Papers Data Compression
“Data Compression” 태그가 달린 논문 459편 · 필터 해제
Quantum Autoencoder for Multivariate Time Series Anomaly Detection
Anomaly Detection (AD) defines the task of identifying observations or events that deviate from typical - or normal - patterns, a critical capability in IT security for recognizing incidents such as system misconfigurati…
Anomaly DetectionData CompressionQuantum Machine LearningTime Series+1FPGA-Based Neural Network Accelerators for Space Applications: A Survey
Space missions are becoming increasingly ambitious, necessitating high-performance onboard spacecraft computing systems. In response, field-programmable gate arrays (FPGAs) have garnered significant interest due to their…
Data CompressionGuided Wave-Based Structural Awareness Under Varying Operating States via Manifold Representations
Guided wave-based structural health monitoring (SHM) remains a powerful strategy for identifying early-stage defects and safeguarding vital aerospace structures. Yet, its practical use is often hindered by the enormous, …
Data CompressionDiagnosticState EstimationStructural Health MonitoringDeepSelective: Feature Gating and Representation Matching for Interpretable Clinical Prediction
The rapid accumulation of Electronic Health Records (EHRs) has transformed healthcare by providing valuable data that enhance clinical predictions and diagnoses. While conventional machine learning models have proven eff…
Data CompressionDecision Makingfeature selectionPrognosis+1VAE-based Feature Disentanglement for Data Augmentation and Compression in Generalized GNSS Interference Classification
Distributed learning and Edge AI necessitate efficient data processing, low-latency communication, decentralized model training, and stringent data privacy to facilitate real-time intelligence on edge devices while reduc…
Data AugmentationData CompressionDisentanglementRepresenting Flow Fields with Divergence-Free Kernels for Reconstruction
Accurately reconstructing continuous flow fields from sparse or indirect measurements remains an open challenge, as existing techniques often suffer from oversmoothing artifacts, reliance on heterogeneous architectures, …
Computational EfficiencyData CompressionSuper-ResolutionAn extrapolated and provably convergent algorithm for nonlinear matrix decomposition with the ReLU function
Nonlinear matrix decomposition (NMD) with the ReLU function, denoted ReLU-NMD, is the following problem: given a sparse, nonnegative matrix $X$ and a factorization rank $r$, identify a rank-$r$ matrix $\Theta$ such that …
Data CompressionMathMatrix CompletionNonlinear Multiple Response Regression and Learning of Latent Spaces
Identifying low-dimensional latent structures within high-dimensional data has long been a central topic in the machine learning community, driven by the need for data compression, storage, transmission, and deeper data …
Data CompressionLEMMAregressionUniPCGC: Towards Practical Point Cloud Geometry Compression via an Efficient Unified Approach
Learning-based point cloud compression methods have made significant progress in terms of performance. However, these methods still encounter challenges including high complexity, limited compression modes, and a lack of…
Data CompressionHighly Efficient Direct Analytics on Semantic-aware Time Series Data Compression
Semantic communication has emerged as a promising paradigm to tackle the challenges of massive growing data traffic and sustainable data communication. It shifts the focus from data fidelity to goal-oriented or task-orie…
Data CompressionOutlier DetectionSemantic CommunicationTime SeriesSeisRDT: Latent Diffusion Model Based On Representation Learning For Seismic Data Interpolation And Reconstruction
Due to limitations such as geographic, physical, or economic factors, collected seismic data often have missing traces. Traditional seismic data reconstruction methods face the challenge of selecting numerous empirical p…
Data CompressionRepresentation LearningEdge-Fog Computing-Enabled EEG Data Compression via Asymmetrical Variational Discrete Cosine Transform Network
The large volume of electroencephalograph (EEG) data produced by brain-computer interface (BCI) systems presents challenges for rapid transmission over bandwidth-limited channels in Internet of Things (IoT) networks. To …
Brain Computer InterfaceData CompressionEEGVariational InferenceLossy Neural Compression for Geospatial Analytics: A Review
Over the past decades, there has been an explosion in the amount of available Earth Observation (EO) data. The unprecedented coverage of the Earth's surface and atmosphere by satellite imagery has resulted in large volum…
Data CompressionEarth ObservationSelf-Supervised LearningVideo CompressionQuantum autoencoders for image classification
Classical machine learning often struggles with complex, high-dimensional data. Quantum machine learning offers a potential solution, promising more efficient processing. The quantum convolutional neural network (QCNN), …
ClassificationData Compressionimage-classificationImage Classification+1Guaranteed Conditional Diffusion: 3D Block-based Models for Scientific Data Compression
This paper proposes a new compression paradigm -- Guaranteed Conditional Diffusion with Tensor Correction (GCDTC) -- for lossy scientific data compression. The framework is based on recent conditional diffusion (CD) gene…
Data CompressionDecoderDenoisingSolving Optimal Power Flow on a Data-Budget: Feature Selection on Smart Meter Data
How much data is needed to optimally schedule distributed energy resources (DERs)? Does the distribution system operator (DSO) have to precisely know load demands and solar injections at each bus of the feeder to solve a…
Data Compressionfeature selectionSemantic Feature Division Multiple Access for Digital Semantic Broadcast Channels
In this paper, we propose a digital semantic feature division multiple access (SFDMA) paradigm in multi-user broadcast (BC) networks for the inference and the image reconstruction tasks. In this SFDMA scheme, the multi-u…
Data CompressionImage ReconstructionHybrid Quantum Neural Networks with Amplitude Encoding: Advancing Recovery Rate Predictions
Recovery rate prediction plays a pivotal role in bond investment strategies, enhancing risk assessment, optimizing portfolio allocation, improving pricing accuracy, and supporting effective credit risk management. Howeve…
Data CompressionManagementQuantum Machine LearningEfficient Bearing Sensor Data Compression via an Asymmetrical Autoencoder with a Lifting Wavelet Transform Layer
Bearing data compression is vital to manage the large volumes of data generated during condition monitoring. In this paper, a novel asymmetrical autoencoder with a lifting wavelet transform (LWT) layer is developed to co…
Data CompressionDecoderDenoisingMathematical model of parameters relevance in adaptive level-crossing sampling for electrocardiogram signals
Digital acquisition of bio-signals has been mostly dominated by uniform time sampling following the Nyquist theorem. However, in recent years, new approaches have emerged, focused on sampling a signal only when certain e…
Bayesian OptimizationData Compression