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检索条件"任意字段=Conference on Algorithms, Devices, and Systems for Optical Information Processing"

共 3212 条记录，以下是611-620 订阅

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Data-set for Event-based optical Flow Evaluation in Robotics Applications 16

Data-set for Event-based Optical Flow Evaluation in Robotics...

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16th International Joint conference on Computer Vision, Imaging and Computer Graphics Theory and Applications (VISIGRAPP) / 16th International conference on Computer Vision Theory and Applications (VISAPP)

作者： Khairallah, Mahmoud Bonardi, Fabien Roussel, David Bouchafa, Samia Univ Paris Saclay Univ Evry IBISC F-91025 Evry France

ISBN: (纸本)9789897584886

Event-Based cameras (also known as Dynamic Vision Sensors "DVS") have been used extensively in robotics during the last ten years and have proved the ability to solve many problems encountered in this domain. Their technology is very different from conventional cameras which requires rethinking the existing paradigms and reviewing all the classical image processing and computer vision algorithms. We show in this paper how Event-Based cameras are naturally adapted to estimate on the fly scene gradients and hence the visual flow. Our work starts with a complete study of existing event-based optical flow algorithms that are suitable to be integrated into real-time robotics applications. Then, we provide a data-set that includes different scenarios along with a set of visual flow ground-truth. Finally, we propose an evaluation of existing event-based visual flow algorithms using the proposed ground truth data-set.

关键词： Event-based Camera optical Flow Estimation Ego-motion Data-sets Frame Alignment

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Automated Recognition of Optic Disc and Blood Vessels in Diabetic Fundoscopy Images Using Real-Time Image Analysis

Automated Recognition of Optic Disc and Blood Vessels in Dia...

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IEEE conference on Multimedia information processing and Retrieval (MIPR)

作者： Kaixuan Li Wei-Bang Chen Yongjin Lu Xiaoliang Wang He Gao Department of Computer Science Virginia State University Petersburg VA USA Department of Mathematics and Statistics Oakland University Rochester MI USA Department of Computer Science SUNY Oswego Oswego NY USA

ISBN: (数字)9798350351422

ISBN: (纸本)9798350351439

Diabetic retinopathy (DR) is a sight-threatening condition associated with diabetes, characterized by damage to the retinal blood vessels. Key to the automation of DR staging is the identification of various symptoms directly or closely associated with retinal blood vessels, as well as the number of these symptoms in the four quadrants of the retina separated by the optic disc. Therefore, precise identification of the optic disc (OD) and blood vessels in fundus images is crucial for DR stage diagnosis but is often time-consuming and requires expert analysis. This study introduces a thresholding-based approach for the automated localization of the OD and the detection of blood vessels in fundus images of diabetic patients. Our algorithm is more robust than some deep learning-based algorithms, achieving more accurate results, particularly in advanced DR stages where the resemblance between various symptoms and blood vessels complicates the extraction of blood vessels. Additionally, our computer vision system can achieve OD localization and blood vessel segmentation in real time. The experimental results on a dataset selected by an ophthalmologist from a Kaggle dataset, ensuring data quality, show that the proposed algorithm can achieve an accuracy higher than 94% for both OD localization and blood vessel detection, outperforming some state-of-the-art algorithms.

关键词： Location awareness Image segmentation Accuracy Image recognition information processing Blood vessels Retina optical imaging Real-time systems Biomedical imaging

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Power Allocation Based on CNN and Long Short-Term Memory Networks in Massive MIMO systems 12th

Power Allocation Based on CNN and Long Short-Term Memory Net...

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12th International conference on Computer Engineering and Networks, CENet 2022

作者： Cui, Zijian Tan, Fangqing School of Information and Communication Guilin University of Electronic Technology Guilin541004 China Guangxi Key Laboratory of Wireless Wideband Communication and Signal Processing Guilin University of Electronic Technology Guilin541004 China

ISBN: (纸本)9789811969003

Massive MIMO is one of the key technologies in 5G and 6G wireless networks, with advantages of low complexity, high throughput, ultra-reliability and low latency. In this study, convolutional neural networks (CNN) and long short-term memory networks are used to carry out power allocation strategies in the downlink of Massive MIMO networks. In particular, deep neural networks are used to learn mathematical relationships between location and the power allocation strategies of user devices. And this trained mapping is used to predict the power allocation of new user devices. Deep learning can significantly reduce the computational complexity of power allocation compared to traditional methods. However, the structure of the existing neural network based on particular scene is relatively simple and basic. Considering the rapid progress of algorithms in the field of recent deep learning, there is a large room for improvement. To this end, the accuracy and performance of the models are improved using CNN and long short-term memory network. Simulation results show that two optimized network models can improve the loss to a lower level in shorter time than fully connected network. © 2022, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： MIMO systems

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Selection of Color Contrast Objects Against a Non-Stationary Background Using Modified HSV Model

Selection of Color Contrast Objects Against a Non-Stationary...

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International conference on Radio Electronics & Info Communications (UkrMiCo)

作者： Anatoliy Popov Irina Vasilyeva Volodymyr Kosharskyi Konstantin Dergachov Department of Aerospace Radioelectronic Systems National Aerospace University “Kharkiv Aviation Institute” Kharkiv Ukraine Department of Information-Communication Technologies named after O.O. Zelensky National Aerospace University “Kharkiv Aviation Institute” Kharkiv Ukraine Department of Aircraft Control Systems National Aerospace University “Kharkiv Aviation Institute” Kharkiv Ukraine

For the development of such smart systems based on the computer vision as, for example, unmanned aerial vehicles, driverless cars, etc. efficient image processing algorithms are required. These algorithms should provide the possibility to process images in a video stream, perform segmentation, select objects and define informative signatures for further recognition. Objects in optical images usually differ in color, shape, may have shadows, and they stand out against the non-stationary background of the terrain; that is why the HSV color model is often used for their selection. To select objects against the background of the terrain it is necessary to define their signatures and train the recognition system. However the variety of objects and the dependence of their color characteristics on changes in illumination pose a considerable problem. The paper proposes an alternative approach – to train the system to recognize the background, and the fragments of the image that are not recognized as the background should be classified as objects. For real-time estimation of the color parameters of the background it is proposed to convert the traditional HSV model into a one-dimensional signature to speed up calculations during processing. processing of the images received from an unmanned aerial vehicle by the proposed method makes it possible to select almost all objects of different colors, shapes and sizes against a heterogeneous background and to define their contours for further recognition.

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A design of frequency encoded dibit-based comparator using reflective semiconductor optical amplifier with simulative verification 4

A design of frequency encoded dibit-based comparator using r...

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4th International conference on devices for Integrated Circuit, DevIC 2021

作者： Bosu, Surajit Bhattacharjee, Baibaswata Bankura Sammilani College Department of Physics Bankura India Ramananda College Department of Physics Bankura India

ISBN: (纸本)9781728199559

In this modern era, optical computing increases the chance of ultra-high-speed information processing. All-optical comparator is one of the necessary components of the optical computing systems. Here we have designed a low power frequency encoded comparator using reflective semiconductor optical amplifier. This proposed comparator can compare a single bit signal. This work is based on dibit concept which improves the bit error problems. The proposed comparator is realized by simulation in MATLAB Simulink (R2018a) software. The devised design with the integrating ability is very purposeful in optical communication and computation. © 2021 IEEE.

关键词： Comparators (optical)

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Deep learning methods for point-of-care ultrasound examination 17

Deep learning methods for point-of-care ultrasound examinati...

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17th International conference on Signal-Image Technology and Internet-Based systems, SITIS 2023

作者： Ignesti, Giacomo Deri, Chiara D'Angelo, Gennaro Pratali, Lorenza Bruno, Antonio Benassi, Antonio Salvetti, Ovidio Moroni, Davide Martinelli, Massimo Institute of Information Science and Technologies National Research Council of Italy Pisa Italy University of Pisa Department of Computer Science Pisa Italy Institute of Clinical Physiology National Research Council of Italy Pisa Italy

ISBN: (纸本)9798350370911

Point-of-care Test (POCT) is the delivery of medical care at or near the patient's bedside. Primarily employed in emergencies, where rapid diagnosis and treatment are critical, POCT is now being used in domestic telehealth solutions, as in the TiAssisto project, thanks to technological advances such as the development of portable and affordable devices, high-speed Internet connections, video conferencing, and Artificial Intelligence (AI). Ultrasound (US) images of internal organs and structures are valuable tools in POCT medicine since this examination is portable, quick, and cost-effective. USs can help diagnose different conditions, including heart problems, abdominal pain, and pneumonia. Deep learning algorithms have proven to be highly effective in image recognition, enabling physicians to make informed decisions on-site. This article presents a pipeline approach providing remarkable and reliable results to handle point-of-care ultrasound examinations, making use of methods for: a) automating text cleaning for privacy based on an optical Character Recognition (OCR) algorithm;b) scrolling through the video frames and annotating them using an ad hoc implemented tool;c) classifying various signs in US using a state of the art deep learning algorithm, that is an adaptive efficient method ensembling two EfficientNet-b0 weak models;d) benchmarking medical plausibility to address transparency and human in the loop setting using a post hoc explanation visual explanation method, i.e. *** involved physician's feedback remarks that this system can detect important signs in pulmonary US imaging. However, the dataset is not yet the final one since the TiAssisto project is still ongoing, with a planned conclusion in February 2024. Our ultimate goal is not merely to develop a classification system but to create an effective healthcare support system that can be used beyond primary healthcare facilities. © 2023 IEEE.

关键词： Decision support systems

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Deep Reinforcement Learning algorithms for Low Latency Edge Computing systems

Deep Reinforcement Learning algorithms for Low Latency Edge ...

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International Symposium on Artificial Intelligence and Signal processing (AISP)

作者： K. Kumaran E. Sasikala Department of Information Technology SRM Institute of Science and Technology Easwari Engineering College Chennai India Department of Data Science & Business Systems SRM Institute of Science and Technology Kattankulathur Chennai India

Nowadays, due to the increase of technological development in smart devices, more computational capabilities are needed with better performance. The maximization of the cloud computing resources used in mobile networks and Internet of Things makes good results. The data processing along with delay in the communication network will be easily handled by edge computing technology. The edge server brought as much as close to the end devices to avoid the frequent cloud access. In edge servers, the processing of the computational task is time consuming, which causes latency. The inference of deep learning along with reinforcement models makes the better resource allocation and task scheduling for complex systems. In this work, deep reinforcement models like Q learning, Double Q learning, Deep Q network, Double Deep Q learning has been compared. The developed Deep Reinforcement learning model has been deployed in the edge-based system. As a result, it is inferred that Double Deep Q learning is having the ability to make better decisions in resource allocation by maximizing the gains and rewards. The simulation result shows that Double Deep Q learning performs better with decreased latency and increases the performance of the edge systems.

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Event Extraction from Telugu-English Code Mixed Social Media Text 7

Event Extraction from Telugu-English Code Mixed Social Media...

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7th International conference on Communication and Electronics systems, ICCES 2022

作者： Burramsetty, Sushma Sri Gonugunta, Nanditha Priya Uppu, Subhanjanaa Nunna, Satya Krishna Velagapudi Ramakrishna Siddhartha Engineering College Dept. Information Technology Vrsec Vijayawada India

ISBN: (数字)9781665496346

ISBN: (纸本)9781665496346

Natural Language processing Event Extraction work is crucial. Social media has become increasingly significant in today's world. Using natural language, people can express their views on a wide range of topics on social media. Social Media tools popularized the devices among the masses making the information distribution faster and easier. The exchange of text is the most popular means of communication across social media users. It had become necessary to understand the semantics of messages communicated as the messages had a wide effect across the users. Event extraction means extracting the events across streams of the social media messages. Event extraction helps in taking corrective actions in case of natural calamities and hence possibly save the lives of many people. The major objective of the task is to draw specific knowledge to predict the events(incidents) specified in the code-mixed digital text. This paper proposes a two-step procedure for the extraction of the events. The first phase is by applying a binary classifier to identify the messages containing the event. The second phase is by applying a sequence labeling technique, conditional random fields (CRF), to extract the event from the message. As social media text is a bit noisy, it is a major challenge to develop learning algorithms for these tasks. Parts of Speech (POS) tags and Named Entity Recognition (NER) are used for the words to address some issues in this challenge. © 2022 IEEE.

关键词： Semantics

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An Ontological Model for the Automated Data Replenishment Method Implementation for the Synthesis of Applied Photogrammetry systems

An Ontological Model for the Automated Data Replenishment Me...

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International Russian Automation conference (RusAutoCon)

作者： A. V. Kozlovskiy Y. E. Melnik V. I. Voloshchuk Advanced Engineering School Southern Federal University Rostov-on-Don Russia

ISBN: (数字)9798350349818

ISBN: (纸本)9798350349825

The article deals with the development of a modified ontological model for the formation and application of a knowledge base for the selection of methods for solving problems of applied photogrammetry in various fields, such as mapping, archaeology and aerial photography. This is relevant, in particular, for automating the design of problem-oriented systems of applied photogrammetry, as well as for fast tracking of new methods, which is difficult to perform manually. The proposed solution is based on the use of a fuzzy ontology containing information about different photogrammetry methods, imaging conditions, equipment parameters and desired results. To integrate new methods into the knowledge base it is proposed to use a combination of modern methods of natural language processing and machine learning, and to store the information about the methods in the form of text descriptions. This allows to automatically determine the optimal methods for a particular photogrammetry task, taking into account the conditions of surveying and the desired results. The paper describes in detail the structure of the ontology with modifications that take into account the current trends in the subject area, in particular the use of mobile devices, as well as the algorithm for integrating new methods and the potential advantages of the proposed solution. The paper also describes the potential of the developed system to improve the efficiency, accuracy and automation of method selection in photogrammetry, which can significantly improve the results of practical applications of photogrammetry in various fields.

关键词： Automation Machine learning algorithms Knowledge based systems Imaging Machine learning Ontologies Natural language processing Data models Satellite images Photogrammetry

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Ultra short pulse generation by Gain Switching in Temperature dependent Electrically Pumped Micro disk Lasers for Medical Applications

Ultra short pulse generation by Gain Switching in Temperatur...

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Intelligent & Innovative Practices in Engineering & Management (IIPEM), International conference on

作者： P Ashok M Ganesh Madhan C G Balaji Symbiosis Institute of Digital and Telecom Management (SIDTM) Symbiosis International (Deemed University) (SIU) Pune Maharashtra India Department of Electronics Engineering Madras Institute of Technology Anna University Chennai Tamilnadu India

ISBN: (数字)9798350390049

ISBN: (纸本)9798350390056

Micro-Disk Lasers (MDLs) operating at 1059nm are compact semiconductor devices that utilize whispering gallery modes for optical confinement. The circular geometry of the microdisk allows for high Q-factor resonances, enabling low threshold lasing. The 1059nm wavelength is particularly useful for applications in optical communications and spectroscopy. Fabrication often involves epitaxial growth techniques followed by etching processes to define the disk structure. Recent Studies focus on improving thermal management and integrating these devices with on-chip photonic circuits. Gain switching involves rapidly modulating the laser's gain above and below the lasing threshold using short electrical pulses. This process creates a population inversion followed by a rapid discharge, resulting in picosecond or sub-picosecond optical pulses. In microdisk lasers, the small cavity volume enhances the speed of this process. These devices find applications in optical communications, spectroscopy, and sensing. They're used in wavelength-division multiplexing systems, gas detection (e.g., methane), and as sources for frequency combs. Their compact size makes them suitable for integrated photonic circuits. In bio photonics, they are employed for fluorescence excitation and optical coherence tomography. They also show potential in quantum information processing. This research article focusses on generating ultra short optical pulses from electrically driven MDLs at various temperatures ranging from 25°C to 100°C. The pulse dynamics and other characteristics are discussed in detail in different sections of the article.

关键词： Biomedical equipment Spectroscopy optical switches Medical services optical fiber communication Laser applications Optimization optical pulses Photonics Switching circuits

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