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Electron Density Specification in the Inner Magnetosphere From the Narrow Band Receiver Onboard DSX

RADIO SCIENCE 2024年第2期59卷 1-20页

作者： Su, Yi-Jiun Carilli, John A. Parham, J. Brent Chu, Xiangning Galkin, Ivan A. Ginet, Gregory P. AF Res Lab Space Vehicles Directorate Kirtland AFB NM 87117 USA MIT Lincoln Lab Cambridge MA USA Univ Colorado Boulder Lab Atmospher & Space Phys Boulder CO USA Univ Massachusetts Lowell Lowell MA USA

Electron density plays an important role in the study of wave propagation and is known to be associated with the index of refraction and radiation belt diffusion coefficients. The primary objective of our investigation is to explore the possibility of implementing an onboard signal processing algorithm to automatically obtain electron densities from the upper hybrid resonance traces of wave spectrograms for future missions. U-Net, developed for biomedical image segmentation, has been adapted as our deep learning architecture with results being compared with those extracted from a more traditional semi-automated method. As a product, electron densities and cyclotron frequencies for the entire DSX mission between 2019 and 2021 are acquired for further analysis and applications. Due to limited space measurements, a synthetic image generator based on data statistics and randomization is proposed as an initial step toward the development of a generative adversarial network in hopes of providing unlimited realistic data sources for advanced machine learning. Plain Language Summary Electron density is the most important fundamental plasma parameter, however, it is very difficult to directly measure in situ due to spacecraft potential. A convolutional neural network (CNN), developed to recognize features from biomedical images, has been adapted to pull out the resonance traces from space wave receivers automatically specifying densities along satellite orbits. The comparison between computer vision based on a CNN and human vision based on a semi-automated extraction is demonstrated in this paper. With additional development and refinement, our proof-of-concept study may be matured to a level suitable for incorporation into onboard signal processing units to reduce human labor and human-in-the-loop induced operational errors during future space missions.

关键词： deep machine learning electron density satellite wave receiver plasmasphere image processing space instrument software development

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A fast, lightweight deep learning vision pipeline for autonomous UAV landing support with added robustness

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ENGINEERING applications OF ARTIFICIAL INTELLIGENCE 2024年 131卷

作者： Pieczynski, Dominik Ptak, Bartosz Kraft, Marek Piechocki, Mateusz Aszkowski, Przemyslaw Poznan Univ Tech Inst Robot & Machine Intelligence Piotrowo 3A PL-60965 Poznan Poland

Despite massive development in aerial robotics, precise and autonomous landing in various conditions is still challenging. This process is affected by many factors, such as terrain shape, weather conditions, and the presence of obstacles. This paper describes a deep learning-accelerated image processing pipeline for accurate detection and relative pose estimation of the UAV with respect to the landing pad. Moreover, the system provides increased safety and robustness by implementing human presence detection and error estimation for both landing target detection and pose computation. Human presence and landing pad location are performed by estimating the presence probability via segmentation. This is followed by the landing pad keypoints' location regression algorithm, which, in addition to coordinates, provides the uncertainty of presence for each defined landing pad landmark. To perform the aforementioned tasks, a set of lightweight neural network models was selected and evaluated. The resulting measurements of the system's performance and accuracy are presented for each component individually and for the whole processing pipeline. The measurements are performed using onboard embedded UAV hardware and confirm that the method can provide accurate, low-latency feedback information for safe landing support.

关键词： Unmanned aerial vehicle Landing support image processing Deep learning On-board processing

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Guest Editorial Introduction to the Special Section on Transformer Models in vision

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IEEE TRANSACTIONS ON PATTERN ANALYSIS AND machine INTELLIGENCE 2023年第11期45卷 12721-12725页

作者： Khan, Salman Khan, Fahad Shahbaz Vaswani, Ashish Parmar, Niki Yang, Ming-Hsuan Shah, Mubarak Mohamed Bin Zayed Univ Artificial Intelligence Abu Dhabi U Arab Emirates Australian Natl Univ Canberra 2601 Australia Linkoping Univ S-58183 Linkoping Sweden Stealth Mt View WY USA Univ Calif Merced Merced CA 95343 USA Univ Cent Florida Orlando FL 32816 USA

Transformer models have achieved outstanding results on a variety of language tasks, such as text classification, ma- chine translation, and question answering. This success in the field of Natural Language processing (NLP) has sparked interest in the computer vision community to apply these models to vision and multi-modal learning tasks. However, visual data has a unique structure, requiring the need to rethink network designs and training methods. As a result, Transformer models and their variations have been suc- cessfully used for image recognition, object detection, seg- mentation, image super-resolution, video understanding, image generation, text-image synthesis, and visual question answering, among other applications.

关键词： Special issues and sections Transformers Text categorization machine translation Natural language processing

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Temporal compressive edge imaging enabled by a lensless diffuser camera

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OPTICS LETTERS 2024年第11期49卷 3058-3061页

作者： Zheng, Ze Liu, Baolei Song, Jiaqi Ding, Lei Zhong, Xiaolan Chang, Lingqian Wu, Xiaojun McGloin, David Wang, Fan Beihang Univ Sch Phys Beijing 100191 Peoples R China Univ Technol Sydney Fac Engn & IT Sch Biomed Engn Sydney NSW 2007 Australia Beihang Univ Beijing Adv Innovat Ctr Biomed Engn Sch Biol Sci & Med Engn Beijing 100191 Peoples R China Beihang Univ Sch Elect & Informat Engn Beijing 100191 Peoples R China Univ Aberdeen Kings Coll Sch Nat & Comp Sci Aberdeen AB24 3FX Scotland

Lensless imagers based on diffusers or encoding masks enable high -dimensional imaging from a single-shot measurement and have been applied in various applications. However, to further extract image information such as edge detection, conventional post -processing filtering operations are needed after the reconstruction of the original object images in the diffuser imaging systems. Here, we present the concept of a temporal compressive edge detection method based on a lensless diffuser camera, which can directly recover a time sequence of edge images of a moving object from a single-shot measurement, without further post -processing steps. Our approach provides higher image quality during edge detection, compared with the "conventional post -processing method." We demonstrate the effectiveness of this approach by both numerical simulation and experiments. The proof-of-concept approach can be further developed with other image post -processing operations or versatile computer vision assignments toward task-oriented intelligent lensless imaging systems.

关键词： Computational imaging Digital image processing Edge detection Imaging systems machine vision Numerical simulation

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Systematic literature review of AI algorithms applied to unmanned aerial vehicle images

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INTERNATIONAL JOURNAL OF image AND DATA FUSION 2025年第1期16卷

作者： El Khadir, Kenza Ait Fadil, Abdelhamid El Brirchi, El Hassan Hassania Sch Publ Works LaGes Lab Km 7 Jadida RdBP 8108 Casablanca Morocco

Artificial Intelligence (AI) combined with image processing has shown significant improvements through new techniques such as machine Learning (ML) models. This paper introduces the key methods and algorithms used for Drone image processing. We discuss the benefits and limitations of using ML models instead of classical techniques. Our goal is to classify, categorize and describe the methods that are used in realistic settings of diverse domains of applications. We conducted a systematic literature review where systems presented in the papers were analysed based on their domain, task, technology, and efficiency. By extensively reviewing the existing literature, we successfully identified key themes and trends that emerged across the various research questions. The overall findings of the research emphasise the potential of AI and drone imagery in numerous fields. However, the review also uncovered several challenges that necessitate attention, such as issues related to data quality and the requirement for more advanced AI algorithms. The paper outlines significant innovations in the field and offers recommendations for future research directions. By highlighting cross-disciplinary insights, it delves into methodological approaches, exploring commonalities in AI algorithms and UAVs technologies.

关键词： Artificial intelligence ML image processing computer vision UAVs (unmanned aerial vehicles) drones image recognition fault detection object detection inspection agriculture environmental pollution CNNs (convolutional neural networks) infrastructure

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Ultrabroadband Detection and Self-Powered Functionality in Quasi-One-Dimensional Nb3Se12I Nanowire Photodetectors for Bionic vision applications

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ACS APPLIED MATERIALS & INTERFACES 2025年第14期17卷 21448-21458页

作者： Zhang, Jianbin Zhao, Yi Liu, Ge Wang, Guangyi Chen, Liangqiang Shang, Conghui Li, Jiaxuan Zhou, Nan Xu, Hua Yang, Rusen Li, Xiaobo Xidian Univ Sch Adv Mat & Nanotechnol Shaanxi Joint Key Lab Graphene Shaanxi Key Lab High Orbits Electron Mat & Protect Xian 710126 Peoples R China Shaanxi Normal Univ Shaanxi Engn Lab Adv Energy Technol Key Lab Appl Surface & Colloid Chem Shaanxi Key Lab Adv Energy DevicesMinist EducSch Xian 710119 Peoples R China

The burgeoning fields of the Internet of things (IoT) and artificial intelligence (AI) have escalated the demands for image sensing technologies, necessitating advancements in sensor efficiency and functionality. Traditional image sensors, structured on von Neumann architectures with discrete processing units, face challenges, such as high power consumption, latency, and escalated hardware costs. In this work, we introduced a unique approach through the development of a quasi-one-dimensional nanowire Nb3Se12I-based double-ended photosensor. The advanced sensor not only replicated the adaptive behavior of biological vision systems but also effectively managed the decreased sensitivity triggered by intense light stimuli. The integration of the photothermoelectric and bolometric effects allows the device to operate in a self-powered mode, offering broadband detectivity ranging from visible (405 nm) to midwave infrared (4060 nm). Additionally, the quasi-one-dimensional structure enables an angle-dependent response to polarized light with a polarization ratio of 1.83. Our findings suggest that the biomimetic vision adaptive sensor based on Nb3Se12I could effectively enhance the capabilities of smart optical sensors and machine vision systems.

关键词： photothermoelectric self-powered Nb3Se12I ultrabroadband detection bionicvisual adaptation

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A Survey on Efficient vision Transformers: Algorithms, Techniques, and Performance Benchmarking

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IEEE TRANSACTIONS ON PATTERN ANALYSIS AND machine INTELLIGENCE 2024年第12期46卷 7682-7700页

作者： Papa, Lorenzo Russo, Paolo Amerini, Irene Zhou, Luping Sapienza Univ Rome Dept Comp Control & Management Engn I-00185 Rome Italy Univ Sydney Sch Elect & Informat Engn Fac Engn Sydney NSW 2006 Australia

vision Transformer (ViT) architectures are becoming increasingly popular and widely employed to tackle computer vision applications. Their main feature is the capacity to extract global information through the self-attention mechanism, outperforming earlier convolutional neural networks. However, ViT deployment and performance have grown steadily with their size, number of trainable parameters, and operations. Furthermore, self-attention's computational and memory cost quadratically increases with the image resolution. Generally speaking, it is challenging to employ these architectures in real-world applications due to many hardware and environmental restrictions, such as processing and computational capabilities. Therefore, this survey investigates the most efficient methodologies to ensure sub-optimal estimation performances. More in detail, four efficient categories will be analyzed: compact architecture, pruning, knowledge distillation, and quantization strategies. Moreover, a new metric called Efficient Error Rate has been introduced in order to normalize and compare models' features that affect hardware devices at inference time, such as the number of parameters, bits, FLOPs, and model size. Summarizing, this paper first mathematically defines the strategies used to make vision Transformer efficient, describes and discusses state-of-the-art methodologies, and analyzes their performances over different application scenarios. Toward the end of this paper, we also discuss open challenges and promising research directions.

关键词： Computer vision computational efficiency vision transformer

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Ensemble of vision transformer architectures for efficient Alzheimer's Disease classification

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BRAIN INFORMATICS 2024年第1期11卷 25页

作者： Shaffi, Noushath Viswan, Vimbi Mahmud, Mufti Sultan Qaboos Univ Coll Sci Dept Comp Sci POB 36 Muscat 123 Oman Univ Technol & Appl Sci Coll Comp & Informat Sci Sohar OM311 Oman Nottingham Trent Univ Dept Comp Sci Nottingham NG11 8NS England Nottingham Trent Univ Med Technol Innovat Facil Nottingham NG11 England Nottingham Trent Univ Comp & Informat Res Ctr Nottingham NG11 8NS England

Transformers have dominated the landscape of Natural Language processing (NLP) and revolutionalized generative AI applications. vision Transformers (VT) have recently become a new state-of-the-art for computer vision applications. Motivated by the success of VTs in capturing short and long-range dependencies and their ability to handle class imbalance, this paper proposes an ensemble framework of VTs for the efficient classification of Alzheimer's Disease (AD). The framework consists of four vanilla VTs, and ensembles formed using hard and soft-voting approaches. The proposed model was tested using two popular AD datasets: OASIS and ADNI. The ADNI dataset was employed to assess the models' efficacy under imbalanced and data-scarce conditions. The ensemble of VT saw an improvement of around 2% compared to individual models. Furthermore, the results are compared with state-of-the-art and custom-built Convolutional Neural Network (CNN) architectures and machine Learning (ML) models under varying data conditions. The experimental results demonstrated an overall performance gain of 4.14% and 4.72% accuracy over the ML and CNN algorithms, respectively. The study has also identified specific limitations and proposes avenues for future research. The codes used in the study are made publicly available.

关键词： vision transformer Convolutional neural networks machine learning models Alzheimer's Disease Swin transformer Data efficient image transformers Bidirectional encoder representation from image transformers

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Computer vision Techniques in Manufacturing

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IEEE TRANSACTIONS ON SYSTEMS MAN CYBERNETICS-SYSTEMS 2023年第1期53卷 105-117页

作者： Zhou, Longfei Zhang, Lin Konz, Nicholas MIT Comp Sci & Artificial Intelligence Lab 77 Massachusetts Ave Cambridge MA 02139 USA Beihang Univ Sch Automat Sci & Elect Engn Beijing 100191 Peoples R China Duke Univ Dept Elect & Comp Engn Durham NC 27708 USA

Computer vision (CV) techniques have played an important role in promoting the informatization, digitization, and intelligence of industrial manufacturing systems. Considering the rapid development of CV techniques, we present a comprehensive review of the state of the art of these techniques and their applications in manufacturing industries. We survey the most common methods, including feature detection, recognition, segmentation, and three-dimensional modeling. A system framework of CV in the manufacturing environment is proposed, consisting of a lighting module, a manufacturing system, a sensing module, CV algorithms, a decision-making module, and an actuator. applications of CV to different stages of the entire product life cycle are then explored, including product design, modeling and simulation, planning and scheduling, the production process, inspection and quality control, assembly, transportation, and disassembly. Challenges include algorithm implementation, data preprocessing, data labeling, and benchmarks. Future directions include building benchmarks, developing methods for nonannotated data processing, developing effective data preprocessing mechanisms, customizing CV models, and opportunities aroused by 5G.

关键词： image edge detection image segmentation Task analysis Robot sensing systems Sensors Feature detection Three-dimensional displays Assembly computer vision (CV) deep learning inspection machine intelligence machine learning manufacturing production robotics survey

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Skywatch: Advanced machine Learning Techniques for Distinguishing UAVs from Birds in Airspace Security

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INTERNATIONAL JOURNAL OF ADVANCED COMPUTER SCIENCE AND applications 2024年第11期15卷 1065-1078页

作者： Alqaraleh, Muhyeeddin Alzboon, Mowafaq Salem Al-Batah, Mohammad Subhi Zarqa Univ Fac Informat Technol Dept Software Engn Zarqa Jordan Jadara Univ Fac Sci & Informat Technol Dept Comp Sci Irbid Jordan

This study addresses the critical challenge of distinguishing Unmanned Aerial Vehicles (UAVs) from birds in real-time for airspace security in both military and civilian contexts. As UAVs become increasingly common, advanced systems must accurately identify them in dynamic environments to ensure operational safety. We evaluated several machine learning algorithms, including K-Nearest Neighbors (kNN), AdaBoost, CN2 Rule Induction, and Support Vector machine (SVM), employing a comprehensive methodology that included data preprocessing steps such as image resizing, normalization, and augmentation to optimize training on the "Birds vs. Drone Dataset." The performance of each model was assessed using evaluation metrics such as accuracy, precision, recall, F1 score, and Area Under the Curve (AUC) to determine their effectiveness in distinguishing UAVs from birds. Results demonstrate that kNN, AdaBoost, and CN2 Rule Induction are particularly effective, achieving high accuracy while minimizing false positives and false negatives. These models excel in reducing operational risks and enhancing surveillance efficiency, making them suitable for real-time security applications. The integration of these algorithms into existing surveillance systems offers robust classification capabilities and real-time decision-making under challenging conditions. Additionally, the study highlights future directions for research in computational performance optimization, algorithm development, and ethical considerations related to privacy and surveillance. The findings contribute to both the technical domain of machine learning in security and broader societal impacts, such as civil aviation safety and environmental monitoring.

关键词： Unmanned Aerial Vehicles (UAVs) machine learning image recognition real-time processing security computer vision image processing

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