Relevant bibliographies by topics / Sparse RLS algorithm

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  1. Journal articles
  2. Dissertations / Theses
  3. Book chapters
  4. Conference papers

Academic literature on the topic 'Sparse RLS algorithm'

Author: Grafiati
Published: 4 June 2021
Last updated: 2 February 2022

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Journal articles on the topic "Sparse RLS algorithm"

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Babadi, Behtash, Nicholas Kalouptsidis, and Vahid Tarokh. "SPARLS: The Sparse RLS Algorithm." IEEE Transactions on Signal Processing 58, no. 8 (August 2010): 4013–25. http://dx.doi.org/10.1109/tsp.2010.2048103.

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Sun, Dajun, Lu Liu, and Youwen Zhang. "Recursive regularisation parameter selection for sparse RLS algorithm." Electronics Letters 54, no. 5 (March 2018): 286–87. http://dx.doi.org/10.1049/el.2017.4242.

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Xia, Qing, Yun Lin, and Hui Luo. "Dynamic RLS-DCD for Sparse System Identification." Applied Mechanics and Materials 602-605 (August 2014): 2411–14. http://dx.doi.org/10.4028/www.scientific.net/amm.602-605.2411.

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In this passage we propose a computationally efficient adaptive filtering algorithm for sparse system identification.The algorithm is based on dichotomous coordinate descent iterations, reweighting iterations,iterative support detection.In order to reduce the complexity we try to discuss in the support.we suppose the support is partial,and partly erroneous.Then we can use the iterative support detection to solve the problem.Numerical examples show that the proposed method achieves an identification performance better than that of advanced sparse adaptive filters (l1-RLS,l0-RLS) and its performance is close to the oracle performance.
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Petrovic, Predrag. "Possible solution of parallel FIR filter structure." Serbian Journal of Electrical Engineering 2, no. 1 (2005): 21–28. http://dx.doi.org/10.2298/sjee0501021p.

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In this paper, a parallel form FIR adaptive filter structure with RLS (Recursive Least Squares) type adaptive algorithm is proposed. The proposed parallel form FIR structure consists of a recursive orthogonal transform stage and sparse FIR sub filters operating in parallel. The adaptive algorithm used to update coefficient vector of the sparse filters is implemented by using modified Hopfield networks. This structure implements the RLS-type adaptive algorithm, without an explicit matrix inversion avoiding numerical instability problems. Simulation results which show the desirable features of proposed structure are given.
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Yang, Cuili, Junfei Qiao, Zohaib Ahmad, Kaizhe Nie, and Lei Wang. "Online sequential echo state network with sparse RLS algorithm for time series prediction." Neural Networks 118 (October 2019): 32–42. http://dx.doi.org/10.1016/j.neunet.2019.05.006.

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Lim, Junseok, Keunhwa Lee, and Seokjin Lee. "A Modified Recursive Regularization Factor Calculation for Sparse RLS Algorithm with l1-Norm." Mathematics 9, no. 13 (July 5, 2021): 1580. http://dx.doi.org/10.3390/math9131580.

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In this paper, we propose a new calculation method for the regularization factor in sparse recursive least squares (SRLS) with l1-norm penalty. The proposed regularization factor requires no prior knowledge of the actual system impulse response, and it also reduces computational complexity by about half. In the simulation, we use Mean Square Deviation (MSD) to evaluate the performance of SRLS, using the proposed regularization factor. The simulation results demonstrate that SRLS using the proposed regularization factor calculation shows a difference of less than 2 dB in MSD from SRLS, using the conventional regularization factor with a true system impulse response. Therefore, it is confirmed that the performance of the proposed method is very similar to that of the existing method, even with half the computational complexity.
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M. Al-Sammna, Ahmed, Marwan Hadri Azmi, and Tharek Abd Rahman. "Time-Varying Ultra-Wideband Channel Modeling and Prediction." Symmetry 10, no. 11 (November 12, 2018): 631. http://dx.doi.org/10.3390/sym10110631.

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This paper considers the channel modeling and prediction for ultra-wideband (UWB) channels. The sparse property of UWB channels is exploited, and an efficient prediction framework is developed by introducing two simplified UWB channel impulse response (CIR) models, namely, the windowing-based on window delay (WB-WD) and the windowing-based on bin delay (WB-BD). By adopting our proposed UWB windowing-based CIR models, the recursive least square (RLS) algorithm is used to predict the channel coefficients. By using real CIR coefficients generated from measurement campaign data conducted in outdoor environments, the modeling and prediction performance results and the statistical properties of the root mean square (RMS) delay spread values are presented. Our proposed framework improves the prediction performances with lower computational complexity compared with the performance of the recommended ITU-R UWB-CIR model. It is shown that our proposed framework can achieved 15% lower prediction error with a complexity reduction by a factor of 12.
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Eksioglu, Ender M. "Group sparse RLS algorithms." International Journal of Adaptive Control and Signal Processing 28, no. 12 (December 11, 2013): 1398–412. http://dx.doi.org/10.1002/acs.2449.

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Fedorov, Roman, and Oleg Berngardt. "Monitoring observations of meteor echo at the EKB ISTP SB RAS radar: algorithms, validation, statistics." Solar-Terrestrial Physics 7, no. 1 (March 29, 2021): 47–58. http://dx.doi.org/10.12737/stp-71202107.

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The paper considers the implementation of algorithms for automatic search for signals scattered by meteor trails according to EKB ISTP SB RAS radar data. In general, the algorithm is similar to the algorithms adopted in specialized meteor systems. The algorithm is divided into two stages: detecting a meteor echo and determining its parameters. We show that on the day of the maximum Geminid shower, December 13, 2016, the scattered signals detected by the algorithm are foreshortening and correspond to scattering by irregularities extended in the direction of the meteor shower radiant. This confirms that the source of the signals detected by the algorithm is meteor trails. We implement an additional program for indirect trail height determination. It uses a decay time of echo and the NRLMSIS-00 atmosphere model to estimate the trail height. The dataset from 2017 to 2019 is used for further testing of the algorithm. We demonstrate a correlation in calculated Doppler velocity between the new algorithm and FitACF. We present a solution of the inverse problem of reconstructing the neutral wind velocity vector from the data obtained by the weighted least squares method. We compare calculated speeds and directions of horizontal neutral winds, obtained in the three-dimensional wind model, and the HWM-14 horizontal wind model. The algorithm allows real-time scattered signal processing and has been put into continuous operation at the EKB ISTP SB RAS radar.
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Li, Yingjun, Wenpeng Zhang, Biao Tian, Wenhao Lin, and Yongxiang Liu. "Scattering Model-Based Frequency-Hopping RCS Reconstruction Using SPICE Methods." Remote Sensing 13, no. 18 (September 15, 2021): 3689. http://dx.doi.org/10.3390/rs13183689.

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RCS reconstruction is an important way to reduce the measurement time in anechoic chambers and expand the radar original data, which can solve the problems of data scarcity and a high measurement cost. The greedy pursuit, convex relaxation, and sparse Bayesian learning-based sparse recovery methods can be used for parameter estimation. However, these sparse recovery methods either have problems in solving accuracy or selecting auxiliary parameters, or need to determine the probability distribution of noise in advance. To solve these problems, a non-parametric Sparse Iterative Covariance Estimation (SPICE) algorithm with global convergence property based on the sparse Geometrical Theory of Diffraction (GTD) model (GTD–SPICE) is employed for the first time for RCS reconstruction. Furthermore, an improved coarse-to-fine two-stage SPICE method (DE–GTD–SPICE) based on the Damped Exponential (DE) model and the GTD model (DE–GTD) is proposed to reduce the computational cost. Experimental results show that both the GTD–SPICE method and the DE–GTD–SPICE method are reliable and effective for RCS reconstruction. Specifically, the DE–GTD–SPICE method has a shorter computational time.
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Dissertations / Theses on the topic "Sparse RLS algorithm"

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(5930993), Vinith Vijayarajan. "Channel sparsity aware polynomial expansion filters for nonlinear acoustic echo cancellation." Thesis, 2019.

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Speech quality is a demand in voice commanded systems and in telephony. The voice communication system in real time often suffers from audible echoes. In order to cancel echoes, an acoustic echo cancellation system is designed and applied to increase speech quality both subjectively and objectively.

In this research we develop various nonlinear adaptive filters wielding the new channel sparsity-aware recursive least squares (RLS) algorithms using a sequential update. The developed nonlinear adaptive filters using the sparse sequential RLS (S-SEQ-RLS) algorithm apply a discard function to disregard the coefficients which are not significant or close to zero in the weight vector for each channel in order to reduce the computational load and improve the algorithm convergence rate. The channel sparsity-aware algorithm is first derived for nonlinear system modeling or system identification, and then modified for application of echo cancellation. Simulation results demonstrate that by selecting a proper threshold value in the discard function, the proposed nonlinear adaptive filters using the RLS (S-SEQ-RLS) algorithm can achieve the similar performance as the nonlinear filters using the sequential RLS (SEQ-RLS) algorithm in which the channel weight vectors are sequentially updated. Furthermore, the proposed channel sparsity-aware RLS algorithms require a lower computational load in comparison with the non-sequential and non-sparsity algorithms. The computational load for the sparse algorithms can further be reduced by using data-selective strategies.

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Book chapters on the topic "Sparse RLS algorithm"

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Tan, Chao, and Genlin Ji. "DKE-RLS: A Manifold Reconstruction Algorithm in Label Spaces with Double Kernel Embedding-Regularized Least Square." In Lecture Notes in Computer Science, 16–28. Cham: Springer International Publishing, 2018. http://dx.doi.org/10.1007/978-3-319-97304-3_2.

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Goodridge, Wayne S., Shyamala C. Sivakumar, William Robertson, and William J. Phillips. "Multiple Optimization of Network Carrier and Traffic Flow Goals Using a Heuristic Routing Decision System." In Intelligent Quality of Service Technologies and Network Management, 113–37. IGI Global, 2010. http://dx.doi.org/10.4018/978-1-61520-791-6.ch007.

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This chapter presents a multiple constraint optimization algorithm called routing decision system (RDS) that uses the concept of preference functions to address the problem of selecting paths in core networks that satisfy traffic-oriented QoS requirements while simultaneously satisfying network resource-oriented performance goals. The original contribution lies in the use of strong scales employed for constructing a multiple criteria preference function in an affine space. The use of preference functions makes it possible for paths that match both traffic-oriented and resource-oriented goals to be selected by the algorithm. The RDS algorithm is used in conjunction with a heuristic path finding algorithm called Constraint Path Heuristic (CP-H) algorithm which is a novel approach to finding a set of constraint paths between source and destination nodes in a network. The CP-H algorithm finds multiple paths for each metric and then passes all the paths to the RDS algorithm. Simulation results showed that the CP-H/RDS algorithm has a success rate of between 93 and 96% when used in Waxman graph topologies, and is shown to be significantly better than other heuristic based algorithms under strict constraints. In addition, it is shown that the associated execution time of the CP-H/RDS algorithm is slightly higher than other heuristic based algorithms but good enough for use in an online traffic engineering (TE) application. Simulations to assess the performance of CP-H/RDS algorithm in a TE environment show that the algorithms has lower call block rates than other TE algorithms. It is also shown that the CP-H/RDS has a 96% probability of providing at least two distinct feasible backup paths in addition to the main QoS path. A framework for implementing the CP-H/RDS as a routing server is proposed. The routing decision system server (RDSS) framework is novel in that the complexity introduced by QoS awareness remains outside the network.
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Parwez, Md Salik, Hasan Farooq, Ali Imran, and Hazem Refai. "Spectral Efficiency Self-Optimization through Dynamic User Clustering and Beam Steering." In Research Anthology on Developing and Optimizing 5G Networks and the Impact on Society, 79–94. IGI Global, 2021. http://dx.doi.org/10.4018/978-1-7998-7708-0.ch005.

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This paper presents a novel scheme for spectral efficiency (SE) optimization through clustering of users. By clustering users with respect to their geographical concentration we propose a solution for dynamic steering of antenna beam, i.e., antenna azimuth and tilt optimization with respect to the most focal point in a cell that would maximize overall SE in the system. The proposed framework thus introduces the notion of elastic cells that can be potential component of 5G networks. The proposed scheme decomposes large-scale system-wide optimization problem into small-scale local sub-problems and thus provides a low complexity solution for dynamic system wide optimization. Every sub-problem involves clustering of users to determine focal point of the cell for given user distribution in time and space, and determining new values of azimuth and tilt that would optimize the overall system SE performance. To this end, we propose three user clustering algorithms to transform a given user distribution into the focal points that can be used in optimization; the first is based on received signal to interference ratio (SIR) at the user; the second is based on received signal level (RSL) at the user; the third and final one is based on relative distances of users from the base stations. We also formulate and solve an optimization problem to determine optimal radii of clusters. The performances of proposed algorithms are evaluated through system level simulations. Performance comparison against benchmark where no elastic cell deployed, shows that a gain in spectral efficiency of up to 25% is possible depending upon user distribution in a cell.
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Ordóñez, Diego, Carlos Dafonte, Bernardino Arcay, and Minia Manteiga. "Connectionist Systems and Signal Processing Techniques Applied to the Parameterization of Stellar Spectra." In Soft Computing Methods for Practical Environment Solutions, 187–203. IGI Global, 2010. http://dx.doi.org/10.4018/978-1-61520-893-7.ch012.

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A stellar spectrum is the finger-print identification of a particular star, the result of the radiation transport through its atmosphere. The physical conditions in the stellar atmosphere, its effective temperature, surface gravity, and the presence and abundance of chemical elements explain the observed features in the stellar spectra, such as the shape of the overall continuum and the presence and strength of particular lines and bands. The derivation of the atmospheric stellar parameters from a representative sample of stellar spectra collected by ground-based and spatial telescopes is essential when a realistic view of the Galaxy and its components is to be obtained. In the last decade, extensive astronomical surveys recording information of large portions of the sky have become a reality since the development of robotic or semi-automated telescopes. The Gaia satellite is one of the key missions of the European Space Agency (ESA) and its launch is planned for 2011. Gaia will carry out the so-called Galaxy Census by extracting precise information on the nature of its main constituents, including the spectra of objects (Wilkinson, 2005). Traditional methods for the extraction of the fundamental atmospheric stellar parameters (effective temperature (Teff), gravity (log G), metallicity ([Fe/H]), and abundance of alpha elements [a/Fe], elements integer multiples of the mass of the helium nucleus) are time-consuming and unapproachable for a massive survey involving 1 billion objects (about 1% of the Galaxy constituents) such as Gaia. This work presents the results of the authors’ study and shows the feasibility of an automated extraction of the previously mentioned stellar atmospheric parameters from near infrared spectra in the wavelength region of the Gaia Radial Velocity Spectrograph (RVS). The authors’ approach is based on a technique that has already been applied to problems of the non-linear parameterization of signals: artificial neural networks. It breaks ground in the consideration of transformed domains (Fourier and Wavelet Transforms) during the preprocessing stage of the spectral signals in order to select the frequency resolution that is best suited for each atmospheric parameter. The authors have also progressed in estimating the noise (SNR) that blurs the signal on the basis of its power spectrum and the application of noise-dependant algorithms of parameterization. This study has provided additional information that allows them to progress in the development of hybrid systems devoted to the automated classification of stellar spectra.
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Gunjal, S. N., Yadav S K, and Kshirsagar D B. "A Distributed Item Based Similarity Approach for Collaborative Filtering on Hadoop Framework." In Intelligent Systems and Computer Technology. IOS Press, 2020. http://dx.doi.org/10.3233/apc200176.

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Now a day’s multiple website provides millions of product to their on-line users. Due to the interaction of millions customer with the e-commerce websites creates massive volume of data. Recommendation system is a dynamic data capturing system filters massive volume of information generated through the interaction of users to web-portals & generate suggestion that fits the user expectations. Recommendation framework is data filtering tools that make use of algorithms and user rating data to recommend the most relevant items to a particular user. Collaborative filtering is one of the successful techniques in the recommendation system to recommend the top N-item. In the majority of the Recommendation framework information sparsity, high dimensionality, adaptability which are normal issue in the RS domain have adversely influence the exhibition of CF. The proposed system is developed to resolve the mentioned problems in most of the recommendation system using item based similarities approach in CF with the help of user sub space clustering approach on hadoop framework. In the proposed system user subspaces formed by considering the interest of the users in the items like Interested, Neither Interested nor Uninterested (NIU), and Uninterested. After the user subspace clustering the neighbor item tree is constructed. To find out the similarities between the items the similarities measures is developed from the neighbor item tree. It observed that in traditional item-based collaborative filtering method requires more computational cost but the computation of item similarities is performed off line & computational cost required for on line prediction is less. In proposed work to improve the computation speed of off line item-item similarity the computation is performed on various nodes in cluster in the hadoop distributed system. The similarity between the two items is used to predict the rating provided by user on the target items. The proposed method tested on the Movie lens 100K, Movie lens 1M in order to make comparisons with the existing techniques. The proposed method improves the performance of the recommendation systems by resolving the issues like scalability, high dimensionality, data sparsity etc.
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Conference papers on the topic "Sparse RLS algorithm"

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Das, Bijit Kumar, and Mrityunjoy Chakraborty. "A new diffusion sparse RLS algorithm with improved convergence characteristics." In 2016 IEEE International Symposium on Circuits and Systems (ISCAS). IEEE, 2016. http://dx.doi.org/10.1109/iscas.2016.7539138.

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Koike-Akino, Toshiaki, Andreas F. Molisch, Man-On Pun, Ramesh Annavajjala, and Philip Orlik. "Order-Extended Sparse RLS Algorithm for Doubly-Selective MIMO Channel Estimation." In ICC 2011 - 2011 IEEE International Conference on Communications. IEEE, 2011. http://dx.doi.org/10.1109/icc.2011.5963228.

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Bao, Donghai, Fang Yang, Qianru Jiang, Sheng Li, and Xiongxiong He. "Block RLS algorithm for surveillance video processing based on image sparse representation." In 2017 29th Chinese Control And Decision Conference (CCDC). IEEE, 2017. http://dx.doi.org/10.1109/ccdc.2017.7978879.

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Gui, Guan, Linglong Dai, Baoyu Zheng, Li Xu, and Fumiyuki Adachi. "Correntropy Induced Metric Penalized Sparse RLS Algorithm to Improve Adaptive System Identification." In 2016 IEEE 83rd Vehicular Technology Conference (VTC Spring). IEEE, 2016. http://dx.doi.org/10.1109/vtcspring.2016.7504179.

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Zhang, Youwen, Lu Liu, and Dajun Sun. "Adaptive turbo equalization with sparse homotopy DCD-RLS algorithm with variable forgetting factor for underwater acoustic communication." In 2016 IEEE/OES China Ocean Acoustics (COA). IEEE, 2016. http://dx.doi.org/10.1109/coa.2016.7535755.

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Qin, Zhen, Jun Tao, Liang An, Shuai Yao, and Xiao Han. "Fast Sparse RLS Algorithms." In 2018 10th International Conference on Wireless Communications and Signal Processing (WCSP). IEEE, 2018. http://dx.doi.org/10.1109/wcsp.2018.8555873.

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Babadi, Behtash, Nicholas Kalouptsidis, and Vahid Tarokh. "Comparison of SPARLS and RLS algorithms for adaptive filtering." In 2009 IEEE Sarnoff Symposium (SARNOFF). IEEE, 2009. http://dx.doi.org/10.1109/sarnof.2009.4850336.

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Yguel, M., C. Tay Meng Keat, C. Braillon, C. Laugier, and O. Aycard. "Dense Mapping for Range Sensors: Efficient Algorithms and Sparse Representations." In Robotics: Science and Systems 2007. Robotics: Science and Systems Foundation, 2007. http://dx.doi.org/10.15607/rss.2007.iii.017.

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Minami Shiguematsu, Yukitoshi, Martim Brandao, Kenji Hashimoto, and Atsuo Takanishi. "Effects of Biped Humanoid Robot Walking Gaits on Sparse Visual Odometry Algorithms." In 2018 IEEE-RAS 18th International Conference on Humanoid Robots (Humanoids). IEEE, 2018. http://dx.doi.org/10.1109/humanoids.2018.8625015.

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Xu, Jie, Cheng Deng, Xinbo Gao, Dinggang Shen, and Heng Huang. "Predicting Alzheimer's Disease Cognitive Assessment via Robust Low-Rank Structured Sparse Model." In Twenty-Sixth International Joint Conference on Artificial Intelligence. California: International Joint Conferences on Artificial Intelligence Organization, 2017. http://dx.doi.org/10.24963/ijcai.2017/542.

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Alzheimer's disease (AD) is a neurodegenerative disorder with slow onset, which could result in the deterioration of the duration of persistent neurological dysfunction. How to identify the informative longitudinal phenotypic neuroimaging markers and predict cognitive measures are crucial to recognize AD at early stage. Many existing models related imaging measures to cognitive status using regression models, but they did not take full consideration of the interaction between cognitive scores. In this paper, we propose a robust low-rank structured sparse regression method (RLSR) to address this issue. The proposed model simultaneously selects effective features and learns the underlying structure between cognitive scores by utilizing novel mixed structured sparsity inducing norms and low-rank approximation. In addition, an efficient algorithm is derived to solve the proposed non-smooth objective function with proved convergence. Empirical studies on cognitive data of the ADNI cohort demonstrate the superior performance of the proposed method.
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