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Summary The advanced technology in recent years that has achieved more attention among researchers and the social community is the wireless sensor network (WSN) that includes a number of nodes that are commonly distributed in remote zones. While deploying the WSN in huge areas, WSNs produce a massive amount of data. Thus, there is a significant need to process the data through efficient models. The data aggregation technique is the common solution widely employed to obstruct congestion on large‐scale WSNs. However, the demanding part of the data aggregation scheme is to mitigate the network overhead without affecting the system efficiency. Most of the data transmitted by sensor nodes are repetitious and thus result in high power consumption. Therefore, sensor nodes should utilize an efficient data aggregation model for data transmission that minimizes duplicate data. In order to maintain such complications, this article proposes a hierarchical Taylor quantized kernel least mean square (HTQKLMS) filter for aggregating data in WSN. For this purpose, WSN is initially simulated, and then data aggregation is accomplished using developed HTQKLMS filter. Additionally, the HTQKLMS is derived by amalgamating the hierarchical fractional quantized kernel least mean square (HFQKLMS) filter with the Taylor series. Here, the data prediction mechanism is done by employing HFQKLMS model that is an integration of quantized kernel least mean square (QKLMS) and hierarchical fractional bidirectional least mean square (HFBLMS). Apart from this, data redundancy is achieved by broadcasting needed data utilizing data detected at the destination. Furthermore, HTQKLMS approach has delivered a minimum energy consumption of 0.0333 J and less prediction error of 0.0326. [ABSTRACT FROM AUTHOR]

The next generation phased array radio telescopes, such as the Square Kilometre Array (SKA) low frequency aperture array, suffer from RF interference (RFI) because of the large field of view of antenna element. The classical station beamformer used in SKA-low is resource efficient but cannot deal with the unknown sidelobe RFI. A real-time adaptive beamforming strategy is proposed for SKA-low station, which trades the capability of adaptive RFI nulling at an acceptably cost, it doesn't require hardware redesign but only modifies the firmware accordingly. The proposed strategy uses a Parallel Least Mean Square (PLMS) algorithm, which has a computational complexity of 4 N + 2 and can be performed in parallel. Beam pattern and output SINR simulation results show deeply nulling performance to sidelobe RFI, as well as good mainlobe response similar to the classical beamformer. The convergence performance depends on the signal-and-interference environments and step size, wherein too large a step size leads to a non-optimal output SINR and too small a step size leads to slow convergence speed. FPGA implementation demonstrations are implemented and tested on a NI FPGA module, and test results demonstrate good real-time performance and low slice resource consumption. [ABSTRACT FROM AUTHOR]

Active Magnetic Bearing (AMB) is a kind of electromagnetic support that makes the rotor movement frictionless and can suppress rotor vibration by controlling the magnetic force. The most common approach to restrain the rotor vibration in AMBs is to adopt a notch filter or adaptive filter in the AMB controller. However, these methods cannot obtain the precise amplitude and phase of the compensation current. Thus, they are not so effective in terms of suppressing the vibrations of the fundamental and other harmonic orders over the whole speed range. To improve the vibration suppression performance of AMBs, an adaptive filter based on Least Mean Square (LMS) is applied to extract the vibration signals from the rotor displacement signal. An Iterative Search Algorithm (ISA) is proposed in this paper to obtain the corresponding relationship between the compensation current and vibration signals. The ISA is responsible for searching the compensating amplitude and shifting phase online for the LMS filter, enabling the AMB controller to generate the corresponding compensation force for vibration suppression. The results of ISA are recorded to suppress vibration using the Look-Up Table (LUT) in variable speed range. Comprehensive simulations and experimental validations are carried out in fixed and variable speed range, and the results demonstrate that by employing the ISA, vibrations of the fundamental and other harmonic orders are suppressed effectively.

In this paper, a type of variable step size LMS named weighted zero attracting variable step size (WZAVSSLMS) is equipped with the control algorithm for governing DSTATCOM. In WZAVSSLMS, the ability of zero attracting nature to a variable step size (VSSLMS) for improving convergence rate is observed. WZAVSSLMS algorithm-based fundamental extractor is developed to identify the fundamental quantity of the load current for a three-phase four-wire DSTATCOM with non-linear load in this work. This type of variable step size LMS has provided a concession for quick convergence rate and steady-state error. Precision and convergence of responses are accomplished by the learning rate control. As a result of these features over conventional LMS, the authors choose WZAVSSLMS based approach in well-known DSTATCOM system to compensate power quality issues. The fundamental extraction from the distorted load current constitutes the main divergences in the proposed study. Further, the Finite control set model predictive control (FCS MPC) of the distribution static compensator (DSTATCOM) using Weighted Aggregated Sum Product Assessment Solution (WASPAS) method is implemented. [ABSTRACT FROM AUTHOR]

In the present paper, Dynamic Voltage Restorer (DVR) controller using the Least Mean Fourth (LMF) algorithm is presented. The proposed DVR is used for protecting various voltage-sensitive loads from voltage-related power quality issues (i.e. balanced voltage sag, unbalanced voltage sag and voltage harmonic distortion, balanced and unbalanced swell, etc.) in a distribution system. The active and reactive power components of distorted source voltage are extracted using an adaptive LMF-based control algorithm for the calculation of reference load voltage for the voltage source converter (VSC) of DVR. The control algorithm doesn't require a phase-locked loop (PLL) and thus is less complex, robust and easy to implement. The adopted control algorithm for DVR is verified in MATLAB by simulation studies. The adaptive LMF-controlled DVR produces excellent dynamics response as compared to conventional adaptive Least Mean Square (LMS) and Synchronous reference frame theory (SRFT)-controlled DVR. The convergence rate of the source voltage fundamental active weight component is higher for the proposed adaptive LMF-controlled DVR. Various power quality issues such as balanced sag, unbalanced sag, balanced and unbalanced swell and a combination of these power quality issues are mitigated with the help of the LMF-controlled DVR. The simulation results are verified through real-time experimentation using an OPAL-RT under an RT lab environment. [ABSTRACT FROM AUTHOR]

Copyright of Journal of Data Acquisition & Processing / Shu Ju Cai Ji Yu Chu Li is the property of Editorial Department of Journal of Nanjing University of Aeronautics & Astronautics and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

The next generation phased array radio telescopes, such as the Square Kilometre Array (SKA) low frequency aperture array, suffer from RF interference (RFI) because of the large field of view of antenna element. The classical station beamformer used in SKA-low is resource efficient but cannot deal with the unknown sidelobe RFI. A real-time adaptive beamforming strategy is proposed for SKA-low station, which trades the capability of adaptive RFI nulling at an acceptably cost, it doesn’t require hardware redesign but only modifies the firmware accordingly. The proposed strategy uses a Parallel Least Mean Square (PLMS) algorithm, which has a computational complexity of 4N+2 and can be performed in parallel. Beam pattern and output SINR simulation results show deeply nulling performance to sidelobe RFI, as well as good mainlobe response similar to the classical beamformer. The convergence performance depends on the signal-and-interference environments and step size, wherein too large a step size leads to a non-optimal output SINR and too small a step size leads to slow convergence speed. FPGA implementation demonstrations are implemented and tested on a NI FPGA module, and test results demonstrate good real-time performance and low slice resource consumption.

Summary: This paper develops a variable step size robust normalized least mean absolute third (VSSRNLMAT)‐based control scheme for a three‐phase grid‐tied multifunctional photovoltaic system (GTMPVS). The GTMPVS delivers PV power to grid‐connected loads and feeds excess power to the grid through a voltage source converter (VSC) when PV power is available. It enhances power quality (PQ) by mitigating current harmonics and compensating for an unbalanced load and reactive power simultaneously. The proposed control scheme accurately extracts the fundamental active and reactive weight components of nonlinear load current. The power exchange at the grid side occurs with balanced and sinusoidal grid currents. During the absence of PV power, the VSC continues its operation as a distribution static compensator (DSTATCOM), thus increasing the effective utilization of the power electronic switches employed in the VSC. The GTMPVS model is created in MATLAB/Simulink. The response of GTMPVS is observed under steady‐state and dynamic operating conditions while feeding power to a nonlinear load. The dynamic operating conditions are created by a change in solar irradiation and an unbalanced nonlinear load. Further, the proposed VSSRNLMAT control scheme is compared with some of the other adaptive filtering‐based control schemes, including least mean square (LMS), least mean fourth (LMF), least mean absolute third (LMAT), and normalized LMAT (NLMAT) in terms of convergence, oscillations, mean square deviation (MSD), computational complexity, and grid current total harmonic distortion (THD). It is found that the VSSRNLMAT control scheme offers THD of grid current as per IEEE‐519 standard. The proposed control scheme is implemented to operate the laboratory‐developed GTMPVS to verify the simulation results. [ABSTRACT FROM AUTHOR]

Speech enhancement is a signal processing technique used to improve the quality and intelligibility of speech recordings that contain noise or interference. Its main goal is to eliminate unwanted background noise while preserving the clarity and naturalness of the speech signal. This paper provides a comprehensive analysis of three widely used adaptive filtering algorithms, Least Mean Square (LMS), Normalized Least Mean Square (NLMS), and Affine Projection Algorithm (APA). The limitations of LMS, such as slow convergence and sensitivity to input variations, are addressed in this study. By incorporating normalization, NLMS improves convergence speed and robustness to input power levels. The Affine Projection Algorithm (APA) is known for its exceptional performance in non-stationary environments, achieved through subspace projection to estimate optimal filter coefficients, resulting in faster convergence and improved tracking capabilities. In this paper, the algorithms are compared using Signal-to-Noise Ratio (SNR), Mean-Square Error (MSE), and Root-Mean-Square-Error (RMSE) values. [ABSTRACT FROM AUTHOR]

Adaptive Digital Filters (ADFs) are computationally demanding Digital Signal Processing (DSP) systems with applications in diverse areas signal processing, such as active noise control, channel equalization, system identification. The most popular implementation structure for ADFs is FIR transversal filter which can be efficiently implemented without multipliers by Distributed Arithmetic (DA) method. DA is used to efficiently implement digital FIR filter with a precomputed weight DA combinations in a memory element. However, due to the operation of ADFs the memory content has to be recomputed at each iteration; the advantage of DA method disappears. The objective of this review is to cover the research gap on DA-ADF implementations. This article reviews the main features and contributions of DA-ADF designs present in literature. The DA-ADF designs have been grouped into three categories: non-pipelined, pipelined and block processing. Pipelining and block processing increase the throughput of the ADF. The reviewed designs implement only Least Mean Square (LMS) type adaptive algorithms due to the simplicity of implementation. Latest and most efficient designs tend to employ conjugate Offset Binary Coding (OBC) DA structure.

Noise contaminates and distorts measurements from wireless sensor networks (WSNs). The sensor node's computations and energy consumption are increased due to the noise in the signal, resulting in the sensor node's shorter lifespan. Therefore, efficient design is required to achieve noise reduction. The finite impulse response (FIR) filter is a significant part of signal preprocessing in the WSN to remove noise. Signal preprocessing could assist in minimizing the amount of energy used in communication between nodes while also improving data transmission efficiency. The multiplier block in an FIR filter involves the generation and addition of partial products (PP), which consumes a large area and enormous power. The most common adaptive filtering technique utilizes the least mean squares (LMS) algorithms. The multiply and accumulate (MAC) process is the spine of LMS adaptive filters. Using multiple MAC units can boost the system's speed, but the cost of the system rises as the multipliers take up a lot of space and consume more energy. When LMS is implemented as a completely reliable architecture on a hardware platform, the multiplier evolves into a bottleneck for higher-order filters, resulting in significant size, expenditure, and energy needs, rendering the design unsuitable for practical implementation. FIR filters are frequently realized using distributed arithmetic (DA) based scheme. DA-based designs replace multipliers with look-up tables (LUT), where the precomputed PPs is saved. LUTs are required for weight update and filtering processes. The size of the LUT rises exponentially with the increasing order of filters. To minimize the size of a LUT, offset binary coding (OBC) is utilized, resulting in smaller memory size. The proposed architecture is based on DA in which PPs of filter coefficients are pre-calculated, and these coefficients are saved in LUTs, and through the use of OBC, the weighted coefficients are updated. The proposed method does not decompose the LUT into two smaller LUT to achieve area and energy efficiency. Typically, each iteration requires recalculating all of the LUT's address locations. This research presents a novel method that iteratively updates the contents of LUT without rotating the address, making the FIR filter more energy efficient. Random access memory-based LUT is utilized to eliminate the physical address rotation. A method for reducing LUT access time is proposed. The proposed design significantly reduces area and energy consumption compared to the existing systems. Also, it saves a large amount of sliced LUT and flip-flops compared to conventional methods. Therefore, high-performance adaptive filtering applications like IoT-based WSN can benefit the most from the suggested approach. [ABSTRACT FROM AUTHOR]

A modern design of fractal antenna arrays, called fractile array, which exhibits a fractal boundary contour within a tiled plane, is explored for enhanced array performance. In this paper, the Eisenstein fractile array is introduced to exploit the unique geometrical features of fractiles that allow multiband and wideband operation and avoid grating lobes in the radiation pattern even, in some cases, when the array elements’ spacing is greater than the half wavelength. To alleviate the large number of elements and the high Side-Lobe Level (SLL) occurred at large scales, the Genetic Algorithm (GA) optimization technique is considered for thinning the proposed antenna array by estimating the optimal set of “on” and “off” elements corresponding to the minimum SLL without degrading the directivity of the radiation pattern. Also, the proposed array configuration is designed with adaptive beamforming capability using the Least Mean Square (LMS) technique. The effectiveness of the proposed GA-LMS approach is investigated by performing several MATLAB simulations under various set of array configurations. Results reveal that the suggested thinned Eisenstein fractile antenna array using GA-LMS approach is superior in terms of multiband and wideband performance, array element reduction, SLL reduction, grating lobe elimination, and beamforming capability. This elucidates the robustness of the suggested thinned Eisenstein fractile array as a promising design for multiband, wideband, compact, inexpensive, and adaptive smart antennas in modern wireless systems.

Distributed arithmetic (DA) is an efficient look-up table (LUT) based approach. The throughput of DA based implementation is limited by the LUT size. This paper presents two high-throughput architectures (Type I and II) of non-pipelined DA based least-mean-square (LMS) adaptive filters (ADFs) using two’s complement (TC) and offset-binary coding (OBC) respectively. We formulate the LMS algorithm using the steepest descent approach with possible extension to its power-normalized LMS version and followed by its convergence properties. The coefficient update equation of LMS algorithm is then transformed via TC DA and OBC DA to design and develop non-pipelined architectures of ADFs. The proposed structures employ the LUT pre-decomposition technique to increase the throughput performance. It enables the same mapping scheme for concurrent update of the decomposed LUTs. An efficient fixed-point quantization model for the evaluation of proposed structures from a realistic point-of-view is also presented. It is found that Type II structure provides higher throughput than Type I structure at the expense of slow convergence rate with almost the same steady-state mean square error. Unlike existing non-pipelined LMS ADFs, the proposed structures offer very high throughput performance, especially with large order DA base units. Furthermore, they are capable of performing less number of additions in every filter cycle. Based on the simulation results, it is found that $256^{\mathrm {th}}$ order filter with $8^{\mathrm {th}}$ order DA base unit using Type I structure provides $9.41 \times $ higher throughput while Type II structure provides $16.68 \times $ higher throughput as compared to the best existing design. Synthesis results show that $32^{\mathrm {nd}}$ order filter with $8^{\mathrm {th}}$ order DA base unit using Type I structure achieves 38.76% less minimum sampling period (MSP), occupies 28.62% more area, consumes 67.18% more power, utilizes 49.06% more slice LUTs and 3.31% more flip-flops (FFs), whereas Type II structure achieves 51.25% less MSP, occupies 21.42% more area, consumes 47.84% more power, utilizes 29.10% more slice LUTs and 1.47% fewer FFs as compared to the best existing design.

The growing integration of distribution grid with solar energy (PV) has resulted in severe power quality (PQ) concerns, particularly in the case of a weak distribution grid. In order to improve the PQ, the effective development of a control algorithm for the solar energy (PV) conversion system, interfaced to the grid, is very vital. In this article, an adaptive robust least mean logarithmic square (RLMLS) filter-based control has been proposed to provide grid integration capabilities of a PV system, for optimal operation. Moreover, it supplies active power to the linear/nonlinear load and grid, along with power factor correction, load balancing, and harmonics mitigation. MATLAB/Simulink (2018a) is used for modelling and evaluation of the proposed system, under various loading scenarios, including nonlinear, unbalance, and load increment. It is also tested under severe grid voltage conditions, such as unbalanced and distorted grid voltage. The system's performance has been verified as per IEEE-519 standard, showing that it is capable of grid integration and efficient in maintaining the PQ under non-ideal grid conditions characterized by a wide variety of load fluctuations, distortion, and unbalance with added benefits of faster convergence speed, reduced complexity, less sampling time, better accuracy, low dynamic oscillations/ripples in the estimation of active component, ease of implementation, and adaptability. Furthermore, a hardware prototype is developed for validation, and test results show that the system can operate efficiently under a wide variety of load fluctuations, distortion, and unbalance conditions. [ABSTRACT FROM AUTHOR]

Copyright of Journal of Chongqing University of Posts & Telecommunications (Natural Science Edition) is the property of Chongqing University of Posts & Telecommunications and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

This study introduces novel short-term wind speed predictors based on multilayer perceptron (MLP), recurrent neural network (RNN), and support vector machine (SVM) by combining them with the data-adaptive censoring (DAC) strategy. Taking into account the multistep ahead prediction mode, we design a DAC strategy based on the least mean square (LMS) algorithm, which iteratively obtains a new training dataset consisting of the most informative input–output wind data from all training set for MLP, RNN, and SVM structures. This enables us to censor less informative training data with high accuracy and thereby the training costs of the MLP, RNN, and SVM are reduced without a considerably adverse effect on their prediction performances in testing processes. The conducted simulation results on real-life large-scale short-term wind speed data verify the mentioned attractive features of the proposed predictors. [ABSTRACT FROM AUTHOR]

This article presents an L2 norm-enabled variable step-size adaptive least mean square controller for grid-connected photovoltaic converter. The variable step size is adopted to damp out the weight oscillations and to enable faster convergence under exposure to disturbances in the load and solar insolation. More precisely, to achieve oscillations free response, a nonlinear relationship is developed between step size and estimated error. This way, it significantly improves the convergence speed and stability. The iteration function of the presented variable step-size feature includes the input signal to study the impact of harmonically polluted load current on the weight extraction performance. Besides, the designing procedure of step size considers both current and previous error states, thereby ensuring noise immunity. With the inclusion of these features, the devised controller is applied to accurately extracting the fundamental weight component of the load current and in turn enabling various ancillary services. The proposed control algorithm is designed in matlab/Simulink software and its effectiveness is examined under both steady-state and transient conditions during grid disturbances. Furthermore, the practicability of the proposed controller is verified and compared with conventional controls using ARM cortex M4 microcontroller-based laboratory test bed. In a nutshell, the results confirm the oscillations free weight component with higher convergence and better stability under various operating conditions. [ABSTRACT FROM AUTHOR]

Imbalanced vibration generated during the operation of a high-speed rotor inside a gyro is transmitted to the magnetically suspended control moment gyro through a magnetic bearing stator, resulting in micro-jitter. This greatly affects the control accuracy of the spacecraft attitude. In order to effectively suppress micro-jitter, the dynamic model of the magnetically suspended rotor with imbalanced mass is established in this article, and the imbalanced characteristics of the high-speed rotor are analyzed. Finally, a double-channel adaptive least mean square error algorithm is proposed. By utilizing that the radial X- and Y- displacement signals from the rotor displacement sensor are orthogonal to each other, a feedforward correction is applied to adaptively compensate the residual displacement stiffness of the magnetically suspended rotor system due to mass imbalance. While eliminating this synchronous current, the proposed algorithm also offsets the adverse effects of the low-pass characteristics of the power amplifier. Simulation and experimental results show that the method compensates the synchronous displacement stiffness force generated by the system with high precision, and realizes the micro-jitter control of the magnetically suspendedcontrol moment gyro. [ABSTRACT FROM AUTHOR]

To eliminate the influence of parameter mismatch for fundamental model based sensorless methods, an effective online position error correction method is proposed for permanent magnet synchronous machines in this paper. Based on the derived position error mechanism, i.e. the error varies proportionally to the dq-axis currents, the proposed method injects a sinusoidal current signal with a small amplitude and low frequency into the $d$ - or $q$ -axis current for a short period. During injection, the corresponding sinusoidal response for current injection can be acquired from the estimated speed of the sensorless position observer. It is found that the amplitude of the response in the estimated speed decreases as the parameter mismatch reduces, and eventually reaches a minimum if there is no parameter mismatch. Thus, by applying the least mean square (LMS) algorithm, the amplitude of the response in the estimated speed can be minimised as the parameters are adaptively adjusted to the actual values, and then the position error can be corrected. The proposed method is validated through experiments on a permanent magnet generator drive system.

Multiply-Accumulate (MAC) operation is the backbone of Least Mean Squares (LMS) digital adaptive filters. Implementing LMS on hardware platform as a Fully Dedicated Architecture (FDA) multiplier becomes bottleneck for higher order filters, prompting high area, cost and power requirements and hence renders the design unsuited for practical implementation. In this paper, we have proposed a composite design that makes use of Distributed Arithmetic (DA) to replace the bottleneck multiplier with memory units that store Partial Products (PPs) to emulate multiplication. The depth of these memory units tends to exponentially grow as the filter order rises. To manage that, we have used Half Memory algorithm (HM) and Offset Binary Coding (OBC) to refine the structure of PPs such that the memory size is reduced at least by a factor of 4 for the same filter order. The proposed design improves system’s Throughput, Critical Path Delay, Power Consumption and FPGA Resource Utilization. However, it introduces Latency in both the output and update segments of the LMS algorithm. To provide an option between resource utilization and latency, we have suggested a mechanism to halve the originally produced latency by the Parallel Processing of input bit steam w.r.t even and odd bits. Moreover, we have also proposed a method that reduces the latency of update module at the slight expense of other design attributes. The fundamental structure of the proposed design is flexible owing to the dynamic memory structure as well as the option to choose between latency and resource minimization. Simulations have been carried out in Xilinx Vivado and conclusions have been drawn by comparing both FDA and DA based designs. Results for a 16-tap filter indicate a remarkable improvement in Throughput, Area Utilization and Power Consumption by 18%, 5% and 3.5% respectively at the expense of $4\times $ escalated latency. The Half Latency method allowed the latency to drop $2\times $ but with slightly elevated power and area attributes.

This paper presents a novel method of power quality enrichment in a grid-connected photovoltaic (PV) system using a distribution static compensator (DSTATCOM). The paper consists of two-step control processes. In the primary step, a fuzzy logic controller (FLC) is employed in the DC-DC converter to extract the peak power point from the PV panel, where the FLC produces a switching signal for the DC-DC converter. In the secondary step, a unit vector template (UVT)/adaptive linear neuron (ADALINE)-based least mean square (LMS) controller is adopted in the DC-AC converter, i.e., voltage source converter (VSC). The input to this VSC is the boosted DC voltage, which originates from the PV panel as a result of DC-DC conversion. The VSC shunted with the power grid is known as a DSTATCOM, which can maintain the power quality in the distribution system. The UVT controller generates reference source currents from the grid voltages and DC-link voltages. The ADALINE-based LMS controller calculates the online weight according to the previous weights by the sensed load current. The UVT/ADALINE-based LMS controller of a DSTAT-COM performs several tasks such as maintaining the sinusoidal source current, achieving a unity power factor, and performing reactive power compensation. The reference current extracted from the UVT/ADALINE-based LMS controller is fed to the hysteresis current controller to obtain the desired switching signal for the VSC. A 100 kW solar PV system integrated into a three-phase four-wire distribution system through a four-leg VSC is designed in MATLAB/Simulink. The performances of the FLC and UVT/ADALINE-based LMS controllers are demonstrated under various irradiances as well as constant temperature and nonlinear loading conditions.

This brief presents a pattern extraction based digital background calibration for Successive Approximation Register (SAR) Analog-to-Digital Converters (ADCs). We demonstrate that major factors limiting the convergence rate of the conventional calibration schemes can be addressed by performing pattern extraction and using the extracted patterns to train the calibration engine. Benefiting from the proposed technique, the convergence speed of the calibration can be improved without sacrificing the stability and accuracy of the calibration loop. A lightweight pattern extraction scheme is also introduced to reduce the hardware cost and power consumption. [ABSTRACT FROM AUTHOR]

This article presents a continuous-time (CT) multistage noise-shaping (MASH) delta–sigma ($\Delta \Sigma$) analog-to-digital converter (ADC) with enhanced tolerance to temperature and operating-frequency variations through on-chip multi-rate (MR) background calibration based on the least-mean-square (LMS) algorithm. The proposed digital calibration, which combines MR operation, post-conditioners, and pseudorandom-noise injection at the quantizer input, efficiently and accurately matches digital transfer functions to the corresponding analog ones for cancellation of quantization error. The first modulator in the proposed MASH ADC considers sufficient quantizer delay and reduces input-signal leakage into the second modulator to enable successful quantization-error transmission while minimizing distortion in the second stage, which cannot be tolerated in the case of calibrated digital noise-cancellation filter. A CT 2–2 MASH $\Delta \Sigma $ ADC prototype fabricated in 40-nm CMOS achieves the dynamic range (DR) of 79 dB and the signal-to-noise and distortion ratio (SNDR) of 78.5 dB over an 8-MHz bandwidth with 7.3-mW analog power and 16-mW digital power, reaching Schreier figure of merit (FoMs) of 168.9 dB, and demonstrates conversion-accuracy robustness within 1-dB SNDR fluctuation for temperature variations from −40° to 125° and within 2-dB SNDR degradation for frequency variations of 20% range. [ABSTRACT FROM AUTHOR]

In Wireless Sensor Network (WSN), the sensor nodes aggregate the environment and transmit the aggregated data to the Cluster Head (CH) or aggregator. Even though various data aggregation model is introduced, the resource‐constrained issues in WSN are complex to solve in the research community. Therefore, the authorization and verification‐based data aggregation model is proposed in this research to perform the secure data transmission. The proposed network model contains three functioning blocks, such as sensor nodes, aggregators, and data centers, respectively. The proposed data aggregation model involves five different phases, setup and key generation phase, signing, verification, authorization, and data aggregation phase. The sensor node creates the ID and private key to perform the data communication between the Cluster Head and data center. The data center generates the control message and transfers the message to CH and the sensor node. Finally, the data aggregation is carried out using the trimodel Least Mean Square (LMS) filter to achieve the data aggregation in WSN. Moreover, the proposed authorization and verification‐based data aggregation model obtained a lower prediction error of 0.0526 and consumed higher energy of 0.5567 J with the computational and the communication cost as 0.008 second, and 0.0139 second, respectively. [ABSTRACT FROM AUTHOR]

• The multi-peak noise is modeled as an MPD and classified into three types. • An adaptive filtering algorithm called MPD is proposed under multi-peak noise. • The MPD algorithm outperforms other commonly used algorithms under multi-peak noise. • The MPD provides a closed-form solution that can approximate the optimal solution. In this paper, we assume the multi-peak noise in adaptive filtering follows a multimodal probability distribution (MPD), and model it as a Gaussian mixture model (GMM). Then a new gradient ascent algorithm is proposed based on the maximum likelihood function. Under the MPD assumption, multiple means and variances of the modes can be estimated by the expectation-maximization (EM) algorithm, and a corresponding closed-form solution can be obtained. Since the GMM can exactly approximate multi-peak probability density function (PDF), the closed-form solution of the proposed MPD algorithm can approximate the optimal solution of the adaptive filtering problem under multi-peak additive noise. According to the interaction between the distance and magnitudes of adjacent peaks, multi-peak noise can be divided into significant multi-peak (SMP) noise, hidden multi-peak (HMP) noise (e.g., Rayleigh), and invisible multi-peak (IMP) noise (e.g., Uniform). Simulations demonstrate that the proposed MPD algorithm outperforms the maximum correntropy criterion (MCC) and minimum error entropy (MEE) under SMP and HMP noise, and outperforms the least mean fourth (LMF) under IMP noise. The experimental steady-state errors align well with the theoretical values. [ABSTRACT FROM AUTHOR]

In general, the background noise degrades the speech quality. Thus, the intelligibility of the speech can be enhanced by mitigating the effects of background noise and echo suppression. So, speech enhancement can also be viewed as one of the optimization problems. In this work, directed search optimization (DSO) method is used to enhance the speech quality which is originally degraded. The performance of DSO-based speech enhancement method is compared with particle swarm optimization (PSO) and least mean square (LMS)-based methods in terms of output average segmental SNR and speech quality. From the experimental results, it was observed that the output spectrogram, output ASSNR and speech quality using DSO algorithm are far better as compared to PSO and LMS-based methods. Moreover, DSO-based method is computationally less complex as compared to the PSO-based method. [ABSTRACT FROM AUTHOR]

Instability of the local oscillator causes phase noise – a phenomenon that is a disadvantage and is considered to be a major obstacle in the functioning of coherent optical orthogonal frequency division multiplexing (CO-OFDM) systems. An attempt has been made in this paper to reduce the effects of common phase errors generated by phase noise. In this paper, a least mean square (LMS) based algorithm is proposed for estimation of phase noise. Using this proposed algorithm, the major problem of phase ambiguity caused by cycle slip is avoided and the bit error rate is greatly improved. Further, there is no requirement for modifying the frame structure of OFDM using this algorithm. A CO-OFDM system with the 8-PSK technique is used to implement the algorithm concerned. Furthermore, the algorithm, using the 8-PSK modulation technique, is analyzed and compared with the existing QPSK technique and with other algorithms. The investigations reveal that 8-PSK outperforms existing LMS algorithms using other techniques and significantly reduces the bit error rate.

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Conventional least-mean-square (LMS) algorithm is one of the most popular algorithms, which is widely used for adaptive beamforming. But the performance of the LMS algorithm degrades significantly because the constant step size is not suitable for varying signal-to-noise ratio (SNR) scenarios. Although numerous variable-step-size LMS (VSS-LMS) algorithms were proposed to improve the performance of the LMS algorithm; however, most of these VSS-LMS algorithms are either computationally complex or not reliable in practical scenarios since they depend on many parameters that are not easy to tune manually. In this letter, a fast and robust VSS-LMS algorithm is proposed for adaptive beamforming. The VSS is obtained based on normalized sigmoid function, where the sigmoid function is calculated by using the mean of instantaneous error first and then normalized by the squared cumulative sum of instantaneous error and estimated signal power. The proposed algorithm can update the step size adaptively without tuning any parameter and outperform state-of-the-art algorithms with low computational complexity. The simulation results show better performance of the proposed algorithm. [ABSTRACT FROM AUTHOR]

Neural oscillations are vital for the functioning of a central nervous system because they assist in brain communication across a huge network of neurons. Alpha frequency oscillations are believed to depict idling or inhibition of task-irrelevant cortical activities. However, recent studies on alpha oscillations (particularly alpha phase) hypothesize that they have an active and direct role in the mechanisms of attention and working memory. To understand the role of alpha oscillations in several cognitive processes, accurate estimations of phase, amplitude, and frequency are required. Herein, we propose an approach for time-series forward prediction by comparing an autoregressive (AR) model and an adaptive method (least mean square (LMS)-based AR model). This study tested both methods for two prediction lengths of data. Our results indicate that for shorter data segments (prediction of 128 ms), the AR model outperforms the LMS-based AR model, while for longer prediction lengths (256 ms), the LMS- based AR model surpasses the AR model. LMS with low computational cost can aid in electroencephalography (EEG) phase prediction (alpha oscillations) in basic research to reveal the functional role of the oscillatory phase as well as for applications for brain-computer interfaces. [ABSTRACT FROM AUTHOR]

Instability of the local oscillator causes phase noise -a phenomenon that is a disadvantage and is considered to be a major obstacle in the functioning of coherent optical orthogonal frequency division multiplexing (CO-OFDM) systems. An attempt has been made in this paper to reduce the effects of common phase errors generated by phase noise. In this paper, a least mean square (LMS) based algorithm is proposed for estimation of phase noise. Using this proposed algo- rithm, the major problem of phase ambiguity caused by cycle slip is avoided and the bit error rate is greatly improved. Further, there is no requirement for modifying the frame struc- ture of OFDM using this algorithm. A CO-OFDM system with the 8-PSK technique is used to implement the algorithm con- cerned. Furthermore, the algorithm, using the 8-PSK modulation technique, is analyzed and compared with the existing QPSK technique and with other algorithms. The investiga- tions reveal that 8-PSK outperforms existing LMS algorithms using other techniques and significantly reduces the bit error rate. [ABSTRACT FROM AUTHOR]

Aiming at background cli^tter of TOFD，the modified variable step size LMS was proposed for suppressing lateral wave of ultrasonic TOFD images. First，step factor of the relationship witli the steady-state error was analyzed for obtaining the optimal variable and variable step iterative formula. Secondly，the memory factor concept was introduced for solving the problems of algorithm does not fully converge step factor has been reduced to a minimum. Finaiy，the algorithim was applied to non-near-surface and near surface defects defect images，the relationship bet^veen eror signal and step size factor under diferent parameters was studied，filtering effect was compared，and the influence of each parameter was discussed. The results indicate that the suppression effect of lateral wave is the best when the value of is 0. 02 and the value of is 0.8. This method can effectively removal the timing jitter lateral waves. [ABSTRACT FROM AUTHOR]

This paper describes the design and analysis of controlled reception pattern antenna (CRPA) arrays. The algorithm of the CRPA system minimizes the array output power with a constraint that the power of the look direction maintains a constant or unity. The proposed approaches use different permittivities and sizes between one reference and auxiliary elements of the CRPA arrays. Due to the low permittivity and large size of one reference element, the CRPA arrays not only have much better sensitivity of a user's GPS receiver but also maintain the performance of the null depth of the CRPA system. [ABSTRACT FROM AUTHOR]

In magnetically suspended rotor (MSR) system, harmonic vibration appears inevitably produced by mass imbalance of the rotor and sensor runout. Least mean square (LMS) is one method to reject the harmonic frequency component of the disturbance in a control system. Nevertheless, the performance of the conventional LMS is not optimal and the transient response is relatively slow. In this paper, the rotor dynamic model including mass imbalance and sensor runout is built, and the mechanism of vibration is analyzed. A harmonic vibration force suppression method based on frequency-domain adaptive LMS is proposed. Compared to the existing adaptive LMS methods, this article introduces a new convergence factor in frequency domain, which improves the updating strategy of the step size and the convergence rate of the algorithm. Simulation results demonstrate that the proposed method can effectively restrain the harmonic vibration force of magnetically suspended rotor system. [ABSTRACT FROM AUTHOR]

In last few years there is escalating growth of teletraffic accompanied with higher power emission resulting in increased global carbon footprint. But as evolution of a technology should be in harmony with environment, controlling transmission energy at base station and mobile phones becomes mandatory. Therefore novel Beamforming technique or precoding technique is a desideratum at receiver as well as transmitter side. This technique searches for receivers and tracks optimal solution for enhancement of power efficiency, signal to interference noise ratio (SINR) and half power beam width. This paper proposes an integration of evolutionary algorithms and standard beamforming techniques such as least mean square (LMS) and recursive least square (RLS). To solve the global optimization problem, evolutionary algorithms such as particle swarm optimization (PSO), non-dominated sorting genetic algorithm, BAT and Firefly are used. The MATLAB simulation of the proposed fusion of LMS and RLS with evolutionary optimization algorithms has shown improvement in power and SINR which resulted in achieving less bit error rate. The results have shown reduction in mean square error (MSE) and improvement in cost function by 89%. [ABSTRACT FROM AUTHOR]

This article presents a novel time-shared and lookup table (LUT)-less pipelined architecture for a least-mean-square (LMS) adaptive filter (ADF). The proposed approach first employs a time-shared architecture for the pipelined LMS ADF to compute each filter partial product and coefficient increment term using a single multiplier. Critical path analysis of this architecture is carried out to determine the pipeline requirements. Next, a novel LUT-less multiplier is suggested by exploiting the symmetries between the odd-multiples. Due to the symmetries between the odd-multiples, offset terms are added using an adder tree. For higher wordlength coefficients, only a few adders are required to generate the odd-multiples, and only one offset adder tree is required. Finally, a novel super-latch is developed to pipeline the LUT-less multiplier with adaptation delays of the pipelined LMS ADF. From the implementation results, it is found that the proposed design for the 32nd-order filter occupies 60.32% less area, consumes 61.93% less power, and utilizes 58.83% less sliced LUTs and 63.28% fewer flip-flops over the best existing design. [ABSTRACT FROM AUTHOR]

As low-cost fully digital array technology becomes more commercialized, techniques need to be developed to be able to deal with many of the inherent problems of these systems. This article focuses on mitigating the intermodulation distortion (IMD) caused by the low-input third-order intercept point (IIP3) of the low-noise amplifier (LNA) in a digital array element’s channel. The performance of solid-state electronics also varies with the temperature of the device, thus changing the device’s nonlinear characteristics. This article proposes an adaptive solution for nonlinear equalization (NLEQ) with the use of the least mean-square (LMS) algorithm. Data from a modern digital array channel are corrected using the proposed method; the channel nonlinearities are also characterized for further simulations. A simulation using these nonlinear coefficients shows that NLEQ can effectively mitigate the IMD of two strong interferers that had previously distorted the weak signal of interest. Finally, a nine-element fully digital array is simulated, showing the correlation of IMD with specific, predictable directions, and the decorrelation of these spurs after NLEQ is applied to each element of the array. [ABSTRACT FROM AUTHOR]