{"about":{"non_affiliation":"Code with Papers and Syntology are not affiliated with, endorsed by, or sponsored by Papers with Code, Meta, or the pwc-archive mirror.","site":"https://codewithpapers.app","licence":"CC BY-SA 4.0","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","attribution":"https://codewithpapers.app/attribution","modified":"archive material modified by Syntology; see the attribution page","syntology":{"site":"https://syntology.ai","developers":"https://syntology.ai/developers","mcp":{"server":"https://syntology.ai/mcp","transport":"streamable-http","server_card":"https://syntology.ai/.well-known/mcp/server-card.json","auth":{"type":"trial token, no account","trial_token":"https://syntology.ai/api/oauth/trial/token","method":"POST","docs":"https://syntology.ai/developers"}},"have":"https://syntology.ai/api/graph/have?x=<method, arXiv id or title> (free, answers coverage only)","paper_base":"https://syntology.ai/paper/","atlas_base":"https://app.syntology.ai/?focus="},"machine_readable":[{"url":"https://codewithpapers.app/llms.txt","what":"the machine catalog: every machine-readable file, counted"},{"url":"https://codewithpapers.app/index/manifest.json","what":"paper-to-code index by arXiv id, with Syntology's counts"},{"url":"https://codewithpapers.app/search/manifest.json","what":"site search index (titles, authors) and its files"},{"url":"https://codewithpapers.app/download","what":"bulk files: Syntology's layer, described there"},{"url":"https://codewithpapers.app/build_manifest.json","what":"the build record: inputs, counts, exclusions, probes"}]},"url":"/task/compressive-sensing/papers/2","list_of":"/task/compressive-sensing","task":"Compressive Sensing","archive":{"snapshot":"2025-07-28"},"key_notes":{"n_ran_checked":"legacy name, kept unchanged so existing readers do not break: it counts the samples that ran with no instrument failure (honoured, violated, and ran with no contract checked); it does not mean a contract was checked, and the pages print it as 'K with no instrument failure', not 'K checked'","n_constructed":"a sub-count of the samples that ran, never subtracted from them and never a failure: an executed sample whose run returned an instance of its own class (fixture_out_type equals the entry name): the run built an object and did not compute a result (Syntology's RAN record, counts.constructed)"},"syntology_read_at":"2026-09-28T10:30:06+00:00","order":"archive","order_definition":"repositories listed in the archive (most first), then date (newest first), then slug","page":2,"pages_in_order":6,"rows_per_page":100,"rows":[101,200],"of":597,"counts":{"archive_papers_tagged":597,"with_a_code_link":123,"where_syntology_ran_a_sample":17,"not_listed_spam_title":0,"listed":597,"listed_where_code_ran":17,"where_syntology_ran_a_sample_split":{"with_a_run_with_no_instrument_failure":14,"every_run_a_failure_of_syntologys_instrument":3,"listed_with_a_run_with_no_instrument_failure":14,"listed_every_run_a_failure_of_syntologys_instrument":3,"filter":{"states":["a run with no instrument failure","any run, instrument failures included"],"default":"a run with no instrument failure","note":"on the 'only where code ran' pages the default hides, in the browser, the rows where every run was a failure of Syntology's instrument; the second state shows them again. Rows are hidden, never re-ordered; these twins list every row"}},"definition":"distinct papers the archive tags; 'where Syntology ran a sample' counts papers with at least one harvested sample that ran, which is not a correctness claim"},"first_page":"/task/compressive-sensing","prev":"/task/compressive-sensing","next":"/task/compressive-sensing/papers/3","papers":[{"url":"/paper/nonconvex-regularization-based-sparse-and-low","slug":"nonconvex-regularization-based-sparse-and-low","title":"A Survey on Nonconvex Regularization Based Sparse and Low-Rank Recovery in Signal Processing, Statistics, and Machine Learning","date":"2018-08-16","arxiv_id":"1808.05403","repositories_listed":1,"syntology":null},{"url":"/paper/lapran-a-scalable-laplacian-pyramid","slug":"lapran-a-scalable-laplacian-pyramid","title":"LAPRAN: A Scalable Laplacian Pyramid Reconstructive Adversarial Network for Flexible Compressive Sensing Reconstruction","date":"2018-07-24","arxiv_id":"1807.09388","repositories_listed":1,"syntology":null},{"url":"/paper/an-efficient-deep-convolutional-laplacian","slug":"an-efficient-deep-convolutional-laplacian","title":"An efficient deep convolutional laplacian pyramid architecture for CS reconstruction at low sampling ratios","date":"2018-04-13","arxiv_id":"1804.04970","repositories_listed":1,"syntology":null},{"url":"/paper/solving-linear-inverse-problems-using-gan","slug":"solving-linear-inverse-problems-using-gan","title":"Solving Linear Inverse Problems Using GAN Priors: An Algorithm with Provable Guarantees","date":"2018-02-23","arxiv_id":"1802.08406","repositories_listed":1,"syntology":{"n":6,"n_ran":1,"n_constructed":0,"n_ran_checked":1,"n_instrument":0,"n_unverified":5,"n_honours":0,"n_violates":0,"n_no_contract":1,"n_pointer_only":0,"phrase":"1 ran (of which 0 constructed an object rather than computing a result; 1 with no instrument failure: 0 honoured, 0 violated, 1 with no contract checked; 0 where Syntology's instrument failed) · 5 unverified","sample_list":"/paper/solving-linear-inverse-problems-using-gan#ran","syntology_url":"https://syntology.ai/paper/1802.08406","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"1802.08406"}},"official":null}},{"url":"/paper/full-image-recover-for-block-based","slug":"full-image-recover-for-block-based","title":"Full Image Recover for Block-Based Compressive Sensing","date":"2018-02-01","arxiv_id":"1802.00179","repositories_listed":1,"syntology":null},{"url":"/paper/perceptual-compressive-sensing","slug":"perceptual-compressive-sensing","title":"Perceptual Compressive Sensing","date":"2018-02-01","arxiv_id":"1802.00176","repositories_listed":1,"syntology":null},{"url":"/paper/nearly-optimal-robust-subspace-tracking","slug":"nearly-optimal-robust-subspace-tracking","title":"Nearly Optimal Robust Subspace Tracking","date":"2017-12-17","arxiv_id":"1712.06061","repositories_listed":1,"syntology":null},{"url":"/paper/fully-convolutional-measurement-network-for","slug":"fully-convolutional-measurement-network-for","title":"Fully Convolutional Measurement Network for Compressive Sensing Image Reconstruction","date":"2017-11-21","arxiv_id":"1712.01641","repositories_listed":1,"syntology":null},{"url":"/paper/one-network-to-solve-them-all-solving-linear-1","slug":"one-network-to-solve-them-all-solving-linear-1","title":"One Network to Solve Them All -- Solving Linear Inverse Problems Using Deep Projection Models","date":"2017-10-01","arxiv_id":null,"repositories_listed":1,"syntology":null},{"url":"/paper/adaptive-measurement-network-for-cs-image","slug":"adaptive-measurement-network-for-cs-image","title":"Adaptive Measurement Network for CS Image Reconstruction","date":"2017-09-23","arxiv_id":"1710.01244","repositories_listed":1,"syntology":{"n":1,"n_ran":1,"n_constructed":0,"n_ran_checked":1,"n_instrument":0,"n_unverified":0,"n_honours":0,"n_violates":0,"n_no_contract":1,"n_pointer_only":0,"phrase":"1 ran (of which 0 constructed an object rather than computing a result; 1 with no instrument failure: 0 honoured, 0 violated, 1 with no contract checked; 0 where Syntology's instrument failed) · 0 unverified","sample_list":"/paper/adaptive-measurement-network-for-cs-image#ran","syntology_url":"https://syntology.ai/paper/1710.01244","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"1710.01244"}},"official":null}},{"url":"/paper/ista-net-interpretable-optimization-inspired","slug":"ista-net-interpretable-optimization-inspired","title":"ISTA-Net: Interpretable Optimization-Inspired Deep Network for Image Compressive Sensing","date":"2017-06-24","arxiv_id":"1706.07929","repositories_listed":1,"syntology":null},{"url":"/paper/sparse-depth-sensing-for-resource-constrained","slug":"sparse-depth-sensing-for-resource-constrained","title":"Sparse Depth Sensing for Resource-Constrained Robots","date":"2017-03-04","arxiv_id":"1703.01398","repositories_listed":1,"syntology":null},{"url":"/paper/dr2-net-deep-residual-reconstruction-network","slug":"dr2-net-deep-residual-reconstruction-network","title":"DR2-Net: Deep Residual Reconstruction Network for Image Compressive Sensing","date":"2017-02-19","arxiv_id":"1702.05743","repositories_listed":1,"syntology":null},{"url":"/paper/learning-to-invert-signal-recovery-via-deep","slug":"learning-to-invert-signal-recovery-via-deep","title":"Learning to Invert: Signal Recovery via Deep Convolutional Networks","date":"2017-01-14","arxiv_id":"1701.03891","repositories_listed":1,"syntology":null},{"url":"/paper/online-learning-sensing-matrix-and","slug":"online-learning-sensing-matrix-and","title":"Online Learning Sensing Matrix and Sparsifying Dictionary Simultaneously for Compressive Sensing","date":"2017-01-04","arxiv_id":"1701.01000","repositories_listed":1,"syntology":null},{"url":"/paper/interpretable-recurrent-neural-networks-using","slug":"interpretable-recurrent-neural-networks-using","title":"Interpretable Recurrent Neural Networks Using Sequential Sparse Recovery","date":"2016-11-22","arxiv_id":"1611.07252","repositories_listed":1,"syntology":null},{"url":"/paper/an-efficient-method-for-robust-projection","slug":"an-efficient-method-for-robust-projection","title":"An Efficient Method for Robust Projection Matrix Design","date":"2016-09-27","arxiv_id":"1609.08281","repositories_listed":1,"syntology":null},{"url":"/paper/deepbinarymask-learning-a-binary-mask-for","slug":"deepbinarymask-learning-a-binary-mask-for","title":"DeepBinaryMask: Learning a Binary Mask for Video Compressive Sensing","date":"2016-07-12","arxiv_id":"1607.03343","repositories_listed":1,"syntology":{"n":4,"n_ran":3,"n_constructed":0,"n_ran_checked":3,"n_instrument":0,"n_unverified":1,"n_honours":0,"n_violates":0,"n_no_contract":3,"n_pointer_only":0,"phrase":"3 ran (of which 0 constructed an object rather than computing a result; 3 with no instrument failure: 0 honoured, 0 violated, 3 with no contract checked; 0 where Syntology's instrument failed) · 1 unverified","sample_list":"/paper/deepbinarymask-learning-a-binary-mask-for#ran","syntology_url":"https://syntology.ai/paper/1607.03343","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"1607.03343"}},"official":{"repos":["miliadis/DeepVideoCS"],"state":"official (archive's flag): 3 ran","n_ran":3,"n_constructed":0,"n_ran_no_instrument_failure":3,"n_unverified":1,"ran_from_kinds":["official"]}}},{"url":"/paper/deep-fully-connected-networks-for-video","slug":"deep-fully-connected-networks-for-video","title":"Deep Fully-Connected Networks for Video Compressive Sensing","date":"2016-03-16","arxiv_id":"1603.04930","repositories_listed":1,"syntology":null},{"url":"/paper/bayesian-sparse-tucker-models-for-dimension","slug":"bayesian-sparse-tucker-models-for-dimension","title":"Bayesian Sparse Tucker Models for Dimension Reduction and Tensor Completion","date":"2015-05-10","arxiv_id":"1505.02343","repositories_listed":1,"syntology":null},{"url":"/paper/group-based-sparse-representation-for-image","slug":"group-based-sparse-representation-for-image","title":"Group-based Sparse Representation for Image Restoration","date":"2014-05-14","arxiv_id":"1405.3351","repositories_listed":1,"syntology":null},{"url":"/paper/the-stone-transform-multi-resolution-image","slug":"the-stone-transform-multi-resolution-image","title":"The STONE Transform: Multi-Resolution Image Enhancement and Real-Time Compressive Video","date":"2013-11-14","arxiv_id":"1311.3405","repositories_listed":1,"syntology":null},{"url":"/paper/fast-l1-minimization-algorithms-for-robust-1","slug":"fast-l1-minimization-algorithms-for-robust-1","title":"Fast L1-Minimization Algorithms For Robust Face Recognition","date":"2010-07-21","arxiv_id":"1007.3753","repositories_listed":1,"syntology":null},{"url":null,"slug":"disentangling-coincident-cell-events-using","title":"Disentangling coincident cell events using deep transfer learning and compressive sensing","date":"2025-07-17","arxiv_id":"2507.13176","repositories_listed":0,"syntology":null},{"url":null,"slug":"zero-shot-solving-of-imaging-inverse-problems","title":"Zero-Shot Solving of Imaging Inverse Problems via Noise-Refined Likelihood Guided Diffusion Models","date":"2025-06-16","arxiv_id":"2506.13391","repositories_listed":0,"syntology":null},{"url":null,"slug":"radiodun-a-physics-inspired-deep-unfolding","title":"RadioDUN: A Physics-Inspired Deep Unfolding Network for Radio Map Estimation","date":"2025-06-10","arxiv_id":"2506.08418","repositories_listed":0,"syntology":null},{"url":null,"slug":"low-complexity-super-resolution-signature","title":"Low-Complexity Super-Resolution Signature Estimation of XL-MIMO FMCW Radar","date":"2025-06-09","arxiv_id":"2506.07979","repositories_listed":0,"syntology":null},{"url":null,"slug":"training-beam-design-for-channel-estimation","title":"Training Beam Design for Channel Estimation in Hybrid mmWave MIMO Systems","date":"2025-06-01","arxiv_id":"2506.00913","repositories_listed":0,"syntology":null},{"url":null,"slug":"altgdmin-alternating-gd-and-minimization-for","title":"AltGDmin: Alternating GD and Minimization for Partly-Decoupled (Federated) Optimization","date":"2025-04-20","arxiv_id":"2504.14741","repositories_listed":0,"syntology":null},{"url":null,"slug":"a-novel-radar-constant-false-alarm-rate","title":"A Novel Radar Constant False Alarm Rate Detection Algorithm Based on VAMP Deep Unfolding","date":"2025-04-14","arxiv_id":"2504.09907","repositories_listed":0,"syntology":null},{"url":null,"slug":"secure-directional-modulation-with-movable","title":"Secure Directional Modulation with Movable Antenna Array Aided by RIS","date":"2025-04-10","arxiv_id":"2504.07417","repositories_listed":0,"syntology":null},{"url":null,"slug":"spectrum-from-defocus-fast-spectral-imaging","title":"Spectrum from Defocus: Fast Spectral Imaging with Chromatic Focal Stack","date":"2025-03-26","arxiv_id":"2503.20184","repositories_listed":0,"syntology":null},{"url":null,"slug":"group-sparsity-methods-for-compressive-space","title":"Group Sparsity Methods for Compressive Space-Frequency Channel Estimation and Spatial Equalization in Fluid Antenna System","date":"2025-03-03","arxiv_id":"2503.02004","repositories_listed":0,"syntology":null},{"url":null,"slug":"compressive-sensing-empirical-wavelet","title":"Compressive Sensing Empirical Wavelet Transform for Frequency-Banded Power Measurement Considering Interharmonics","date":"2025-02-14","arxiv_id":"2502.09847","repositories_listed":0,"syntology":null},{"url":null,"slug":"channel-estimation-for-ris-aided-mu-mimo","title":"Channel Estimation for RIS-Aided MU-MIMO mmWave Systems with Practical Hybrid Architecture","date":"2025-02-08","arxiv_id":"2502.05559","repositories_listed":0,"syntology":null},{"url":null,"slug":"multi-target-range-doppler-and-angle","title":"Multi-target Range, Doppler and Angle estimation in MIMO-FMCW Radar with Limited Measurements","date":"2025-02-03","arxiv_id":"2502.01147","repositories_listed":0,"syntology":null},{"url":null,"slug":"exploiting-dynamic-sparsity-for-near-field","title":"Exploiting Dynamic Sparsity for Near-Field Spatial Non-Stationary XL-MIMO Channel Tracking","date":"2024-12-27","arxiv_id":"2412.19475","repositories_listed":0,"syntology":null},{"url":null,"slug":"analysis-and-synthesis-denoisers-for-forward","title":"Analysis and Synthesis Denoisers for Forward-Backward Plug-and-Play Algorithms","date":"2024-11-20","arxiv_id":"2411.13276","repositories_listed":0,"syntology":null},{"url":null,"slug":"palms-parallel-adaptive-lasso-with-multi","title":"PALMS: Parallel Adaptive Lasso with Multi-directional Signals for Latent Networks Reconstruction","date":"2024-11-18","arxiv_id":"2411.11464","repositories_listed":0,"syntology":null},{"url":null,"slug":"block-based-adaptive-compressive-sensing-with","title":"Block based Adaptive Compressive Sensing with Sampling Rate Control","date":"2024-11-15","arxiv_id":"2411.10200","repositories_listed":0,"syntology":null},{"url":null,"slug":"user-activity-detection-with-delay","title":"User Activity Detection with Delay-Calibration for Asynchronous Massive Random Access","date":"2024-11-04","arxiv_id":"2411.01923","repositories_listed":0,"syntology":null},{"url":null,"slug":"prior-information-aided-admm-for-multi-user","title":"Prior Information-Aided ADMM for Multi-User Detection in Codebook-Based Grant-Free NOMA: Dynamic Scenarios","date":"2024-10-18","arxiv_id":"2410.14224","repositories_listed":0,"syntology":null},{"url":null,"slug":"compressive-radio-interferometric-sensing","title":"Compressive radio-interferometric sensing with random beamforming as rank-one signal covariance projections","date":"2024-09-23","arxiv_id":"2409.15031","repositories_listed":0,"syntology":null},{"url":null,"slug":"on-the-restricted-isometry-property-of-1","title":"A Hierarchical View of Structured Sparsity in Kronecker Compressive Sensing","date":"2024-09-13","arxiv_id":"2409.08699","repositories_listed":0,"syntology":null},{"url":null,"slug":"a-lightweight-human-pose-estimation-approach","title":"A Lightweight Human Pose Estimation Approach for Edge Computing-Enabled Metaverse with Compressive Sensing","date":"2024-08-26","arxiv_id":"2409.00087","repositories_listed":0,"syntology":null},{"url":null,"slug":"subspace-constrained-variational-bayesian","title":"Subspace Constrained Variational Bayesian Inference for Structured Compressive Sensing with a Dynamic Grid","date":"2024-07-24","arxiv_id":"2407.16947","repositories_listed":0,"syntology":null},{"url":null,"slug":"high-dimensional-confidence-regions-in-sparse","title":"High-Dimensional Confidence Regions in Sparse MRI","date":"2024-07-18","arxiv_id":"2407.18964","repositories_listed":0,"syntology":null},{"url":"/paper/congo-compressive-online-gradient","slug":"congo-compressive-online-gradient","title":"CONGO: Compressive Online Gradient Optimization","date":"2024-07-08","arxiv_id":"2407.06325","repositories_listed":0,"syntology":{"n":3,"n_ran":1,"n_constructed":0,"n_ran_checked":1,"n_instrument":0,"n_unverified":2,"n_honours":0,"n_violates":0,"n_no_contract":1,"n_pointer_only":0,"phrase":"1 ran (of which 0 constructed an object rather than computing a result; 1 with no instrument failure: 0 honoured, 0 violated, 1 with no contract checked; 0 where Syntology's instrument failed) · 2 unverified","sample_list":"/paper/congo-compressive-online-gradient#ran","syntology_url":"https://syntology.ai/paper/2407.06325","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2407.06325"}},"official":null}},{"url":null,"slug":"extremely-large-scale-dynamic-metasurface","title":"Extremely Large-Scale Dynamic Metasurface Antennas (XL-DMAs): Near-Field Modeling and Channel Estimation","date":"2024-07-06","arxiv_id":"2407.04954","repositories_listed":0,"syntology":null},{"url":null,"slug":"ai-based-beam-level-and-cell-level-mobility","title":"AI-Driven Mobility Management for High-Speed Railway Communications: Compressed Measurements and Proactive Handover","date":"2024-07-05","arxiv_id":"2407.04336","repositories_listed":0,"syntology":null},{"url":null,"slug":"low-complexity-csi-feedback-for-fdd-massive","title":"Low-Complexity CSI Feedback for FDD Massive MIMO Systems via Learning to Optimize","date":"2024-06-24","arxiv_id":"2406.16323","repositories_listed":0,"syntology":null},{"url":null,"slug":"linear-inverse-problems-using-a-generative","title":"Linear Inverse Problems Using a Generative Compound Gaussian Prior","date":"2024-06-16","arxiv_id":"2406.10767","repositories_listed":0,"syntology":null},{"url":null,"slug":"iterative-sparse-identification-of-nonlinear","title":"Iterative Sparse Identification of Nonlinear Dynamics","date":"2024-06-06","arxiv_id":"2406.03779","repositories_listed":0,"syntology":null},{"url":null,"slug":"sparse-multi-baseline-sar-cross-modal-3d","title":"CMAR-Net: Accurate Cross-Modal 3D SAR Reconstruction of Vehicle Targets with Sparse-Aspect Multi-Baseline Data","date":"2024-06-06","arxiv_id":"2406.04158","repositories_listed":0,"syntology":null},{"url":null,"slug":"electromagnetic-property-sensing-in-isac-with","title":"Electromagnetic Property Sensing in ISAC with Multiple Base Stations: Algorithm, Pilot Design, and Performance Analysis","date":"2024-05-10","arxiv_id":"2405.06364","repositories_listed":0,"syntology":null},{"url":null,"slug":"imaging-signal-recovery-using-neural-network","title":"Imaging Signal Recovery Using Neural Network Priors Under Uncertain Forward Model Parameters","date":"2024-05-05","arxiv_id":"2405.02944","repositories_listed":0,"syntology":null},{"url":null,"slug":"compressive-sensing-imaging-using-caustic","title":"Compressive Sensing Imaging Using Caustic Lens Mask Generated by Periodic Perturbation in a Ripple Tank","date":"2024-05-01","arxiv_id":"2405.00407","repositories_listed":0,"syntology":null},{"url":null,"slug":"on-generalization-bounds-for-deep-compound","title":"On Generalization Bounds for Deep Compound Gaussian Neural Networks","date":"2024-02-20","arxiv_id":"2402.13106","repositories_listed":0,"syntology":null},{"url":null,"slug":"a-comparative-study-of-compressive-sensing","title":"A Comparative Study of Compressive Sensing Algorithms for Hyperspectral Imaging Reconstruction","date":"2024-01-26","arxiv_id":"2401.14762","repositories_listed":0,"syntology":null},{"url":null,"slug":"study-of-the-gomp-algorithm-for-recovery-of","title":"Study of the gOMP Algorithm for Recovery of Compressed Sensed Hyperspectral Images","date":"2024-01-26","arxiv_id":"2401.14786","repositories_listed":0,"syntology":null},{"url":null,"slug":"snapcap-efficient-snapshot-compressive-video","title":"SnapCap: Efficient Snapshot Compressive Video Captioning","date":"2024-01-10","arxiv_id":"2401.04903","repositories_listed":0,"syntology":null},{"url":null,"slug":"msdc-deq-net-deep-equilibrium-model-deq-with","title":"MsDC-DEQ-Net: Deep Equilibrium Model (DEQ) with Multi-scale Dilated Convolution for Image Compressive Sensing (CS)","date":"2024-01-05","arxiv_id":"2401.02884","repositories_listed":0,"syntology":null},{"url":null,"slug":"reconstruction-free-cascaded-adaptive","title":"Reconstruction-free Cascaded Adaptive Compressive Sensing","date":"2024-01-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"ufc-net-unrolling-fixed-point-continuous","title":"UFC-Net: Unrolling Fixed-point Continuous Network for Deep Compressive Sensing","date":"2024-01-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"electromagnetic-property-sensing-a-new","title":"Electromagnetic Property Sensing: A New Paradigm of Integrated Sensing and Communication","date":"2023-12-27","arxiv_id":"2312.16428","repositories_listed":0,"syntology":null},{"url":null,"slug":"a-fast-algorithm-for-low-rank-sparse-column","title":"A Fast Algorithm for Low Rank + Sparse column-wise Compressive Sensing","date":"2023-11-07","arxiv_id":"2311.03824","repositories_listed":0,"syntology":null},{"url":null,"slug":"pipo-net-a-penalty-based-independent","title":"PIPO-Net: A Penalty-based Independent Parameters Optimization Deep Unfolding Network","date":"2023-11-04","arxiv_id":"2311.02443","repositories_listed":0,"syntology":null},{"url":null,"slug":"experimental-results-of-underwater-sound","title":"Experimental Results of Underwater Sound Speed Profile Inversion by Few-shot Multi-task Learning","date":"2023-10-18","arxiv_id":"2310.11708","repositories_listed":0,"syntology":null},{"url":null,"slug":"underwater-sound-speed-profile-construction-a","title":"Underwater Sound Speed Profile Construction: A Review","date":"2023-10-12","arxiv_id":"2310.08251","repositories_listed":0,"syntology":null},{"url":null,"slug":"compression-ratio-learning-and-semantic","title":"Compression Ratio Learning and Semantic Communications for Video Imaging","date":"2023-10-10","arxiv_id":"2310.06246","repositories_listed":0,"syntology":null},{"url":null,"slug":"sparsity-based-channel-estimation-exploiting","title":"Sparsity-Based Channel Estimation Exploiting Deep Unrolling for Downlink Massive MIMO","date":"2023-09-24","arxiv_id":"2309.13545","repositories_listed":0,"syntology":null},{"url":null,"slug":"finite-compressive-sensing","title":"Fractal Compressive Sensing","date":"2023-09-15","arxiv_id":"2309.08641","repositories_listed":0,"syntology":null},{"url":null,"slug":"interpretable-and-efficient-beamforming-based","title":"Interpretable and Efficient Beamforming-Based Deep Learning for Single Snapshot DOA Estimation","date":"2023-09-14","arxiv_id":"2309.07411","repositories_listed":0,"syntology":null},{"url":null,"slug":"optimization-guarantees-of-unfolded-ista-and","title":"Optimization Guarantees of Unfolded ISTA and ADMM Networks With Smooth Soft-Thresholding","date":"2023-09-12","arxiv_id":"2309.06195","repositories_listed":0,"syntology":null},{"url":null,"slug":"multi-uav-enabled-distributed-sensing","title":"Multi UAV-enabled Distributed Sensing: Cooperation Orchestration and Detection Protocol","date":"2023-09-10","arxiv_id":"2309.05114","repositories_listed":0,"syntology":null},{"url":null,"slug":"communication-efficient-decentralized-2","title":"Communication-Efficient Decentralized Federated Learning via One-Bit Compressive Sensing","date":"2023-08-31","arxiv_id":"2308.16671","repositories_listed":0,"syntology":null},{"url":null,"slug":"sparse-models-for-machine-learning","title":"Sparse Models for Machine Learning","date":"2023-08-26","arxiv_id":"2308.13960","repositories_listed":0,"syntology":null},{"url":null,"slug":"in-sector-compressive-beam-alignment-for","title":"In-sector Compressive Beam Alignment for MmWave and THz Radios","date":"2023-08-25","arxiv_id":"2308.13268","repositories_listed":0,"syntology":null},{"url":null,"slug":"a-compressive-sensing-based-method-for","title":"A Compressive Sensing Based Method for Harmonic State Estimation","date":"2023-08-11","arxiv_id":"2308.06398","repositories_listed":0,"syntology":null},{"url":null,"slug":"coded-aperture-radar-imaging-using","title":"Coded Aperture Radar Imaging Using Reconfigurable Intelligent Surfaces","date":"2023-08-11","arxiv_id":"2308.05949","repositories_listed":0,"syntology":null},{"url":null,"slug":"learning-a-common-dictionary-for-csi-feedback","title":"Learning a Common Dictionary for CSI Feedback in FDD Massive MU-MIMO-OFDM Systems","date":"2023-07-29","arxiv_id":"2307.15943","repositories_listed":0,"syntology":null},{"url":null,"slug":"signal-processing-after-quadratic-random","title":"Signal processing after quadratic random sketching with optical units","date":"2023-07-27","arxiv_id":"2307.14672","repositories_listed":0,"syntology":null},{"url":null,"slug":"sampling-priors-augmented-deep-unfolding","title":"Sampling-Priors-Augmented Deep Unfolding Network for Robust Video Compressive Sensing","date":"2023-07-14","arxiv_id":"2307.07291","repositories_listed":0,"syntology":null},{"url":null,"slug":"multipath-time-delay-estimation-with","title":"Multipath Time-delay Estimation with Impulsive Noise via Bayesian Compressive Sensing","date":"2023-07-05","arxiv_id":"2307.02113","repositories_listed":0,"syntology":null},{"url":null,"slug":"mosaic-masked-optimisation-with-selective","title":"MOSAIC: Masked Optimisation with Selective Attention for Image Reconstruction","date":"2023-06-01","arxiv_id":"2306.00906","repositories_listed":0,"syntology":null},{"url":null,"slug":"joint-channel-estimation-and-turbo","title":"Joint Channel Estimation and Turbo Equalization of Single-Carrier Systems over Time-Varying Channels","date":"2023-05-16","arxiv_id":"2305.09226","repositories_listed":0,"syntology":null},{"url":null,"slug":"nl-cs-net-deep-learning-with-non-local-prior","title":"NL-CS Net: Deep Learning with Non-Local Prior for Image Compressive Sensing","date":"2023-05-06","arxiv_id":"2305.03899","repositories_listed":0,"syntology":null},{"url":null,"slug":"dynamic-compressive-sensing-based-on-rls-for","title":"Dynamic Compressive Sensing based on RLS for Underwater Acoustic Communications","date":"2023-04-24","arxiv_id":"2304.11838","repositories_listed":0,"syntology":null},{"url":null,"slug":"hierarchical-interactive-reconstruction","title":"Hierarchical Interactive Reconstruction Network For Video Compressive Sensing","date":"2023-04-15","arxiv_id":"2304.07473","repositories_listed":0,"syntology":null},{"url":null,"slug":"accelerated-parallel-mri-using-memory","title":"Accelerated parallel MRI using memory efficient and robust monotone operator learning (MOL)","date":"2023-04-03","arxiv_id":"2304.01351","repositories_listed":0,"syntology":null},{"url":null,"slug":"compressive-sensing-with-tensorized","title":"Compressive Sensing with Tensorized Autoencoder","date":"2023-03-10","arxiv_id":"2303.06235","repositories_listed":0,"syntology":null},{"url":null,"slug":"multi-target-range-and-angle-detection-for","title":"Multi-target Range and Angle detection for MIMO-FMCW radar with limited antennas","date":"2023-02-28","arxiv_id":"2302.14327","repositories_listed":0,"syntology":null},{"url":null,"slug":"star-ris-enabled-simultaneous-indoor-and","title":"STAR-RIS-Enabled Simultaneous Indoor and Outdoor 3D Localization: Theoretical Analysis and Algorithmic Design","date":"2023-02-07","arxiv_id":"2302.03342","repositories_listed":0,"syntology":null},{"url":null,"slug":"compressed-domain-vibration-detection-and","title":"Compressed domain vibration detection and classification for distributed acoustic sensing","date":"2022-12-27","arxiv_id":"2212.14735","repositories_listed":0,"syntology":null},{"url":null,"slug":"lr-csnet-low-rank-deep-unfolding-network-for","title":"LR-CSNet: Low-Rank Deep Unfolding Network for Image Compressive Sensing","date":"2022-12-18","arxiv_id":"2212.09088","repositories_listed":0,"syntology":null},{"url":null,"slug":"unrolling-svt-to-obtain-computationally","title":"Unrolling SVT to obtain computationally efficient SVT for n-qubit quantum state tomography","date":"2022-12-17","arxiv_id":"2212.08852","repositories_listed":0,"syntology":null},{"url":null,"slug":"proximal-gradient-based-unfolding-for-massive","title":"Proximal Gradient-Based Unfolding for Massive Random Access in IoT Networks","date":"2022-12-04","arxiv_id":"2212.01839","repositories_listed":0,"syntology":null},{"url":null,"slug":"signal-processing-with-optical-quadratic","title":"Signal processing with optical quadratic random sketches","date":"2022-12-01","arxiv_id":"2212.00660","repositories_listed":0,"syntology":null},{"url":null,"slug":"a-model-data-driven-network-embedding","title":"A Model-data-driven Network Embedding Multidimensional Features for Tomographic SAR Imaging","date":"2022-11-28","arxiv_id":"2211.15002","repositories_listed":0,"syntology":null},{"url":null,"slug":"compressive-spectrum-sensing-using-blind","title":"Compressive Spectrum Sensing Using Blind-Block Orthogonal Least Squares","date":"2022-11-14","arxiv_id":"2211.07182","repositories_listed":0,"syntology":null}],"record_sha256":"0b2e012d10cb90c3babffe8f699b58ba7f68f68b00dc160c41d3775c337102fb","record_changed_at":"2026-09-28","record_changed_at_basis":"first_hashed"}