Least Mean Square Research Articles

Low Delay Short Word Length Sigma Delta Active Noise Control

Active noise control (ANC) requires a controller with a small delay. However, in most ANC applications, this delay is significant due to the typically low sample rate, the anti-aliasing (AA), and reconstruction filters (RC) of the analog to digital (AD) and digital to analog (DA) converters. This delay can be reduced by increasing the sampling frequency at the expense of a significant increase in computational complexity. Sigma-delta AD and DA converters work at high sampling frequencies, but their use in ANC is limited due to the delay of AA and RC filters. This work proposes removing the AA and RC filters of the sigma-delta converters and using the oversampled signals directly in an ANC system. This proposal allows the ANC system to be implemented at a large sample frequency but using signals with fewer bits per sample (word-length). This is the first short-word length implementation of the least mean squares (LMS) algorithm to the authors' knowledge. Gains of up to 6 times may be achieved in the computational complexity when compared with a long-word length implementation. Theoretical and simulation results validate the current solution.

Electrical and environmental parameters of the performance of polymer solar cells based on P3HT:PCBM

<p>The electrical and environmental parameters of polymer solar cells (PSC) provide important information on their performance. In the present article we study the influence of temperature on the voltage-current (I-V) characteristic at different temperatures from 10 °C to 90 °C, and important parameters like bandgap energy Eg, and the energy conversion efficiency η. The one-diode electrical model, normally used for semiconductor cells, has been tested and validated for the polemeral junction. The PSC used in our study are formed by the poly(3-hexylthiophene) (P3HT) and [6,6]-phenyl C61-butyric acid methyl ester (PCBM). Our technique is based on the combination of two steps; the first use the Least Mean Squares (LMS) method while the second use the Newton-Raphson algorithm. The found results are compared to other recently published works, they show that the developed approach is very accurate. This precision is proved by the minimal values of statistical errors (RMSE) and the good agreement between both the experimental data and the I-V simulated curves. The obtained results show a clear and a monotonic dependence of the cell efficiency on the studied parameters.</p>

Performance Analysis of Clock Gated Coordinate Rotation Digital Computer Based Least Mean Square Filter Design for Video Processing Applications

Design of a low power FIR filter has always been an area of intense research concern. Application of Co-ordinate Rotation digital computer (CORDIC) algorithm for designing a low power FIR filter is presented in this paper. CORDIC algorithm helps in the computation of complex trigonometric functions by using shift and adds mechanisms avoiding the need for a separate processor in hardware implementation. The challenge of noise interruption in real time video signal capturing is rejected greatly by using CORDIC LMS filter architecture. Also the area, power, and delay of the LMS filter are reduced to a good extent because of CORDIC multipliers. When compared to the previously discussed filter techniques in various works, the proposed technique shows 21.13% reduction in delay and 90.28% decrease in leakage power. Further improvement in dynamic power savings is obtained through clock gating technique applied to the sequential elements in the circuit, which reduces dynamic power dissipation by 70% from the previous works. The comparative study of benchmark filters with their specifications is presented with a detailed analysis of area, delay, leakage, and dynamic power. The video processing using CORDIC LMS filter is verified with MATLAB R 2021A software. The hardware implementation of the proposed architecture is done on a SPARTAN 3E FPGA kit with Xilinx ISE design suite interface. The design is also verified for delay, area, leakage, and dynamic power using CADENCE with GPDK 180 nm library.

Summarisation, Simulation and Comparison of Nine Control Algorithms for an Active Control Mount with an Oscillating Coil Actuator

With the further development of the automotive industry, the traditional vibration isolation method is difficult to meet the requirements for wide frequency bands under multiple operating conditions, the active control mount (ACM) is gradually paid attentions, and the control algorithm plays a decisive role. In this paper, the ACM with oscillating coil actuator (OCA) is taken as the object, and the comparative study of the control algorithms is performed to select the optimal one for ACM. Through the modelling of ACM, the design of controller and the system simulations, the force transmission rate is used to compare the vibration isolation performance of the nine control algorithms, which are least mean square (LMS) adaptive feedforward control, recursive least square (RLS) adaptive feedforward control, filtered reference signal LMS (FxLMS) adaptive control, linear quadratic regulator (LQR) optimal control, H2 control, H∞ control, proportional integral derivative (PID) feedback control, fuzzy control and fuzzy PID control. In summary, the FxLMS adaptive control algorithm has the better performance and the advantage of easier hardware implementation, and it can apply in ACMs.

An adaptive speech signal processing for COVID-19 detection using deep learning approach.

Researchers and scientists have been conducting plenty of research on COVID-19 since its outbreak. Healthcare professionals, laboratory technicians, and front-line workers like sanitary workers, data collectors are putting tremendous efforts to avoid the prevalence of the COVID-19 pandemic. Currently, the reverse transcription polymerase chain reaction (RT-PCR) testing strategy determines the COVID-19 virus. This RT-PCR processing is more expensive and induces violation of social distancing rules, and time-consuming. Therefore, this research work introduces generative adversarial network deep learning for quickly detect COVID-19 from speech signals. This proposed system consists of two stages, pre-processing and classification. This work uses the least mean square (LMS) filter algorithm to remove the noise or artifacts from input speech signals. After removing the noise, the proposed generative adversarial network classification method analyses the mel-frequency cepstral coefficients features and classifies the COVID-19 signals and non-COVID-19 signals. The results show a more prominent correlation of MFCCs with various COVID-19 cough and breathing sounds, while the sound is more robust between COVID-19 and non-COVID-19 models. As compared with the existing Artificial Neural Network, Convolutional Neural Network, and Recurrent Neural Network, the proposed GAN method obtains the best result. The precision, recall, accuracy, and F-measure of the proposed GAN are 96.54%, 96.15%, 98.56%, and 0.96, respectively

A Takagi-Sugeno Fuzzy Model for Greenhouse Climate

This paper investigates the identification and modeling of a greenhouse's climate using real climate data from a greenhouse installed in the LAPER laboratory in Tunisia. The objective of this paper is to propose a solution to the problem of nonlinear time-variant inputs and outputs of greenhouse internal climate. Combining fuzzy logic technique with Least Mean Squares (LMS), a robust greenhouse climate model for internal temperature prediction is proposed. The simulation results demonstrate the effectiveness of the identification approach and the power of the implemented Takagi-Sugeno Fuzzy model-based algorithm.

Microwave photonic frequency down-conversion with self-interference cancellation and SSBI mitigation

In this paper, we proposed a microwave photonic frequency down-conversion scheme with the functions of self-interference cancellation (SIC) and signal–signal beat interference (SSBI) mitigation. The scheme is based on a dual-polarization quadrature phase-shift keying (DP-QPSK) modulator. One of the dual-parallel Mach–Zehnder modulator (DP-MZM) which is located in the upper part of the DP-QPSK modulator is used to achieve SIC by properly biasing the modulator. A local oscillator signal is loaded on the other DP-MZM which is located in the lower part of the DP-QPSK modulator. After photoelectric conversion of the combined optical signal by a photo-detector (PD), the signal of interest (SOI) is down-converted to an intermediate frequency (IF) signal. The SSBI is mitigated by Kramers–Kronig (KK) detection to achieve lower IF down-conversion. Experimental results show that the SIC for a single-tone interference signal is around 53 dB, and for an interference signal with a bandwidth of 100 MHz, the SIC achieved at least 29 dB. A simulation verifies the effect which the least mean square (LMS) algorithm mitigated on the influence of multipath After SIC, we used KK algorithm to reduce error vector magnitude (EVM) of the down-converted 16-QAM signal from 17.54% to 9.07%, which indicates a remarkable SSBI mitigation effect.

Magnitude - Delay Least Mean Squares Equalization for Accurate Estimation of Time of Arrival

Multipath signals are known to be the main source of error in localization procedures; they are generated by the reflection from different objects through non-line of sight (NLOS) paths and overlap with the line of sight (LOS) signal. The time delay and strength of the LOS signal are critical parameters for ranging which are both affected by the overlapped NLOS components. The situation is worse when the frequency bandwidth (BW) is limited that inherently leads to decrease in the resolution of system obeying the Fourier limit ( <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$3^\ast 10^{\wedge }8$ </tex-math></inline-formula> /BW). Leveraging the classical super-resolution techniques such as Maximum Likelihood (ML), MUSIC and compressive sensing (CS), the Fourier limit can be overcome but at the cost of having higher complexity and more computational time. In this paper, the Magnitude-Delay Least Mean Squares Equalization (MD-LMSE) is proposed uniquely for super-resolution time delay estimation. The proposed estimation algorithm is based on the recursive time domain Least Mean Squares (LMS) minimization, which is established through an equalizer structure. The MD-LMSE approach provides high accuracy, super resolution and low computational time that makes it suitable for real time implementation. The proposed MDLMSE algorithms is used through an experimental setup consisting one LOS signal and one NLOS signal to improve the range resolution of a narrowband system 95% with low error of 6.66%.The obtained results verify that the proposed approach is a good candidate for accurate ranging, especially in low frequency applications (with limited bandwidth) by enabling higher penetration depths.

A model-based variable step-size strategy for proximal multitask diffusion LMS algorithm

Several practical applications, such as distributed spectrum sensing and channel identification in underwater communication networks with multiple sensors, can be modeled as a distributed network with jointly sparse structure. For such a network, the proximal multitask diffusion least mean square (LMS) algorithm has been proposed in the literature, and its performance has been studied thoroughly. Due to the trade-off between convergence speed and steady-state performance in the proximal multitask diffusion LMS algorithm, it is important but not trivial, to set the step-size parameter properly. To address this issue, a variable step-size strategy for the proximal multitask diffusion LMS algorithm is proposed in this paper. Based on the transient model of the proximal multitask diffusion LMS algorithm, and by minimizing an upper-bound of the excess mean-square error (EMSE) at each iteration on the basis of a white input assumption, we obtain a closed-form expression of the step-size parameter. Simulation results illustrate the effectiveness of the proposed strategy and highlight its performance through comparison with other existing variable step-size strategies, in the cases of white and moderately correlated inputs.

Development of Wireless and Passive SAW Temperature Sensor with Very High Accuracy

A surface acoustic wave (SAW) temperature sensor with high accuracy was developed and wirelessly characterized in this work. The sensing chip with reflective delay line pattern was simulated using typical coupling of modes (COM) model and prepared by the standard photolithographic technique. Sharp reflection peaks with high signal-to-noise (SNR) were observed from the developed sensing chip operating at 433 MHz. Referring to the frequency-stepped continuous wave (FSCW)-based transceiver, planar antennas, and the developed SAW chip, the wireless and passive temperature sensor system was built. Adaptive Least Mean Square (LMS) algorithm was used for the first time in the SAW sensor signal processing to significantly improve the system SNR, and the corresponding phase fluctuation is down to only 3°. High temperature sensitivity of 36.5 °C and very high accuracy of ±0.2 °C in the range of −30 °C∼100 °C were achieved successfully by wireless measurement.

Simulation of LMS based adaptive noise cancellation using Labview

The audio signals processed in the signal measurement systems are inevitably susceptible to unwanted noise which significantly affects the quality of the signal and the overall performance of the signal communication systems. Due to its' random and unpredictable nature, the amount of noise in signals has proven to be a significant issue in designing these systems and recently has been a trending research topic. In this regard, the active noise cancellation method has proven to be an effective technique for eliminating the noise effects on signal processing. The concept of active noise cancellation is based on the application of adaptive filters and algorithms proposed to reduce the signal corruption and distortion caused by the noise due to the principle of destructive interference. In this paper a simulation model of active noise reduction technique using the LMS (Least Mean Square) algorithm in Labview is presented. The purpose of the work is to investigate the noise cancellation effect on a recorded audio file in terms of analyzing the audio file before and after filtering out the noise by using the LMS algorithm and discuss the results thereof.

Deep learning-based stereophonic acoustic echo suppression without decorrelation.

Traditional stereophonic acoustic echo cancellation algorithms need to estimate acoustic echo paths from stereo loudspeakers to a microphone, which often suffers from the nonuniqueness problem caused by a high correlation between the two far-end signals of these stereo loudspeakers. Many decorrelation methods have already been proposed to mitigate this problem. However, these methods may reduce the audio quality and/or stereophonic spatial perception. This paper proposes to use a convolutional recurrent network (CRN) to suppress the stereophonic echo components by estimating a nonlinear gain, which is then multiplied by the complex spectrum of the microphone signal to obtain the estimated near-end speech without a decorrelation procedure. The CRN includes an encoder-decoder module and two-layer gated recurrent network module, which can take advantage of the feature extraction capability of the convolutional neural networks and temporal modeling capability of recurrent neural networks simultaneously. The magnitude spectra of the two far-end signals are used as input features directly without any decorrelation preprocessing and, thus, both the audio quality and stereophonic spatial perception can be maintained. The experimental results in both the simulated and real acoustic environments show that the proposed algorithm outperforms traditional algorithms such as the normalized least-mean square and Wiener algorithms, especially in situations of low signal-to-echo ratio and high reverberation time RT60.

VSS-LMS: LMS Algorithm Experimental Approach

Optimizing the error between the estimated signal and expected signal is the major goal of a filtering algorithm and the Least Mean Square (LMS) is a well-known adaptive filtering algorithm which plays a significant role in achieving this aim. Nonetheless, the LMS algorithm is usually characterised with low convergence speed in respect to the minimum Mean Square Error (MSE) and flexibility in application. In this paper the Least Mean Square (LMS) algorithm is dealt with a different approach. Contrary to designing LMS filters with fixed step size, variable step size is introduced to improve its convergence speed. An experimental study is considered to formulate a new method for adjusting the step size of the LMS algorithm in this work. Simulation results as well as performance evaluation of the formulated variable step size (VSS-LMS) are presented and compared with the conventional LMS algorithm in terms of MSE and convergence speed.

Capacity and BER performance improvement in integrated MIMO‐OFDM system using optimal power allocation, channel estimation, and turbo coding

SummaryAmong communication systems, multiple input multiple output (MIMO) system has the capability of providing spectral efficiency, high data rates, diversity gain, and improved reliability. Suitably designed orthogonal frequency division multiplexing (OFDM) transmits the signal without inter symbol interference and multipath fading. Hence, MIMO‐OFDM is a strong candidate for 4G and 5G wireless systems. However, variations in signal‐to‐noise ratio (SNR) due to the random nature of the channel and poor capacity at low SNR are critical issues in conventional MIMO‐OFDM system. This paper explains how optimal power allocation (OPA), channel estimation, and coding improve the capacity and bit error rate (BER) performance of MIMO‐OFDM system. In OPA, the transmitter power is distributed adaptively to the channels based on the SNR statistics using singular value decomposition and water filling algorithm. In case of low SNR, less number of channels are selected for power allocation. Least squares (LS), minimum mean square error (MMSE), and least mean square (LMS) estimators are considered in comb type pilot channel estimation while turbo code is used with code rates of 1/2 and 1/3. From the simulation results, capacity improvement of 5 b/s/Hz and 11 b/s/Hz is observed for MIMO‐OFDM systems with four antennas and eight antennas, respectively. It is also found that performance of LMS estimator improved by 3.5‐dB SNR over LS and 1 dB SNR over MMSE. In this paper is shown how LMS estimator and 1/3 turbo code have been integrated with MIMO‐OFDM, and this has resulted in around 4‐dB SNR improvement over conventional MIMO‐OFDM.

Improving the backpropagation algorithm with consequentialism weight updates over mini-batches

Normalized least mean squares (NLMS) and affine projection algorithm (APA) are two successful algorithms that improve the stability of least mean squares (LMS) by reducing the necessity to change the learning rate during the training process. In this paper, we extend them to multi-layer neural networks. We first prove that it is possible to consider a multi-layer neural network as a stack of adaptive filters. It opens the door to bring successful algorithms from adaptive filters to neural networks. We additionally introduce a more comprehensible interpretation than the complicated geometric interpretation in APA for a single fully-connected (FC) layer that can easily be generalized, for instance, to convolutional neural networks and mini-batch training. With this new viewpoint, we introduce a more robust algorithm by predicting and then amending the adverse consequences of some actions that take place in mini-batch backpropagation (BP), even before they happen. The proposed method is a modification to the BP that can be used alongside stochastic gradient descent (SGD) and its momentum variants like Adam and Nesterov. Our experiments show the usefulness of the proposed method in the training of deep neural networks. It is less sensitive to hyper-parameters and needs less intervention during the training process. Besides, it usually converges more smoothly and in fewer iterations. Such predictable behavior helps it to get tuned easier, be resilient during the training, and reduce or eliminate its reliance on other techniques such as momentum.

Modified GSC Method to Reduce the Distortion of the Enhanced Speech Signal Using Cross-Correlation and Sidelobe Neutralization

The generalized sidelobe canceller (GSC) method is a common algorithm to enhance audio signals using a microphone array. Distortion of the enhanced audio signal consists of two parts: the residual acoustic noise and the distortion of the desired audio signal, which means that the desired audio signal is damaged. This paper proposes a modified GSC method to reduce both kinds of distortion when the desired audio signal is a non-stationary speech signal. First, the cross-correlation coefficient between the canceling signal and the error signal of the least mean square (LMS) algorithm was added to the adaptive process of the GSC method to reduce the distortion of the enhanced signal while the energy of the desired signal frame was increased suddenly. The sidelobe pattern of beamforming was then presented to estimate the noise signal in the beamforming output signal of the GSC method. The noise component of the beamforming output signal was decreased by subtracting the estimated noise signal to improve the denoising performance of the GSC method. Finally, the GSC-SN-MCC method was proposed by merging the above two methods. The experiment was performed in an anechoic chamber to validate the proposed method in various SNR conditions. Furthermore, the simulated calculation with inaccurate noise directions was conducted based on the experiment data to inspect the robustness of the proposed method to the error of the estimated noise direction. The experiment data and calculation results indicated that the proposed method could reduce the distortion effectively under various SNR conditions and would not cause more distortion if the estimated noise direction is far from the actual noise direction.

Transfer-RLS method and transfer-FORCE learning for simple and fast training of reservoir computing models

Reservoir computing is a machine learning framework derived from a special type of recurrent neural network. Following recent advances in physical reservoir computing, some reservoir computing devices are thought to be promising as energy-efficient machine learning hardware for real-time information processing. To realize efficient online learning with low-power reservoir computing devices, it is beneficial to develop fast convergence learning methods with simpler operations. This study proposes a training method located in the middle between the recursive least squares (RLS) method and the least mean squares (LMS) method, which are standard online learning methods for reservoir computing models. The RLS method converges fast but requires updates of a huge matrix called a gain matrix, whereas the LMS method does not use a gain matrix but converges very slow. On the other hand, the proposed method called a transfer-RLS method does not require updates of the gain matrix in the main-training phase by updating that in advance (i.e., in a pre-training phase). As a result, the transfer-RLS method can work with simpler operations than the original RLS method without sacrificing much convergence speed. We numerically and analytically show that the transfer-RLS method converges much faster than the LMS method. Furthermore, we show that a modified version of the transfer-RLS method (called transfer-FORCE learning) can be applied to the first-order reduced and controlled error (FORCE) learning for a reservoir computing model with a closed-loop, which is challenging to train.

Prediction of the motion of chest internal points using a recurrent neural network trained with real-time recurrent learning for latency compensation in lung cancer radiotherapy.

During the radiotherapy treatment of patients with lung cancer, the radiation delivered to healthy tissue around the tumor needs to be minimized, which is difficult because of respiratory motion and the latency of linear accelerator (LINAC) systems. In the proposed study, we first use the Lucas-Kanade pyramidal optical flow algorithm to perform deformable image registration (DIR) of chest computed tomography (CT) scan images of four patients with lung cancer. We then track three internal points close to the lung tumor based on the previously computed deformation field and predict their position with a recurrent neural network (RNN) trained using real-time recurrent learning (RTRL) and gradient clipping. The breathing data is quite regular, sampled at approximately 2.5 Hz, and includes artificially added drift in the spine direction. The amplitude of the motion of the tracked points ranged from 12.0 mm to 22.7 mm. Finally, we propose a simple method for recovering and predicting three-dimensional (3D) tumor images from the tracked points and the initial tumor image, based on a linear correspondence model and the Nadaraya-Watson non-linear regression. The root-mean-square (RMS) error, maximum error and jitter corresponding to the RNN prediction on the test set were smaller than the same performance measures obtained with linear prediction and least mean squares (LMS). In particular, the maximum prediction error associated with the RNN, equal to 1.51 mm, is respectively 16.1% and 5.0% lower than the error given by a linear predictor and LMS. The average prediction time per time step with RTRL is equal to 119 ms, which is less than the 400 ms marker position sampling time. The tumor position in the predicted images appears visually correct, which is confirmed by the high mean cross-correlation between the original and predicted images, equal to 0.955. The standard deviation of the Gaussian kernel and the number of layers in the optical flow algorithm were the parameters having the most significant impact on registration performance. Their optimization led respectively to a 31.3% and 36.2% decrease in the registration error. Using only a single layer proved to be detrimental to the registration quality because tissue motion in the lower part of the lung has a high amplitude relative to the resolution of the CT scan images. The random initialization of the hidden units and the number of these hidden units were found to be the most important factors affecting the performance of the RNN. Increasing the number of hidden units from 15 to 250 led to a 56.3% decrease in the prediction error on the cross-validation data. Similarly, optimizing the standard deviation of the initial Gaussian distribution of the synaptic weights σinitRNN led to a 28.4% decrease in the prediction error on the cross-validation data, with the error minimized for σinitRNN=0.02 with the four patients.

Signed Convex Combination of Fast Convergence Algorithm to Generalized Sidelobe Canceller Beamformer for Multi-Channel Speech Enhancement

In speech communication applications such as teleconferences, mobile phones, etc., the real-time noises degrade the desired speech quality and intelligibility. For these applications, in the case of multichannel speech enhancement, the adaptive beamforming algorithms play a major role compared to fixed beamforming algorithms. Among the adaptive beamformers, Generalized Sidelobe Canceller (GSC) beamforming with Least Mean Square (LMS) Algorithm has the least complexity but provides poor noise reduction whereas GSC beamforming with Combined LMS (CLMS) algorithm has better noise reduction performance but with high computational complexity. In order to achieve a tradeoff between noise reduction and computational complexity in real-time noisy conditions, a Signed Convex Combination of Fast Convergence (SCCFC) algorithm based GSC beamforming for multi-channel speech enhancement is proposed. This proposed SCCFC algorithm is implemented using a signed convex combination of two Fast Convergence Normalized Least Mean Square (FCNLMS) adaptive filters with different step-sizes. This improves the overall performance of the GSC beamformer in real-time noisy conditions as well as reduces the computation complexity when compared to the existing GSC algorithms. The performance of the proposed multi-channel speech enhancement system is evaluated using the standard speech processing performance metrics. The simulation results demonstrate the superiority of the proposed GSC-SCCFC beamformer over the traditional methods.

A new diffusion variable spatial regularized LMS algorithm

This paper develops a new diffusion (Diff) least mean squares (LMS) algorithm for the identification of a network of systems that have distinct parameters at each node. The mean and mean squares behavior of the Diff-LMS algorithm in the so called multitask environment is studied in order to obtain an explicit expression of the estimation bias and variance in terms of the spatial regularization (SR) parameter. An optimal SR formula for the Diff LMS algorithm is then derived via minimizing the estimation error. An approximation is made to the formula such that a new practical Diff variable SR LMS (Diff-VSR-LMS) algorithm is obtained. This paper also provides a framework for the design of other LMS-like algorithms that incorporate diffusion technology to solve multitask problems. The theoretical analysis is evaluated via computer simulations and the performance of the proposed algorithm is compared with conventional Diff LMS algorithms under the multitask environment.