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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]

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.)

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]

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]

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]

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]

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]

Copyright of Systems Engineering & Electronics is the property of Journal of Systems Engineering & Electronics Editorial Department 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.)

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]

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]

Copyright of Journal of Mechanical & Electrical Engineering is the property of Mechanical & Electrical Engineering Magazine 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.)

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]

Unity grid power factor is essential for the indirect matrix converter (IMC) applications due to the grid-friendly requirement. The existing closed-loop grid power factor control methods usually consist of complex structures, resulting in high requirements both in hardware and software aspects. In this paper, a low-cost phase-angle compensation method is proposed to reduce the costs. Based on the modeling for the IMC, the influences from the system parameters on the grid power factor angle are analyzed. Then, a virtual adaptive resistance model is denoted and calculated with the least mean square algorithm to generate a virtual voltage signal, which is consequently utilized to generate the phase compensation angle by a newly designed proportional-integral closed loop to regulate the grid power factor angle. Compared to the typical existing method, the proposed low-cost phase-angle compensation method requires only one grid current measurement in hardware and reduces 18.9% computational burden in software, while the advantages being adaptive to the system parameters and suitable for a wide load-range are reserved. Simulation and experimental research for the proposed method is carried out to validate its steady-state and dynamic-state compensation effectiveness under various load conditions. [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.)

Conventional direction-of-arrival (DOA) estimation methods based on subspace decomposition are computationally complex and difficult to implement in practical applications. Although a fixed-step-size least mean square (FSS-LMS) algorithm for DOA estimation has overcome the complexity problem; however, the manual selection of suitable step size is difficult. In this letter, a variable-step-size LMS algorithm is proposed for DOA estimation. In the proposed method, the step size is updated by using the estimated signal power and the cumulative sum of squared instantaneous error, and the spatial spectrum is obtained by the reciprocal of array pattern, where the peak values indicate the estimated DOAs of signals. The proposed method can choose step size adaptively and provide better performance with low computational complexity. The performance of proposed method is verified by the numerical simulations and compared with some existing methods. [ABSTRACT FROM AUTHOR]

A low-jitter, low-power ring oscillator (RO)-based injection-locked clock multiplier (ILCM) is presented. It employs a background-calibrated reference frequency doubler to increase the RO noise suppression bandwidth, a digital delay-locked loop (DLL) to achieve second-order suppression of RO noise, and a digital frequency-tracking loop (FTL) to continuously tune the oscillator’s free-running frequency and ensure a robust operation across process, voltage, and temperature (PVT) variations. A least-mean-square (LMS) algorithm is used to accurately cancel the deterministic jitter (DJ) caused by input duty cycle errors. Fabricated in the 65-nm CMOS process, the prototype ILCM occupies an active area of 0.09 mm2 and generates an output clock in the range of 2.5–5.75 GHz using a 125-MHz reference clock. At 5 GHz, it achieves an integrated jitter of 335 fsrms, while consuming 5.3 mW of power. This translates to the best reported figure-of-merit (FoM) of −242.4 dB for a ring-based ILCM at this high frequency. [ABSTRACT FROM AUTHOR]

Copyright of Journal of Measurement Science & Instrumentation is the property of Journal of Measurement Science & Instrumentation 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.)

Implementation of low-noise power-efficient clock multipliers requires low-noise high-frequency reference clocks. This paper presents ways to generate such reference clocks at four times the frequency of a standard crystal oscillator (XO) output frequency. Using extensive digital correction techniques, a 216-MHz reference clock with an integrated jitter of 77 $\text {fs}_{\mathrm{ rms}}$ is generated from a 54-MHz Pierce XO. A ring oscillator-based injection locking clock multiplier driven by the proposed quadrupler is used to demonstrate the efficacy of the quadrupler. Fabricated in a 65-nm CMOS process, the proposed clock multiplier occupies an active area of 0.16 mm2 and achieves 366 $\text {fs}_{\mathrm{ rms}}$ integrated jitter at 4.752-GHz output frequency while consuming 6.5-mW power from a 1.0-V supply of which 1.5 mW is consumed in the quadrupler. [ABSTRACT FROM AUTHOR]

A novel digital signal processing (DSP) scheme for special‐shaped circular (7,1) modulated signals based on quasi blind carrier phase estimation (QBCPE) is proposed. The results show that applying 0.33‰ overhead QBCPE provides similar performance as 3% training sequence data‐aided equalization of least mean square (LMS) algorithm. [ABSTRACT FROM AUTHOR]

This paper deals with the use of a distribution static compensator (DSTATCOM) to the system constituted by a nonlinear load fed from a distorted grid. Moreover, an improved proportionate normalized least mean square (i-PNLMS) control algorithm is proposed for the DSTATCOM. The system is first modeled on a MATLAB/Simulink platform and its performance is analyzed under steady state and load increment cases. The frequency response of the compensated system is also obtained. Tests are conducted on a laboratory prototype to analyze the system's performance under steady state and load unbalancing. This algorithm helps in achieving the desired grid currents easily and efficiently, and it shows an inherent tendency to adapt to the system's requirements. The obtained total harmonic distortion of grid currents and point of common coupling voltages are within the limits defined in the IEEE-519 standard. [ABSTRACT FROM AUTHOR]

Sensors are used to determine a variety of health and security parameters. Pressure mats and camera analysis are frequently used to determine a person’s chair posture. Measurement of parameters can be derived from a variety of different sensor types, which may already be present in an environment. This paper presents the use of the concept of “sensory substitution,” where a sensor designed to measure Quantity X is used to measure Quantity Y. The concept is used to enable alternative sensing techniques for medical and security applications, such as determining chair occupancy, postural shift times, and even current postural state, without using cameras or pressure sensors. Specifically, the postural state of a subject is determined in a laboratory study using various arrays of both temperature sensors and acoustic sensors. This method can be used standalone, can augment other sensors, or can validate data from pressure or visual-based systems. These alternative sensors could also be used for other aspects of smart infrastructure. The system was tested with different types and styles of furniture, including chairs, armrests, cushions, and beds. A least mean square algorithm was deployed to remove noises enabling acoustic posture detection. Using a thresholding algorithm, postural changes are then identified using audio. In a controlled environment, over 90% of postural change events were detected. Thus, the paper shows two alternative instrumentation and measurement methods to determine occupancy, postural change timings, and even posture states of a person in a chair. [ABSTRACT FROM AUTHOR]

The radar echo signal of non-stationary and singular points usually contains false echoes, which affects the recognition and measurement of liquid level echo signal. In order to eliminate false echo interference and improve the recognition and measurement accuracy of the liquid level gauge, a method of echo recognition and correction based on adaptive least mean square (LMS) is proposed. The short-time amplitude function and short-time zero crossing rate function are combined to recognize the echo signal. The weight vector iteration and updating weight coefficients are obtained by LMS method. The echo signal is recognized and the false echo interference is suppressed. The experimental results show that the level echo signal can be accurately recognized by this method, and level measurement accuracy can reach 0.47% F. S. Compared with other denoising methods, adaptive LMS can keep the signal singularity characteristics while suppressing the noise. Moreover, it has better robustness. [ABSTRACT FROM AUTHOR]

Nowadays, the vein based recognition system becomes an emerging and facilitating biometric technology in the recognition system. Vein recognition exploits the different modalities such as finger, palm and hand image for the person identification. In this work, the fuzzy least brain storm optimization and Euclidean distance (EED) are proposed for the vein based recognition system. Initially, the input image is fed into the region of interest (ROI) extraction which obtains the appropriate image for the subsequent step. Then, features or vein pattern is extracted by the image enlightening, circular averaging filter and holoentropy based thresholding. After the features are obtained, the entropy based Euclidean distance is proposed to fuse the features by the score level fusion with the weight score value. Finally, the optimal matching score is computed iteratively by the newly developed fuzzy least brain storm optimization (FLBSO) algorithm. The novel algorithm is developed by the least mean square (LMS) algorithm and fuzzy brain storm optimization (FBSO). Thus, the experimental results are evaluated and the performance is compared with the existing systems using false acceptance rate (FAR), false rejection rate (FRR) and accuracy. The performance outcome of the proposed algorithm attains the higher accuracy of 89.9% which ensures the better recognition rate. [ABSTRACT FROM AUTHOR]

In this paper we briefly describe the design, implementation, and evaluation of a novel adaptive optimization approach for the feed-forward software defined equalization (FFSDE) method using the least mean squared (LMS) algorithm. In our design, we adaptively change the filter length ( N) and step size ( $$\mu$$ ) to achieve the optimal bit error rate value. We used a vector signal generator RF PXI-5670 and a vector signal analyzer (VSA) RF PXI-5660 to test the validity of our approach. We implemented our method for the M-ary quadrature amplitude modulation (M-QAM) scheme in the VSA (which served as a receiver). The experimental results showed that we achieved high convergence speed and accuracy for rapidly changing transmitter channel characteristics. The automatic optimal setting feature of the LMS Algorithm parameters N and $$\mu$$ , enabled us to solve the hardware configuration problem for the FFSDE method. Determination of the LMS Algorithm training sequence size for the particular M-QAM allowed us to eliminate redundant data of the training sequence and increase the throughput. [ABSTRACT FROM AUTHOR]

A joint-optimization method is proposed for enhancing the behavior of the l1-norm- and sum-log norm-penalized NLMS algorithms to meet the requirements of sparse adaptive channel estimations. The improved channel estimation algorithms are realized by using a state stable model to implement a joint-optimization problem to give a proper trade-off between the convergence and the channel estimation behavior. The joint-optimization problem is to optimize the step size and regularization parameters for minimizing the estimation bias of the channel. Numerical results achieved from a broadband sparse channel estimation are given to indicate the good behavior of the developed joint-optimized NLMS algorithms by comparison with the previously proposed l1-normand sum-log norm-penalized NLMS and least mean square (LMS) algorithms. [ABSTRACT FROM AUTHOR]

In this paper a predistorter linearizer is designed to reduce the memory less and also memory effect in different power amplifier models. The proposed method is based on the joint working principle of RLS filter model (polynomial) which compensate intermodulates and the multi-level system excludes the trade-off between precision and adaptation speed. The Predistorter model is validated by utilizing the orthogonal frequency division multiplexing in 16-QAM modulation. The Matlab realization of the proposed system shows 32×-34× faster convergence time than predistorter based on conventional LMS model with same adaptation step. [ABSTRACT FROM AUTHOR]

This letter examines the problem of clutter cancellation in airborne passive radar. This problem is exacerbated by the Doppler spread of clutter due to the receiver motion, since targets falling in the clutter Doppler band will be canceled. To address this problem, a least mean square-based algorithm is proposed for the sidelooking airborne passive radar. Making use of the directional dependence of the clutter Doppler frequency, this algorithm cancels clutter along the clutter ridge and thus avoids the cancellation of targets. Simulations with the experimental data show that clutter can be canceled ideally with the proposed algorithm and signal-to-noise ratio losses are slight, for example, the losses are within 1 dB for targets with radial velocity greater than 10 m/s. [ABSTRACT FROM PUBLISHER]

In this paper, the performances of well-known adaptive beamforming algorithms, namely, Sample Matrix Inversion (SMI) and Least Mean Square (LMS), are analyzed. From the simulation results, it is observed that for SMI, as the number of user increases the main lobe spreads in all directions. As a result, beamforming in the user direction may not be correct. To overcome this, LMS algorithm can be used. The LMS algorithm is the best choice in most of the commercial wireless applications due to its low complexity. 5G and beyond 5G networks require beamforming algorithms with high directivity and fast convergence rate. Hence, in this paper, LMS algorithm is modified to improve the convergence rate. Furthermore, improved LMS algorithms are applied to horizontal - vertical Uniform Linear Array. This is also called 2D-ULA. These improved LMS algorithms are called 2D-LMS 1 and 2D-LMS 2. The main drawback of LMS algorithm is its low convergence rate; because of this, LMS algorithm may not track the desired signal when the signal conditions are changing rapidly. Simulation results show that the proposed 2D-LMS 1 and 2D-LMS 2 algorithms have improved convergence rate, higher directivity, low complexity and fast execution time as compared to conventional LMS algorithm. Hence, the proposed algorithms are best suited for 5G and beyond wireless communications. [ABSTRACT FROM AUTHOR]

Compressive sensing (CS) is able to measure sparse signals with fewer measurements than Nyquist rate. The reconstruction of sparse signals in CS is an underdetermined problem. For realistic applications, the sparsity information may not be available, the measurements are used to be disturbed by noise, and efficient hardware realization is important. Existing CS reconstruction algorithms are tradeoff between noise tolerance and low complexity. In this paper, we propose a novel reconstruction algorithm called stochastic gradient pursuit (SGP). The stochastic gradient approach is applied to replace least square process of well-known orthogonal matching pursuit (OMP) algorithm. Since stochastic gradient approach iterates toward a minimum mean square error solution, proposed algorithm shows robustness against noise. Furthermore, proposed algorithm inherits the low-complexity property of OMP and shows feasibility of hardware implementation. The SGP algorithm can achieve 36% higher success rate as well as 27% less complexity than OMP when SNR is 20 dB. Finally, we implement the algorithm with designed hardware sharing architecture in TSMC 90 nm technology. The postlayout result shows that the core size is only 1.08 mm 2 at 150 MHz operation frequency. Compared with state-of-the-art ASIC designs, the proposed engine achieves 29% higher throughput-to-area ratio. [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]

This paper explains the implementation based on adaptive filter algorithm with shadow technique, which belongs to the class of LMS algorithm on FPGA. The objective of this paper is to cancel out additive noise due to the effect of environmental conditions in the communication system. The additive noise is one of the major problems in the communication, especially in the digital electronic circuits design these days. Generally, the coefficient of filter updation is not in a basic filter time to time, as it may be an effect on the desired information. By updating coefficient of filter time to time, this problem could be eradicated and by increasing the number of iterations for the filtering process, it would give an efficient result. The popular method used to cancel out the additive noise in the communication systems is the Least Mean Square (LMS) algorithm. Here, a novel shadow based technique is employed in fixed LMS adaptive filter for improving the spectral characteristics like Side Lobe Attenuation (SLA), Main Lobe Width (MLW), Side Lobe Fall of Ratio (SLFR) of the filter by changing the feedback factor 'β'. In this project, it is found that the performance of shadow based fixed LMS is improved when compared to the existed fixed LMS in terms of signal-to-noise ratio (SNR) and Mean Squared Error (MSE) done with the help of MATLAB tool, and this proposed project was implemented on FPGA with utilization of minimum number of logic gates, flip flops, LUT's, and registers that are operated at maximum frequencies. This paper also compares the number of flip flops, logic gates, LUT's, and registers utilized for different resources in the family of FPGA. In real time applications, the adaptive filter algorithm implementation on FPGA plays a crucial role, especially in the DSP processors for effective communication. By introducing the concept of shadow technique in the adaptive filtering, the additive noise would be suppressed in the communication systems. The core FPGA is designed such that, it can be implemented in any brand of SoPC. In this paper, SPARTAN 3E and Vertex 4 are the application platform of FPGA. The achieved results gives an improvement in area resource utilization, convergence rate, speed, and performance in design pure LMS hardware core. Implementing the LMS adaptive filter algorithm on FPGA is achieved in VERILOG hardware description languages. [ABSTRACT FROM AUTHOR]

This paper presents a wideband fractional- N frequency synthesizer design with a low-effort adaptive calibration technique for \Sigma \Delta quantization noise cancellation. After adopting from the classical single-ended loop filter structure, this least mean square algorithm based calibration technique can precisely and efficiently adjust the noise cancellation digital–analog convertor current with high linearity and immunity. Besides, as long as the desired current is achieved, the calibration circuits are turned off and disconnected to save the power consumption and isolate from the signal paths. With the proposed phase-noise cancellation technique, small area and low power circuit design are achieved, meanwhile the fractional and reference spurs are highly attenuated, allowing the wideband direct frequency/phase modulation with high data rates. With low effort modification, it can be directly implemented as straightforward phase-noise enhancement for any wideband phase-locked loop applications. [ABSTRACT FROM PUBLISHER]

This paper gives information about the comparison of adaptive filtering techniques like fixed LMS, Normalized Least Mean Square, and RLS for noise elimination in speech communication systems. The main objective of this paper is to suppress the additive noise which is due to the effect of environmental conditions in the communication systems. In these days, additive noise is one of the major problems in the communication, especially in the digital electronic circuit design. The origins of additive noise are because of atmospheric conditions, weather situations around the system and any other disturbances. Generally, the coefficients of filter updation in a basic filter does not occur time to time, as it may affect the desired information. By updating the coefficients of the filter time to time, this problem could be eradicated and thereby increasing the number of iterations for the filtering process, which gives efficient results. In the communication systems, the performance of these adaptive filters are in terms of mean square error, signal to noise ratio, rate of convergence, etc. In this research paper, the authors have discussed about how to cancel out the additive noise which is combined to the input speech signals that observes the records of signal to noise ratio, and mean square error. Finally, this article compares those results experimentally with the help of MATLAB programming and calculation tool. The mean square error improvement with the number of iterations for different noise signals are represented graphically. By the observation of the graphical results, the rate of convergence and reception level for the given speech and noise signals were found. [ABSTRACT FROM AUTHOR]

We present a digital phase-locked loop (DPLL) operating from 2.8 to 3.8 GHz with an on-chip 40-MHz reference crystal oscillator. The DPLL makes use of a class-D digitally controlled oscillator and a digital-to-time converter with a single-bit (bang-bang) phase detector. The DPLL displays an excellent behavior in terms of in-band fractional spurs, which are consistently below −65 dBc across the tuning range, thanks to a number of digital correction algorithms running in the background. The 65-nm CMOS DPLL consumes 18.2 mW for an in-band phase noise of −102 dBc/Hz at 100-kHz offset. [ABSTRACT FROM PUBLISHER]

SUMMARY Normalized least mean square (NLMS) was considered as one of the classical adaptive system identification algorithms. Because most of systems are often modeled as sparse, sparse NLMS algorithm was also applied to improve identification performance by taking the advantage of system sparsity. However, identification performances of NLMS type algorithms cannot achieve high-identification performance, especially in low signal-to-noise ratio regime. It was well known that least mean fourth (LMF) can achieve high-identification performance by utilizing fourth-order identification error updating rather than second-order. However, the main drawback of LMF is its instability and it cannot be applied to adaptive sparse system identifications. In this paper, we propose a stable sparse normalized LMF algorithm to exploit the sparse structure information to improve identification performance. Its stability is shown to be equivalent to sparse NLMS type algorithm. Simulation results show that the proposed normalized LMF algorithm can achieve better identification performance than sparse NLMS one. Copyright © 2013 John Wiley & Sons, Ltd. [ABSTRACT FROM AUTHOR]