573 skills found · Page 8 of 20
eugenbobrov / Adaptive Regularized Zero Forcing Beamforming In Massive MIMO With Multi Antenna UsersModern wireless cellular networks use massive multiple-input multiple-output (MIMO) technology. This technology involves operations with an antenna array at a base station that simultaneously serves multiple mobile devices which also use multiple antennas on their side. For this, various precoding and detection techniques are used, allowing each user to receive the signal intended for him from the base station. There is an important class of linear precoding called Regularized Zero-Forcing (RZF). In this work, we propose Adaptive RZF (ARZF) with a special kind of regularization matrix with different coefficients for each layer of multi-antenna users. These regularization coefficients are defined by explicit formulas based on SVD decompositions of user channel matrices. We study the optimization problem, which is solved by the proposed algorithm, with the connection to other possible problem statements. We also compare the proposed algorithm with state-of-the-art linear precoding algorithms on simulations with the Quadriga channel model. The proposed approach provides a significant increase in quality with the same computation time as in the reference methods.
ldecicco / TapasTAPAS is a tool for rapid prototyping of adaptive streaming algorithms and video streaming traffic generation
acguez / BamcpBayes-Adaptive Monte-Carlo Planning algorithm
slaventiysvat / Sound ProcessorAdaptive Noise Reduction using RLS algorithm, NLMS algorithm for Echo Cancelation, VAD of G729 to separate noise and voice
AndrewYung724 / Adaptive Cruise ControlDesign of a game theoretic adaptive cruise control algorithm using a one player dynamic game model with full information structure and a receding horizon control algorithm.
shwinshaker / LipGrowAn adaptive training algorithm for residual network
WeberZheng / ALNSAdaptive Large Neighborhood Search Algorithm for paper "Robust Dual Sourcing Inventory Routing Optimization for Disaster Relief"
Hossamomar / EM070 New FPGA Family For CNN Architectures High Speed Soft Neuron DesignWho doesn’t dream of a new FPGA family that can provide embedded hard neurons in its silicon architecture fabric instead of the conventional DSP and multiplier blocks? The optimized hard neuron design will allow all the software and hardware designers to create or test different deep learning network architectures, especially the convolutional neural networks (CNN), more easily and faster in comparing to any previous FPGA family in the market nowadays. The revolutionary idea about this project is to open the gate of creativity for a precise-tailored new generation of FPGA families that can solve the problems of wasting logic resources and/or unneeded buses width as in the conventional DSP blocks nowadays. The project focusing on the anchor point of the any deep learning architecture, which is to design an optimized high-speed neuron block which should replace the conventional DSP blocks to avoid the drawbacks that designers face while trying to fit the CNN architecture design to it. The design of the proposed neuron also takes the parallelism operation concept as it’s primary keystone, beside the minimization of logic elements usage to construct the proposed neuron cell. The targeted neuron design resource usage is not to exceeds 500 ALM and the expected maximum operating frequency of 834.03 MHz for each neuron. In this project, ultra-fast, adaptive, and parallel modules are designed as soft blocks using VHDL code such as parallel Multipliers-Accumulators (MACs), RELU activation function that will contribute to open a new horizon for all the FPGA designers to build their own Convolutional Neural Networks (CNN). We couldn’t stop imagining INTEL ALTERA to lead the market by converting the proposed designed CNN block and to be a part of their new FPGA architecture fabrics in a separated new Logic Family so soon. The users of such proposed CNN blocks will be amazed from the high-speed operation per seconds that it can provide to them while they are trying to design their own CNN architectures. For instance, and according to the first coding trial, the initial speed of just one MAC unit can reach 3.5 Giga Operations per Second (GOPS) and has the ability to multiply up to 4 different inputs beside a common weight value, which will lead to a revolution in the FPGA capabilities for adopting the era of deep learning algorithms especially if we take in our consideration that also the blocks can work in parallel mode which can lead to increasing the data throughput of the proposed project to about 16 Tera Operations per Second (TOPS). Finally, we believe that this proposed CNN block for FPGA is just the first step that will leave no areas for competitions with the conventional CPUs and GPUs due to the massive speed that it can provide and its flexible scalability that it can be achieved from the parallelism concept of operation of such FPGA-based CNN blocks.
Taehooie / Congestion Aware CACC AlgorithmCongestion aware cooperative adaptive cruise control algorithm for mitigation of self organized traffic jam
olixu / Adaptive Dynamic Programming HubAdaptive Dynamic Programming Algorithms and Simulations
jparkerholder / ASEBOCode to run the ASEBO algorithm from the paper: From Complexity to Simplicity: Adaptive ES-Active Subspaces for Blackbox Optimization... please get in touch if interested!!
SankhaSubhra / Ada KNNA MATLAB implementation of Adaptive k-Nearest Neighbor Algorithms called Ada-kNN and Ada-kNN2 (alongside a global weighting scheme for handling class imbalance).
Songinpyo / SFTrackOfficial repository of " SFTrack: A Robust Scale and Motion Adaptive Algorithm for Tracking Small and Fast Moving Objects" (IROS 2024)
laibrary42121 / Adaptive Beamforming For Directional Signal EnhancementDeveloped an adaptive beamforming system using Kalman filter for DOA denoising and MUSIC algorithm for spatial filtering. Achieved better SNR and interference suppression than LCMV beamformer in dynamic scenarios with fluctuating source and interference directions.
seanwu1105 / Adaptive Huffman CodingAdaptive Huffman coding algorithm in Python.
intelligent-control-lab / AGenAdaptable generative prediction using recursive least square algorithm
mohofar / NICE KLMS QkLMSAn implementation to "Transfer Learning in Adaptive Filters: The Nearest Instance Centroid-Estimation Kernel Least-Mean-Square Algorithm" with additional work on blood pressure prediction.
mikebianco / Locally Sparse TomographyLocally sparse travel time tomography (LST) is a tomography algorithm which uses sparse modeling and dictionary learning to estimate 2D geophysical images based on wave travel times across sensor arrays. This repository is an implementation of the IEEE paper: M.J. Bianco and P. Gerstoft, "Travel time tomography with adaptive dictionaries," IEEE Trans. on Computational Imaging, Vol. 4, No. 4, 2018.
Slifers / MS TLDIn order to solve tracking failures caused by objects deformation, occlusion and fast motion, a novel algorithm called MS-TLD which under the Tracking-Learning-Detection framework is proposed. The algorithm reconstructs a new tracker with the scale-adaptive mean-shift method. By introducing color histogram features and scale-adaptive, the new tracker can track objects with deformation and fast moving. We establish a new tracking-detection feedback strategy—the inter-correction between tracker and detector. Therefore, the new algorithm has better robustness when objects are occluded. We use TB-50 standard dataset to verify and evaluate our method. The experimental results show that the proposed algorithm can overcome the tracking failures caused by objects with deformation, occlusion, fast motion, as well as background clutters, and has better tracking accuracy and robustness compared with TLD and other 3 classic algorithms.
abubakar1107 / Fuzzy Adaptive RRT N Path Planning And Control Of Autonomous Vehicle In CARLAThis project implements a Fuzzy Adaptive RRT*N algorithm for autonomous vehicle path planning in CARLA. By integrating fuzzy logic with RRT*N, the vehicle adapts dynamically to obstacles and traffic, ensuring efficient navigation in complex urban environments with optimized control.
