of the moving targets. As such, by tracking the shadows of moving targets in the video SAR image sequence, it becomes feasible to keep track of these targets. Nevertheless, due to the small pixel size and time-varying characteristics of the target shadow, current prevailing tracking methods often prove insufficient for direct tracking of the shadow. In this letter, a shadow-assisted tracking method for moving targets based on a multilevel discriminant correlation filters network (MDCFnet) is proposed. Primarily, we designed a reverse feature pyramid network (RFPN) that integrates multiple high-level features into low-level features to obtain multiple features with higher distinguishability and resolution, thereby enhancing the final tracking accuracy and precision. Furthermore, we devised multilevel discriminant correlation filters (MDCFs) to perform filtering tracking under multiple feature maps. Real dataset processing results are provided to demonstrate that the proposed method outperforms other state-of-the-art methods. [ABSTRACT FROM AUTHOR]

Noncooperative bistatic radars (NCBRs) do not transmit electromagnetic waves but use emitters of other host radars as illuminators for target detection. If the noncooperative bistatic radar and the host radar belong to the opposing camps, respectively, the noncooperative bistatic radar will be a great challenge to the host radar. To ensure the electromagnetic information security of host radars, a joint design of host radar waveforms and mismatched filters (MMFs) is proposed to reduce the detection performance of NCBRs. For NCBRs, we raise the autocorrelation function sidelobe levels of the transmitted waveform to deteriorate its detection performance. For host radars, we design an MMF to improve the target detection performance of such transmitted waveforms. The simulation results show that the effects of raising and suppressing the range sidelobes in the model are both achieved. The price is an additional SNR loss (about 2 dB). Two types of transmission waveforms are designed including mainlobe broadening (MLB) waveforms and false peaks (FPs) waveforms, by controlling the autocorrelation function elevated position parameter. The constant false alarm detection results show that the designed waveforms fulfill the requirements of noncooperative bistatic radar countermeasures. [ABSTRACT FROM AUTHOR]

Extraction road from remote sensing (RS) images is a challenging topic because of the inhomogeneous intensity, nonconsistent contrast, and very cluttered background of satellite images. Most previous approaches, relying on convolutions or self-attention, are built on the local operation or global modeling on the spatial domain but are difficult to capture weak and continuous road objects. The spectral representation of road image features and modulation learning on it provides a novel long-range-dependent and fine-grained feature representation mechanism. Based on it, we propose a novel road extraction network on RS images, called an adaptive Fourier filtered U-shaped network (AFU-Net) in this letter, which relies on modulation learning on the Fourier domain. The AFU-Net is composed of modulation learner (MoL) basic blocks and follows the pipeline of the classical U-Net model. The basic MoL block includes a global MoL (GML) block for global spectral modulation learning and an attentive MoL (AML) block which contains two parallel layers, i.e., phase-modulated filter (PMF) and magnitude-modulated filter (MMF), for fine-grained spectral modulation on the Fourier spectrum. The experiments on two public datasets, such as Massachusetts roads and DeepGlobe road datasets have shown the outstanding performance of AFU-Net on the metrics of accuracy, precision, recall, and mean intersection over union (mIoU). [ABSTRACT FROM AUTHOR]

This paper investigates the secrecy performance for simultaneously transmitting and reflecting reconfigurable intelligent surface (STAR-RIS) assisted downlink multi-carrier non-orthogonal multiple access (NOMA) networks, consisting of multiple legitimate users and eavesdroppers. We propose two STAR-RIS-NOMA schemes for maximizing the secrecy performance by jointly optimizing the transmission and reflection beamforming of the STAR-RIS, the transmit beamforming of the base station (BS), the power allocation coefficients and the user pairing vector under the full channel state information (CSI) and the statistical CSI of the eavesdropping channel, respectively. For the full CSI available to the BS, an alternating beamforming algorithm is proposed for maximizing the secrecy sum rate. Specifically, we first propose a user pairing scheme based on the differences of user’s channel gains. Then the beamforming vectors and the power allocation coefficients are optimized based on the techniques of semidefinite programming and surrogate lower bound approximation, respectively. For the statistical CSI available to the BS, the problem of minimizing the maximum secrecy outage probability (SOP) is investigated. By invoking the subroutines of alternating beamforming algorithm, we first derive an exact SOP given the user pairing. Then, we conceive the beamforming vectors and the power allocation coefficients by linear matrix inequality and linear programming, respectively. Simulation results show that: 1) the secrecy performance of the proposed STAR-RIS-NOMA scheme outperforms the existing conventional RIS-NOMA scheme and RIS assisted orthogonal multiple access (RIS-OMA) scheme; 2) the proposed alternating beamforming algorithm is capable of achieving a near-optimal performance with low complexity compared to the exhaustive search. [ABSTRACT FROM AUTHOR]

The concept of complex Pythagorean fuzzy set $(\mathbf {CPFS})$ is recent development in the field of fuzzy set $(\mathbf {FS})$ theory. The significance of this concept lies in the fact that this theory assigned membership grades $\psi $ and non-membership grades $\hat {\psi }$ from unit circle in plane, i.e., in the form of a complex number with the condition $(\psi)^{2}+ (\hat {\psi })^{2}\le 1$ instead from [0, 1] interval. This is an expressive technique for dealing with uncertain circumstances. The aim of this study is to proceed the classification of the unique framework of $\mathbf {CPFS}$ in algebraic structure that is field theory and examine its numerous algebraic features. Also, we initiate the important examples and results of certain field. Furthermore, we illustrate that every complex Pythagorean fuzzy subfield ( $\mathbf {CPFSF}$ ) generates two Pythagorean fuzzy subfields $(\mathbf {PFSFs})$ . We also prove many useful algebraic aspects of this notion for a $\mathbf {CPFSF}$ . Moreover, we demonstrate that intersection of two complex Pythagorean fuzzy subfields $(\mathbf {CPFSFs})$ is also $\mathbf {CPFSF}$ . Additionally, we discuss the novel idea of level subsets of $\mathbf {CPFSFs}$ and demonstrate that level subset of $\mathbf {CPFSF}$ form subfield. Additionally, we improve the application of this theory to show the concept of the direct product of two $\mathbf {CPFSFs}$ is also a $\mathbf {CPFSF}$ and produce several novel results on direct product of $\mathbf {CPFSFs}$ . Finally, we explore the homomorphic images and inverse images of $\mathbf {CPFSFs}$ .

This article proposes a new grid impedance detection method incorporating the complex coefficient filter (CCF) with full-order capacitor current observer for a T-type three-level grid-connected inverter controlled by the inverter output current feedback. Compared with conventional CCF impedance detection algorithms, the proposed method reduces the number of current sensors and detects the grid impedance accurately. First, based on the sampled inverter output current and grid-connected voltage signals, the grid-connected current is estimated. Then, the CCF method is used to extract harmonics from the grid-connected current and voltage signals to calculate the grid impedance. Finally, the correctness of the full-order capacitor current observer is verified by simulation and the feasibility and effectiveness of the proposed algorithm are verified experimentally based on a laboratory prototype. [ABSTRACT FROM AUTHOR]

This article explores the unification and simplification of the small-signal model for representative inductive power transfer systems. Various famous compensations are included, such as the S–S, LCC–S, and LCC–LCC. Through system decomposition, the uniform model concept is applied to the inverter, rectifier, and resonant tank. Meanwhile, the model complexity and similarity are discussed for various resonant tanks, and proper simplification is used to reduce the model order. The uniform equivalent circuit model is finally proposed for the target systems, based on which the explicit transfer function can be represented by a general polynomial equation. The model order of the S–S, LCC–S, and LCC–LCC systems are five, two, and five, respectively. The experiment shows the model is accurate up to one-fifth of the switching frequency for all the target systems. [ABSTRACT FROM AUTHOR]

Rotating blades are key turbomachinery components with high failure risk due to harsh working conditions. Thus, monitoring the running state of blades is urgently required. Blade tip timing (BTT) is regarded as a potential technology for blade vibration monitoring owing to its noncontact nature and low cost. However, severe undersampling and strict probe layout hinder the application of BTT. As a result of severe undersampling, aliasing and its suppression are the main research topics in BTT. However, in this article, a biprobes method for BTT based on the utilization of aliasing is proposed to identify the natural frequencies of blades. Active aliasing is used in this method to produce aliasing findings at various equivalent sampling frequencies, and the closed-form robust Chinese remainder theorem is used for dealiasing. Unlike the conventional multirate sampling systems for BTT that use a multiprobe configuration or varied speed, the suggested method utilizes varying delay durations to obtain varying equivalent sample frequencies. Furthermore, to ensure that active aliasing and dealiasing form a closed loop, an included angle coprime layout is proposed. Finally, the robustness and effectiveness of the proposed method are verified through numerical and experimental validation. [ABSTRACT FROM AUTHOR]

This article investigates a decoupled vector current control strategy for grid-tied single-phase nine-level Packed E-Cell (PEC) inverters for vehicle-to-grid (V2G) technologies. The control design is based on a loop shaping approach, where performance specifications such as desired closed-loop bandwidth, closed-loop stability, robustness to pure time-delays in control loops/process, and decoupling between direct ($d$) and quadrature ($q$) axis of control signals are specified in the frequency domain. The control design problem is formulated as a convex optimization problem whose solution results in a multivariable controller in the $dq$ framework. The effectiveness of the proposed control strategy in terms of robust stability, current tracking, and improved total harmonic distortion is evaluated by experimental and simulation results under several case studies. The results also show that the designed controller provides the desired transient response as well as decoupling between the $dq$ output currents. [ABSTRACT FROM AUTHOR]

Objective quantification of the balancing mechanisms in humans is strongly needed in health care of older people, yet is largely missing among current clinical balance assessment methods. Hence, the main goal of this literature review is to identify methods that have the potential to meet that need. We searched in the PubMed and IEEE Xplore databases using predefined criteria, screened 1064 articles, and systematically reviewed and categorized methods from 73 studies that deal with identification of neuromuscular controller models of human upright standing from empirical data. These studies were then analyzed with the particular aim to understand to what degree such methods would be useful solutions for assessing the balance of older individuals aged above 60 years. The 16 studies that included an older subject population were especially examined with this in mind. The majority of the reviewed articles focused on research questions related to the general function of human balance control rather than clinical applicability. Further efforts need to be made to adapt these methods for more accessible and mobile technologies and to ensure that the outcomes are valid for balance assessment of a general older population. [ABSTRACT FROM AUTHOR]

In this article, we propose a monitoring procedure based on a multilayer neural network with multivalued neurons (MLMVN) capable of preventing catastrophic failures of dc–dc converters. The neural classifier allows both the detection of any malfunction and its localization. Thanks to the low computational complexity, the proposed method operates online, estimating the deviations of the passive components from their nominal values: this allows control strategies to be promptly adopted and operation of the dc–dc converter to be kept in high efficiency and reliability conditions. Since measuring the voltage and current on each component increases the complexity of the system, a testability analysis is proposed with the aim of identifying the minimum number of measurements needed to distinguish the classes of failure. To make the testability phase easier and more intuitive, a graphical representation is proposed. As a case study, prognostic analysis has been applied to prevent catastrophic failures in a synchronous Zeta converter. Several fault conditions have been analyzed through simulations and experimental tests. The obtained results confirm the ability of the proposed method to prevent failures and, also, show that the application of MLMVN results in a better performance than classic solutions available in the literature, such as support vector machine. [ABSTRACT FROM AUTHOR]

Approximate message passing (AMP) is a low-cost iterative parameter-estimation technique for certain high-dimensional linear systems with non-Gaussian distributions. AMP only applies to independent identically distributed (IID) transform matrices, but may become unreliable (e.g., perform poorly or even diverge) for other matrix ensembles, especially for ill-conditioned ones. To solve this issue, orthogonal/vector AMP (OAMP/VAMP) was proposed for general right-unitarily-invariant matrices. However, the Bayes-optimal OAMP/VAMP (BO-OAMP/VAMP) requires a high-complexity linear minimum mean square error (MMSE) estimator. This prevents OAMP/VAMP from being used in large-scale systems. To address the drawbacks of AMP and BO-OAMP/VAMP, this paper offers a memory AMP (MAMP) framework based on the orthogonality principle, which ensures that estimation errors in MAMP are asymptotically IID Gaussian. To realize the required orthogonality for MAMP, we provide an orthogonalization procedure for the local memory estimators. In addition, we propose a Bayes-optimal MAMP (BO-MAMP), in which a long-memory matched filter is used for interference suppression. The complexity of BO-MAMP is comparable to AMP. To asymptotically characterize the performance of BO-MAMP, a state evolution is derived. The relaxation parameters and damping vector in BO-MAMP are optimized based on state evolution. Most crucially, the state evolution of the optimized BO-MAMP converges to the same fixed point as that of the high-complexity BO-OAMP/VAMP for all right-unitarily-invariant matrices, and achieves the Bayes optimal MSE predicted by the replica method if its state evolution has a unique fixed point. Finally, simulations are provided to verify the theoretical results’ validity and accuracy. [ABSTRACT FROM AUTHOR]

Pursley and Sarwate established a lower bound on a combined measure of autocorrelation and crosscorrelation for a pair $(f,g)$ of binary sequences (i.e., sequences with terms in {−1, 1}). If $f$ is a nonzero sequence, then its autocorrelation demerit factor, $\text {ADF}(f)$ , is the sum of the squared magnitudes of the aperiodic autocorrelation values over all nonzero shifts for the sequence obtained by normalizing $f$ to have unit Euclidean norm. If $(f,g)$ is a pair of nonzero sequences, then their crosscorrelation demerit factor, $\text {CDF}(f,g)$ , is the sum of the squared magnitudes of the aperiodic crosscorrelation values over all shifts for the sequences obtained by normalizing both $f$ and $g$ to have unit Euclidean norm. Pursley and Sarwate showed that for binary sequences, the sum of $\text {CDF}(f,g)$ and the geometric mean of $\text {ADF}(f)$ and $\text {ADF}{(g)}$ must be at least 1. For randomly selected pairs of long binary sequences, this quantity is typically around 2. In this paper, we show that Pursley and Sarwate’s bound is met for binary sequences precisely when $(f,g)$ is a Golay complementary pair. We also prove a generalization of this result for sequences whose terms are arbitrary complex numbers. We investigate constructions that produce infinite families of Golay complementary pairs, and compute the asymptotic values of autocorrelation and crosscorrelation demerit factors for such families. [ABSTRACT FROM AUTHOR]

In phase-contrast magnetic resonance imaging (PC-MRI), spin velocity contributes to the phase measured at each voxel. Therefore, estimating velocity from potentially wrapped phase measurements is the task of solving a system of noisy congruence equations. We propose Phase Recovery from Multiple Wrapped Measurements (PRoM) as a fast, approximate maximum likelihood estimator of velocity from multi-coil data with possible amplitude attenuation due to dephasing. The estimator can recover the fullest possible extent of unambiguous velocities, which can greatly exceed twice the highest venc. The estimator uses all pairwise phase differences and the inherent correlations among them to minimize the estimation error. Correlations are directly estimated from multi-coil data without requiring knowledge of coil sensitivity maps, dephasing factors, or the actual per-voxel signal-to-noise ratio. Derivation of the estimator yields explicit probabilities of unwrapping errors and the probability distribution for the velocity estimate; this, in turn, allows for optimized design of the phase-encoded acquisition. These probabilities are also incorporated into spatial post-processing to further mitigate wrapping errors. Simulation, phantom, and in vivo results for three-point PC-MRI acquisitions validate the benefits of reduced estimation error, increased recovered velocity range, optimized acquisition, and fast computation. A phantom study at 1.5T demonstrates 48.5% decrease in root mean squared error using PRoM with post-processing versus a conventional “dual-venc” technique. Simulation and 3T in vivo results likewise demonstrate the proposed benefits. [ABSTRACT FROM AUTHOR]

This brief develops a novel spectrum sharing scheme for radar and communication systems with multiple input multiple output (MIMO) structure in non-homogeneous interference which crosses the set of controlled radar multiple range gates. Under a space-time design model involving modulus, power, and similarity constraints on the transmit waveforms, we optimize the signal-to-interference-plus noise ratio (SINR) of radar and communication systems simultaneously with the weighted sum design. To tackle the nonconvex formulation, the alternating iteration is exploited to successively solve a series of sub-problems based on Taylor expansion, and the alternating direction method of multipliers (ADMM) design that enjoys low computational burden. Finally, numerical results are performed to demonstrate the merits of the proposed design in terms of the output performance, convergence and computational complexity. [ABSTRACT FROM AUTHOR]

With the increasing penetration of distributed generations (DGs) in three-phase distribution networks, the traditional radial passive distribution networks are gradually transformed into active distribution networks (ADNs) with DGs, accompanied by a dramatic increase in operational scenarios. To fast analyze the massive scenarios of ADNs, this brief proposes a multi-scenario-based power flow analytical method based on the multi-dimensional holomorphic embedding method. First, DGs are partitioned according to their spatial correlation, and then a power flow analytical method is cast for unbalanced three-phase ADNs. Finally, the method is applied to the ADN power flow analysis for multiple scenarios. Comparisons with other power flow calculation methods validate the high efficiency and computational tractability of the proposed method. [ABSTRACT FROM AUTHOR]

In this brief, the physical interpretation of phase-shift has been discussed using the Thevenin model of a matching network with an ideal voltage source. The concept of the desired phased matching network is extended to a multi-section matching network. The idea is based on cascading of s-parameter with matched port. The junction between two s-parameter blocks will be matched if the first network’s load impedance is conjugate with the second block’s source impedance. If the junction is matched, then the total phase shift will be the addition of the individual blocks phase shift. Based on these properties, it has been shown here that the desired phased matching network can be represented as a cascade connection of multiple sub-matching networks. The idea is numerically validated using lumped $\Pi $ -match network, and an algorithm is proposed to design a multi-section $\Pi $ -match network with the desired phase shift. [ABSTRACT FROM AUTHOR]

The phase-to-ground admittance in distribution networks is asymmetric. This asymmetry is aggravated by the arc suppression coil, resulting in zero-sequence overvoltage. The magnitude of zero-sequence overvoltage can exceed the line-to-ground voltage, endangering the operation of electrical equipment. Moreover, after the occurrence of a single-line-to-ground fault, the change law of the three-phase voltage is affected by the zero-sequence overvoltage, leading to misjudgment of the fault phase. Accordingly, this paper proposes a fault phase selection method based on active current injection that can adapt to asymmetric distribution networks. The fault phase is selected by injecting current and calculating the equivalent power supply voltage of the faulty line without increasing the ground-fault current. The high sensitivity of the proposed fault phase selection method was verified for high impedance faults through software simulations and prototype experiments. Moreover, the zero-sequence overvoltage can be suppressed by a single-phase flexible arc suppression device during the normal operation of distribution networks. The ground-fault current can be quickly suppressed at different ground-fault locations and resistance values, ensuring the extinguishment of the ground-fault arc. [ABSTRACT FROM AUTHOR]

The recent evolution of passive optical network standards and related research activities for physical layer solutions that achieve bit rates well above 10 Gbps per wavelength (λ) is discussed. We show that the advancement toward 50, 100, and 200 Gbps/λ will certainly require a strong introduction of advanced digital signal processing (DSP) technologies for linear, and maybe nonlinear, equalization and for forward error correction. We start by reviewing in detail the current standardization activities in the International Telecommunication Union and the Institute of Electrical and Electronics Engineers, and then we present a comparison of the DSP approaches for traditional direct detection solutions and for future coherent detection approaches. [ABSTRACT FROM AUTHOR]

In this article, a targeted step skewing of rotor segments scheme is proposed to reveal the modulated vibration reduction mechanism in integral-slot interior permanent magnet synchronous machines (IPMSMs). First, the vibration characteristics of a 72-slot/12-pole integral-slot IPMSM are investigated. The dominant vibration at specific frequency band is introduced. Then, in order to analyze the main source of vibration, the magnetic field modulation effect and radial force modulation effect are investigated in detail. The results show that the high-order radial force can excite large vibration by radial force modulation effect. This kind of vibration is defined as modulated vibration. Afterwards, the optimal shift angle and number of rotor segments are determined by comparison. Finally, the conventional and proposed 72-slot/12-pole integral-slot IPMSMs are manufactured to validate the theoretical analysis. The experimental results verify that the proposed method can effectively reduce the modulated vibration of the 72-slot/12-pole integral-slot IPMSM. [ABSTRACT FROM AUTHOR]

Aiming at the influence on the actual speed caused by the suspension winding′s rotor induced current of a squirrel-cage bearingless induction motor (SBIM), a novel three-speed wound bearingless induction motor (WBIM) is designed based on establishing the stator–rotor equivalent circuit, deriving the exciting current-suspension slip ratio equation and analyzing the magnetic field distribution with odd pole pairs and dipole pairs. The stator is embedded with a fixed four pole winding and a 2/6 pole-changing winding, while the rotor is wound with series–parallel windings connecting at a slip ratio ring. By changing the connection method of the stator coils, adjusting the three-phase voltage source, and controlling the short circuit of the rotor winding central point, it can realize that the rotor only induces the torque winding′s magnetic field under three speeds. Combining with the finite-element analysis, the induced current, air gap magnetic density, suspension force, and electromagnetic torque of the traditional SBIM and the new WBIM are quantitatively compared. Finally, an experimental platform is built for further verification. The results show that the designed three-speed WBIM can not only effectively suppress the induced current of the suspension winding and reduce its influence on the motor torque and speed, but also makes the motor has a better start-up and steady-state performance. [ABSTRACT FROM AUTHOR]

In this letter, the attenuation by spheroidal sand particles under different relative humidity (RH) is investigated with consideration of contributions of water vapor and excess charges carried by particles, which is much closer to real conditions. The microwave (MW) attenuation will be enhanced when the sand particles are considered as the spheroids. The contributions of RH and excess charges to the MW attenuation are much greater by the spheroidal sand particles than that by the equal-volume spherical ones. The increasing effects of excess charges and RH on the MW attenuation are mutually reinforced. The contribution of water vapor to the MW attenuation by sand–dust storms is significant when the visibility is high, which makes up the gap between the experimental results and calculated results. An improved attenuation model by sand and dust storms is given with an incident wave frequency of 4–45 GHz. [ABSTRACT FROM AUTHOR]

Target localization is widely applied in wireless sensor networks (WSNs). In this article, we tackle the problem of position estimation for multiple stationary targets using Doppler frequency shifts and angles by moving sensor arrays. The computational load for the exhaustive maximum likelihood (ML) direct position determination (DPD) search is insufferable. Based on Pincus’ theorem and the importance sampling (IS) concept, we propose a novel noniterative ML DPD method, and the circular mean is used for superior position estimation performance; meanwhile, a concrete criterion is also proposed for the choice of the importance function. The computational complexity of the proposed method is modest, and the off-grid problem that most existing DPD techniques face is significantly alleviated. Moreover, it can be implemented in parallel separately. In addition, we present the closed-form Cramér–Rao lower bound (CRLB) for the DPD with moving sensor arrays and stationary targets. Simulation results demonstrate that the proposed ML DPD estimator can achieve better estimation accuracy than state-of-the-art DPD techniques. With a reasonable parameter choice, the estimation performance of the proposed technique is very close to the CRLB, even in the adverse conditions of low signal-to-noise ratio (SNR) levels. [ABSTRACT FROM AUTHOR]

This article investigates the problems of structural balance and distributed control for a high-order signed network with generic linear dynamics. A novel approach is proposed to analyze the structural balance of a general network based on the strongly connected components of the topology graph and the Frobenius normal form of the adjacency matrix. To address the distributed control of high-order signed networks, both state-feedback and observer-type algorithms are developed on the basis of low-gain strategies, where a unified framework is presented to construct full- or reduced-order observers. The dynamical behaviors of the signed network under the proposed algorithms are systematically explored, where the nontrivial final network state is computed by employing an eigenvector-based approach. The theoretical results are illustrated by numerical examples. [ABSTRACT FROM AUTHOR]