Journal articles: 'Least mean square (LMS) algorithm'

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Ibrahim Khan, Muhammad, Muhammad Juanid Mughal, and Rana Liaqat Ali. "Cosine Least Mean Square Algorithm for Adaptive Beamforming." International Journal of Engineering & Technology 7, no. 3.16 (2018): 94. http://dx.doi.org/10.14419/ijet.v7i3.16.16191.

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Beamforming and multiple-input multiple-output (MIMO) antenna configurations have received worldwide interest during the recent time. Various beamforming algorithm has been proposed and employed in different applications. The Least Mean Square (LMS) algorithm has become one of the most widespread adaptive beamforming techniques because of its simplicity and robustness. This paper presents a new variant of LMS algorithm named as Cosine Least Mean Square (Cos-LMS) which uses the efficient computation of array factor for linear antenna array.This algorithm gives improved performance in beam width reduction, side lobe level reduction, null depth, and stability as compared to standard LMS and other variants of LMS algorithm. The performance improvement by Cos-LMS algorithm is accomplished without increasing the computationalcomplexity of standard LMS algorithm.

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Mishra, Swastika, and Jibendu Roy. "Sparse echo cancellation using variants of least mean fourth and least mean square algorithms." Facta universitatis - series: Electronics and Energetics 36, no. 4 (2023): 519–32. http://dx.doi.org/10.2298/fuee2304519m.

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Echo cancellation is the most essential and indispensable component of telephone networks. The impulse responses of most of the networks are sparse in nature; that is, the impulse response has a small percentage of its components with a significant magnitude (large energy), while the rest are zero or small. In these sparse environments, conventional adaptive algorithms like least mean square (LMS) and normalized LMS (NLMS) show substandard and inferior performances. In this paper, the performances of the normalized least mean square (NLMS) algorithm, the normalized least mean fourth (NLMF) and the proportionate normalized least mean fourth (PNLMF) are compared for sparse echo cancellation. The sparseness of both the echo response and the input signal is exploited in this algorithm to achieve improved results at a low computational cost. The PNLMF algorithm showed better results and faster convergence in sparse and non sparse systems, but its results in sparse environments are more impressive. The NLMF algorithm shows good results in sparse environments but not in non-sparse environments. The PNLMS algorithm can be considered superior to the NLMF and NLMS algorithms with respect to the error profile. A modified algorithm, the sparse controlled modified proportionate normalized LMF (SCMPNLMF) algorithm, is proposed, and its performances are compared with the other algorithms.

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Sandu, Ajay Kumar. "Design and Implementation of Least Mean Square Adaptive Filter Using Verilog." International Journal of Innovative Science and Modern Engineering (IJISME) 12, no. 12 (2024): 1–7. https://doi.org/10.35940/ijisme.C4566.12121224.

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Abstract: Adaptive filters are ability of adaptation to an unknown environment. These filters have been used widely because of its capable of operating in an unknown system and low power implementation of hardware. Adaptive filters have great range of signal processing and control operations for the tracking time variations of input statistics and Robust to the noise immunity. These filters used various Areas like Noise cancelling (interface cancelling), system identification, inverse modelling and echo predication. Adaptive filters structures have the adaptive algorithms to perform the time variations of the input statics and Robust to the noise cancelling. The most popular algorithm is LMS (Least Mean Square) it produces the least mean square of error signal in the adaptive filter to minimize noise power. Adaptive filter structures follow the two algorithms RLS and LMS. RLS algorithms excellent performance with increased complexity and the filter coefficients that minimize waited linear least squares cost function relating to the input signals. It requires infinite memory for error signal. LMS algorithms are simplest to understand and describe the hardware of the system compare to the RLS (Recursive Least Square). LMS algorithms are follows the stochastic gradient descent method to minimize the error signal and de-nosing task. It estimates the gradient vector from the input data and LMS incorporates an iterative procedure that makes successive corrections to the weight vector in the direction of the negative of the gradient vector which leads to minimum mean square error. It doesn’t require correlation functions for calculations. The main aim of the project is to design the LMS algorithm based adaptive filter using Verilog HDL to reduce the power consumption, hardware complexity and improving the noise cancelling for the adaptive filter on the FGPA boards. An important challenge in the LMS adaptive filters design implementation of structural model in the Verilog HDL for image processing to target the noise cancelling, power and hardware complexity. Tool use for implementation the structural model of LMS filter is vivado tool and Xilinx software for FPGA board implementation.

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Kalkar, Purvika, and John Sahaya Rani Alex. "FIELD PROGRAMMABLE GATE ARRAY IMPLEMENTATION OF A VARIABLE LEAKY LEAST MEAN SQUARE ADAPTIVE ALGORITHM." Asian Journal of Pharmaceutical and Clinical Research 10, no. 13 (2017): 69. http://dx.doi.org/10.22159/ajpcr.2017.v10s1.19566.

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Adaptive noise cancellation is an extensively researched area of signal processing. Many algorithms had been studied such as least mean square algorithm (LMS), recursive least square algorithm, and normalized LMS algorithm. The statistical characteristics of noise are fast in nature and the algorithms for noise cancellation should converge fast. Since LMS algorithm has slow convergence; in this paper, a variable leaky LMS (VLLMS) algorithm is explored. VLLMS is implemented using the concept of hardware-software cosimulation using Xilinx System Generator. The design is implemented on Virtex-6 ML605 field programmable gate array board. The implemented design is tested for sinusoidal signal added with an additivewhite Gaussian noise. The design summary and the utilization summary are presented.

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Javed, Shazia, and Noor Atinah Ahmad. "A Stochastic Total Least Squares Solution of Adaptive Filtering Problem." Scientific World Journal 2014 (2014): 1–6. http://dx.doi.org/10.1155/2014/625280.

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An efficient and computationally linear algorithm is derived for total least squares solution of adaptive filtering problem, when both input and output signals are contaminated by noise. The proposed total least mean squares (TLMS) algorithm is designed by recursively computing an optimal solution of adaptive TLS problem by minimizing instantaneous value of weighted cost function. Convergence analysis of the algorithm is given to show the global convergence of the proposed algorithm, provided that the stepsize parameter is appropriately chosen. The TLMS algorithm is computationally simpler than the other TLS algorithms and demonstrates a better performance as compared with the least mean square (LMS) and normalized least mean square (NLMS) algorithms. It provides minimum mean square deviation by exhibiting better convergence in misalignment for unknown system identification under noisy inputs.

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Rahman, Aviv Yuniar, Mamba’us Sa’adah, and Istiadi. "Noise Reduction in RTL-SDR using Least Mean Square and Recursive Least Square." Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) 4, no. 2 (2020): 286–95. http://dx.doi.org/10.29207/resti.v4i2.1667.

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Noise reduction is an important process in a communication system, one of which is radio communication. In the process of broadcasting radio Frequency Modulation (FM) often encountered noise so that listeners find it difficult to understand the information provided. In the past, noise reduction used traditional filters that were only able to filter certain frequencies. However, for future technologies an adaptive filter is needed that can dynamically reduce noise effectively. Register Level-Software Defined Radio (RTL-SDR) can capture signals with a very wide frequency range but has a less clear sound quality. So it needs to be done noise reduction. In this study, two methods are used, namely Least Mean Square (LMS) and Recursive Least Square (RLS). The data used five radio stations in Malang. The results showed that the LMS algorithm is stable but has a slow convergence speed, whereas the RLS algorithm has poor stability but has a high convergence speed. From the test, it can be concluded that the performance of RLS is better than LMS for noise reduction in RTL-SDR. The best performance is the reduction of White Noise using RLS on the Oryza radio station with an Normalized Weight Differences (NWD) value of -13.93 dB.

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Kumar, Sandu Ajay, and Dr T. Satya Savithri. "Design and Implementation of Least Mean Square Adaptive Filter Using Verilog." International Journal of Innovative Science and Modern Engineering 12, no. 12 (2024): 1–7. https://doi.org/10.35940/ijisme.c4566.12121224.

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Adaptive filters are ability of adaptation to an unknown environment. These filters have been used widely because of its capable of operating in an unknown system and low power implementation of hardware. Adaptive filters have great range of signal processing and control operations for the tracking time variations of input statistics and Robust to the noise immunity. These filters used various Areas like Noise cancelling (interface cancelling), system identification, inverse modelling and echo predication. Adaptive filters structures have the adaptive algorithms to perform the time variations of the input statics and Robust to the noise cancelling. The most popular algorithm is LMS (Least Mean Square) it produces the least mean square of error signal in the adaptive filter to minimize noise power. Adaptive filter structures follow the two algorithms RLS and LMS. RLS algorithms excellent performance with increased complexity and the filter coefficients that minimize waited linear least squares cost function relating to the input signals. It requires infinite memory for error signal. LMS algorithms are simplest to understand and describe the hardware of the system compare to the RLS (Recursive Least Square). LMS algorithms are follows the stochastic gradient descent method to minimize the error signal and de-nosing task. It estimates the gradient vector from the input data and LMS incorporates an iterative procedure that makes successive corrections to the weight vector in the direction of the negative of the gradient vector which leads to minimum mean square error. It doesn’t require correlation functions for calculations. The main aim of the project is to design the LMS algorithm based adaptive filter using Verilog HDL to reduce the power consumption, hardware complexity and improving the noise cancelling for the adaptive filter on the FGPA boards. An important challenge in the LMS adaptive filters design implementation of structural model in the Verilog HDL for image processing to target the noise cancelling, power and hardware complexity. Tool use for implementation the structural model of LMS filter is vivado tool and Xilinx software for FPGA board implementation.

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Chander, Ramavath, Edara Venkata Chandra Sekhara Rao, and Erukula Vidyasagar. "Least mean square based adaptive control of active power filter." International Journal of Power Electronics and Drive Systems (IJPEDS) 15, no. 2 (2024): 1072–80. https://doi.org/10.11591/ijpeds.v15.i2.pp1072-1080.

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The proposed control scheme is based on the least mean square (LMS) algorithm. The LMS algorithm is employed to estimate the necessary reference tracking current for the active power filter (APF). The proposed control scheme aims to enhance the dynamic response of the APF and minimize steady-state error. The weights of the LMS technique are calculated based on the estimated current of the APF. This algorithm is employed to minimize the error difference between the desired system output and its actual output, known as the mean square error (MSE). The estimated weights are utilized to modify the reference current weights, enabling them to follow the intended current of the APF. The online adaption of the LMS method involves the real-time adjustment of the weights. The performance of the LMS-based APF control is evaluated through a simulation study in MATLAB/Simulink, where it is compared with the conventional control method.

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Lee, Han-Sol, Changgyun Jin, Chanwoo Shin, and Seong-Eun Kim. "Sparse Diffusion Least Mean-Square Algorithm with Hard Thresholding over Networks." Mathematics 11, no. 22 (2023): 4638. http://dx.doi.org/10.3390/math11224638.

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This paper proposes a distributed estimation technique utilizing the diffusion least mean-square (LMS) algorithm, specifically designed for sparse systems in which many coefficients of the system are zeros. To efficiently utilize the sparse representation of the system and achieve a promising performance, we have incorporated L0-norm regularization into the diffusion LMS algorithm. This integration is accomplished by employing hard thresholding through a variable splitting method into the update equation. The efficacy of our approach is validated by comprehensive theoretical analysis, rigorously examining the mean stability as well as the transient and steady-state behaviors of the proposed algorithm. The proposed algorithm preserves the behavior of large coefficients and strongly enforces smaller coefficients toward zero through the relaxation of L0-norm regularization. Experimental results show that the proposed algorithm achieves superior convergence performance compared with conventional sparse algorithms.

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Tanpreeyachaya, Jirasak, Ichi Takumi, and Masayasu Hata. "A New Partial-normalized Least Mean Square Algorithm." IEEJ Transactions on Electronics, Information and Systems 116, no. 1 (1996): 57–65. http://dx.doi.org/10.1541/ieejeiss1987.116.1_57.

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11

Chander, Ramavath, Edara Venkata Chandra Sekhara Rao, and Erukula Vidyasagar. "Least mean square based adaptive control of active power filter." International Journal of Power Electronics and Drive Systems (IJPEDS) 15, no. 2 (2024): 1072. http://dx.doi.org/10.11591/ijpeds.v15.i2.pp1072-1080.

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The proposed control scheme is based on the least mean square (LMS) algorithm. The LMS algorithm is employed to estimate the necessary reference tracking current for the active power filter (APF). The proposed control scheme aims to enhance the dynamic response of the APF and minimize steady-state error. The weights of the LMS technique are calculated based on the estimated current of the APF. This algorithm is employed to minimize the error difference between the desired system output and its actual output, known as the mean square error (MSE). The estimated weights are utilized to modify the reference current weights, enabling them to follow the intended current of the APF. The online adaption of the LMS method involves the real-time adjustment of the weights. The performance of the LMS-based APF control is evaluated through a simulation study in MATLAB/Simulink, where it is compared with the conventional control method.

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Tanmay, A. Lonare, and Mrinal R. Bachute Prof. "NOISE CANCELLATION USING LEAST MEAN SQUARE AND WAVELET TRANSFORM FOR SPEECH ENHANCEMENT." JournalNX - A Multidisciplinary Peer Reviewed Journal 2, no. 6 (2016): 13–17. https://doi.org/10.5281/zenodo.1469522.

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This paper presents about Adaptive Filter Algorithms used in Embedded Signal Processing for Speech Enhancement. Filters are generally used to select or to remove or to separate out particular fixed frequency, but in Adaptive Filters the frequency selection is important as well as the coefficients of Adaptive filter are being updated by the Adaptive Algorithms. Adaptive Filters are the filters whose filter coefficients are updated automatically by the process of steepest descent algorithm. An Adaptive Filter is defined as a self- adjusting system that relies for its operation on a recursive algorithm, which makes it possible for the Filter to perform satisfactorily in an environment where knowledge of the relevant statistics is not available. Least Mean Square (LMS) is the algorithm used to update filter coefficients by subtracting the desired signal from input signal producing error signal which updates the algorithm variables at each iteration repeated iterating process trains itself to the input signal and cancels noise. Wavelet transform is taking the overlapped windowed frames of input signal transforming it from time domain to frequency to understand the spectrogram of signal apply thresholding depending upon the parameters to consider and denoise the signal. Databases of clean speech and Noise speech can be downloaded freely from TIMIT, NOIZEUS, and SpEAR database. Implement the both the filters LMS and Wavelet and compare them to conclude which algorithm works well. https://journalnx.com/journal-article/20150084

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Panigrahi, T., P. M. Pradhan, G. Panda, and B. Mulgrew. "Block Least Mean Squares Algorithm over Distributed Wireless Sensor Network." Journal of Computer Networks and Communications 2012 (2012): 1–13. http://dx.doi.org/10.1155/2012/601287.

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In a distributed parameter estimation problem, during each sampling instant, a typical sensor node communicates its estimate either by the diffusion algorithm or by the incremental algorithm. Both these conventional distributed algorithms involve significant communication overheads and, consequently, defeat the basic purpose of wireless sensor networks. In the present paper, we therefore propose two new distributed algorithms, namely, block diffusion least mean square (BDLMS) and block incremental least mean square (BILMS) by extending the concept of block adaptive filtering techniques to the distributed adaptation scenario. The performance analysis of the proposed BDLMS and BILMS algorithms has been carried out and found to have similar performances to those offered by conventional diffusion LMS and incremental LMS algorithms, respectively. The convergence analyses of the proposed algorithms obtained from the simulation study are also found to be in agreement with the theoretical analysis. The remarkable and interesting aspect of the proposed block-based algorithms is that their communication overheads per node and latencies are less than those of the conventional algorithms by a factor as high as the block size used in the algorithms.

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Shah, Syed Asghar Ali, Tariqullah Jan, Syed Muslim Shah, Muhammad Asif Zahoor Raja, Mohammad Haseeb Zafar, and Sana Ul Haq. "Self correction fractional least mean square algorithm for application in digital beamforming." PLOS ONE 19, no. 6 (2024): e0304018. http://dx.doi.org/10.1371/journal.pone.0304018.

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Fractional order algorithms demonstrate superior efficacy in signal processing while retaining the same level of implementation simplicity as traditional algorithms. The self-adjusting dual-stage fractional order least mean square algorithm, denoted as LFLMS, is developed to expedite convergence, improve precision, and incurring only a slight increase in computational complexity. The initial segment employs the least mean square (LMS), succeeded by the fractional LMS (FLMS) approach in the subsequent stage. The latter multiplies the LMS output, with a replica of the steering vector (Ŕ) of the intended signal. Mathematical convergence analysis and the mathematical derivation of the proposed approach are provided. Its weight adjustment integrates the conventional integer ordered gradient with a fractional-ordered. Its effectiveness is gauged through the minimization of mean square error (MSE), and thorough comparisons with alternative methods are conducted across various parameters in simulations. Simulation results underscore the superior performance of LFLMS. Notably, the convergence rate of LFLMS surpasses that of LMS by 59%, accompanied by a 49% improvement in MSE relative to LMS. So it is concluded that the LFLMS approach is a suitable choice for next generation wireless networks, including Internet of Things, 6G, radars and satellite communication.

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Momoh, M. O., O. C. Ubadike, I. A. Kachalla, M. A. Isa-Bello, P. U. Chinedu, and M. B. Abdullahi. "VSS-LMS: LMS Algorithm Experimental Approach." MEKATRONIKA 3, no. 2 (2021): 31–36. http://dx.doi.org/10.15282/mekatronika.v3i2.7136.

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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.

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Mohammed, Hussein Miry, and Hussien Mary Ali. "Efficient combined fuzzy logic and LMS algorithm for smart antenna." TELKOMNIKA 21, no. 05 (2023): 975–80. https://doi.org/10.12928/telkomnika.v21i5.24370.

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The smart antennas are broadly used in wireless communication. The least mean square (LMS) algorithm is a procedure that is concerned in controlling the smart antenna pattern to accommodate specified requirements such as steering the beam toward the desired signal, in addition to placing the deep nulls in the direction of unwanted signals. The conventional LMS (C-LMS) has some drawbacks like slow convergence speed besides high steady state fluctuation error. To overcome these shortcomings, the present paper adopts an adaptive fuzzy control step size least mean square (FC-LMS) algorithm to adjust its step size. Computer simulation outcomes illustrate that the given model has fast convergence rate as well as low mean square error steady state.

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Silfarney Alves Araújo, Rafael, Wemerson Delcio Parreira, and Renata Coelho Borges. "Análise Comparativa de Algoritmos Adaptativos Baseados em Least-Mean-Square para o Controle Ativo de Vibração." Anais do Computer on the Beach 14 (May 3, 2023): 303–10. http://dx.doi.org/10.14210/cotb.v14.p303-310.

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ABSTRACTAdaptive algorithms have been applied in noise and vibrationsuppression in several engineering areas. This work investigatesthe practicality of analysis between techniques of active vibrationcontrol for the suppression of movements arising from handtremors. The signals generated by the pathological motion havefrequencies between 3Hz and 6Hz, with the highest concentrationof energy in the gestures. These signals are acquired in thefundamental harmonic and the second harmonic, whereas physiologicaltremors vary between 2Hz and 12Hz. We have used computationaltools in Matlab to simulate these signals. We have usedthe least mean square (LMS) based algorithms, namely Filtered-xLeast Mean Square (Fx-LMS), Filtered-x Normalized Least MeanSquare (Fx-NLMS), and a hybrid Fx-NLMS&LMS. In our results, weidentify a faster response by the Fx-LMS&LMS algorithm in theactive vibration control for physiological tremors. In this example,the algorithm has required 3000 samples for steady-state.

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Araújo, Rafael Silfarney Alves, Jéssica Cristina Tironi, Wemerson Delcio Parreira, Renata Coelho Borges, Juan Francisco De Paz Santana, and Valderi Reis Quietinho Leithardt. "Analysis of Adaptive Algorithms Based on Least Mean Square Applied to Hand Tremor Suppression Control." Applied Sciences 13, no. 5 (2023): 3199. http://dx.doi.org/10.3390/app13053199.

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The increase in life expectancy, according to the World Health Organization, is a fact, and with it rises the incidence of age-related neurodegenerative diseases. The most recurrent symptoms are those associated with tremors resulting from Parkinson’s disease (PD) or essential tremors (ETs). The main alternatives for the treatment of these patients are medication and surgical intervention, which sometimes have restrictions and side effects. Through computer simulations in Matlab software, this work investigates the performance of adaptive algorithms based on least mean squares (LMS) to suppress tremors in upper limbs, especially in the hands. The signals resulting from pathological hand tremors, related to PD, present components at frequencies that vary between 3 Hz and 6 Hz, with the more significant energy present in the fundamental and second harmonics, while physiological hand tremors, referred to ET, vary between 4 Hz and 12 Hz. We simulated and used these signals as reference signals in adaptive algorithms, filtered-x least mean square (Fx-LMS), filtered-x normalized least mean square (Fx-NLMS), and a hybrid Fx-LMS–NLMS purpose. Our results showed that the vibration control provided by the Fx-LMS–LMS algorithm is the most suitable for physiological tremors. For pathological tremors, we used a proposed algorithm with a filtered sinusoidal input signal, Fsinx-LMS, which presented the best results in this specific case.

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Pramod Kodgirwar, Vidya, Kalyani R. Joshi, and Shankar B. Deosarkar. "Design of adaptive array using least mean square beamformer." Indonesian Journal of Electrical Engineering and Computer Science 33, no. 2 (2024): 932. http://dx.doi.org/10.11591/ijeecs.v33.i2.pp932-941.

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This paper introduces an 8-element linear array designed for adaptive array applications, using least mean square (LMS) algorithm to enhance the directivity of the array. Microstrip antenna has been optimized at 2.3 GHz, a pivotal frequency ranges relevant to 4G and 5G applications. This design is thoughtfully extended to encompass 8-elements, achieved through the art of parameterization using computer simulation technology (CST) microwave studio. This geometry of 8-element exhibits considerable promise, significantly elevating the gain from 6.13 dBi for a single element to an impressive 15.5 dBi for all eight-element array. To further empower the array’s adaptability and beam-steering capabilities, the LMS algorithm is simulated. This intelligent algorithm computes complex weights, thoughtfully with various angles, including those for the interested user at 60° and 30°, as well as potential interferers at 10° and 15°, as simulated in MATLAB. These meticulously calculated weights are effectively applied to antenna elements using CST, facilitating beam steering in various directions. During CST simulations, notable peaks in performance emerge at 54° and 28°, strategically aligned with nulls at 10° and 15°. Remarkably, these results exhibit a remarkable degree of concurrence with those obtained through MATLAB simulations, affirming effectiveness of the proposed adaptive array design.

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Kodgirwar, Vidya Pramod, Kalyani R. Joshi, and Shankar B. Deosarkar. "Design of adaptive array using least mean square beamformer." Indonesian Journal of Electrical Engineering and Computer Science 33, no. 2 (2024): 932–41. https://doi.org/10.11591/ijeecs.v33.i2.pp932-941.

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This paper introduces an 8-element linear array designed for adaptive array applications, using least mean square (LMS) algorithm to enhance the directivity of the array. Microstrip antenna has been optimized at 2.3 GHz, a pivotal frequency ranges relevant to 4G and 5G applications. This design is thoughtfully extended to encompass 8-elements, achieved through the art of parameterization using computer simulation technology (CST) microwave studio. This geometry of 8-element exhibits considerable promise, significantly elevating the gain from 6.13 dBi for a single element to an impressive 15.5 dBi for all eight-element array. To further empower the array’s adaptability and beam-steering capabilities, the LMS algorithm is simulated. This intelligent algorithm computes complex weights, thoughtfully with various angles, including those for the interested user at 60° and 30°, as well as potential interferers at 10° and 15°, as simulated in MATLAB. These meticulously calculated weights are effectively applied to antenna elements using CST, facilitating beam steering in various directions. During CST simulations, notable peaks in performance emerge at 54° and 28°, strategically aligned with nulls at 10° and 15°. Remarkably, these results exhibit a remarkable degree of concurrence with those obtained through MATLAB simulations, affirming effectiveness of the proposed adaptive array design.

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Javed, Shazia, and Noor Atinah Ahmad. "Optimal preconditioned regularization of least mean squares algorithm for robust online learning1." Journal of Intelligent & Fuzzy Systems 39, no. 3 (2020): 3375–85. http://dx.doi.org/10.3233/jifs-191728.

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Despite its low computational cost, and steady state behavior, some well known drawbacks of the least means squares (LMS) algorithm are: slow rate of convergence and unstable behaviour for ill conditioned autocorrelation matrices of input signals. Several modified algorithms have been presented with better convergence speed, however most of these algorithms are expensive in terms of computational cost and time, and sometimes deviate from optimal Wiener solution that results in a biased solution of online estimation problem. In this paper, the inverse Cholesky factor of the input autocorrelation matrix is optimized to pre-whiten input signals and improve the robustness of the LMS algorithm. Furthermore, in order to have an unbiased solution, mean squares deviation (MSD) is minimized by improving convergence in misalignment. This is done by regularizing step-size adaptively in each iteration that helps in developing a highly efficient optimal preconditioned regularized LMS (OPRLMS) algorithm with adaptive step-size. Comparison of OPRLMS algorithm with other LMS based algorithms is given for unknown system identification and noise cancelation from ECG signal, that results in preference of the proposed algorithm over the other variants of LMS algorithm.

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Prof., R. B. Gaikwad Vaishali Sharma. "COMPARISON OF ADAPTIVE FILTERS ALGORITHMS FOR SPEECH ENHANCEMENT WITH DIFFERENT CHANNELS." GLOBAL JOURNAL OF ADVANCED ENGINEERING TECHNOLOGIES AND SCIENCES 5, no. 6 (2018): 64–72. https://doi.org/10.5281/zenodo.1302253.

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In many application of noise cancellation, the changes in signal characteristics could be quite fast. This requires the utilization of adaptive algorithms, which converge rapidly. Least Mean Squares (LMS), Normalized Least Mean Squares (NLMS) and Unbiased and Normalized Adaptive Noise Reduction UNANR adaptive filters have been used in a wide range of signal processing application because of its simplicity in computation and implementation. The UNANR algorithm has established itself as the "ultimate" adaptive filtering algorithm in the sense that it is the adaptive filter exhibiting the best convergence behavior. Various Adaptive filter algorithms have been derived such as LMS, NLMS and UNANR to solve the dilemma of fast convergence rate or low excess root mean-square (RMS) error in the past two decades. This paper presented a new, easy to implement, LMS, NLMS and UNANR algorithm with various channels such as AWGN and Rician channel using the MATLAB R2013a that employs the RMS and the PSNR estimated system noise power to control the quality of online speech signal. Simulation experiments show that the NLMS and UNANR algorithm performs very well than LMS.

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Prasetyowati, Sri Arttini Dwi, Bustanul Arifin, Agus Adhi Nugroho, and Muhammad Khosyi’in. "Exploration of Generator Noise Cancelling Using Least Mean Square Algorithm." Journal of Electrical Technology UMY 6, no. 1 (2022): 22–32. http://dx.doi.org/10.18196/jet.v6i1.14826.

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Generator noise can be categorized as monotonous noise, which is very annoying and needs to be eliminated. However, noise-cancelling is not easy to do because the algorithm used is not necessarily suitable for each noise. In this study, generator noise was obtained by recording near the generator (outdoor signal) and from the room (indoor signal). Noise generator exploration is carried out to determine whether the noise signal can be removed using the Adaptive LMS method. Exploration was carried out by analyzing statistical signals, spectrum with Fast Fourier Transform (FFT) and Inverse FFT (IFFT), and analyzing the frequency distribution of the remaining noise. The results showed that the correlation coefficients were close to each other. Outdoor and indoor signals are at low frequency. The behavior of FFT and IFFT if described in two dimensions, namely real and imaginary axes, formed a circle with a zero center and has parts that come out of the circle. It confirms that noise-cancelling with adaptive LMS can be realized well even though some noise is still left. The residual noise has formed an impulse that showed normally distributed with mean=-0.0000735 and standard deviation =0.000735. This indicates that the residual noise was no longer disturbing.

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Zhang, Xiaohui, Songnan Yang, Yuanyuan Liu, and Wei Zhao. "Improved Variable Step Size Least Mean Square Algorithm for Pipeline Noise." Scientific Programming 2022 (February 9, 2022): 1–16. http://dx.doi.org/10.1155/2022/3294674.

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In this study, we employ the active noise control (ANC) method to eliminate the low-frequency part of the noise generated by the rotation of the axial fan in heating, ventilation, and air-conditioning (HVAC) pipelines. Because the traditional variable step size least mean square (VSS-LMS) algorithm has poor tracking performance, we propose a variable step size filtered-X least mean square (FXLMS) algorithm based on the arctangent function to improve the adaptive filtering method of the convergence speed and noise cancellation effect. The step size of the proposed algorithm can be adjusted according to the error. When the error signal is significant, a larger step can be obtained, and when the error is small, the step size smoothness of the algorithm can be optimized. Compared with the traditional VSS-LMS algorithm, the convergence speed of the proposed algorithm is increased by 29%, the noise reduction effect is enhanced by 19%, and the mean square error (MSE) is reduced by 23% (0.0084). In addition, we developed a hardware experimental platform based on noise characteristics. In the noise reduction test using a GB/T 5836.2-06 standard PVC pipeline, the system reduced the noise by 12–17 dB.

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Asma, Issa Mohsin, S. Daghal Asaad., and Hasan Sallomi Adheed. "A beamforming comparative study of least mean square, genetic algorithm and grey wolf optimization algorithms for multipath smart antenna system." TELKOMNIKA Telecommunication, Computing, Electronics and Control 18, no. 6 (2020): 2911~2920. https://doi.org/10.12928/TELKOMNIKA.v18i6.16970.

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Multipath environment is a limitation fact in optimized usage of wireless networks. Using smart antenna and beamforming algorithms contributed to that subscribers get a higher-gain signal and better directivity as well as reduce the consumed power for users and the mobile base stations by adjusting the appropriate weights for each element in the antenna array that leads to reducing interference and directing the main beam to wanted user. In this paper, the performance of three of beamforming algorithms in multipath environment in terms of Directivity and side lobe level reduction has been studied and compared, which are least mean square (LMS), genetic algorithm (GA) and grey wolf optimization (GWO) technique. The simulation result appears that LMS algorithm aids us to get the best directivity followed by the GWO, and we may get most sidelobe level reduction by using the GA algorithm, followed by LMS algorithm in second rank.

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Xu, Fangmin, Chenyang Zheng, and Haiyan Cao. "Memory Distributed LMS for Wireless Sensor Networks." Mathematical Problems in Engineering 2018 (2018): 1–8. http://dx.doi.org/10.1155/2018/9831378.

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Due to the limited communication resource and power, it is usually infeasible for sensor networks to gather data to a central processing node. Distributed algorithms are an efficient way to resolve this problem. In the algorithms, each sensor node deals with its own input data and transmits the local results to its neighbors. Each node fuses the information from neighbors and its own to get the final results. Different from the existing work, in this paper, we present an approach for distributed parameter estimation in wireless sensor networks based on the use of memory. The proposed approach consists of modifying the cost function by adding extra statistical information. A distributed least-mean squares (d-LMS) algorithm, called memory d-LMS, is then derived based on the cost function and analyzed. The theoretical performances of mean and mean square are analyzed. Moreover, simulation results show that the proposed algorithm outperforms the traditional d-LMS algorithm in terms of convergence rate and mean square error (MSE) performance.

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Prasetyowati, Sri Arttini Dwi, Munaf Ismail, and Badieah Badieah. "Implementation of Least Mean Square Adaptive Algorithm on Covid-19 Prediction." JUITA: Jurnal Informatika 10, no. 1 (2022): 139. http://dx.doi.org/10.30595/juita.v10i1.11963.

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This study used Corona Virus Disease-19 (Covid-19) data in Indonesia from June to August 2021, consisting of data on people who were infected or positive Covid-19, recovered from Covid-19, and passed away from Covid-19. The data were processed using the adaptive LMS algorithm directly without pre-processing cause calculation errors, because covid-19 data was not balanced. Z-score and min-max normalization were chosen as pre-processing methods. After that, the prediction process can be carried out using the LMS adaptive method. The analysis was done by observing the error prediction that occurred every month per case. The results showed that data pre-processing using min-max normalization was better than with Z-score normalization because the error prediction for pre-processing using min-max and z-score were 18% and 47%, respectively.

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Karchi, Nalini, Deepak Kulkarni, Rocío Pérez de Prado, Parameshachari Bidare Divakarachari, Sujata N. Patil, and Veena Desai. "Adaptive Least Mean Square Controller for Power Quality Enhancement in Solar Photovoltaic System." Energies 15, no. 23 (2022): 8909. http://dx.doi.org/10.3390/en15238909.

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The objective of the proposed work is to develop a Maximum Power Point Tracking (MPPT) controller and inverter controller by applying the adaptive least mean square (LMS) algorithm to control the total harmonics distortion of a solar photovoltaic system. The advantage of the adaptive LMS algorithm is given by its simplicity and reduced required computational time. The adaptive LMS algorithm is applied to modify the Perturb and Observe (P&O), MPPT controller. In this controller, the adaptive LMS algorithm is used to predict solar photovoltaic power. The adaptive LMS maximum power point tracking controller gives better optimal solutions with less steady error 0.7% (6 watts) and 0% peak overshot in power with the tradeoff being more settling time at 0.33 s. The development of the inverter control law is performed using the d-q frame theory. This helps to reduce the number of equations to build a control law. The load current, grid current and grid voltage are sensed and transformed into d and q components. This adaptive LMS control law is used to extract the reference grid currents and, later, to compare them with the actual grid currents. The result of this comparison is used to generate the switching gate pulses for the inverter switches. The proposed controllers are developed and implemented with a solar PV system in MATLAB Simulink. The total harmonics distortion in grid and load current (3.25% and 7%) and voltage (0%) is investigated under linear and non-linear load conditions with changes in solar irradiations. The analysis is performed by selecting step incremental values and sampling time.

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Fang, Yubin, Xiaojin Zhu, Zhiyuan Gao, Jiaming Hu, and Jian Wu. "New feedforward filtered-x least mean square algorithm with variable step size for active vibration control." Journal of Low Frequency Noise, Vibration and Active Control 38, no. 1 (2018): 187–98. http://dx.doi.org/10.1177/1461348418812326.

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The step size of least mean square (LMS) algorithm is significant for its performance. To be specific, small step size can get small excess mean square error but results in slow convergence. However, large step size may cause instability. Many variable step size least mean square (VSSLMS) algorithms have been developed to enhance the control performance. In this paper, a new VSSLMS was proposed based on Kwong’s algorithm to evaluate the robustness. The approximate analysis of dynamic and steady-state performance of this developed VSSLMS algorithm was given. An active vibration control system of piezoelectric cantilever beam was established to verify the performance of the VSSLMS algorithms. By comparing with the current VSSLMS algorithms, the proposed method has better performance in active vibration control applications.

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Martinek, Radek, Jaroslav Rzidky, Rene Jaros, Petr Bilik, and Martina Ladrova. "Least Mean Squares and Recursive Least Squares Algorithms for Total Harmonic Distortion Reduction Using Shunt Active Power Filter Control." Energies 12, no. 8 (2019): 1545. http://dx.doi.org/10.3390/en12081545.

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This paper deals with the use of least mean squares (LMS, NLMS) and recursive least squares (RLS) algorithms for total harmonic distortion (THD) reduction using shunt active power filter (SAPF) control. The article presents a pilot study necessary for the construction of our own controlled adaptive modular inverter. The objective of the study is to find an optimal algorithm for the implementation. The introduction contains a survey of the literature and summarizes contemporary methods. According to this research, only adaptive filtration fulfills our requirements (adaptability, real-time processing, etc.). The primary benefit of the paper is the study of the efficiency of two basic approaches to adaptation ((N)LMS and RLS) in the application area of SAPF control. The study examines the impact of parameter settings (filter length, convergence constant, forgetting factor) on THD, signal-to-noise ratio (SNR), root mean square error (RMSE), percentage root mean square difference (PRD), speed, and stability. The experiments are realized with real current and voltage recordings (consumer electronics such as PC source without power factor correction (PFC), HI-FI amplifier, etc.), which contain fast dynamic transient phenomena. The realized model takes into account a delay caused by digital signal processing (DSP) (the implementation of algorithms on field programmable gate array (FPGA), approximately 1–5 μs) and a delay caused by the reaction time of the proper inverter (approximately 100 μs). The pilot study clearly showed that the RLS algorithm is the most suitable for the implementation of an adaptive modular inverter because it achieved the best results for all analyzed parameters.

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He, Hong, Cong Cong Wu, Tong Yang, Lin He, and Dan Li. "Analysis of Smart Antenna Interference Suppression Base on LMS Improved Algorithm." Key Engineering Materials 474-476 (April 2011): 1019–23. http://dx.doi.org/10.4028/www.scientific.net/kem.474-476.1019.

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Smart antenna technology can increase channel capacity, improve spectrum efficiency and enlarge cover area by using its spatial diversity ability , which greatly improve system performance . A least mean squares (LMS) is posed for the smart antenna adaptive interference suppression system based on the training sequence. Also , the least mean square (LMS) and least squares (RLS) algorithm are proposed for the design and simulation about interference suppression and compare and analyze the result which can prove the effectiveness about algorithm in TD-SCDMA system .According to the results, the new method with a faster convergence speed, which doesn`t matter with interference environments, is better than LMS.

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Chen, Yuan Yuan, Run Jie Liu, Jin Yuan Shen, and Dan Dan He. "The Use of Adaptive Algorithms on Smart Antenna Device." Advanced Materials Research 548 (July 2012): 730–34. http://dx.doi.org/10.4028/www.scientific.net/amr.548.730.

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Adaptive beamforming is one of the core technology of the smart antenna system. Two different adaptive algorithms which adopt the minimum mean square algorithm (LMS) and recursive least squares algorithm (RLS) are employed to realize the beamforming in smart antenna system. The smart antenna system based on LMS and RLS is simulated and realized by the MATLAB software in which a uniform linear adaptive antenna array is used. The results show that the smart antenna systems based on RLS and LMS algorithms can significantly reduce the bit error rate especially with the low SNR.

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Sano, Hisashi, Shuichi Adachi, and Hideki Kasuya. "Application of a Least Squares Lattice Algorithm to Active Noise Control for an Automobile." Journal of Dynamic Systems, Measurement, and Control 119, no. 2 (1997): 318–20. http://dx.doi.org/10.1115/1.2801256.

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The purpose of this paper is to propose an alternative approach to active noise control (ANC) using the least squares lattice (LSL) algorithm. Typically, in ANC applications, the least-mean-square (LMS) algorithm has been used because of its simplicity. However, the LMS algorithm has the disadvantage of slow convergence speed in the case of broadband noise, such as the road noise present in the passenger compartment of automobiles traveling on rough road surfaces. In order to solve this problem, the LSL algorithm for ANC is considered. By computer simulation using actual car data, the LSL algorithm proves to be more effective than the LMS one.

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PavanKalyan, I., G. Jaya Santosh, K. H. K. Prasad, and Durgesh Nandan. "Study of Echo Cancellation approach by using Least Mean Square (LMS) Algorithm." Journal of Physics: Conference Series 1714 (January 2021): 012053. http://dx.doi.org/10.1088/1742-6596/1714/1/012053.

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Radhi Shabib Kaned. "Investigation of Phase Noise on the Performance of LMS-RLS Adaptive Equalizer." Diyala Journal of Engineering Sciences 6, no. 1 (2013): 27–35. http://dx.doi.org/10.24237/djes.2013.06103.

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This paper investigates the effect of phase noise on equalization of communication channels using least mean square (LMS) and recursive least square (RLS) adaptive algorithms. The aim of the investigation is to mitigate inter-symbol interference (ISI) caused by the channel and to impose the bit error rate (BER) in the received signals. The equalizerusestwobasicadaptivealgorithms: LMS algorithmand RLS algorithm. Without LMS-RLS equalizer,theBER ismorethan when the system modelincludesLMS-RLS equalizer as indicated in table (1) and table (2). Equalizer algorithm is analyzed using MATLAB v.9 Communication Block Set.

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PENTAKOTA, KRISHNA, MARIO A. RAMIREZ, and SEBASTIAN HOYOS. "LEAST MEAN SQUARED BACKGROUND CALIBRATION FOR OFDM MULTICHANNEL RECEIVERS." Journal of Circuits, Systems and Computers 21, no. 01 (2012): 1250014. http://dx.doi.org/10.1142/s0218126612500144.

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This paper presents a data estimation scheme for wide band multichannel charge sampling filter bank receivers together with a complete system calibration algorithm based on the least mean squared (LMS) algorithm. A unified model has been defined for the receiver containing all first order mismatches, offsets, imperfections, and the LMS algorithm is employed to track these errors. The performance of this technique under noisy channel conditions has been verified. Moreover, a detailed complexity analysis of the calibration algorithm is provided which shows that sinc filter banks have much lower complexity than traditional continuous-time filter banks.

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Yasin, Muhammad, and Muhammad Junaid Hussain. "A Novel Adaptive Algorithm Addresses Potential Problems of Blind Algorithm." International Journal of Antennas and Propagation 2016 (2016): 1–8. http://dx.doi.org/10.1155/2016/5983924.

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A hybrid algorithm called constant modulus least mean square (CMLMS) algorithm is proposed in order to address the potential problems existing with constant modulus algorithm (CMA) about its convergence. It is a two-stage adaptive filtering algorithm and based on least mean square (LMS) algorithm followed by CMA. A hybrid algorithm is theoretically developed and the same is verified through MatLab Software. Theoretical model is verified through simulation and its performance is evaluated in smart antenna in presence of a cochannel interfering signal and additive white Gaussian noise (AWGN) of zero mean. This is also tested in Rayleigh fading channel using digital modulation technique for Bit Error Rate (BER). Finally, a few computer simulations are presented in order to substantiate the theoretical findings with respect to proposed model. Corresponding results obtained with the use of only CMA and LMS algorithms are also presented for further comparison.

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COMINGUEZ, A. H. "A NEW GENERALIZED LEAST MEAN-SQUARE ALGORITHM FOR PROCESSING NON-STATIONARY SEISMIC DATA." Geofísica Internacional 26, no. 3 (1987): 393–406. http://dx.doi.org/10.22201/igeof.00167169p.1987.26.3.1312.

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Se presenta un algoritmo adaptable apropiado para deconvolver trazas, el cual está basado sobre una expresión generalizada de la técnica de mínimo error cuadrático medio. El uso del nuevo proceso se recomienda especialmente para elaborar sismogramas de reflexión sísmica que contengan reverberaciones variables en el tiempo. Mediante la aplicación del sistema adaptable los coeficientes del operador se recalculaban para cada tiempo de la señal de entrada. Tanto las características de convergencia del algoritmo como sus propiedades de estabilidad se analizan y comparan con las del algoritmo tradicional LMS. Para tal efecto se presentan ilustraciones con sismogramas sintéticos. La aplicabilidad del método expuesto parece promisoria para pruebas sísmicas en agues proco profundas.

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Vázquez, Ángel A., Eduardo Pichardo, Juan G. Avalos, et al. "Multichannel Active Noise Control Based on Filtered-x Affine Projection-Like and LMS Algorithms with Switching Filter Selection." Applied Sciences 9, no. 21 (2019): 4669. http://dx.doi.org/10.3390/app9214669.

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Affine projection (AP) algorithms have been demonstrated to have faster convergence speeds than the conventional least mean square (LMS) algorithms. However, LMS algorithms exhibit smaller steady-state mean square errors (MSEs) when compared with affine projection (AP) algorithms. Recently, several authors have proposed alternative methods based on convex combinations to improve the steady-state MSE of AP algorithms, even with the increased computational cost from the simultaneous use of two filters. In this paper, we present an alternative method based on an affine projection-like (APL-I) algorithm and least mean square (LMS) algorithm to solve the ANC under stationary Gaussian noise environments. In particular, we propose a switching filter selection criteria to improve the steady-state MSE without increasing the computational cost when compared with existing models. Here, we validate the proposed strategy in a single and a multichannel system, with and without automatically adjusting the scaling factor of the APL-I algorithm. The results demonstrate that the proposed scheme exploits the best features of each filter (APL-I and LMS) to guarantee rapid convergence with a low steady-state MSE. Additionally, the proposed approach demands a low computational burden compared with existing convex combination approaches, which will potentially lead to the development of real-time ANC applications.

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Sawant, Vishal V., and Mahesh Chavan. "Performance of Beamforming for Smart Antenna using Traditional LMS Algorithm for Various Parameters." International Journal of Computers and Communications 15 (April 14, 2021): 8–13. http://dx.doi.org/10.46300/91013.2021.15.2.

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Adaptive signal processing sensor arrays, known also as smart antennas .The smart antenna adaptive algorithms achieve the best weight vector for beam forming by iterative means. The Least Mean Square (LMS) algorithm, is an adaptive algorithm .LMS incorporates an iterative procedure that makes successive corrections to the weight vector in the direction of the negative of the gradient vector which eventually leads to the minimum mean square error. Beam forming is directly determined by the two factors. The performance of the traditional LMS algorithm for different parameters is analysed in this paper. This algorithm can be applied to beam forming with the software Matlab. The result obtain can achieve faster convergence and lower steady state error. The algorithms can be simulated in MATLAB 7.10 version.

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Manjula, B. M., H. S. Prasantha, and M. A. Goutham. "Delayed LMS Algorithm for Ballistocardiogram Biomedical Signal." International Journal of Engineering and Advanced Technology (IJEAT) 9, no. 6 (2020): 77–81. https://doi.org/10.35940/ijeat.F1252.089620.

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The Ballisto-cardiogram (BCG) is a biomedical signal which is basically a measure of ballistic forces on heart. Just like ECG to detect the abnormalities in heart Ballisto-cardiography technique is used extensively now a day in research to analyze the abnormalities of the patient. When the blood pumps from heart to different parts of the body is represented in the form of graph for each heart beat. The frequency of Ballisto-cardiography signal is 1-20Hz .Ballisto-cardiography is most emerging techniques which is used to test the diseases related to heart called as cardiovascular disease. Various devices [1] like chairs, beds and weighing scales are projected to improvise the extraction of the BCG, but noise is one of the main issues with this BCG signal processing, noise is generated because of motion artifacts, shaking of the devices or may be power line noise. This noise [2] affects the quality of signal which we need to test. In order to overcome such issue this paper proposes a new architecture making use of LMS filtering algorithm Here weight update algorithm is used to update the error in extracted signal. The architecture proposed here includes FIR filter and also error computation blocks. Here the author has implemented 5-tap filtering algorithm. MATLAB and system generators are used to carry out the work.

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Alhaj, H. M. M., N. M. Nor, Vijanth S. Asirvadam, M. F. Abdullah, and T. Ibrahim. "Estimation of Power System Harmonic Using Modified Normalized Least Mean Square." Applied Mechanics and Materials 785 (August 2015): 378–82. http://dx.doi.org/10.4028/www.scientific.net/amm.785.378.

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A new adaptive power system harmonic estimator is presented, which is competent of tracking power system harmonic components. The proposed estimator technique is based on the normalized Least Mean Square (LMS), which is a stochastic gradient descent algorithm. The learning method of the proposed estimator is based upon the recursive estimate of the signal power, and is faster tracking of harmonic components as compared to the introduced Adaptive Linear Element (ADALINE).

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Anna, Rahul Kasukurthy, and Sankar Chintala D. Uma. "Speech enhancement for noisy signals using adaptive algorithms." i-manager’s Journal on Electronics Engineering 13, no. 4 (2023): 26. http://dx.doi.org/10.26634/jele.13.4.20099.

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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.

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Sulthana, Asiya, and Md Zia Ur Rahman. "Efficient adaptive noise cancellation techniques in an IOT Enabled Telecardiology System." International Journal of Engineering & Technology 7, no. 2.17 (2018): 74. http://dx.doi.org/10.14419/ijet.v7i2.17.11562.

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An increasing number of elderly and disabled people urge the need for a health care monitoring system which has the capabilities for analyzing patient health care data to avoid preventable deaths. Medical Telemetry is becoming a key tool in assisting patients living remotely where a “Real-time Remote Critical Health Care Monitoring System” (RRCHCMS) can be utilized for the same. The RRCHCMS is capable of receiving and transmitting data from a remote location to a location that has the capability to diagnose the data and affect decision making and further providing assistance to the patient. During the cardiac analysis, several artifacts solidly affect the ST segment, humiliate the signal quality, frequency resolution, and results in large amplitude signals in ECG that simulate PQRST waveform and cover up the miniature features that are useful for clinical monitoring and diagnosis. In this paper, several leaky based adaptive filter structures for cardiac signal improvement are discussed. The Circular Leaky Least Mean Square (CLLMS) algorithm being the steepest drop strategy for dropping the mean squared error gives a better result in comparison with the Least Mean Square (LMS) algorithm. To enlarge the filtering ability some variants of LMS, Normalized Least Mean Square (NLMS), CLLMS, Variable Step Size CLLMS (VSS-CLLMS) algorithms are used in both time domain (TD) and frequency domain (FD). At last, we applied this algorithm on cardiac signals occurred due to MIT-BIH database. The performance of CLLMS algorithm is better compared to LLMS counterparts in conditions of Signal to Noise Ratio Improvement (SNRI), Excess Mean Square Error (EMSE) and Misadjustment (MSD). When compared to all other algorithms VSS-CLLMS gives superior SNRI. These values are 13.5616dB and 13.7592dB for Baseline Wander (BW) and Muscle Artifact (MA) removal.

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Praveen, Kumar Reddy, and G. S. Rajanna. "Performance Analysis of Beam Forming for Mobile Communication." Journal of Advancement in Communication System 6, no. 3 (2023): 10–15. https://doi.org/10.5281/zenodo.8416126.

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The beam is formed in the direction of the desired signal in the adaptive antenna system, while nulls are inserted in the direction of the unwanted signal (interference). In this study, the analysis of the Least Mean Square and Normalized Least Mean Square algorithms is covered. These algorithms can change the adequacy and stage values shipped off the different radio antenna array elements. Apparently, the Normalized Least Mean Square Algorithm converges significantly more quickly than the Least Mean Square Algorithm, according to the simulation findings. Utilising the MATLAB programme, the adaptive beam forming algorithms are tested and simulated.

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Mohammad, Shukri Salman, Eleyan Alaa, and Al-Sheikh Bahaa. "Discrete wavelet transform-based RI adaptive algorithm for system identification." International Journal of Electrical and Computer Engineering (IJECE) 10, no. 3 (2020): 2383–91. https://doi.org/10.11591/ijece.v10i3.pp2383-2391.

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In this paper, we propose a new adaptive filtering algorithm for system identification. The algorithm is based on the recursive inverse (RI) adaptive algorithm which suffers from low convergence rates in some applications; i.e., the eigenvalue spread of the autocorrelation matrix is relatively high. The proposed algorithm applies discrete-wavelet transform (DWT) to the input signal which, in turn, helps to overcome the low convergence rate of the RI algorithm with relatively small step-size(s). Different scenarios has been investigated in different noise environments in system identification setting. Experiments demonstrate the advantages of the proposed DWT recursive inverse (DWT-RI) filter in terms of convergence rate and mean-square-error (MSE) compared to the RI, discrete cosine transform LMS (DCT-LMS), discrete wavelet transform LMS (DWT-LMS) and recursive-least-squares (RLS) algorithms under same conditions.

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Reddy, Praveen, and Dr Baswaraj Gadgay. "FPGA based least mean square algorithm for noise cancellation in communication system." International Journal of Engineering & Technology 7, no. 3.3 (2018): 165. http://dx.doi.org/10.14419/ijet.v7i2.32.15588.

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We present modified Distributed Arithmetic (DA) based architecture for LMS Adaptive filter which has improved the throughput of the filter also area and power has been comparatively been reduced. As we know, the adaptive filter uses continuous recalculation and generation of new coefficients will generate the negative effect on the use of algorithm. We have used a special temporary LUT addressing technique has overcome the issues resulting in better performance and good results. In this paper, we have discussed about the adaptive filter and implementation of DA adaptive filter and also discussed the results obtained from the design. Comparison with traditional de-sign has also been done to show the effectiveness of the algorithm.

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Lapointe, Marcel, Huu Tue Huynh, and Paul Fortier. "Highly parallel architecture for the least mean squares (LMS) algorithm." Canadian Journal of Electrical and Computer Engineering 16, no. 3 (1991): 93–104. http://dx.doi.org/10.1109/cjece.1991.6592939.

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Binti, Mohd. Zailan Khalida Adeeba, Hasan Mohd. Hilmi Bin, and Gunawan Witjaksono. "COMPARATIVE ANALYSIS OF MACHINE LEARNING ALGORITHMS FOR OPTIMIZING VARIABLE STEP-SIZE LEAST MEAN SQUARE IN MOTION ARTIFACT REDUCTION." COMPUSOFT: An International Journal of Advanced Computer Technology 09, no. 03 (2020): 3590–95. https://doi.org/10.5281/zenodo.14912129.

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Optical sensor like Photoplethysmographs (PPG), is widely used in generating real time information such as current heart rate. Existing studies on PPG demonstrated that the weakness of this technology is the sensor will capture the motion artifact reading when there is excessive motion exerts on the sensor. Numerous algorithms had been developed to reduce the motion artifact on PPG and increase the accuracy of the health monitoring device reading. However, these existing solutions using least mean square (LMS) algorithm failed to achieve high accuracy of heart rate reading. This paper presents and compares three types of machine learning algorithms that are widely used in classification of wearable signals, which are support vector machine (SVM), artificial neural network (ANN) and random forest (RF). The machine learning algorithms optimize variable step-size LMS (VSSLMS) accuracy by classifying the speed of the motion and giving suitable step size values based on the classification. The result shows that SVM is the best machine learning algorithm in classifying the speed category of the heart rate to eventually get the suitable step size value for VSSLMS. 

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Tajdari, Teimour. "Adaptive method to predict and track unknown system behaviors using RLS and LMS algorithms." Facta universitatis - series: Electronics and Energetics 34, no. 1 (2021): 133–40. http://dx.doi.org/10.2298/fuee2101133t.

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This study investigates the ability of recursive least squares (RLS) and least mean square (LMS) adaptive filtering algorithms to predict and quickly track unknown systems. Tracking unknown system behavior is important if there are other parallel systems that must follow exactly the same behavior at the same time. The adaptive algorithm can correct the filter coefficients according to changes in unknown system parameters to minimize errors between the filter output and the system output for the same input signal. The RLS and LMS algorithms were designed and then examined separately, giving them a similar input signal that was given to the unknown system. The difference between the system output signal and the adaptive filter output signal showed the performance of each filter when identifying an unknown system. The two adaptive filters were able to track the behavior of the system, but each showed certain advantages over the other. The RLS algorithm had the advantage of faster convergence and fewer steady-state errors than the LMS algorithm, but the LMS algorithm had the advantage of less computational complexity.

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Journal articles on the topic 'Least mean square (LMS) algorithm'

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