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

共 3227 条记录，以下是851-860 订阅

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TTC4MCP: Monocular Collision Prediction Based on Self-Supervised TTC Estimation

TTC4MCP: Monocular Collision Prediction Based on Self-Superv...

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IEEE/RSJ International conference on Intelligent Robots and systems (IROS)

作者： Changlin Li Yeqiang Qian Cong Sun Weihao Yan Chunxiang Wang Ming Yang Department of Automation Shanghai Jiao Tong University Shanghai China Key Laboratory of System Control and Information Processing Ministry of Education of China Shanghai China Global Institute of Future Technology Shanghai Jiao Tong University Shanghai China University of Michigan-Shanghai Jiao Tong University Joint Institute ShanghaiJiao Tong University Shanghai China

Vision-based collision prediction for autonomous driving is a challenging task due to the dynamic movement of vehicles and diverse types of obstacles. Most existing methods rely on object detection algorithms, which only predict predefined collision targets, such as vehicles and pedestrians, and cannot anticipate emergencies caused by unknown obstacles. To address this limitation, we propose a novel approach using pixel-wise time-to-collision (TTC) estimation for monocular collision prediction (TTC4MCP). Our approach predicts TTC and optical flow from monocular images and identifies potential collision areas using feature clustering and motion analysis. To overcome the challenge of training TTC estimation models without ground truth data in new scenes, we propose a self-supervised TTC training method, enabling collision prediction in a wider range of scenarios. TTC4MCP is evaluated on multiple road conditions and demonstrates promising results in terms of accuracy and robustness.

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Time-Space-Varying Resource Graph-Based Dependent Task Offloading for Satellite-Terrestrial Integrated Computing Power Networks

Time-Space-Varying Resource Graph-Based Dependent Task Offlo...

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IEEE conference on Wireless Communications and Networking

作者： Xin Zou Renchao Xie Qinqin Tang Zehui Xiong Gaochang Xie Tao Huang State Key Laboratory of Networking and Switching Technology Beijing University of Posts and Telecommunications Beijing China Purple Mountain Laboratories Nanjing China Information Systems Technology and Design Pillar Singapore University of Technology and Design Singapore Singapore

ISBN: (数字)9798350368369

ISBN: (纸本)9798350368376

With the continuous advancement of network technologies and hardware devices, computation-intensive and latency-sensitive tasks have emerged worldwide, requiring networks to provide extensive coverage, low latency, and robust computing capabilities. Leveraging the global coverage of LowEarth Orbit (LEO) satellites and the flexible resource invocation capabilities of the Computing Power Network (CPN), we propose a Satellite-Terrestrial Computing Power Network (ST-CPN) framework that integrates both strengths. In this framework, tasks can be offloaded to satellites closer to users for processing, ensuring high-quality services anytime and anywhere. However, due to the dynamic nature of the network and the limited resources of individual nodes, efficiently executing complex dependent tasks presents significant challenges. Therefore, we investigate the dependent task offloading problem in the dynamic ST-CPN environment. Considering dynamic changes of topology and available resources caused by satellite mobility, we propose a Time-Space-Varying Resource Graph (TSVRG) to capture the status of the communication, storage, and computation resources. On this basis, given that individual nodes struggle to process dependent tasks, we offload multiple subtasks of a task to different nodes for collaborative processing. In this paper, we model the task as a Directed Acyclic Graph (DAG) and transform the offloading problem into a mapping problem from the DAG to TSVRG. We then introduce a Delay Predictionbased Graph Mapping Algorithm (DPGMA) to address this problem. Simulation results indicate that our scheme achieves better performance than the benchmark schemes.

关键词： Satellites Computational modeling Simulation Transforms Benchmark testing Space-air-ground integrated networks Dynamic scheduling Prediction algorithms Delays Topology

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The photorefractive effect in integrated chalcogenide nonlinear photonic device and its application

The photorefractive effect in integrated chalcogenide nonlin...

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conference on Lasers and Electro-Optics (CLEO)

作者： Jiayue Wu Jianteng Huang Di Xia Jiaxin Zhao Zifu Wang Yufei Li Liyang Luo Bin Zhang Zhaohui Li School of Electrical and Information Technology Guangdong Provincial Key Laboratory of Optoelectronic Information Processing Chips and Systems Sun Yat-sen University Guangzhou China Key Laboratory of Optoelectronic Materials and Technologies Sun Yat-Sen University Guangzhou China Southern Marine Science and Engineering Guangdong Laboratory (Zhuhai) Zhuhai China

We demonstrate the photorefractive effect of chalcogenide (ChG) microresonators in the optical parametric oscillation (OPO) process by our homemade in-situ dispersion measurement. By fine thermal annealing post-treatm... 详细信息

ISBN: (数字)9781957171050

ISBN: (纸本)9781665466660

关键词： Chalcogenides Photorefractive effect Photorefractive materials optical variables measurement Nonlinear optical devices Microcavities System-on-chip

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Design of a Third-Order Filter with 10 GHz Center Frequency 9th

Design of a Third-Order Filter with 10 GHz Center Frequency

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9th International conference on Communications, Signal processing, and systems, CSPS 2020

作者： Wang, Hai Wang, Zhihong Sun, Guiling Guo, Rong He, Ming Zhang, Yi Zhang, Shengli Teaching Center for Experimental Electronic Information College of Electronic Information and Optical Engineering Nankai University Tianjin300350 China Department of Electronic Information Science and Technology College of Electronic Information and Optical Engineering Nankai University Tianjin300350 China Institute of Photoelectronic Thin Film Devices and Technology College of Electronic Information and Optical Engineering Nankai University Tianjin300350 China

ISBN: (纸本)9789811584107

Filters are extensively applied in the field of communication artificial intelligence. The input–output coupling structure and the stage coupling structure of substrate integrated waveguide (SIW) resonator are introduced in this paper. The transmission characteristics of SIW are studied by means of the electromagnetic field distribution and surface current of rectangular waveguide. Combined with the basic theory and the general design method of coupled resonant filter, as well as the metal via hole design requirements of SIW, a third-order filter with center frequency of 10 GHz, relative bandwidth of about 2.4%, and return loss in the pass band better than −12.5db is designed by using the electromagnetic simulation software HFSS. © 2021, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Substrate integrated waveguides

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Implicit Channel Learning For Machine Learning Applications In 6G Wireless Networks

Implicit Channel Learning For Machine Learning Applications ...

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Acoustics, Speech, and Signal processing Workshops (ICASSPW), IEEE International conference on

作者： Ahmet M. Elbir Wei Shi Kumar Vijay Mishra Anastasios K. Papazafeiropoulos Symeon Chatzinotas Interdisciplinary Centre for Security Reliability and Trust University of Luxembourg Luxembourg School of Information Technology Carleton University Ottawa Canada United States DEVCOM Army Research Laboratory Adelphi USA University of Hertfordshire Hatfield U. K

With the deployment of the fifth generation (5G) wireless systems gathering momentum across the world, possible technologies for 6G are under active research discussions. In particular, the role of machine learning (ML) in 6G is expected to enhance and aid emerging applications such as virtual and augmented reality, vehicular autonomy computer vision and internet of everything. This will result in large segments of wireless data traffic comprising image, video and speech. The ML algorithms process these for classification/recognition/estimation through the learning models located on cloud servers. This requires wireless transmission of data from edge devices to the cloud server. Channel estimation, handled separately from recognition step, is critical for accurate learning performance. Toward combining the learning for both channel and the ML data, we introduce implicit channel learning to perform the ML tasks without estimating the wireless channel. Here, the ML models are trained with channel-corrupted datasets in place of nominal data. Without channel estimation, the proposed approach exhibits approximately 60% improvement in image and speech classification tasks for diverse scenarios such as millimeter wave and IEEE 802.11p vehicular channels.

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Robust Adaptive Quantum-Limited Super-Resolution Imaging

Robust Adaptive Quantum-Limited Super-Resolution Imaging

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Asilomar conference on Signals, systems & Computers

作者： Tianrui Tan Kwan Kit Lee Amit Ashok Animesh Datta Boulat A. Bash Department of Electrical and Computer Engineering University of Arizona Tucson AZ Wyant College of Optical Sciences University of Arizona Tucson AZ Department of Physics University of Warwick Coventry UK

Inspired by the results from quantum information processing, spatial-mode demultiplexing offers resolution of objects at the quantum limit, below the well-known Rayleigh-Abbe diffraction limit. An intriguing aspect of these results is that, while the analysis leading to the ultimate sub-diffraction limit is quantum, semi-classical devices can be used to build “super-resolving” sensors that achieve this limit. Therefore, these developments have potential to significantly improve the quality of imaging systems in the near term. However, these quantum-inspired systems are sensitive to fluctuation in nuisance parameters, whether they are natural or adversarial. In this paper we analyze the impact of such fluctuations and provide methods to mitigate it. We focus on a problem of resolving two point sources, which has immediate practical applications in, e.g., space situation awareness.

关键词： Fluctuations Perturbation methods Superresolution Imaging Estimation Receivers information processing

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Applications, analytics, and algorithms—3 a’s of stream data: A complete survey 3rd

Applications, analytics, and algorithms—3 a’s of stream da...

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3rd International conference on Big-Data and Cloud Computing, ICBDCC 2019

作者： Amudha, L. Pushpalakshmi, R. Department of CSE K. Ramakrishnan College of Engineering TiruchirappalliTamil Nadu India Department of Information Technology PSNA College of Engineering and Technology DindigulTamil Nadu India

ISBN: (纸本)9789811552847

Recent decades have shown tremendous growth in both hardware and software. Today, data is generated everywhere from schools, colleges, hospitals, institutions, industries, supermarkets, railway stations, traffic system, communication industry, and so on. Most of this data is generated through digital devices and electronic gadgets. This digital flooding paved the way for business analytics where the data can be analyzed on the business perspective to identify the needs and scope of the consumers and thereby to increase the profit margin. Particularly nowadays, streaming data is generated abundantly everywhere. Storing, processing, and analyzing stream data in real time are a major challenge today. Time-critical applications generate fast streams of temporal data. Analytics with stream data is meaningful only if there is quick and immediate response. Delayed response is of no use in the case of stream data analytics. This paper does an extensive study of the applications, analytic methods, and algorithms that can be applied on continuous streaming data to achieve better performance. A comparative analysis of the traditional, deep learning and reinforcement algorithms is also described. Finally, the challenges in handling stream data are analyzed and defined along with its future scope. © Springer Nature Singapore Pte Ltd 2021.

关键词： Digital devices

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Fine-tuned LSTM-Based Model for Efficient Honeypot-Based Network Intrusion Detection System in Smart Grid Networks

Fine-tuned LSTM-Based Model for Efficient Honeypot-Based Net...

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International conference on Communications, Signal processing, and their Applications (ICCSPA)

作者： Abdullatif Albaseer Mohamed Abdallah Division of Information and Computing Technology College of Science and Engineering Hamad Bin Khalifa University Doha Qatar

ISBN: (纸本)9781665482387

Honeypot is considered a powerful complement to the Network Intrusion Detection System (NIDS) in smart grid (SG) systems, which minimizes the workload of NIDSs while providing access to information about the attacker's actions. This assists in further tracing the attack surface and, in return, enables the NIDSs to prevent such behaviors. Machine learning (ML) has recently attracted considerable attention in the SG security domain as a stringent technique for designing and implementing algorithms to predict security threats. However, large data sets collected by honeypots require more effort for faster response, real-time processing, and decision-making, especially for limited resources SG's devices. Thus, this paper proposes an approach to address this challenge, including feature extraction, oversampling and weak label combinations. We demonstrate that all classic ML algorithms cannot maintain the desired performance level when reducing the number of selected features (i.e., using only 25% of the features). As a result, we resort to the Deep Learning approach and propose an LSTM-based model that outperforms the state-of-the-art in terms of accuracy, precision, recall, and f1-score. We conduct extensive simulations using a realistic dataset that includes large log files. The proposed approach can employ just 25% of the features from each collected network packet while attaining 99.8% testing accuracy with a 13% improvement compared to the benchmarks.

关键词： Performance evaluation Training Decision making Network intrusion detection Signal processing algorithms Prediction algorithms Feature extraction

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User-Centric Drink Customization with Cloud-Powered Collaborative Filtering with Raspberry Pi Integration

User-Centric Drink Customization with Cloud-Powered Collabor...

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International conference on Reliability, Infocom Technologies and Optimization (ICRITO) (Trends and Future Directions)

作者： S Gowriswari S Shafla Fathima D. Antony Joseph Rajan N. Mohankumar V. Sasirekha M. Rajmohan Department of Instrumentation and Communication Engineering Sri Sairam Engineering College Chennai Tamil Nadu India Department of Information Technology B.S. Abdur Rahman Crescent Institute of Science and Technology Chennai Tamil Nadu India Department of Computer Science and Engineering Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Chennai Tamil Nadu India Department of Electronics and Communication Engineering B.V. Raju Institute of Technology Telangana India Faculty of Management Studies SRM Institute of Science and Technology Chennai Tamil Nadu India Department of Electronics and Communication Engineering Hindustan Institute of Technology and Science Chennai Tamil Nadu India

ISBN: (数字)9798350350357

ISBN: (纸本)9798350350364

This paper proposes an approach for user-centric drink customization by combining Raspberry Pi technology with cloud-powered collaborative filtering. By using collaborative filtering algorithms housed in cloud architecture, the proposed solution seeks to improve the beverage customization experience. A specialized Raspberry Pi interface allows users to input their preferences and comments, which are automatically included in the collaborative filtering mechanism. With the help of this cloud-based system, user data can be processed and analyzed in real time, enabling personalized drink suggestions. Adding Raspberry Pi devices to a system makes it more responsive and makes data processing more localized, which is great for users. The combination of Raspberry Pi with cloud-powered collaborative filtering shows that it can provide a highly customized and adaptable drink experience. This study adds to the growing body of knowledge in cloud-based collaborative filtering and Internet of Things (IoT) applications, and it provides a potential way to improve customer happiness in the beverage business.

关键词： Cloud computing Collaborative filtering Predictive models Market research Data processing Real-time systems Reliability

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Hash Based Message Authentication Code Performance with Different Secure Hash Functions

Hash Based Message Authentication Code Performance with Diff...

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International conference on Signal processing and Communication (ICSC)

作者： Ankush Soni Sanjay K Sahay Vaishali Soni Department of CSIS BITS Pilani K. K. Birla Goa Campus Goa India Department of Information Technology Netaji Subhas University of Technology Dwarka Delhi India

ISBN: (数字)9798331511890

ISBN: (纸本)9798331511906

Hash-based Message Authentication Code (HMAC) is a widely used cryptographic algorithm that combines a hash function with a secret key to ensure the integrity and authenticity of data. The performance of HMAC relies on the underlying hash function. In the past, Message Digest - 5 (MD5) and Secure Hash Algorithm - 1 (SHA-1) were commonly used for HMAC, but due to exponential increase of computational power, both MD5 and SHA-1 are no more secure. Consequently, Secure Hash Algorithm - 2 (SHA2-256, SHA2-384, SHA2-512) and Secure Hash Algorithm -3 (SHA3) are recommended. In these, SHA2-256 are mostly used in high end applications viz. Bitcoin, Blockchain, Smart Communication, Transport Layer Security (TLS) etc. However, understanding the quantum computation power, SHA2-512 and SHA3-512 shall be used and also it will be match the security level of AES-256. Therefore, in this paper present the comprhensive performance of the HMAC with SHA2-256, SHA2-512 and SHA3-512 with input message sizes ranging from 1 KB to 1 GB on three different platforms viz. intel i7 processor running Ubuntu and Windows, and resource-constrained Raspberry Pi 4B system. The analysis shows that SHA2-512 is significantly more efficient than SHA2-256 and SHA3-512 across all platforms and obviously provide double security than SHA2-256 which is mostly used. The experimental analysis shows that SHA2-512 processes 1 GB of data ~70% faster than SHA2-256 in i7 processors and ~90% faster on resource-constrained devices like Raspberry Pi. Hence, SHA2-512 shall be used in HMAC or in any other application which demands huge power for the systems e.g., Bitcoin or other blockchain which basically demands high security and efficient hash function. The findings show the potential of SHA2-512 and needs to replace SHA-256 in HMAC or any other applications/systems which uses hash functions.

关键词： Hash functions Codes Quantum computing Signal processing algorithms Bitcoin Signal processing Distance measurement Blockchains Message authentication

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