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Relevant bibliographies by topics / Novel recovery algorithms

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Academic literature on the topic 'Novel recovery algorithms'

Author: Grafiati

Published: 6 September 2023

Last updated: 31 July 2025

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Journal articles on the topic "Novel recovery algorithms"

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Liang, Rui Hua, Xin Peng Du, Qing Bo Zhao, and Li Zhi Cheng. "Sparse Signal Recovery Based on Simulated Annealing." Applied Mechanics and Materials 321-324 (June 2013): 1295–98. http://dx.doi.org/10.4028/www.scientific.net/amm.321-324.1295.

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Sparse signal recovery is a hot topic in the fields of optimization theory and signal processing. Two main algorithmic approaches, i.e. greedy pursuit algorithms and convex relaxation algorithms have been extensively used to solve this problem. However, these algorithms cannot guarantee to find the global optimum solution, and then they perform poorly when the sparsity level is relatively large. Based on the simulated annealing algorithm and greedy pursuit algorithms, we propose a novel algorithm on solving the sparse recovery problem. Numerical simulations show that the proposed algorithm has

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Zhu, Ying, Yong Xing Jia, Chuan Zhen Rong, and Yu Yang. "Study on Compressed Sensing Recovery Algorithms." Applied Mechanics and Materials 433-435 (October 2013): 322–25. http://dx.doi.org/10.4028/www.scientific.net/amm.433-435.322.

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Abastruct. Compressive sensing is a novel signal sampling theory under the condition that the signalis sparse or compressible.In this case,the small amount of signal values can be reconstructed when signal is sparse or compressible.This paper has reviewed the idea of OMP,GBP and SP,given algorithms and analyzed the experiment results,suggested some improvements.

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Battiston, Adrian, Inna Sharf, and Meyer Nahon. "Attitude estimation for collision recovery of a quadcopter unmanned aerial vehicle." International Journal of Robotics Research 38, no. 10-11 (2019): 1286–306. http://dx.doi.org/10.1177/0278364919867397.

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An extensive evaluation of attitude estimation algorithms in simulation and experiments is performed to determine their suitability for a collision recovery pipeline of a quadcopter unmanned aerial vehicle. A multiplicative extended Kalman filter (MEKF), unscented Kalman filter (UKF), complementary filter, [Formula: see text] filter, and novel adaptive varieties of the selected filters are compared. The experimental quadcopter uses a PixHawk flight controller, and the algorithms are implemented using data from only the PixHawk inertial measurement unit (IMU). Performance of the aforementioned

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An, Qi, Li Wang та Nana Zhang. "Novel Iterative Reweighted ℓ1 Minimization for Sparse Recovery". Mathematics 13, № 8 (2025): 1219. https://doi.org/10.3390/math13081219.

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Data acquisition and high-dimensional signal processing often require the recovery of sparse representations of signals to minimize the resources needed for data collection. ℓp quasi-norm minimization excels in exactly reconstructing sparse signals from fewer measurements, but it is NP-hard and challenging to solve. In this paper, we propose two distinct Iteratively Re-weighted ℓ1 Minimization (IRℓ1) formulations for solving this non-convex sparse recovery problem by introducing two novel reweighting strategies. These strategies ensure that the ϵ-regularizations adjust dynamically based on the

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Wang, Runsong, Xuelian Li, Juntao Gao, Hui Li, and Baocang Wang. "Quantum rotational cryptanalysis for preimage recovery of round-reduced Keccak." Quantum Information & Computation 23, no. 3&4 (2023): 223–34. http://dx.doi.org/10.26421/qic23.3-4-3.

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The Exclusive-OR Sum-of-Product (ESOP) minimization problem has long been of interest to the research community because of its importance in classical logic design (including low-power design and design for test), reversible logic synthesis, and knowledge discovery, among other applications. However, no exact minimal minimization method has been presented for more than seven variables on arbitrary functions. This paper presents a novel quantum-classical hybrid algorithm for the exact minimal ESOP minimization of incompletely specified Boolean functions. This algorithm constructs oracles from s

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Shukla, Vasundhara, and Preety D. Swami. "Sparse Signal Recovery through Long Short-Term Memory Networks for Compressive Sensing-Based Speech Enhancement." Electronics 12, no. 14 (2023): 3097. http://dx.doi.org/10.3390/electronics12143097.

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This paper presents a novel speech enhancement approach based on compressive sensing (CS) which uses long short-term memory (LSTM) networks for the simultaneous recovery and enhancement of the compressed speech signals. The advantage of this algorithm is that it does not require an iterative process to recover the compressed signals, which makes the recovery process fast and straight forward. Furthermore, the proposed approach does not require prior knowledge of signal and noise statistical properties for sensing matrix optimization because the used LSTM can directly extract and learn the requ

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Acharya, Deep Shekhar, and Sudhansu Kumar Mishra. "Optimal Consensus Recovery of Multi-agent System Subjected to Agent Failure." International Journal on Artificial Intelligence Tools 29, no. 06 (2020): 2050017. http://dx.doi.org/10.1142/s0218213020500177.

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Multi-Agent Systems are susceptible to external disturbances, sensor failures or collapse of communication channel/media. Such failures disconnect the agent network and thereby hamper the consensus of the system. Quick recovery of consensus is vital to continue the normal operation of an agent-based system. However, only limited works in the past have investigated the problem of recovering the consensus of an agent-based system in the event of a failure. This work proposes a novel algorithmic approach to recover the lost consensus, when an agent-based system is subject to the failure of an age

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Jo, Hwi-Jeong, Heewoo Lee, Jihoon Choi, and Wookyung Lee. "Hybrid Deterministic Sensing Matrix for Compressed Drone SAR Imaging and Efficient Reconstruction of Subsurface Targets." Remote Sensing 17, no. 4 (2025): 595. https://doi.org/10.3390/rs17040595.

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Drone-based synthetic aperture radar (SAR) systems have increasingly gained attention due to their potential for rapid surveillance in localized areas. This paper presents a novel approach to SAR processing for subsurface target detection from a lightweight drone platform. The limited processing capacity and memory resources of small SAR platforms demand efficient recovery performance for high-resolution imaging. Compressed sensing (CS) algorithms are widely used to mitigate data storage requirements, yet they often suffer from challenges related to computational burden and detection errors. C

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Malik, Jameel, Ahmed Elhayek, and Didier Stricker. "WHSP-Net: A Weakly-Supervised Approach for 3D Hand Shape and Pose Recovery from a Single Depth Image." Sensors 19, no. 17 (2019): 3784. http://dx.doi.org/10.3390/s19173784.

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Hand shape and pose recovery is essential for many computer vision applications such as animation of a personalized hand mesh in a virtual environment. Although there are many hand pose estimation methods, only a few deep learning based algorithms target 3D hand shape and pose from a single RGB or depth image. Jointly estimating hand shape and pose is very challenging because none of the existing real benchmarks provides ground truth hand shape. For this reason, we propose a novel weakly-supervised approach for 3D hand shape and pose recovery (named WHSP-Net) from a single depth image by learn

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Zhang, Hongyang, Zhouchen Lin, Chao Zhang, and Junbin Gao. "Relations Among Some Low-Rank Subspace Recovery Models." Neural Computation 27, no. 9 (2015): 1915–50. http://dx.doi.org/10.1162/neco_a_00762.

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Recovering intrinsic low-dimensional subspaces from data distributed on them is a key preprocessing step to many applications. In recent years, a lot of work has modeled subspace recovery as low-rank minimization problems. We find that some representative models, such as robust principal component analysis (R-PCA), robust low-rank representation (R-LRR), and robust latent low-rank representation (R-LatLRR), are actually deeply connected. More specifically, we discover that once a solution to one of the models is obtained, we can obtain the solutions to other models in closed-form formulations.

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Dissertations / Theses on the topic "Novel recovery algorithms"

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Allam, Vineel Reddy. "A novel recovery algorithm for distributed computing environment /." Available to subscribers only, 2008. http://proquest.umi.com/pqdweb?did=1594491041&sid=2&Fmt=2&clientId=1509&RQT=309&VName=PQD.

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Mohsin, Yasir Qasim. "Novel MR image recovery using patch-smoothness iterative shrinkage algorithm." Diss., University of Iowa, 2018. https://ir.uiowa.edu/etd/6538.

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Obtaining high spatial or spatiotemporal resolution along with good slice coverage is challenging in dynamic magnetic resonance imaging, MRI, due to the slow nature of the acquisition process. In recent years, there has been a rapid growth of MRI techniques that allow faster scan speed by exploiting spatial or spatiotemporal redundancy of the images. These techniques can improve the performance of imaging significantly across multiple clinical applications, including cardiac functional examinations, perfusion imaging, blood flow assessment, contrast-enhanced angiography, functional MRI, and in

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Jupally, Vamshi Krishna Rao. "A novel recovery algorithm for concurrent failures in distributed computing environment /." Available to subscribers only, 2008. http://proquest.umi.com/pqdweb?did=1594491061&sid=13&Fmt=2&clientId=1509&RQT=309&VName=PQD.

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Joseph, Geethu. "Linear Dynamical Systems with Sparsity Constraints: Theory and Algorithms." Thesis, 2019. https://etd.iisc.ac.in/handle/2005/5044.

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This thesis develops new mathematical theory and presents novel recovery algorithms for discrete linear dynamical systems (LDS) with sparsity constraints on either control inputs or initial state. The recovery problems in this framework manifest as the problem of reconstructing one or more sparse signals from a set of noisy underdetermined linear measurements. The goal of our work is to design algorithms for sparse signal recovery which can exploit the underlying structure in the measurement matrix and the unknown sparse vectors, and to analyze the impact of these structures on the effic

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Book chapters on the topic "Novel recovery algorithms"

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Abuzneid, Abdelshakour, Chennaipattinam Raghuram Vijay Iyengar, and Ramaswamy Gandhi Dasan Prabhu. "VDisaster recovery with the help of real time video streaming using MANET support." In Novel Algorithms and Techniques in Telecommunications and Networking. Springer Netherlands, 2009. http://dx.doi.org/10.1007/978-90-481-3662-9_87.

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Chun, Kwang Ho, and Myoung Seob Lim. "Novel Symbol Timing Recovery Algorithm for Multi-level Signal." In Lecture Notes in Computer Science. Springer Berlin Heidelberg, 2004. http://dx.doi.org/10.1007/978-3-540-30134-9_8.

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Ramponi, Giorgia. "Learning in the Presence of Multiple Agents." In Special Topics in Information Technology. Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-031-15374-7_8.

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AbstractReinforcement Learning (RL) has emerged as a powerful tool to solve sequential decision-making problems, where a learning agent interacts with an unknown environment in order to maximize its rewards. Although most RL real-world applications involve multiple agents, the Multi-Agent Reinforcement Learning (MARL) framework is still poorly understood from a theoretical point of view. In this manuscript, we take a step toward solving this problem, providing theoretically sound algorithms for three RL sub-problems with multiple agents: Inverse Reinforcement Learning (IRL), online learning in

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Molisz, Wojciech, and Jacek Rak. "A Novel Class-Based Protection Algorithm Providing Fast Service Recovery in IP/WDM Networks." In NETWORKING 2008 Ad Hoc and Sensor Networks, Wireless Networks, Next Generation Internet. Springer Berlin Heidelberg, 2008. http://dx.doi.org/10.1007/978-3-540-79549-0_29.

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Koh, Sungshik. "A Novel Recovery Algorithm of Incomplete Observation Matrix for Converting 2-D Video to 3-D Content." In Advances in Machine Vision, Image Processing, and Pattern Analysis. Springer Berlin Heidelberg, 2006. http://dx.doi.org/10.1007/11821045_28.

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Luo, Dan, Joseph M. Gattas, and Poah Shiun Shawn Tan. "Real-Time Defect Recognition and Optimized Decision Making for Structural Timber Jointing." In Proceedings of the 2020 DigitalFUTURES. Springer Singapore, 2021. http://dx.doi.org/10.1007/978-981-33-4400-6_4.

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AbstractNon-structural or out-of-grade timber framing material contains a large proportion of visual and natural defects. A common strategy to recover usable material from these timbers is the marking and removing of defects, with the generated intermediate lengths of clear wood then joined into a single piece of full-length structural timber. This paper presents a novel workflow that uses machine learning based image recognition and a computational decision-making algorithm to enhance the automation and efficiency of current defect identification and re-joining processes. The proposed workflo

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Huntley, David, Drew Rotheram-Clarke, Roger MacLeod, Robert Cocking, Jamel Joseph, and Philip LeSueur. "Landslide Monitoring with RADARSAT Constellation Mission InSAR, RPAS-Derived Point-Clouds and RTK-GNSS Time-Series in the Thompson River Valley, British Columbia, Canada." In Progress in Landslide Research and Technology, Volume 2 Issue 1, 2023. Springer Nature Switzerland, 2023. http://dx.doi.org/10.1007/978-3-031-39012-8_19.

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AbstractIn this paper, we evaluate the effectiveness of four land-deformation measurement techniques for monitoring slow-moving landslides along a high-risk section of the national railway corridor traversing the Thompson River valley, British Columbia, Canada. The geomorphically active North Slide acts as an ideal field laboratory for testing and evaluating novel monitoring techniques and methods. We compare differential processing of Structure from Motion (SfM) products such as point-cloud elevation models and orthophotos derived from Remotely Piloted Aircraft Systems (RPAS), along with sate

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Hamidi, Hodjatollah. "A General Framework of Algorithm-Based Fault Tolerance Technique for Computing Systems." In Analyzing Security, Trust, and Crime in the Digital World. IGI Global, 2014. http://dx.doi.org/10.4018/978-1-4666-4856-2.ch001.

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The Algorithm-Based Fault Tolerance (ABFT) approach transforms a system that does not tolerate a specific type of faults, called the fault-intolerant system, to a system that provides a specific level of fault tolerance, namely recovery. The ABFT philosophy leads directly to a model from which error correction can be developed. By employing an ABFT scheme with effective convolutional code, the design allows high throughput as well as high fault coverage. The ABFT techniques that detect errors rely on the comparison of parity values computed in two ways. The parallel processing of input parity

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Raju, Sanjay, Rishiikeshwer B.S., Aswin Shriram T., Brindha G.R., Santhi B., and Bharathi N. "COVID-19 - Novel Short Term Prediction Methods." In Mobile Computing Solutions for Healthcare Systems. BENTHAM SCIENCE PUBLISHERS, 2023. http://dx.doi.org/10.2174/9789815050592123010006.

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The recent outbreak of Severe Acute Respiratory Syndrome Corona Virus (SARS-CoV-2), also called COVID-19, is a major global health problem due to an increase in mortality and morbidity. The virus disturbs the respirational process of a human being and is highly spreadable. The current distressing COVID-19 pandemic has caused heavy financial crashing and the assets and standards of the highly impacted countries being compromised. Therefore, prediction methods should be devised, supporting the development of recovery strategies. To make accurate predictions, understanding the natural progression

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Liu, Xin, Shen Wang, Jianzhi Sang, and weizhe zhang. "A Novel Pixel Merging-Based Lossless Recovery Algorithm for Basic Matrix VSS." In Cryptography. IGI Global, 2020. http://dx.doi.org/10.4018/978-1-7998-1763-5.ch032.

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Lossless recovery in visual secret share (VSS) is very meaningful. In this paper, a novel lossless recovery algorithm for the basic matrix VSS is proposed. The secret image is reconstructed losslessly by using simple exclusive XOR operation and merging pixel. The algorithm not only can apply to the VSS without pixel expansion but also can apply to VSS with pixel expansion. The condition of lossless recovery of a VSS is given by analyzing the XOR all columns of basic matrixes. Simulations are conducted to evaluate the efficiency of the proposed scheme.

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Conference papers on the topic "Novel recovery algorithms"

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Cao, Ha H., and Ha H. Nguyen. "Novel Multi-Dimensional Spatially-Adaptive Image Recovery Using Spline Interpolation." In 2019 Signal Processing: Algorithms, Architectures, Arrangements, and Applications (SPA). IEEE, 2019. http://dx.doi.org/10.23919/spa.2019.8936721.

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Chae, Jeongmin, and Song-Nam Hong. "A Novel B-MAP Proxy for Greedy Sparse Signal Recovery Algorithms." In 2020 IEEE International Symposium on Information Theory (ISIT). IEEE, 2020. http://dx.doi.org/10.1109/isit44484.2020.9174022.

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Lei, Lei, and Yuefeng Ji. "Study on novel recovery strategies and algorithms in IP over WDM networks." In Asia-Pacific Optical and Wireless Communications, edited by S. J. Ben Yoo, Kwok-wai Cheung, Yun-Chur Chung, and Guangcheng Li. SPIE, 2004. http://dx.doi.org/10.1117/12.520189.

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Fernandez, Marcel, Grigory Kabatiansky, and Ying Miao. "A Novel Support Recovery Algorithms and Its Applications to Multiple-Access Channels." In 2022 IEEE International Multi-Conference on Engineering, Computer and Information Sciences (SIBIRCON). IEEE, 2022. http://dx.doi.org/10.1109/sibircon56155.2022.10017094.

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Faritha Banu, J., G. Hariprasad, T. Archana, and Prahadees Srivatsan. "Novel Framework for Dengue Classification and Early Recovery using Machine Learning Algorithms." In 2024 11th International Conference on Computing for Sustainable Global Development (INDIACom). IEEE, 2024. http://dx.doi.org/10.23919/indiacom61295.2024.10499095.

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Hendraningrat, Luky, Saeed Majidaie, Nor Idah Ketchut, Fraser Skoreyko, and Seyed Mousa MousaviMirkalaei. "Advanced Reservoir Simulation: A Novel Robust Modelling of Nanoparticles for Improved Oil Recovery." In SPE Annual Technical Conference and Exhibition. SPE, 2021. http://dx.doi.org/10.2118/205927-ms.

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Abstract The potential of nanoparticles, which are classified as advanced fluid material, have been unlocked for improved oil recovery in recent years such as nanoparticles-assisted waterflood process. However, there is no existing commercial reservoir simulation software that could properly model phase behaviour and transport phenomena of nanoparticles. This paper focuses on the development of a novel robust advanced simulation algorithms for nanoparticles that incorporate all the main mechanisms that have been observed for interpreting and predicting performance. The general algorithms were

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Velasco-Lozano, Moises, and Matthew T. Balhoff. "Convolutional Neural Networks to Estimate Residual Oil Saturation Through Partitioning Inter-Well Tracer Tests." In SPE Improved Oil Recovery Conference. SPE, 2024. http://dx.doi.org/10.2118/218187-ms.

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Abstract Modeling of chemical tracers represents one of the most powerful dynamic tools for reservoir characterization and estimation of oil saturation. However, the continuous monitoring during long times in partitioning inter-well tracer tests (PITTs), which extend to months or years in some field tests, limits the use of this technology. The large distance between wells and high partitioning coefficients are some of the main reasons for the slow production of tracers, where time-consuming numerical simulations are required to analyze the tracer data in reservoir models before field applicat

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Suranthiran, Sugathevan, and Suhada Jayasuriya. "Robust Signal Recovery From Distorted Nonlinear Sensor Data." In ASME 2003 International Mechanical Engineering Congress and Exposition. ASMEDC, 2003. http://dx.doi.org/10.1115/imece2003-41487.

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In an attempt to facilitate the design and implementation of memory-less nonlinear sensors, the signal reconstruction schemes are analyzed and necessary modifications are proposed to improve the accuracy and minimize errors in sensor measurements. The problem of recovering chirp signal from the distorted nonlinear output is considered and an efficient reconstruction approach is developed. Model uncertainty is a serious issue with any model-based algorithms and a novel technique, which uses a norminal model instead of an accurate model and produces the results that are robust to model uncertain

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Chełchowski, Adam, Elżbieta Jarzebowska, and Andrzej Debowski. "The CCA Missing Data Completion Algorithm for Machine Learning Purposes." In ASME 2024 International Design Engineering Technical Conferences and Computers and Information in Engineering Conference. American Society of Mechanical Engineers, 2024. http://dx.doi.org/10.1115/detc2024-143253.

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Abstract The paper deals with multivariate time series classification, a widely used concept of supervised machine learning for automation in data analysis. Many algorithms have been proposed to deal with such classification, yet a majority of them require equal length data inputs, which often cannot be primarily satisfied. For this purpose different techniques of data adjustment are applied, such as truncation, padding or forecasting. In this paper we present a novel approach to data recovery, which yields an algorithm allowing to align variable-length time series, basing on cross-correlation

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Kumar, Rahul, Dr Suman Sharma, and Hima Saxena. "DISASTER GUARD AI: A SYNERGISTIC SOLUTION FOR INTELLIGENT DISASTER MANAGEMENT." In Computing for Sustainable Innovation: Shaping Tomorrow’s World. Innovative Research Publication, 2024. http://dx.doi.org/10.55524/csistw.2024.12.1.21.

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Disaster Guard AI is a novel and synergistic approach to intelligent disaster management, combining advanced artificial intelligence, advanced sensor networks, and real-time data analytics. This innovative solution aims to revolutionize the way we prepare for, respond to, and recover from disasters. As the frequency and intensity of natural disasters continue to rise, there is an urgent need for advanced technologies to enhance disaster response and mitigation strategies. Disaster Guard AI combines machine learning algorithms, real-time data analytics, and advanced sensor networks to provide a

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Reports on the topic "Novel recovery algorithms"

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Aroney, Sam, Rhys Newell, Gene Tyson, and Ben Woodcroft. Recovering novel genomes from the rare biosphere using Bin Chicken. Queensland University of Technology, 2024. http://dx.doi.org/10.5204/rep.eprints.253145.

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Recovery of microbial genomes from metagenomic datasets has provided genomic representation for hundreds of thousands of species from diverse biomes. However, low abundance microorganisms are often missed due to insufficient genomic coverage. Here we present Bin Chicken, an algorithm which substantially improves genome recovery through automated, targeted selection of metagenomes for coassembly based on shared marker gene sequences derived from raw reads. Marker gene sequences that are divergent from known reference genomes can be further prioritised, providing an efficient means of recovering

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