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

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Improved Defogging Algorithm Based on Fusion of Dark-Bright Channel and Sky Segmentation

Improved Defogging Algorithm Based on Fusion of Dark-Bright ...

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International conference on Artificial Intelligence and Computer information Technology (AICIT)

作者： Kun Gao Deng Chen Xiancheng Feng School of Electrical and Information Engineering Wuhan Institute of Technology Wuhan China Yingcheng Xindu Chemical Co. Ltd. Xiaogan China

ISBN: (数字)9798350365443

ISBN: (纸本)9798350365450

In adverse weather conditions with low visibility such as smog and rain, the large amounts of water vapor and dust particles suspended in the air cause scattering, refraction, and absorption of visible light. This results in images captured by imaging devices having low contrast and color distortion, severely affecting the normal operation of outdoor visual monitoring systems. This paper proposes a rapid image dehazing method based on dark and bright channel priors and adaptive thresholds. First, the dark channel, bright channel, and grays cale images of the hazy image are calculated. The threshold and atmospheric light value are estimated through the bright channel image, and the dark channel image is processed using an improved Sobel edge detection algorithm to create a mask for refining the transmission map. Next, compensation is performed for the sky region to address the block effect problem present in the sky area. Finally, the clear, haze-free image is restored using the transmission map and atmospheric light value. This method effectively improves image contrast and clarity, reduces artifacts and noise problems present in traditional dehazing algorithms, significantly enhances dehazing effects, and meets the application needs of autonomous driving, intelligent transportation, and photogrammetry. Experimental results show that this method outperforms traditional algorithms in terms of dehazing effect, processing speed, and image quality, demonstrating significant practical value and broad application prospects.

关键词： Image segmentation Visualization Rain Refining Noise Channel estimation Transportation Scattering Photogrammetry Monitoring

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Anomaly Detectors for Self-Aware Edge and IoT devices

Anomaly Detectors for Self-Aware Edge and IoT Devices

引用

IEEE International conference on Software Quality, Reliability and Security (QRS)

作者： Tommaso Zoppi Giovanni Merlino Andrea Ceccarelli Antonio Puliafito Andrea Bondavalli Department of Information Engineering & Computer Science Via Sommarive 9 University of Trento Povo IT SmartMe.io via Salita Larderia Messina IT Department of Engineering University of Messina Messina IT Department of Mathematics and Informatics Viale Morgagni 65 University of Florence Florence IT

With the growing processing power of computing systems and the increasing availability of massive datasets, machine learning algorithms have led to major breakthroughs in many different areas. This applies also to resource-constrained IoT and edge devices, which will often benefit from relatively small – but smart – local anomaly detection tasks that aim at protecting the device, or the information they convey from sensors towards a central node. This provides the device with fault detection capabilities that are typically required when engineering dependable devices, services or systems. This paper overviews a pitfall-free process to provide small devices with anomaly detection capabilities, to make them self-aware of their health condition, and possibly take appropriate countermeasures. Our methodology applies to a wide range of Linux-based devices: we show an application to a specific ARANCINO device, which has already been successfully used in many smart cities and sensing applications. We craft anomaly detectors that are very effective in detecting most of the anomalies. Additionally, we comment on the beneficial impact of time-series analysis, which could help improve detection performance even further, allowing to equip any small device with responsive and accurate anomaly detection machinery.

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Attention-Based Deep Learning Algorithm in Natural Language processing for optical Character Recognition

Attention-Based Deep Learning Algorithm in Natural Language ...

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Evolutionary algorithms and Soft Computing Techniques (EASCT), International conference on

作者： Prem Kumar M R. Logesh Babu G Sunil J. Sampathkumar Subhra Chakraborty Global Technical Marketing Element 14 Bangalore India Department of Information Technology KPR Institute of Engineering & Technology Coimbatore India School of Computer Science & Artificial Intelligence SR University Warangal India Department of ECE Mahendra College of Engineering Salem India Department of ECE Nitte Meenakshi Institute of Technology Bengaluru India

optical Character Recognition (OCR) is commonly referred as text recognition which poses a substantial issue in the computer vision tasks. Conventional optical character recognition systems frequently suffer in handwritten document recognition. To solve this, Deep Learning (DL) models have emerged as a powerful and advanced solution for character recognition. The present research offers a unique CNN-RNN model with an Attention Mechanism (CNN-RNN-AM) for English image character recognition. The process comprised many important phases, beginning with image collection from a user-defined dataset, then image pre-processesing includes grayscale conversion and noise reduction. For effective character recognition, the proposed approach integrates the segmentation process at multiple levels, including line segmentation, word segmentation, and character segmentation. Finally, the CNN-RNN with an attention mechanism is deployed for character recognition. The experimental findings demonstrated the remarkable efficacy of the suggested CNN-RNN-AM model. It outperformed other compared models by attaining an excellent character recognition accuracy of 99.89%.

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Realtime Angular Measurement Design of a Stable Platform Based on FPGA

Realtime Angular Measurement Design of a Stable Platform Bas...

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IEEE International conference on Signal processing, Communications and Computing (ICSPCC)

作者： Zixuan Zhang Zhanchao Wang Lulu Qian Yan Sun Wenhao Zhao Min Huang Chinese Academy of Sciences Key Laboratory of computational Optical Imaging Technology Chinese Academy of Sciences Aerospace Information Research Institute Beijing China Aerospace Information Research Institute Chinese Academy of Sciences School of Optoelectronics University of Chinese Academy of Sciences Beijing China

Stable platform, as an important electromechanical integrated equipment for isolating disturbances and stabilizing visual axes, has been widely applied in fields such as aviation reconnaissance, search and rescue, and tracking systems. Accurate measurement of each axis angle is crucial for stable platforms to achieve high-precision pointing in two-axis or three-axis directions. his article presents the design of a high-precision angle measurement system based on FPGA, utilizing high-precision electronic encoders as the angle measurement devices. FPGA is used to realize protocol conversion between the encoder and the main controller, and it has the characteristics of flexible expansion, allowing compatibility with MCU and DSP, among others. Experimental data shows that the angular measurement accuracy of the system reaches ±0.01°/ms, and the maximum frequency of angle output can reach 100kHz, fully meeting the requirements of practical scenarios. The high-precision angle measurement system designed in this article can be used in optoelectronic imaging devices, optoelectronic tracking platforms, and other applications, laying a solid hardware foundation for subsequent technologies such as adaptive control.

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Smart Medical Box using Arduino

Smart Medical Box using Arduino

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International conference on ent Computing and Control systems (ICICCS)

作者： Sweadha. M M. Selvi Dept. of Computer Science and Engineering Sathyabama Institute of Science and Technology Chennai Tamil Nadu

ISBN: (数字)9798331512088

ISBN: (纸本)9798331512095

The Smart Medical Box is a sophisticated IoT-driven solution designed to enhance medication adherence by utilizing computer vision and machine learning. The system automates the identification and extraction of essential information from medication labels, ensuring precision in dosage reminders and reducing human error. It incorporates a high-resolution camera, an OCR engine, and an Arduino-based microcontroller to enable seamless image processing and real-time analysis. With an intuitive interface and GPS-enabled location services, users can efficiently manage their medication schedules while accessing nearby pharmacies when needed. This research focuses on designing and implementing a scalable and cost-effective system that improves medication compliance and minimizes health risks associated with missed or incorrect dosages. By integrating advanced image processing techniques and AI-driven recognition algorithms, the Smart Medical Box ensures high accuracy in identifying medications. Additionally, features such as automated alerts, real-time monitoring, and pharmacy locator tools enhance the system's usability and accessibility. This innovation is particularly beneficial for elderly individuals and patients with chronic conditions, offering a simplified and automated approach to medication tracking. Performance evaluations of the system indicate a significant improvement in medication adherence, a high recognition accuracy rate, and positive user feedback. Future enhancements, including AI-based OCR, voice assistance, and mobile application connectivity, are expected to further improve functionality and ease of use. This paper presents the conceptualization, development, and testing of the Smart Medical Box, highlighting its potential to revolutionize personal healthcare management by providing an intelligent, efficient, and user-friendly solution.

关键词： Accuracy Image recognition Microcontrollers optical character recognition Medical services Real-time systems Usability Older adults Biomedical imaging Global Positioning System

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Digital Twin Use Case in Telecommunications Transport Network

Digital Twin Use Case in Telecommunications Transport Networ...

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IEEE conference on Wireless Communications and Networking

作者： Ibrahim Fatih Mercimek Kemal Polat Muhammed Duran Yazar Network Architecture Department Turk Telekom Istanbul Türkiye Electrical and Electronics Engineering Department Bolu Abant Izzet Baysal University Bolu Türkiye

ISBN: (数字)9798350368369

ISBN: (纸本)9798350368376

Digital Twin (DT) is a high-fidelity simulation technique that converts a real-world workplace into a virtual environment. information technology and the industrial industry are now deeply integrated and developing as a result of the advent of DT technology. The application of this technology to telecommunications has come to the forefront. In this study, artificial intelligence methods were applied for failure root cause analysis in transport systems adapted to digital twin technology used in telecommunication systems. Three different classification methods were used to analyze the obtained data. The Synthetic Minority Oversampling Technique (SMOTE) model was used to stabilize the data. The study includes the analysis of a data pool created by correlating the data received from the devices belonging to the fiber optic cable (Fiber), Dense Wavelength Division Multiplexing (DWDM) systems, and Internet Protocol Multi-Protocol Label Switching (IP-MPLS) layer, which constitute the transport layer of telecommunication systems. The use of DT terminology in telecommunication technologies is directly related to determining which use case to address, which domains, and which parameters to take in the existing network. For this purpose, the DT structure created by topologically associating DWDM technology and IP-MPLS technology, and then matching them at fault and alarm level, is used in root cause analysis prediction using artificial intelligence algorithms. As the output of the data pool; 3 basic error conditions “MPLS Flap”, “Short Term Interruption”, and “Long Term Interruption”, which cause interruptions on the user side, are discussed. Random Forest (RF), Naive Bayes (NB), and K-Nearest Neighbors (KNN) methods were used as artificial intelligence classification methods. When the results are analyzed, it is seen that the results of the Random Forest (RF) method are the most accurate prediction.

关键词： Radio frequency Root cause analysis optical fiber cables Nearest neighbor methods Wavelength division multiplexing Telecommunications Digital twins Classification algorithms Artificial intelligence Random forests

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HeteroEdge: Addressing Asymmetry in Heterogeneous Collaborative Autonomous systems

HeteroEdge: Addressing Asymmetry in Heterogeneous Collaborat...

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IEEE Internatonal conference on Mobile Adhoc and Sensor systems (MASS)

作者： Mohammad Saeid Anwar Emon Dey Maloy Kumar Devnath Indrajeet Ghosh Naima Khan Jade Freeman Timothy Gregory Niranjan Suri Kasthuri Jayarajah Sreenivasan Ramasamy Ramamurthy Nirmalya Roy Department of Information Systems University of Maryland Baltimore County (UMBC) CARDS UMBC DEVCOM Army Research Lab Department of Computer Science Bowie State University

Gathering knowledge about surroundings and generating situation awareness for autonomous systems is of utmost importance for systems developed for smart urban and uncontested environments. For example, a large area surveillance system is typically equipped with multi-modal sensors such as cameras and LIDARs and is required to execute deep learning algorithms for action, face, behavior, and object recognition. However, these systems are subjected to power and memory limitations due to their ubiquitous nature. As a result, optimizing how the sensed data is processed, fed to the deep learning algorithms, and the model inferences are communicated is critical. In this paper, we consider a testbed comprising two Unmanned Ground Vehicles (UGVs) and two NVIDIA Jetson devices and posit a self-adaptive optimization framework that is capable of navigating the workload of multiple tasks (storage, processing, computation, transmission, inference) collaboratively on multiple heterogenous nodes for multiple tasks simultaneously. The self-adaptive optimization framework involves compressing and masking the input image frames, identifying similar frames, and profiling the devices for various tasks to obtain the boundary conditions for the optimization framework. Finally, we propose and optimize a novel parameter split-ratio, which indicates the proportion of the data required to be offloaded to another device while considering the networking bandwidth, busy factor, memory (CPU, GPU, RAM), and power constraints of the devices in the testbed. Our evaluations captured while executing multiple tasks (e.g., PoseNet, SegNet, ImageNet, DetectNet, DepthNet) simultaneously, reveal that executing 70% (split-ratio=70%) of the data on the auxiliary node minimizes the offloading latency by ≈ 33% (18.7 ms/image to 12.5 ms/image) and the total operation time by ≈ 47% (69.32s to 36.43s) compared to the baseline configuration (executing on the primary node).

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Digital-Twin-Based Clock Synchronization in Industrial IoT systems

Digital-Twin-Based Clock Synchronization in Industrial IoT S...

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IEEE International conference on Cyber Technology in Automation, Control, and Intelligent systems

作者： Ting Cheng Shuai Liu Qimin Xu Shanying Zhu Department of Automation Shanghai Jiao Tong University Shanghai China Key Laboratory of System Control and Information Processing Ministry of Education of China Shanghai China Shanghai Engineering Research Center of Intelligent Control and Management Shanghai China School of Control Science and Engineering Shandong University Jinan China

Close and efficient cooperation between devices in the Industrial Internet of Things (IIoT) requires precise clock synchronization as a prerequisite. The uncertainty of IIoT networks and the complexity of industrial environments have led to less precise clock synchronization achieved by traditional algorithms and higher network resource consumption. This paper proposes a digital-twin-based clock synchronization scheme for IIoT systems. By building the digital model for each local clock to predict their clock skew, the information interaction required to achieve synchronization is reduced. In order to model the clock accurately, this paper proposes to use the extended state Kalman filter to handle uncertain dynamics and unknown measurement bias to improve the estimation accuracy of clock skew further. The simulation results show that the proposed algorithm can achieve high- precision clock synchronization while reducing the consumption of network resources.

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IUI 2021 Tutorial on Conversational Recommendation systems 26

IUI 2021 Tutorial on Conversational Recommendation Systems

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26th International conference on Intelligent User Interfaces (IUI)

作者： Fu, Zuohui Xian, Yikun Zhang, Yongfeng Zhang, Yi Rutgers State Univ Piscataway NJ 08854 USA UC Santa Cruz Santa Cruz CA USA

ISBN: (纸本)9781450380188

Recent years have witnessed the emerging of conversational systems, including both physical devices and mobile-based applications. Both the research community and industry believe that conversational systems will have a major impact on human-computer interaction, and specifically, the CHI/IR/DM/RecSys communities have begun to explore Conversational Recommendation systems. Conversational recommendation aims at finding or recommending the most relevant information (e.g., web pages, answers, movies, products) for users based on textual- or spoken-dialogs, through which users can communicate with the system more efficiently using natural language conversations. Due to users' constant need to look for information to support both work and daily life, conversational recommendation system will be one of the key techniques towards an intelligent web. The tutorial focuses on the foundations and algorithms for conversational recommendation, as well as their applications in real-world systems such as search engine, e-commerce and social networks. The tutorial aims at introducing and communicating conversational recommendation methods to the community, as well as gathering researchers and practitioners interested in this research direction for discussions, idea communications, and research promotions.

关键词： Conversational Recommendation Dialog systems Natural Language processing Intelligent Interface Human-in-the-Loop AI

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optical Impairment Compensation in Fiber Communication systems Based on Artificial Intelligence: A Comprehensive Survey 1

Optical Impairment Compensation in Fiber Communication Syste...

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1st Babylon International conference on information Technology and Science, BICITS 2021

作者： Jarad, Alaa H. Murdas, Ibrahim A. College of Engineering Babyllon University Babil Iraq

ISBN: (纸本)9781665415736

The global demand for high-speed communication has increased dramatically over the past few years when data beginning to dominating of the traffic according to the Cisco Visual Networking Index (VNI). Data traffic is triple between 2014 and 2020, mainly, due to developing applications that consume bandwidth such as cloud services, HD video, high quality of real-time video transmission, virtual- augmented reality (VR-AR), online-games (video games), exchange of multimedia via smartphones and the more like. In fact, in 2020, more than a million minutes of multimedia (video) content is transiting the IP network every second according to the VNI;and the demands will exceed the capability of the current (core) internet backbone systems, in which optical communications are the main infrastructure. In this paper, the focus was on reviewing the mechanisms used for the most important and most effective techniques used to increase the capacity of optical transmission systems, namely Nonlinear Compensation (NLC) which work to reduce the nonlinear impairments that represent the main intrinsic challenges and the main capacity limitations facing the optical systems. The traditional NLC techniques were determined based on the approximate solution of the Nonlinear Schrodinger Equation (NLSE) through Digital Back Propagation (DBP), or Split-step Fourier Method (SSFM). however, their implementation demands excessive signal processing resources, and high-level accurate knowledge. A completely new approach that uses artificial intelligence (AI) algorithms to identify and solve these impairments has been studied in this paper. Traditional NLC techniques are reviewed in the first part to mitigation the nonlinearities and estimate the quality of transmission (QoT). Whereas in the second part, we review the uses of AI techniques that have been studied in applications related to monitoring performance, reduce nonlinearity, and quantify QoT. Finally, this paper presents a summary with a conc

关键词： Digital signal processing

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