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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]

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.

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.

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]

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]

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

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

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

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

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

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

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

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]

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]

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.

Adaptive filtering has the advantages of real-time processing, small computational complexity, and good adaptability and robustness. It has been widely used in communication, navigation, signal processing, optical fiber sensing, and other fields. In this paper, by adding an interferometer with the same parameters as the signal interferometer as the reference channel, the sensing signal of the interferometric fiber-optic hydrophone is denoised by two adaptive filtering schemes based on the least mean square (LMS) algorithm and the normalized least mean square (NLMS) algorithm respectively. The results show that the LMS algorithm is superior to the NLMS algorithm in reducing total harmonic distortion, improving the signal-to-noise ratio and filtering effect.

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]

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]

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]

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 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]

Online estimation techniques are extensively used to determine the parameters of various uncertain dynamic systems. In this paper, online estimation of the open-circuit voltage (OCV) of lithium-ion batteries is proposed by two different adaptive filtering methods (i.e., recursive least square, RLS, and least mean square, LMS), along with an adaptive observer. The proposed techniques use the battery’s terminal voltage and current to estimate the OCV, which is correlated to the state of charge (SOC). Experimental results highlight the effectiveness of the proposed methods in online estimation at different charge/discharge conditions and temperatures. The comparative study illustrates the advantages and limitations of each online estimation method.

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]

In OFDM system, channel estimator with fixed-coefficient interpolation gives rise to significant estimation errors when the estimator mismatches the experienced time-varying channel. This paper presents a pilot-aided channel estimator adopting a double one-dimensional (1-D) polyphase subfilter structure, in which a few interpolating coefficients are applicable to all subcarriers of an OFDM symbol. By analyzing the channel interpolation for OFDM receiver, we propose a two-dimensional (2-D) adaptation scheme of the interpolating coefficient, based on a block least-mean-square algorithm, which requires none of knowledge on channel statistics. The 2-D adaptation scheme is simplified as two types of 1-D schemes respectively for the time and frequency dimensions, based on the known frequency- and time-direction interpolating coefficients. By simultaneously exploiting channel correlation in the time and frequency dimensions, the adaptive interpolators are trained to match channel properties. Furthermore, the proposed schemes of adaptive interpolation demand lower implementation cost, compared with the existing methods. Simulation results show that the adaptive interpolators converge at a high rate, and that the simplified schemes outperform the conventional methods in the high-speed mobile channel and the single-frequency-networks channel with long-delay echoes, while preserving a low implementation complexity. [ABSTRACT FROM AUTHOR]

To suppress unbalanced vibrations of low-speed rotors suspended by magnetic bearings, such as a magnetically suspended flywheel, an autobalancing control method based on elimination of synchronous force has been applied. But its precision may decrease for high-speed rotors such as magnetically suspended control moment gyros. The reason is that the low-pass characteristic of an amplifier in a magnetic bearing control system causes errors in the force elimination. Moreover, this low-pass characteristic increases with rotational speed. To resolve this problem, we propose an autobalancing control scheme using adaptive feedforward compensation based on a least mean square (LMS) algorithm. In this LMS algorithm, two input signals are synchronous displacement and its orthogonal signal. The weights of the input signals are introduced into the algorithm. They are updated in the principle of least mean square to minimize the error between actual and reference values of synchronous current. In simulation and experiments, this method reduces synchronous vibration to less than 40% of that with conventional proportional feedforward. The results demonstrate that this method counteracts the negative effect of low-pass characteristics of an amplifier adaptively and suppresses the synchronous vibration force more precisely. Accordingly, high-precision autobalancing of high-speed rotors can be achieved. [ABSTRACT FROM PUBLISHER]

A high-performance implementation scheme for a least mean square adaptive filter is presented. The architecture is based on distributed arithmetic in which the partial products of filter coefficients are precomputed and stored in lookup tables (LUTs) and the filtering is done by shift-and-accumulate operations on these partial products. In the case of an adaptive filter, it is required that the filter coefficients be updated and, hence, these LUTs are to be recalculated. A new strategy based on the offset binary coding scheme has been proposed in order to update these LUTs from time to time. Simulation results show that the proposed scheme consumes very less chip area and operates at high throughput for large base unit size k\ (= N/m), where m is an integer and N is the number of filter coefficients. For example, a 128-tap finite-impulse-response adaptive filter with the proposed implementation produces 12 times more throughput (for k = 8) and consumes almost 26% less area when compared to the best of existing architectures. [ABSTRACT FROM PUBLISHER]

An all-digital adaptive module for input-referred second-order intercept point (IIP2) calibration in CMOS downconverters is proposed. The proposed automatic background calibration scheme utilizes a modified least-mean-square algorithm with a variable update step to achieve fast convergence. An easily acquired and highly second-order-intermodulation-distortion-correlated reference input is used with delay estimation/optimization to improve the convergence efficiency and reduce the implementation complexity. The proposed all-digital adaptive module is implemented on a field-programmable gate array to calibrate the IIP2 of a 2.62-GHz downconverter fabricated in a 0.18-\mu\m CMOS process. With the proposed automatic background IIP2 calibration, the measured IIP2 of the CMOS downconverter is improved by 21 dB from 62 to 83 dBm, while the IIP2 calibration converges in less than 0.1 ms with a 10-MHz clock, ten times faster than the conventional adaptive algorithm. [ABSTRACT FROM AUTHOR]

Recently, the subspace-least mean square (S-LMS) method has been proposed for power system measurement. The high accuracy and high resolution of the S-LMS method are achieved at the cost of being computationally expensive. In this paper, the S-LMS method is enhanced in two aspects: 1) speed and 2) accuracy. The computation burden of S-LMS is significantly reduced in three ways: 1) exploring the sparsity of power system signals; 2) using an iterative multisectional search scheme; and 3) the combination of these two techniques. Further, detection of the harmonic components based on the fact that they are multiples of the fundamental frequency has been effectively employed, resulting in a more accurate and robust algorithm for fundamental and harmonic estimation in the presence of noise. The enhanced S-LMS algorithm, which detects harmonics more accurately, is more than 150 times faster than the original S-LMS if the interharmonic level is negligibly low. The dynamic behavior of the method is discussed and the method is compared with Prony and DFT. Simulations show the methods are highly resilient to off-nominal conditions and noise. [ABSTRACT FROM AUTHOR]

A high-resolution method for harmonic and interharmonic measurements in power systems is proposed based on the concepts of subspace and least mean square. A subspace function is constructed by using the noise eigenvectors of the autocorrelation matrix of the test signal. The harmonic and interharmonic frequencies of the signal are derived by finding the zeros of the subspace function. A least mean square approach is introduced to compute the amplitudes and phase angles of harmonic and interharmonic components based on the computed frequencies and time-domain measurements of the signal. The proposed method is compared with some of the recently proposed harmonic/interharmonic analysis methods, including discrete Fourier transform (DFT), windowed interpolation DFT, Prony, iterative DFT, and min-norm methods. The effects of noise, fundamental frequency deviation, and subharmonics have been considered. Numerical results show that the proposed method can perform accurate harmonic/interharmonic measurements for power system signals. [ABSTRACT FROM AUTHOR]

For image-based data hiding, it is difficult to achieve good image quality when high embedding capacity and 100% data extraction are also demanded. In this study, the proposed method, namely, overall minimal-error searching (OMES) is developed to meet the aforementioned requirements. Moreover, the concept of secret sharing is also adopted to distribute watermarks into multiple halftone images, and the embedded information can only be extracted when all of the marked images are gathered. The OMES modifies the halftone values at the same position of all host images with the trained substitution table (S-Table). The S-Table makes the original combination of these halftone values as another meaningful combination for embedding watermark, which is the key part in determining the image quality. Thus, an optimization procedure is proposed to achieve the optimized S-Table. Two different encoders, called error-diffused-based and least-mean-square-based approaches are also developed to cooperate with the proposed OMES to cope with high processing speed and high image quality applications, respectively. Finally, for resisting the issues caused by the print-and-scan attack, such as zooming, rotation, and dot gain effect, a compensation correction procedure is also proposed. As demonstrated in the experimental results, the proposed approach provides good image quality, and is able to guard against some frequent happened attacks in printing applications. [ABSTRACT FROM AUTHOR]

The sequential partial-update least mean square (S-LMS)-based algorithms are efficient methods for reducing the arithmetic complexity in adaptive system identification and other industrial informatics applications. They are also attractive in acoustic applications where long impulse responses are encountered. A limitation of these algorithms is their degraded performances in an impulsive noise environment. This paper proposes new robust counterparts for the S-LMS family based on M-estimation. The proposed sequential least mean M-estimate (S-LMM) family of algorithms employ nonlinearity to improve their robustness to impulsive noise. Another contribution of this paper is the presentation of a convergence performance analysis for the S-LMS/S-LMM family for Gaussian inputs and additive Gaussian or contaminated Gaussian noises. The analysis is important for engineers to understand the behaviors of these algorithms and to select appropriate parameters for practical realizations. The theoretical analyses reveal the advantages of input normalization and the M-estimation in combating impulsive noise. Computer simulations on system identification and joint active noise and acoustic echo cancellations in automobiles with double-talk are conducted to verify the theoretical results and the effectiveness of the proposed algorithms. [ABSTRACT FROM AUTHOR]

Distributed arithmetic (DA) is performed to design bit-level architectures for vector–vector multiplication with a direct application for the implementation of convolution, which is necessary for digital filters. In this brief, two novel DA-based implementation schemes are proposed for adaptive finite-impulse response filters. Different from conventional DA techniques, our proposed schemes use coefficients as addresses to access a series of lookup tables (LUTs) storing sums of delayed and scaled input samples. Two smart LUT updating methods are developed, and least-mean-square adaptation is performed to update the weights and minimize the mean square error between the estimated and desired output. Results show that our two high-performance designs achieve high speed, low computation complexities, and low area cost. [ABSTRACT FROM PUBLISHER]

This paper describes new strategies to improve the transient response time of harmonic detection using adaptive filters applied to shunt active power filters. Two cases are presented and discussed, both using an adaptive notch filter, but one uses the least mean square algorithm to adjust the coefficients and the other uses the recursive least squares algorithm. The synchronization of the adaptive notch filter orthogonal input signals, which are generated by the Clarke transformation of the load currents, is achieved automatically without the need of a phase-locked loop. This procedure significantly reduces the real-time computation burden. Simulations using Matlab/Simulink are presented to clarify the algorithm, and practical implementation is performed using the DSP Texas Instruments TMS320F2812. The experimental results are presented and discussed. [ABSTRACT FROM AUTHOR]

Abstract: Auscultation, the technique of listening to heart sounds with a stethoscope can be used as a primary detection system for diagnosing heart valve disorders. Phonocardiogram, the digital recording of heart sounds is becoming increasingly popular as it is relatively inexpensive. In this paper, a technique to improve the performance of the Least Square Support Vector Machine (LSSVM) is proposed for classification of normal and abnormal heart sounds using wavelet based feature set. In the proposed technique, the Lagrange multiplier is modified based on Least Mean Square (LMS) algorithm, which in turn modifies the weight vector to reduce the classification error. The basic idea is to enlarge the separating boundary surface, such that the separability between the clusters is increased. The updated weight vector is used at the time of testing. The performance of the proposed systems is evaluated on 64 different recordings of heart sounds comprising of normal and five different pathological cases. It is found that the proposed technique classifies the heart sounds with higher recognition accuracy than competing techniques. [ABSTRACT FROM AUTHOR]

The super-resolution reconstruction (SRR) of images is an ill posed problem. Traditionally, it is treated as a regularized minimization problem. Moreover, one of the major problems concerning SRR is its dependence on an accurate registration. In this paper, we show that a certain amount of registration error may, in fact, be beneficial for the performance of the least mean square SRR (LMS-SRR) adaptive algorithm. In these cases, the regularization term may be avoided, leading to reduction in computational cost that can be important in real-time SRR applications. [ABSTRACT FROM AUTHOR]

Digital hearing aids usually suffer from acoustic feedback. This feedback corrupts the speech signal, causes instability, and damages the speech intelligibility. To solve these problems, an acoustic feedback reduction (AFR) subsystem using adaptive algorithms such as the least mean square (LMS) algorithm is needed. Although this algorithm has a reduced computational cost, it is very unstable. To avoid this situation, other AFR subsystems based on modifications of the LMS algorithm are used. Such algorithms are given as follows: 1) normalized LMS (NLMS); 2) filtered-X LMS (FXLMS); and 3) normalized FXLMS (NFXLMS). These algorithms are tested in three digital hearing aid categories: 1) in the ear (ITE); 2) in the canal (ITC); and behind the ear (BTE). The first and second categories under study suffer from great feedback effects due to the short distance between the loudspeaker and the microphone, whereas the third category suffers from these effects due to the high signal level at the hearing aid output; thus, robust AFR subsystems are needed. The added stable gains (ASGs) over the limit gain when AFR subsystems are working in the digital hearing aids are studied for all the categories. The ASG is determined as a tradeoff between two measurements: 1) segmented signal-to-noise ratio (objective measurement) and 2) speech quality (subjective measurement). The results show how the digital hearing aids that work with AFR subsystems adapted with the NLMS or the NFXLMS algorithms can achieve up to 18 dB of increase over the limit gain. After analyzing the results, it is observed that the subjective measurement always limits the achieved ASG, but when the NLMS algorithm is used, it is appreciated that the objective measurement is a good approximation for estimating the maximum achieved ASG. Finally, taking into consideration the hearing aid performances and the computational cost of each AFR subsystem implementation, an AFR subsystem based on the NLMS algorithm to adapt feedback-reduction filters that are 128 coefficients long is proposed. [ABSTRACT FROM AUTHOR]

In multiple-input-multiple-output (MIMO) communication systems, multiple antennas are employed at the transmitter and the receiver. Compared with conventional systems, it has been recognized that MIMO is an important technology in obtaining higher data rate and increasing the system capacity. This paper proposes two new linear multiuser detectors and their adaptive implementations for the synchronous multiuser direct-sequence code division multiple-access systems in a MIMO multipath fading environment. First, we employ the total least squares (TLS) method and the estimation of signal parameters via rotational invariance techniques (ESPRIT) to construct the multiuser detector structures. Second, we use the least mean square algorithm in the TLS-based and ESPRIT-based multiuser detectors to develop four adaptive receivers. We also analyze the probability of bit error of the proposed receivers. Simulation resuIts show the performance comparisons of the proposed receivers. [ABSTRACT FROM AUTHOR]

A system model is presented of adaptive arrays with trellis coded modulation (TCM) to investigate the bit-error-rate (BER) of a wireless system in a co-channel interference environment. Adaptive arrays with different geometries, uniform circular array (UCA), uniform rectangular array (URA) and UCA with center element (UCA-CE), are implemented with the least mean square (LMS) algorithm to combat interference at the same frequency. The wireless systems are investigated in well-separated signals and random direction signals scenarios with additive white Gaussian noise. The URA and UCA-CE have faster convergence and their BERs, in some cases, are slightly influenced by the number of interferers. However, although the UCA converges slowly and its performance degrades as the number of interferers increases, it generally has the best BER. The recursive least square (RLS) algorithm is also investigated. It achieves much better performance than the LMS algorithm when the training sequences are short. However, the optimum BERs attained by these two algorithms are the same. [ABSTRACT FROM PUBLISHER]

This paper studies the statistical behavior of an affine combination of the outputs of two least mean-square (LMS) adaptive filters that simultaneously adapt using the same white Gaussian inputs. The purpose of the combination is to obtain an LMS adaptive filter with fast convergence and small steady-state mean-square deviation (MSD). The linear combination studied is a generalization of the convex combination, in which the combination factor λ(n) is restricted to the interval (0,1). The viewpoint is taken that each of the two filters produces dependent estimates of the unknown channel. Thus, there exists a sequence of optimal affine combining coefficients which minimizes the mean-square error (MSE). First, the optimal unrealizable affine combiner is studied and provides the best possible performance for this class. Then two new schemes are proposed for practical applications. The mean-square performances are analyzed and validated by Monte Carlo simulations. With proper design, the two practical schemes yield an overall MSD that is usually less than the MSDs of either filter. [ABSTRACT FROM AUTHOR]