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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]

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]

• 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]

• Echo, an audio quality communicated over the microphone that is undesired, is eliminated using nonlinear kernel adaptive filtering. • Adaptive filters make it possible to identify time-varying traits and monitor dynamic changes in the signal. • Online iterative optimisation algorithms are used by Adaptive Filters. • Artificial Bee Colony algorithm optimizes the step size and coefficient of Kernal Improved Proportionate and Normalised Least Mean Square adaptive filter. • 10 dB improvement of echo return loss enhancement is achieved by this method. This research work uses nonlinear kernel adaptive filtering to eliminate echo, a type of voice that is undesirable and communicated over the microphone. Adaptive filters enable the detection of time-varying potentials and the tracking of the signal's dynamic changes. In this work, a hybrid metaheuristic technique that combines the Artificial Bee Colony (ABC) with the Kernel Adaptive Improved Proportionate and Normalized Least Mean Square (KIPNLMS) filter is developed to significantly boost echo cancellation efficiency. The unique adaptive filter based on sub-filter and proportionate adaptation is combined in the advanced technique, which presents an improved Non-linear Acoustic Echo Cancellation (NAEC) framework. An essential part of kernel techniques is that they translate the information on to a high-dimensional feature space, where linear filtering is performed. In the initial space, the arithmetic operations are carried out by evaluating inner products among pairs of input patterns, also known as kernels. Along with the ABC step size regulation technique, kernel-based adaptive filtering improves Signal to Noise Ratio (SNR) and accelerates convergence. The algorithm's effectiveness is assessed through simulation using signal Echo Return Loss Enhancement (ERLE). The performance of Kernel LMS, Kernal Normalized LMS (KNLMS), Kernal Proportionate NLMS, and KIPNLMS algorithms are compared. In contrast to typical kernel adaptive filters, the suggested ABC-KIPNLMS increases the ERLE more than 10 dB compared to other algorithms. [ABSTRACT FROM AUTHOR]

This paper presents a dedicated digital signal process (DSP) for four pulse amplitude modulation (PAM4) SerDes receivers. It is targeted to implement data recovery and adaptive equalization under ultra-high-speed and large channel attenuation with a small area and high power efficiency. The DSP consists of a clock data recovery (CDR), a 16-tap feed forward equalizer (FFE), a 1-tap decision feedback equalizer (DFE), and an automatic adaptation engine. An adaptive least mean square (LMS) algorithm is utilized to make the system more intelligent in calculating tap coefficients of the FFE and DFE. To address the timing limitation associated with traditional digital DFE that cannot handle large amounts of parallel data at a high speed, speculative techniques and a customized 4-to-1 multiplexer (MUX) unit are employed to remove the summation time and reduce the selection time, respectively. A first-order sigma-delta modulator is used to replace the traditional moving average to calculate average voltages, which could prominently save the hardware resources and power consumption. Additionally, the influence of input quantization resolution on the equalization ability is analyzed. Implemented in a 28-nm CMOS, the DSP could compensate for up to 33-dB loss at 100 Gb/s with a power consumption of 7.22 pJ/bit. [ABSTRACT FROM AUTHOR]

• A dual parameters optimization l p -LMS (DPO- l p -LMS) algorithm is derived by iteratively adjusting the step-size and the parameters p. • Numerical simulations and l p experimental results based on shallow water UWA communication data demonstrated that the proposed algorithm achieves better performance. • The convergence analysis of the proposed algorithm is also theoretically given. The sparse norm constraint ( l 0 , l 1 , l 2 and l p ) least mean square algorithm (LMS) is established technique for modeling sparse systems. However, when applied in target systems with uncertain sparsity, such as temporal-spatial-varying sparse underwater acoustic (UWA) channel, the parameters tuning (the step-size and parameter p) of l p -LMS faces significant challenges. In this paper, with the purpose to simplify the complicated dual-parameter selection problem via gradient strategy, a dual parameters optimization l p -LMS (DPO- l p -LMS) algorithm is derived by iteratively adjusting the step-size and the parameters p in parallel along the descent gradient. Convergence analysis of the proposed algorithm is given. A numerical simulation under varying sparsity systems exhibits that the proposed algorithm outperforms the l p -LMS algorithms driven by the existing optimization approaches in convergence speed and steady-state error. Meanwhile, a field shallow water experiment of UWA communication demonstrated that the proposed algorithm achieves superior performance under the framework of direct adaptation turbo equalization (DA-TEQ). [ABSTRACT FROM AUTHOR]

A 12-bit 250-MS/s adaptive asynchronous successive approximation registers analog-to-digital converter (SAR ADC) with a Non-binary segmented split capacitor array is proposed. The Non-binary bridge split capacitor array leads to much larger units with reduced total capacitance and allows for reducing digital-to-analog converter (DAC) settling time and power consumption. The reset time of the comparator is automatically and precisely adjusted according to the settling time required by each capacitor of the non-binary capacitor array, which accelerates the ADC quantization. With the proposed adaptive asynchronous logic (AAL), the settling time utilization will reach 98.6% and the overall ADC's speed can be increased by 30%. The circuit was designed in 28 nm CMOS technology. The mismatch of the capacitor is calibrated by the Least Mean Square (LMS) background calibration for high speed and low power consumption. Based on post-layout simulations, the ADC achieves a signal-to-noise distortion ratio (SNDR) of 67.92 dB at 250 MS/s sampling rate and consumes 2.76 mW, resulting in a figure of merit of 5.43fJ/conversion-step. The ADC core occupies an active area of only 100 μm × 350 μm. [ABSTRACT FROM AUTHOR]

Across the globe, solar photovoltaic (PV) sources are treated as the most favorable renewable sources which can fulfill a larger percentage of the total electricity demand with its clean form of energy. But due to its intermittent characteristics, the PV injects lots of uncertainty into the power system. Because of variation in input solar intensity due to climatic conditions, it will impact on the power quality (PQ) issues in power industries. Therefore, in this proposal, a solar PV integrated with shunt hybrid active filter (SHAF) is taken to fulfill the objective of (a) reducing the current harmonics and (b) supplying the active power generated from the PV system. The challenging task in the proposal is to control the PV variables and current control in voltage source inverter (VSI) of the SHAF. Therefore, three-key contributions are made as follows: (a) a control strategy designed which is based on adaptive notch filters (ANF) for reducing the harmonics by generating accurate reference currents; (b) a hysteresis controller is implemented for gating signal pulses; and (c) for maximum power tracking, a fuzzy based maximum power point tracking (MPPT) is developed for the solar tracker. We showed embedded applications of a SHAF for regulating the grid interfaced PV system. Besides a SHAF operated with the ANF and based on the direct current control (DCC) method is used for computing the compensated current. A comparative analysis has been done between ANF (least mean square (LMS) and recursive least square (RLS) method) and the existing non-adaptive notch filters (NNF). Rigorous computer simulations are performed to determine the effectiveness of the proposed system. The real-time digital simulator using OP 5142 designed with low cost and advanced monitoring capability is employed for validating results. • A three-phase shunt hybrid active filter with integrated photovoltaic system is presented. • An adaptive fuzzy logic controller is used to track the maximum power from solar photovoltaic system. • An adaptive notch filter is implemented to compute the compensated current. • Extensive simulations are performed to validate with the IEEE-519 standards. [ABSTRACT FROM AUTHOR]