Data-Induced Interactions of Sparse Sensors Using Statistical Physics

• URL: http://arxiv.org/abs/2307.11838v2
• Date: Sun, 14 Sep 2025 22:12:29 GMT
• Title: Data-Induced Interactions of Sparse Sensors Using Statistical Physics
• Authors: Andrei A. Klishin, J. Nathan Kutz, Krithika Manohar,
• Abstract summary: We take a statistical mechanics view to compute the full landscape of sensor interactions induced by the training data.<n>The two key advances of this paper are the recasting of the sensor placement landscape in an Ising model form and a regularized reconstruction that significantly decreases reconstruction error for few sensors.
• Score: 2.2029818765681077
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Large-dimensional empirical data in science and engineering frequently have a low-rank structure and can be represented as a combination of just a few eigenmodes. Because of this structure, we can use just a few spatially localized sensor measurements to reconstruct the full state of a complex system. The quality of this reconstruction, especially in the presence of sensor noise, depends significantly on the spatial configuration of the sensors. Multiple algorithms based on gappy interpolation and QR factorization have been proposed to optimize sensor placement. Here, instead of an algorithm that outputs a single "optimal" sensor configuration, we take a statistical mechanics view to compute the full landscape of sensor interactions induced by the training data. The two key advances of this paper are the recasting of the sensor placement landscape in an Ising model form and a regularized reconstruction that significantly decreases reconstruction error for few sensors. In addition, we provide first uncertainty quantification of the sparse sensing reconstruction and open questions about the shape of reconstruction risk curve. Mapping out these data-induced sensor interactions allows combining them with external selection criteria and anticipating sensor replacement impacts.

Related papers

• SMARTIES: Spectrum-Aware Multi-Sensor Auto-Encoder for Remote Sensing Images [17.304106221330414]
  Recent deep learning models are often specific to single sensors or to fixed combinations.<n>We introduce SMARTIES, a generic and versatile foundation model lifting sensor-specific/dependent efforts.<n>On both single and multi-modal tasks across diverse sensors, SMARTIES outperforms previous models that rely on sensor-specific pretraining.
  arXiv  Detail & Related papers  (2025-06-24T12:51:39Z)
• SensorLM: Learning the Language of Wearable Sensors [50.95988682423808]
  We present SensorLM, a family of sensor-language foundation models that enable wearable sensor data understanding with natural language.<n>We introduce a hierarchical caption generation pipeline designed to capture statistical, structural, and semantic information from sensor data.<n>This approach enabled the curation of the largest sensor-language dataset to date, comprising over 59.7 million hours of data from more than 103,000 people.
  arXiv  Detail & Related papers  (2025-06-10T17:13:09Z)
• PhySense: Sensor Placement Optimization for Accurate Physics Sensing [53.58729034895666]
  PhySense is a framework that learns to jointly reconstruct physical fields and to optimize sensor placements.<n>It achieves state-of-the-art physics sensing accuracy and discovers informative sensor placements previously unconsidered.
  arXiv  Detail & Related papers  (2025-05-19T14:59:11Z)
• Integrating Inverse and Forward Modeling for Sparse Temporal Data from Sensor Networks [2.4280350854512673]
  We present CavePerception, a framework for the analysis of sparse data from sensor networks. We aim to improve the interpretability of sparse, noisy, and potentially incomplete sensor data.
  arXiv  Detail & Related papers  (2025-02-19T11:24:51Z)
• MSSIDD: A Benchmark for Multi-Sensor Denoising [55.41612200877861]
  We introduce a new benchmark, the Multi-Sensor SIDD dataset, which is the first raw-domain dataset designed to evaluate the sensor transferability of denoising models. We propose a sensor consistency training framework that enables denoising models to learn the sensor-invariant features.
  arXiv  Detail & Related papers  (2024-11-18T13:32:59Z)
• A physics-driven sensor placement optimization methodology for temperature field reconstruction [9.976807723785006]
  We propose a novel physics-driven sensor placement optimization (PSPO) method for temperature field reconstruction. The PSPO method significantly outperforms random and uniform selection methods, improving the reconstruction accuracy by nearly an order of magnitude.
  arXiv  Detail & Related papers  (2024-09-27T03:26:38Z)
• SenPa-MAE: Sensor Parameter Aware Masked Autoencoder for Multi-Satellite Self-Supervised Pretraining [1.4528189330418977]
  SenPa-MAE encodes the sensor parameters of an observed multispectral signal into the image embeddings. SenPa-MAE can be pre-trained on imagery of different satellites with non-matching spectral or geometrical sensor characteristics.
  arXiv  Detail & Related papers  (2024-08-20T16:53:30Z)
• Bridging Remote Sensors with Multisensor Geospatial Foundation Models [15.289711240431107]
  msGFM is a multisensor geospatial foundation model that unifies data from four key sensor modalities. For data originating from identical geolocations, our model employs an innovative cross-sensor pretraining approach. msGFM has demonstrated enhanced proficiency in a range of both single-sensor and multisensor downstream tasks.
  arXiv  Detail & Related papers  (2024-04-01T17:30:56Z)
• Leveraging arbitrary mobile sensor trajectories with shallow recurrent decoder networks for full-state reconstruction [4.243926243206826]
  We show that a sequence-to-vector model, such as an LSTM (long, short-term memory) network, with a decoder network, dynamic information can be mapped to full state-space estimates. The exceptional performance of the network architecture is demonstrated on three applications.
  arXiv  Detail & Related papers  (2023-07-20T21:42:01Z)
• Constrained optimization of sensor placement for nuclear digital twins [1.7247618645684337]
  We develop a data-driven technique that incorporates constraints into an optimization framework for sensor placement. We demonstrate the efficacy of sensors optimized by exhaustively computing all feasible configurations for a low-dimensional dynamical system.
  arXiv  Detail & Related papers  (2023-06-23T17:47:06Z)
• Machine Learning Based Compensation for Inconsistencies in Knitted Force Sensors [1.0742675209112622]
  Knitted sensors frequently suffer from inconsistencies due to innate effects such as offset, relaxation, and drift. In this paper, we demonstrate a method for counteracting this by applying processing using a minimal artificial neural network (ANN) By training a three-layer ANN with a total of 8 neurons, we manage to significantly improve the mapping between sensor reading and actuation force.
  arXiv  Detail & Related papers  (2023-06-21T09:19:33Z)
• Environmental Sensor Placement with Convolutional Gaussian Neural Processes [65.13973319334625]
  It is challenging to place sensors in a way that maximises the informativeness of their measurements, particularly in remote regions like Antarctica. Probabilistic machine learning models can suggest informative sensor placements by finding sites that maximally reduce prediction uncertainty. This paper proposes using a convolutional Gaussian neural process (ConvGNP) to address these issues.
  arXiv  Detail & Related papers  (2022-11-18T17:25:14Z)
• Information Entropy Initialized Concrete Autoencoder for Optimal Sensor Placement and Reconstruction of Geophysical Fields [58.720142291102135]
  We propose a new approach to the optimal placement of sensors for reconstructing geophysical fields from sparse measurements. We demonstrate our method on the two examples: (a) temperature and (b) salinity fields around the Barents Sea and the Svalbard group of islands. We find out that the obtained optimal sensor locations have clear physical interpretation and correspond to the boundaries between sea currents.
  arXiv  Detail & Related papers  (2022-06-28T12:43:38Z)
• Learning Online Multi-Sensor Depth Fusion [100.84519175539378]
  SenFuNet is a depth fusion approach that learns sensor-specific noise and outlier statistics. We conduct experiments with various sensor combinations on the real-world CoRBS and Scene3D datasets.
  arXiv  Detail & Related papers  (2022-04-07T10:45:32Z)
• Real-time detection of uncalibrated sensors using Neural Networks [62.997667081978825]
  An online machine-learning based uncalibration detector for temperature, humidity and pressure sensors was developed. The solution integrates an Artificial Neural Network as main component which learns from the behavior of the sensors under calibrated conditions. The obtained results show that the proposed solution is able to detect uncalibrations for deviation values of 0.25 degrees, 1% RH and 1.5 Pa, respectively.
  arXiv  Detail & Related papers  (2021-02-02T15:44:39Z)
• Global field reconstruction from sparse sensors with Voronoi tessellation-assisted deep learning [0.0]
  We propose a data-driven spatial field recovery technique based on a structured grid-based deep-learning approach for arbitrary positioned sensors of any numbers. The presented technique opens a new pathway towards the practical use of neural networks for real-time global field estimation.
  arXiv  Detail & Related papers  (2021-01-03T03:43:53Z)
• Ant Colony Inspired Machine Learning Algorithm for Identifying and Emulating Virtual Sensors [0.0]
  It should be possible to emulate the output of certain sensors based on other sensors. In order to identify the subset of sensors whose readings can be emulated, the sensors must be grouped into clusters. This paper proposes an end-to-end algorithmic solution, to realise virtual sensors in such systems.
  arXiv  Detail & Related papers  (2020-11-02T09:06:14Z)
• Deep Soft Procrustes for Markerless Volumetric Sensor Alignment [81.13055566952221]
  In this work, we improve markerless data-driven correspondence estimation to achieve more robust multi-sensor spatial alignment. We incorporate geometric constraints in an end-to-end manner into a typical segmentation based model and bridge the intermediate dense classification task with the targeted pose estimation one. Our model is experimentally shown to achieve similar results with marker-based methods and outperform the markerless ones, while also being robust to the pose variations of the calibration structure.
  arXiv  Detail & Related papers  (2020-03-23T10:51:32Z)

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.