Tetris-inspired detector with neural network for radiation mapping

• URL: http://arxiv.org/abs/2302.07099v1
• Date: Tue, 7 Feb 2023 22:17:18 GMT
• Title: Tetris-inspired detector with neural network for radiation mapping
• Authors: Ryotaro Okabe (1 and 2), Shangjie Xue (1 and 3 and 4), Jiankai Yu (3), Tongtong Liu (1 and 5), Benoit Forget (3), Stefanie Jegelka (4), Gordon Kohse (6), Lin-wen Hu (6), and Mingda Li (1 and 3) ((1) Quantum Measurement Group, Massachusetts Institute of Technology, Cambridge, MA, USA, (2) Department of Chemistry, Massachusetts Institute of Technology, Cambridge, MA, USA, (3) Department of Nuclear Science and Engineering, Massachusetts Institute of Technology, Cambridge, MA, USA, (4) Department of Electrical Engineering and Computer Science, Massachusetts Institute of Technology, Cambridge, MA, USA, (5) Department of Physics, Massachusetts Institute of Technology, Cambridge, MA, USA, (6) Nuclear Reactor Laboratory, Massachusetts Institute of Technology, Cambridge, MA, USA)
• Abstract summary: We present a framework using Tetris-inspired detector pixels and machine learning for radiation mapping. Using inter-pixel padding to increase the contrast between pixels and neural network to analyze the detector readings, a detector with as few as four pixels can achieve high-resolution directional mapping. Non-square, Tetris-shaped detector can further improve performance beyond the conventional grid-shaped detector.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: In recent years, radiation mapping has attracted widespread research attention and increased public concerns on environmental monitoring. In terms of both materials and their configurations, radiation detectors have been developed to locate the directions and positions of the radiation sources. In this process, algorithm is essential in converting detector signals to radiation source information. However, due to the complex mechanisms of radiation-matter interaction and the current limitation of data collection, high-performance, low-cost radiation mapping is still challenging. Here we present a computational framework using Tetris-inspired detector pixels and machine learning for radiation mapping. Using inter-pixel padding to increase the contrast between pixels and neural network to analyze the detector readings, a detector with as few as four pixels can achieve high-resolution directional mapping. By further imposing Maximum a Posteriori (MAP) with a moving detector, further radiation position localization is achieved. Non-square, Tetris-shaped detector can further improve performance beyond the conventional grid-shaped detector. Our framework offers a new avenue for high quality radiation mapping with least number of detector pixels possible, and is anticipated to be capable to deploy for real-world radiation detection with moderate validation.

Related papers

• Enabling Visual Recognition at Radio Frequency [13.399148413043411]
  PanoRadar is a novel RF imaging system that brings RF resolution close to that of LiDAR. Results enable, for the first time, a variety of visual recognition tasks at radio frequency. Our results demonstrate PanoRadar's robust performance across 12 buildings.
  arXiv  Detail & Related papers  (2024-05-29T20:52:59Z)
• Pano-NeRF: Synthesizing High Dynamic Range Novel Views with Geometry from Sparse Low Dynamic Range Panoramic Images [82.1477261107279]
  We propose the irradiance fields from sparse LDR panoramic images to increase the observation counts for faithful geometry recovery. Experiments demonstrate that the irradiance fields outperform state-of-the-art methods on both geometry recovery and HDR reconstruction.
  arXiv  Detail & Related papers  (2023-12-26T08:10:22Z)
• Trash to Treasure: Low-Light Object Detection via Decomposition-and-Aggregation [76.45506517198956]
  Object detection in low-light scenarios has attracted much attention in the past few years. A mainstream and representative scheme introduces enhancers as the pre-processing for regular detectors. In this work, we try to arouse the potential of enhancer + detector.
  arXiv  Detail & Related papers  (2023-09-07T08:11:47Z)
• Detecting the Sensing Area of A Laparoscopic Probe in Minimally Invasive Cancer Surgery [6.0097646269887965]
  In surgical oncology, it is challenging for surgeons to identify lymph nodes and completely resect cancer. A novel tethered laparoscopic gamma detector is used to localize a preoperatively injected radiotracer. Gamma activity visualization is challenging to present to the operator because the probe is non-imaging and it does not visibly indicate the activity on the tissue surface.
  arXiv  Detail & Related papers  (2023-07-07T15:33:49Z)
• Improving Radioactive Material Localization by Leveraging Cyber-Security Model Optimizations [1.2835555561822447]
  Current detection methods are often costly, slow to use, and can be inaccurate in complex, changing, or new environments. We show how machine learning methods used successfully in cyber domains, such as malware detection, can be leveraged to substantially enhance physical space detection. We show that the ML-based approaches can significantly exceed traditional table-based approaches in predicting angular direction.
  arXiv  Detail & Related papers  (2022-02-21T17:35:58Z)
• Using Machine Learning for Anomaly Detection on a System-on-Chip under Gamma Radiation [1.920987512094627]
  A few types of radiation like Total Ionizing Dose (TID) effects often cause permanent damages on nanoscale electronic devices. This paper focuses on using machine learning algorithms on consumer electronic level Field Programmable Gate Arrays (FPGAs) to tackle TID effects and monitor them to replace before they stop working.
  arXiv  Detail & Related papers  (2022-01-05T13:02:55Z)
• Radio-Assisted Human Detection [61.738482870059805]
  We propose a radio-assisted human detection framework by incorporating radio information into the state-of-the-art detection methods. We extract the radio localization and identifer information from the radio signals to assist the human detection. Experiments on the simulative Microsoft COCO dataset and Caltech pedestrian datasets show that the mean average precision (mAP) and the miss rate can be improved with the aid of radio information.
  arXiv  Detail & Related papers  (2021-12-16T09:53:41Z)
• Radiation-Induced Dark Counts for Silicon Single-Photon Detectors in Space [55.41644538483948]
  Displacement damage to the material lattice of a detector from the ionizing radiation can result in a permanent dark count increase in the detector. In the event of a high-altitude nuclear explosion, the artificial radiation belts produced by the explosion will cause too much damage to silicon single-photon detectors on low Earth orbit satellites.
  arXiv  Detail & Related papers  (2021-11-17T22:36:41Z)
• Infrared Small-Dim Target Detection with Transformer under Complex Backgrounds [155.388487263872]
  We propose a new infrared small-dim target detection method with the transformer. We adopt the self-attention mechanism of the transformer to learn the interaction information of image features in a larger range. We also design a feature enhancement module to learn more features of small-dim targets.
  arXiv  Detail & Related papers  (2021-09-29T12:23:41Z)
• Deep Learning Based Defect Detection for Solder Joints on Industrial X-Ray Circuit Board Images [14.139826850432339]
  In this paper, deep learning is incorporated in X-ray imaging based quality control during PCB quality inspection. Two artificial intelligence (AI) based models are proposed and compared for joint defect detection. The efficacy of the proposed methods are verified through experimenting on a real-world 3D X-ray dataset.
  arXiv  Detail & Related papers  (2020-08-06T12:25:18Z)
• Exploring Thermal Images for Object Detection in Underexposure Regions for Autonomous Driving [67.69430435482127]
  Underexposure regions are vital to construct a complete perception of the surroundings for safe autonomous driving. The availability of thermal cameras has provided an essential alternate to explore regions where other optical sensors lack in capturing interpretable signals. This work proposes a domain adaptation framework which employs a style transfer technique for transfer learning from visible spectrum images to thermal images.
  arXiv  Detail & Related papers  (2020-06-01T09:59:09Z)

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.