Cross-Channel Unlabeled Sensing over a Union of Signal Subspaces

• URL: http://arxiv.org/abs/2506.09773v1
• Date: Wed, 11 Jun 2025 14:10:59 GMT
• Title: Cross-Channel Unlabeled Sensing over a Union of Signal Subspaces
• Authors: Taulant Koka, Manolis C. Tsakiris, Benjamín Béjar Haro, Michael Muma,
• Abstract summary: Cross-channel unlabeled sensing addresses the problem of recovering a multi-channel signal from measurements that were shuffled across channels.<n>This work expands the cross-channel unlabeled sensing framework to signals that lie in a union of subspaces.
• Score: 14.612673151889615
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Cross-channel unlabeled sensing addresses the problem of recovering a multi-channel signal from measurements that were shuffled across channels. This work expands the cross-channel unlabeled sensing framework to signals that lie in a union of subspaces. The extension allows for handling more complex signal structures and broadens the framework to tasks like compressed sensing. These mismatches between samples and channels often arise in applications such as whole-brain calcium imaging of freely moving organisms or multi-target tracking. We improve over previous models by deriving tighter bounds on the required number of samples for unique reconstruction, while supporting more general signal types. The approach is validated through an application in whole-brain calcium imaging, where organism movements disrupt sample-to-neuron mappings. This demonstrates the utility of our framework in real-world settings with imprecise sample-channel associations, achieving accurate signal reconstruction.

Related papers

• Controllable diffusion-based generation for multi-channel biological data [66.44042377817074]
  This work proposes a unified diffusion framework for controllable generation over structured and spatial biological data.<n>We show state-of-the-art performance across both spatial and non-spatial prediction tasks, including protein imputation in IMC and gene-to-protein prediction in single-cell datasets.
  arXiv  Detail & Related papers  (2025-06-24T00:56:21Z)
• A Hybrid Transformer-Mamba Network for Single Image Deraining [70.64069487982916]
  Existing deraining Transformers employ self-attention mechanisms with fixed-range windows or along channel dimensions. We introduce a novel dual-branch hybrid Transformer-Mamba network, denoted as TransMamba, aimed at effectively capturing long-range rain-related dependencies.
  arXiv  Detail & Related papers  (2024-08-31T10:03:19Z)
• Unmixing Optical Signals from Undersampled Volumetric Measurements by Filtering the Pixel Latent Variables [5.74378659752939]
  Latent Unmixing is a new approach which applies a band-pass filter to the latent space of a multi-spectralal neural network. It enables better isolation and quantification of individual signal contributions, especially in the context of undersampled distributions. We showcase the method's practical use in experimental physics through two test cases that highlight the versatility of our approach.
  arXiv  Detail & Related papers  (2023-12-08T20:34:37Z)
• Cocktail: Mixing Multi-Modality Controls for Text-Conditional Image Generation [79.8881514424969]
  Text-conditional diffusion models are able to generate high-fidelity images with diverse contents. However, linguistic representations frequently exhibit ambiguous descriptions of the envisioned objective imagery. We propose Cocktail, a pipeline to mix various modalities into one embedding.
  arXiv  Detail & Related papers  (2023-06-01T17:55:32Z)
• Shuffled Multi-Channel Sparse Signal Recovery [16.333381000882486]
  We pose it as a signal reconstruction problem where we have lost correspondences between the samples and their respective channels. We show that the problem is equivalent to a structured unlabeled sensing problem, and establish sufficient conditions for unique recovery. We propose a robust reconstruction method that combines sparse signal recovery with robust linear regression for the two-channel case.
  arXiv  Detail & Related papers  (2022-12-14T17:46:17Z)
• Beurling-Selberg Extremization for Dual-Blind Deconvolution Recovery in Joint Radar-Communications [38.440005068230576]
  Recent interest in integrated sensing and communications has led to the design of novel signal processing techniques. We focus on a spectral coexistence scenario, wherein the channels and transmit signals of both radar and communications systems are unknown to the common receiver.
  arXiv  Detail & Related papers  (2022-11-16T22:55:12Z)
• Meta-Learning Sparse Implicit Neural Representations [69.15490627853629]
  Implicit neural representations are a promising new avenue of representing general signals. Current approach is difficult to scale for a large number of signals or a data set. We show that meta-learned sparse neural representations achieve a much smaller loss than dense meta-learned models.
  arXiv  Detail & Related papers  (2021-10-27T18:02:53Z)
• CAnet: Uplink-aided Downlink Channel Acquisition in FDD Massive MIMO using Deep Learning [51.72869237847767]
  In frequency-division duplexing systems, the downlink channel state information (CSI) acquisition scheme leads to high training and feedback overheads. We propose an uplink-aided downlink channel acquisition framework using deep learning to reduce these overheads.
  arXiv  Detail & Related papers  (2021-01-12T10:12:28Z)
• FedRec: Federated Learning of Universal Receivers over Fading Channels [92.15358738530037]
  We propose a neural network-based symbol detection technique for downlink fading channels. Multiple users collaborate to jointly learn a universal data-driven detector, hence the name FedRec. The performance of the resulting receiver is shown to approach the MAP performance in diverse channel conditions without requiring knowledge of the fading statistics.
  arXiv  Detail & Related papers  (2020-11-14T11:29:55Z)
• Dual Attention GANs for Semantic Image Synthesis [101.36015877815537]
  We propose a novel Dual Attention GAN (DAGAN) to synthesize photo-realistic and semantically-consistent images. We also propose two novel modules, i.e., position-wise Spatial Attention Module (SAM) and scale-wise Channel Attention Module (CAM) DAGAN achieves remarkably better results than state-of-the-art methods, while using fewer model parameters.
  arXiv  Detail & Related papers  (2020-08-29T17:49:01Z)
• Using Data Imputation for Signal Separation in High Contrast Imaging [6.024602799136753]
  circumstellar signals are unavoidably altered by over-fitting and/or self-subtraction. We present a forward modeling--free solution with data imputation using sequential non-negative matrix factorization (DI-sNMF) We mathematically prove it to have negligible alteration to circumstellar signals when the imputation region is relatively small.
  arXiv  Detail & Related papers  (2020-01-02T18:55:18Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.