Quadratic Formulation of Mutual Information for Sensor Placement Optimization using Ising and Quantum Annealing Machines

• URL: http://arxiv.org/abs/2407.14747v2
• Date: Tue, 15 Oct 2024 06:52:42 GMT
• Title: Quadratic Formulation of Mutual Information for Sensor Placement Optimization using Ising and Quantum Annealing Machines
• Authors: Yuta Nakano, Shigeyasu Uno,
• Abstract summary: We address a problem to determine the placement of sensors from multiple candidate positions, aiming to maximize information acquisition with the minimum number of sensors. We defined mutual information (MI) between the data from selected sensor positions and the data from the others as an objective function. As an example, we calculated optimal solutions of the objective functions for 3 candidates of sensor placements using a quantum annealing machine.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: We address a combinatorial optimization problem to determine the placement of a predefined number of sensors from multiple candidate positions, aiming to maximize information acquisition with the minimum number of sensors. Assuming that the data from predefined candidates of sensor placements follow a multivariate normal distribution, we defined mutual information (MI) between the data from selected sensor positions and the data from the others as an objective function, and formulated it in a Quadratic Unconstrainted Binary Optimization (QUBO) problem by using a method we proposed. As an example, we calculated optimal solutions of the objective functions for 3 candidates of sensor placements using a quantum annealing machine, and confirmed that the results obtained were reasonable. The formulation method we proposed can be applied to any number of sensors, and it is expected that the advantage of quantum annealing emerges as the number of sensors increases.

Related papers

• Deep Optimal Sensor Placement for Black Box Stochastic Simulations [1.638332186726632]
  We propose a novel approach, the joint distribution over input parameters and solution with a joint energy-based model. We demonstrate the validity of our framework on a variety of problems, showing that our method provides highly informative sensor locations at a lower computational cost compared to conventional approaches.
  arXiv  Detail & Related papers  (2024-10-15T20:10:25Z)
• Optimal Multiparameter Metrology: The Quantum Compass Solution [0.0]
  We study optimal quantum sensing of multiple physical parameters using repeated measurements. We identify the combination of input states and measurements that satisfies both optimality criteria. We refer to the resulting optimal sensor as a quantum compass' solution.
  arXiv  Detail & Related papers  (2024-04-22T14:03:46Z)
• Finding the optimal probe state for multiparameter quantum metrology using conic programming [61.98670278625053]
  We present a conic programming framework that allows us to determine the optimal probe state for the corresponding precision bounds. We also apply our theory to analyze the canonical field sensing problem using entangled quantum probe states.
  arXiv  Detail & Related papers  (2024-01-11T12:47:29Z)
• CoSS: Co-optimizing Sensor and Sampling Rate for Data-Efficient AI in Human Activity Recognition [5.215675032361843]
  Recent advancements in Artificial Neural Networks have significantly improved human activity recognition using multiple time-series sensors. While employing numerous sensors with high-frequency sampling rates usually improves the results, it often leads to data inefficiency and unnecessary expansion of the ANN. We introduce a pragmatic framework for data-efficient utilization in HAR tasks, considering the optimization of both sensor modalities and sampling rate simultaneously.
  arXiv  Detail & Related papers  (2024-01-03T22:04:40Z)
• A Quantum-Inspired Binary Optimization Algorithm for Representative Selection [0.0]
  We propose a selector algorithm for selecting the most representative subset of data from a larger dataset. The selector algorithm can be used to build a diversified portfolio from a more extensive selection of assets. We show two use cases for the selector algorithm with real data.
  arXiv  Detail & Related papers  (2023-01-04T22:07:22Z)
• Task-Oriented Sensing, Computation, and Communication Integration for Multi-Device Edge AI [108.08079323459822]
  This paper studies a new multi-intelligent edge artificial-latency (AI) system, which jointly exploits the AI model split inference and integrated sensing and communication (ISAC) We measure the inference accuracy by adopting an approximate but tractable metric, namely discriminant gain.
  arXiv  Detail & Related papers  (2022-07-03T06:57:07Z)
• 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)
• Automatic Extrinsic Calibration Method for LiDAR and Camera Sensor Setups [68.8204255655161]
  We present a method to calibrate the parameters of any pair of sensors involving LiDARs, monocular or stereo cameras. The proposed approach can handle devices with very different resolutions and poses, as usually found in vehicle setups.
  arXiv  Detail & Related papers  (2021-01-12T12:02:26Z)
• ESAD: End-to-end Deep Semi-supervised Anomaly Detection [85.81138474858197]
  We propose a new objective function that measures the KL-divergence between normal and anomalous data. The proposed method significantly outperforms several state-of-the-arts on multiple benchmark datasets.
  arXiv  Detail & Related papers  (2020-12-09T08:16:35Z)
• Data-aided Sensing for Gaussian Process Regression in IoT Systems [48.523643863141466]
  We use data-aided sensing to learn data sets collected from sensors in Internet-of-Things systems. We show that modified multichannel ALOHA with predictions can help improve the performance of Gaussian process regression with data-aided sensing.
  arXiv  Detail & Related papers  (2020-11-23T20:59:51Z)
• Multi-Objective Bayesian Optimisation and Joint Inversion for Active Sensor Fusion [22.04258832800079]
  We propose a framework for multi-objective optimisation and inverse problems given an expensive cost function for allocating new measurements. This new method is devised to jointly solve multi-linear forward models of 2D-sensor data and 3D-geophysical properties. We demonstrate the advantages on a specific example of a joint inverse problem, recommending where to place new drill-core measurements given 2D gravity and magnetic sensor data.
  arXiv  Detail & Related papers  (2020-10-12T01:23:41Z)

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.