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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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Architecture and CAD Software Solutions for PCB Design 12th

Architecture and CAD Software Solutions for PCB Design

引用

12th International conference on High-Performance Computing systems and Technologies in Scientific Research, Automation of Control and Production, HPCST 2022

作者： Tarasov, Ilya Mirzoyan, Dmitry Sovietov, Peter MIREA – Russian Technological University Vernadsky Avenue 78 Moscow119454 Russia

ISBN: (纸本)9783031237430

The article discusses the CAD architecture for PCB design, designed to create a new generation design system, focused on a large number of components, additional routing layers. The identified disadvantage of modern CAD printed circuit boards is a large load on the CPU when constructing a two-dimensional image in the construction area. Zooming while viewing the PCB results in image rebuilding, where elements such as circles, arcs, and polygon cutouts put additional CPU load and slow down system performance. The article discusses the CAD architecture based on the preparation of data on design elements in a format suitable for transfer to the GPU using the API of the OpenGL library. This approach transfers a significant part of the computational load to the GPU, which increases the overall performance of the CAD graphics subsystem. Additionally, the issues of using scripting languages for the automated creation of regular structures, such as external component pins, buses and vias for connecting signals in a regular way, are considered. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Graphics processing unit

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Motor Imagery EEG classification method: based on a novel BiLSTM-Attention-CNN hybrid neural network 2

Motor Imagery EEG classification method: based on a novel Bi...

引用

2nd International conference on Signal Image processing and Communication, ICSIPC 2022

作者： Chu, Hongxing Zhang, Chaozhu School of Information and Automation Engineering Qilu University of Technology Shandong Academy of Sciences Jinan250353 China

ISBN: (纸本)9781510657694

Brain-computer interface (BCI) technology is a human-computer interaction technology that realizes the connection and communication between the human brain and external equipment by collecting and decoding electroencephalo-graph (EEG) signals. It is a major direction for the development of BCI to analyze and decode the incoming EEG signal in real time through online BCI systems to enable the control of external devices. In order to improve the efficiency of online BCI, it is important to further improve the recognition accuracy and reduce the classification time. Long Short-Term Memory (LSTM) network can improve the recognition accuracy by using the temporal characteristics of EEG signals through its temporal recursive structure. Therefore, the optimized Bi-directional Long Short-Term Memory (BiLSTM) can well express the long-term dependent information in the input. In the study, a new model was proposed, the Attention mechanism was introduced to the BiLSTM while integrating Convolutional Neural Networks (CNN). In the case of avoiding gradient disappearance or gradient explosion, the problem of ignoring correlation information between channels is solved, and learning high-dimensional features in EEG data. This study improves the accuracy of motor image (MI) EEG signal classification under the condition of saving the steps of feature extraction, which is of great significance for the development of online BCI systems. © 2022 SPIE.

关键词： Brain computer interface

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Design of High-Capacity, Low-Loss, Multi-Layer Few Mode Fiber with Negatively-Doped Cladding Over C-Band

Design of High-Capacity, Low-Loss, Multi-Layer Few Mode Fibe...

引用

Asia Communications and Photonics conference and Exhibition (ACP)

作者： Haiqin Ye Jiajing Tu Mingjuan Zhuang Shecheng Gao Zhaohui Li Guangdong Provincial Key Laboratory of Optoelectronic Information Processing Chips and Systems and School of Electronics and Information Technology Sun Yat-sen University Guangzhou China Department of Electronic Engineering College of Information Science and Technology Jinan University Guangzhou China

ISBN: (数字)9798350379266

ISBN: (纸本)9798350379273

To accommodate more modes, the refractive index of core material in few mode fiber will become large, causing high Rayleigh scattering loss. To solve this issue, we adopt fluorine-dopled quartz cladding to reduce the doping content in the core. In this paper, we design weakly-coupled few-mode fibers with ultra-low loss and 10 mode groups, from the points of numerical simulation and intelligence algorithm.

关键词： Claddings Rayleigh scattering Design methodology Refractive index Doping Prediction algorithms Numerical simulation optical fiber communication Photonics Genetic algorithms

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The Design of Signal Denoising and Event Location Method based on OTDR

The Design of Signal Denoising and Event Location Method bas...

引用

Electronic information Engineering and Computer Communication (EIECC), International conference on

作者： Fan Zhang Hao Ran Bin Li School of Communications and Information Engineering Chongqing University of Posts and Telecommunications Chongqing China

ISBN: (数字)9798331534622

ISBN: (纸本)9798331534639

optical time domain reflectometer (OTDR) is a device that uses Fresnel reflection and Rayleigh scattering principles for optical fiber detection, which is widely used in the fields of fiber characteristic measurement, fiber event localization and fiber link quality assessment. However, during the testing process, the OTDR signal is often affected by system thermal noise, scattering noise, dark current noise, and environmental noise, especially when the interference is strong, the event localization becomes difficult, which seriously affects the reliability of the detection results. In addition, the traditional event localization methods usually have the problems of complicated process and low localization accuracy. Therefore, this paper proposes an improved wavelet threshold denoising method to effectively denoise the OTDR signal. At the same time, the event localization is performed by combining the wavelet transform (WT) and cell averaging constant false alarm rate (CA-CFAR) method, so as to improve the localization accuracy and the overall performance of the system.

关键词： Location awareness optical fibers Wavelet transforms Noise reduction Noise Signal processing algorithms optical fiber devices Signal denoising optical fiber testing Time-domain analysis

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AI Driven Rat Detection for Warehouse

AI Driven Rat Detection for Warehouse

引用

Emerging Smart Computing and Informatics (ESCI), conference on

作者： Ann Vincent Bhavesh Patil Aditya Murbade Hemalata Mote Shruti Mehata Dept. Elec and Telecomm. Engg. Don Bosco Institute of Technology Mumbai Mumbai India BharatGodam Solutions LLP Ahmedabad India

ISBN: (数字)9798331515683

ISBN: (纸本)9798331515690

The project’s goal is to create a system for controlling insect activity and monitoring of warehouses in sizable grain storage facilities. The idea behind the system uses neural networks, deep learning algorithms, and imagery processing methods to identify, monitor, track, and notify users of the activity of pests, notably rats, in real-time. By using algorithms that use deep learning for real-time object recognition and authentication, rats may be accurately identified. In addition, the sensors augment optical detection by using non-visual methods to detect motion and activity, providing full surveillance regardless of regions that are completely concealed. The system’s network of carefully positioned imaging devices and sensors allows for constant, continuous surveillance of the warehouse ambiance. The system activates and displays an indication once it recognises a rat, and it logs the precise time and place of the event. To ensure prompt action and response, warehouse administrators get updates in real time via email, SMS, or a mobile app. Through the application of artificial intelligence (AI), this initiative seeks to improve the security of food, reduce monetary damage, and revolutionize pest control at storage facilities for grain.

关键词： Deep learning Tracking Surveillance Neural networks Real-time systems Sensor systems optical sensors Object recognition Artificial intelligence Intelligent sensors

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Design and Implementation of an Intelligent IoT Platform Based on information Technology

Design and Implementation of an Intelligent IoT Platform Bas...

引用

Computing, Robotics and System Sciences (ICRSS), International conference on

作者： Yanhua Zhong Shanghai Construction Management Vocational College Shanghai China

ISBN: (数字)9798331511296

ISBN: (纸本)9798331511302

With the development of the Internet of Things (IoT) technology, various intelligent devices generate massive heterogeneous data, posing higher requirements for real-time processing and efficient analysis of the data. Achieving accurate clustering and anomaly detection of dynamic data has become a critical issue in enhancing the intelligence level of IoT platforms. To address this challenge, this study proposes a solution based on an optimized K-means algorithm and deploys it in a smart community environment to improve the system's real-time response and data processing capabilities. Experimental results demonstrate that the algorithm performs excellently in data clustering and anomaly detection. The average clustering accuracy ranges from 93.8% to 94.1%, with a noise point identification rate of 89.2% and a false positive rate controlled at around 4.1%. In tests conducted at different time periods, the running time of the optimized algorithm remained around 120 milliseconds, and the anomaly detection response time was 83 to 86 milliseconds, significantly outperforming DBSCAN, GMM, and hierarchical clustering algorithms. The optimized K-means algorithm exhibits excellent performance in practical applications of IoT platforms, achieving a balance between data processing efficiency and detection accuracy.

关键词： Accuracy Smart cities Heuristic algorithms Noise Software algorithms Clustering algorithms Approximation algorithms Real-time systems Time factors Anomaly detection

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Load balancing scheduling algorithm based on edge clustering in heterogeneous systems 2

Load balancing scheduling algorithm based on edge clustering...

引用

2nd International conference on Internet of Things and Smart City, IoTSC 2022

作者： Li, Na Gao, Bo Xie, Zongfu Liu, Jinjin Ji, Yawei Shen, Xiaolong School of Cyber Science and Engineering Zhengzhou University Zhengzhou China Institute of Information Systems Engineering Strategic Support Force Information Engineering University Zhengzhou China

ISBN: (纸本)9781510654990

In order to give full play to the high parallel and high efficiency characteristics of heterogeneous signal processing platforms, relevant scholars at home and abroad have proposed many task scheduling algorithms with their own advantages to improve processing efficiency. However, for communication intensive tasks, the existing task deployment methods are no longer applicable because of their higher communication requirements and communication delay becomes the key to limit the performance of the platform. For communication-intensive signal tasks, this paper proposes a load balancing scheduling method based on edge clustering. Tasks are abstracted as directed acyclic graph, we aggregate the qualified task edges according to the rating function to reduce the computational complexity, and task duplication is used to replicate the tasks on the critical path to reduce communication overhead and dynamically balance processor load. Through the experimental verification of random task graphs and radar task, it is concluded that the algorithm has a good scheduling result for communication intensive tasks, which can achieve good processor load balancing and system scheduling length reduction. © 2022 SPIE

关键词： Scheduling

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Efficient Latency Optimization in Batch processing Server systems for Multi-User Co-Inference

Efficient Latency Optimization in Batch Processing Server Sy...

引用

IEEE International conference on Communications in China Workshops (ICCC)

作者： Yiming Ma Ke Gao Chao Zhang Department of Electronic Engineering Tsinghua University Beijing China Beijing National Research Center for Information Science and Technology (BNRist)

ISBN: (数字)9798350377675

ISBN: (纸本)9798350377682

With the progression of internet of things (IoT) and artificial intelligence (AI), the demand for real-time, high-performance edge computing in multi-user collaborative inference scenarios has intensified. Batch-processing edge servers play a pivotal role in optimizing resource allocation, throughput, and overall system performance. This paper delves into the utilization of servers equipped with batch processing capabilities in edge computing for multi-user collaborative inference contexts. The inference tasks are partitioned into sub-tasks, and the minimization of user latency under energy consumption constraints is investigated. Initially, we assume a constant inference latency for the edge server and propose an iterative algorithm that explores partition points. Additionally, we introduce a traversal algorithm for the largest batch (TALB), taking into account the actual latency of the edge server. These algorithms demonstrate a significant reduction in task processing latencies of up to 79.5 % and enhance system performance across various conditions, particularly in scenarios with limited server bandwidth or significant capability differences among user devices.

关键词： Energy consumption System performance Collaboration Batch production systems Inference algorithms Partitioning algorithms Servers Internet of Things Optimization Edge computing

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Enhancing Detection Capacity and Security for Network-based Intrusion Detection systems in Internet of Things

Enhancing Detection Capacity and Security for Network-based ...

引用

IEEE International conference on Research, Innovation and Vision for the Future

作者： Duc Pham-Phuc Trung Nguyen-Nhat-Hieu Luan Van-Thien Bao Nguyen-Van Thuat Nguyen-Khanh Quan Le-Trung Faculty of Computer Networks and Communications University of Information Technology Ho Chi Minh City Vietnam Vietnam National University Ho Chi Minh City Vietnam

ISBN: (数字)9798331505073

ISBN: (纸本)9798331505080

As the number of Internet of Things devices continues to grow, so does the risk of attacks, which significantly impacts financial aspects and the potential loss of sensitive data. This makes Network-based Intrusion Detection systems (NIDS) using machine learning algorithms more critical than ever. However, a key concern is the input data for the machine learning models. Most models require large datasets to achieve high accuracy, but this data often needs to maintain privacy if it includes personal information. Additionally, the trustworthiness of devices in the system must be ensured, as unreliable devices can reduce the model's effectiveness or lead to significant data loss. To address these challenges, we propose using Generative Adversarial Networks (GAN) to enrich training data and combining Blockchain with Federated Learning (FL) to ensure data privacy and component validation. This approach results in a robust, efficient model that balances lightweight design with high reliability.

关键词： Data privacy Machine learning algorithms Accuracy Federated learning Intrusion detection Graphics processing units Generative adversarial networks Data models Blockchains Internet of Things

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Identification of Retinal Ischemia for Medical Pronouncement Through Fundus Images and Machine Learning Techniques

Identification of Retinal Ischemia for Medical Pronouncement...

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International conference on Signal processing, Communication, Power and Embedded System (SCOPES)

作者： V. Indu Srinivas Pasupula Gill Santosh Kumar Cheekatla Swapnapriya S NagaMallik Raj T. Ravibabu Dept of CSE(AIML) BVRIT College of Engineering for Women Hyderabad Telengana Department of CSE (AI&ML) Vignana Bharathi Institute of Technology Hyderabad Telengana Dept.of Mechanical Engineering Vignan's institute of information technology (A) Visakhapatnam AP India Dept of CSE Vignan's Institute of Information Technology (A) Visakhapatnam AP India Department of ECE Miracle Educational Society Vizianagaram AP India

ISBN: (数字)9798331506452

ISBN: (纸本)9798331506469

Retinal ischemia is caused by diabetes which affects small arteries including retinopathy. It is a condition marked by the deterioration of the retina's *** worse progression of Retinal ischemia may lead to the complete blindness. In this case, early detection of diabetes and retinal ischemia is more crucial to detect the lesions affected and provide the proper guidance and treatment to the patients suffering from diabetes. The identification of RI can be done manually and automatically. In the manual process, analysis and explanation is given by ophthalmologists and opticians. It is a time taking process and expensive. In case of automation process, early detection of RI can be done by automated systems using Artificial intelligence and deep learning over traditional methods. Most of the research work has done based on early detection of RI by taking two major facets fundus images and RI detection techniques.

关键词： Photography Biomedical optical imaging Signal processing algorithms Retina optical imaging Real-time systems Diabetes optical sensors optical signal processing Arteries

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