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

共 3200 条记录，以下是391-400 订阅

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Concentration prediction of binary mixed gases based on random forest algorithm in the electronic nose system 13

Concentration prediction of binary mixed gases based on rand...

引用

13th International conference on Signal processing systems, ICSPS 2021

作者： Wang, Tao Wu, Yu Zhang, Yongwei Lv, Wen Chen, Xinwei Yang, Zhi Department of Micro/Nano Electronics School of Electronic Information and Electrical Engineering Shanghai Jiao Tong University Shanghai200240 China

ISBN: (数字)9781510653184

ISBN: (纸本)9781510653177

The concentration prediction of mixed gases is crucial to the pattern recognition research of electronic nose (E-nose) systems. The response experiments of the E-nose towards the ethanol and propanol mixture with different concentrations are carried out. Five kinds of machine learning algorithms, including linear regression, support vector machine, K-nearest neighbor, random forest, and decision tree, are used for training the multiple output regressors to predict the content of each component simultaneously. The R2 score, root mean squared error, and mean absolute error are used to evaluate the performance of these models. The relationship between prediction accuracy and concentration distribution has also been studied. The results show that the model based on the random forest has superior performance for forecasting the concentration of ethanol and propanol, with the R2 score more than 0.98 in the 5-fold cross-validation. This study provides a significant inspiration for designing a multi-output regression model to realize the quantitative prediction of mixed gases by the E-nose. © COPYRIGHT SPIE.

关键词： Electronic nose

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Passive Isolator Based on Electromagnetically Induced Transparent Coupling Resonance in Metamaterials

Passive Isolator Based on Electromagnetically Induced Transp...

引用

International conference on Communications, information System and Computer Engineering (CISCE)

作者： Xinming Fu College of Science University of Shanghai for Science and Technology Shanghai China

ISBN: (数字)9798350353174

ISBN: (纸本)9798350353181

Non-reciprocal optical devices in integrated systems on a chip have attracted great interest due to the increasing demand for researching integrated photonics and silicon photonics. The non-reciprocal devices include optical isolators, circulators, and optical amplifiers. In this paper, We analyze the dynamic nonreciprocity of an optical isolator based on the principle of electromagnetically induced transparency, and show a comparison of the nonreciprocal transmission when waves are transmitted forward and backward. The results show that when energy is transmitted, this isolator can realize signal isolation with specific direction transmission at non-single frequency. This device can be used in a wide range of signal processing and optical communication applications.

关键词： Isolators Stimulated emission Resonant frequency optical resonators Metamaterials System-on-chip optical amplifiers

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Enhancing Delay-Sensitive Task Offloading: A Multi-agent Deep Reinforcement Learning Algorithm for MEC-Based AIoT systems 24th

Enhancing Delay-Sensitive Task Offloading: A Multi-agent Dee...

引用

24th International conference on algorithms and Architectures for Parallel processing, ICA3PP 2024

作者： Tang, Fengjie Xu, Jia Liu, Xiao Yao, Aiting Fang, Mingyan Li, Xuejun Anhui Provincial International Joint Research Center for Advanced Technology in Medical Imaging School of Computer Science and Technology Anhui University Hefei China School of Information Technology Deakin University Geelong Australia

ISBN: (纸本)9789819615414

Artificial Intelligence of Things (AIoT) systems are widely utilized in various domains such as smart logistics and smart health. In AIoT systems, computational tasks earmarked for offloading by IoT devices often have different task priorities and heterogeneous resource requirements. However, these factors will affect the QoS of the solution and the task response time. Therefore, motivated by a typical smart warehouse system, a Multi-access Edge Computing (MEC) based distributed task offloading framework is proposed. Afterward, a delay-sensitive mixed-integer programming model is defined to address the imbalanced utilization of computing resources between edge and cloud servers during the offloading process. Finally, based on the attention mechanism, a Priority-Driven Multi-Agent Deep Reinforcement Learning algorithm (PDMA-DRL) is proposed to fulfill real-time resource requirements while reducing the task response time. The PDMA-DRL algorithm consists of two agents, and each agent corresponds to a sub-problem. Comprehensive experimental results demonstrate that our proposed PDMA-DRL algorithm can outperform representative existing methods in generating delay-sensitive task offloading plans and effectively reducing the task response time. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Deep reinforcement learning

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Revolutionizing Waste Management: Harnessing the Power of IoT 7

Revolutionizing Waste Management: Harnessing the Power of Io...

引用

7th IET Smart Cities Symposium, SCS 2023

作者： Karale, Ankita Rajawat, Anand Singh Goyal, S.B. Computer Engineering Sandip Foundation’s Sandip Institute of Research Center Nashik School of Computer Science & Engineering Sandip University Nashik India Faculty of Information Technology City University Petaling Jaya Malaysia

ISBN: (纸本)9781839539831

Waste management has become a pressing problem in a world that is becoming more and more urbanised. Traditional ways of dealing with trash are usually inefficient and bad, leading to pollution, health problems, and wasted resources. As an answer to these problems, the Internet of Things (IoT) has changed the way trash is handled in a big way. This abstract looks at how smart waste management based on the Internet of Things could totally change the business of garbage disposal. Through a network of sensors and devices, the Internet of Things (IoT) connects trash cans, garbage trucks, and waste processing facilities so that they can be monitored and improved in real time. A smart waste management system that is built on IoT has many benefits. With the real-time information it gives about how much trash is being made, routes for collecting trash can be made more efficient in terms of time and fuel use. This cuts down on the number of times trash needs to be picked up, reduces the amount of greenhouse gases released, and saves money. Sensors in trash cans can also find dangerous or flammable items, so they can be dealt with right away and any problems can be avoided. Also, the Internet of Things makes it possible for predictive analytics, which help waste management officials predict how much trash will be made and use their resources well. Using smart waste sorting methods like optical sensors and machine learning algorithms, recyclable and non-recyclable trash can be correctly found and sorted. This helps people recycle more and puts less pressure on landfills. Smart trash management systems that are based on the Internet of Things (IoT) get more people involved and help them understand. Mobile apps and smart devices can tell a person when their trash will be picked up, where the nearest recycling sites are, and how much trash they make. People are encouraged to act responsibly and throw away trash in ways that are good for the environment. Smart waste management base

关键词： Internet of things

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Anti-interference Intelligent Decision-making Technology Based on Deep Reinforcement Learning

Anti-interference Intelligent Decision-making Technology Bas...

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IEEE International conference on information Communication and Signal processing (ICICSP)

作者： Shuo Ma Haitao Xiao Qinyao Li Linkun Ma The Key Laboratory of Intelligent Network and Network Security Ministry of Education China School of Information and Communication Engineering Xi'an Jiaotong University Xi'an China

ISBN: (数字)9798350355895

ISBN: (纸本)9798350355901

In recent years, with the rapid development of wireless communication technology in the military field and the proliferation of the number of various types of wireless communication devices, the battlefield electromagnetic environment has become more and more complex, which puts forward higher requirements for the anti-interference ability of communication systems. In order to enhance the reliability and adaptability of communication systems in complex electromagnetic environments, this paper combines Deep Q-Learning (DQN) with Long Short-Term Memory (LSTM), and proposes a communication anti-jamming intelligent decision- making algorithm based on DQN-LSTM, which realizes adaptive frequency use. In this method, dynamic s-greedy strategy and LSTM are utilized to improve the convergence and stability of DQN. Simulation results show better anti-jamming performance compared to traditional anti-jamming methods and traditional DQN decision-making algorithms.

关键词： Wireless communication Heuristic algorithms Decision making Signal processing algorithms Interference Deep reinforcement learning Stability analysis Electromagnetics Jamming Long short term memory

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Design of VLSI Architecture Using AES Algorithm

Design of VLSI Architecture Using AES Algorithm

引用

Artificial Intelligence For Internet of Things (AIIoT), International conference on

作者： J Indra A Shiny Pershiya Department of ECE KPR Institute of Engineering and Technology Coimbatore

ISBN: (数字)9798350372120

ISBN: (纸本)9798350372137

Security in signal processing involves implementing robust encryption techniques and authentication measures to safeguard sensitive information from unauthorized access or manipulation, ensuring the integrity and confidentiality of processed data in various applications. This paper presents a pioneering VLSI architecture merging Discrete Wavelet Transform (DWT) with robust encryption algorithms for fortified security in data processing. Aimed at embedded systems, it ensures data integrity and confidentiality. By marrying DWT’s computational efficiency for analysis and encryption prowess of AES, it enables real-time processing while safeguarding sensitive information. The design harnesses parallel processing, augmenting throughput and reducing latency for extensive data volumes. Comparative results highlight superior performance in speed, security, and resource utilization over traditional systems. This adaptable framework finds application in secure communications, biomedical signal processing, multimedia encryption, and IoT devices, addressing the critical intersection of signal processing and data security. This VLSI integration of DWT and advanced encryption presents a potent solution for secure, efficient data processing in resource-limited environments. The proposed system exhibits an on-chip power consumption of 535mW, a memory capacity of 819.922 MB, and a gain of 519.445 dB, all while maintaining a stable junction temperature of 33°C.

关键词： Performance evaluation Signal processing algorithms Very large scale integration Parallel processing Data processing Throughput Real-time systems

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Efficient and Secure Data Handling in IoT Networks: Robust Lightweight Approach

Efficient and Secure Data Handling in IoT Networks: Robust L...

引用

Innovations in High Speed Communication and Signal processing (IHCSP), International conference on

作者： Pooja Sharma Nishanthi Nishanthi Noopur Pandey Krishna Reddy B N Bhandari Mahesh Ashok S. Prabagaran Centre of Research Impact and Outcome Chitkara University Rajpura Punjab India Department of CSE Prince Shri Venkateshwara Padmavathy Engineering College Chennai Chitkara Centre for Research and Development Chitkara University Himachal Pradesh India School of Mangement - UG Management JAIN (Deemed to be University) Bangalore Karnataka IT Vishwakarma Institute of Technology Pune India Department of Mechanical Engineering Karpagam Academy of Higher Education Coimbatore India

ISBN: (数字)9798350368949

ISBN: (纸本)9798350368956

A network of physical things that are outfitted with sensors, smart networking, and RFID technology is referred to as the "Internet of Things" (IoT), which is an abbreviation for the phrase. The items in question are able to communicate with one another and share data with other systems and devices respectively. information security is of the utmost importance since by the year 2020, there will be more than fifty billion devices connected to the internet. Traditional cryptography, such as the Advanced Encryption Standard (DES) and the Advanced Encryption Standard (AES), does not take into account the necessary hardware and software, the amount of power consumed, the size of the code, or the amount of memory overhead. NLBSIT, an Energy Efficient and Low-Pooled Memory Cryptographic Algorithm, and a Robust Lightweight Algorithm are the three lightweight encryption methods that are described in this thesis. NLBSIT is an acronym for natural language based secure information transfer. These algorithms are intended for use with devices connected to the Internet of Things. In order to solve the problems that Internet of Things devices encounter, such as excessive memory usage, power consumption, and energy consumption, these algorithms have been developed.

关键词： Power demand Data handling Memory management Software algorithms Signal processing algorithms Energy efficiency Encryption Complexity theory Internet of Things Standards

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An Identification and Categorization of Plastic Material using Deep Learning Approach 3

An Identification and Categorization of Plastic Material usi...

引用

3rd International conference on Pervasive Computing and Social Networking, ICPCSN 2023

作者： Viswanadham, Yedlapalli Kasi Rani, Ravipati Sandhya Mai, Varanasi Madhu Sri Lakshmi Sudha Krishna, Vaka Naga Vamsi Vamsi, Yaragani Veera Seshadri Rao Gudlavalleru Engineering College Department of Information Technology Andhra Pradesh India

ISBN: (纸本)9798350322842

Efforts to reduce the negative effects of plastic trash are hampered by a lack of information on the local distribution of plastic items. Scientists have created AI-based computerized management systems, including deep learning and image processing algorithms, to enhance the effectiveness of preprocessing. Artificial intelligence allows for the automatic recognition of plastic trash on conveyor belts at landfills. Yet, it might be difficult to sort out the many shades of glass and plastic within a collection. To address this, one approach is to use a convolutional neural network (CNN) with intensive training. Due to their prevalence in everyday life, plastics such as polyethylene terephthalate (PET), polypropylene (PP), high-density polyethylene (HDPE), and low-density polyethylene (LDPE) pose a significant environmental threat and necessitate the development of an automated process for sorting plastic garbage. PET plastic is difficult to biodegrade due to its robust and durable structure, which is composed of repeating units of ethylene glycol and terephthalic acid. PET detritus can accumulate in the environment due to its resistance to biodegradation, causing harm to flora, fauna, and ecosystems. The strategy that may be utilized on mobile devices has been developed to implement this procedure in filtration plants and people's homes to address the waste management problems that plague major cities. © 2023 IEEE.

关键词： Polyethylene terephthalates

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Leveraging Machine Learning for Predicting Molecular Refractivity in Drug Toxicology

Leveraging Machine Learning for Predicting Molecular Refract...

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International conference on Signal processing and Communication (ICSC)

作者： Saloni Joshi Reema Gabrani Ruby Beniwal Dept. of Biotechnology Jaypee Institute of Information Technology Noida UP India Dept. of Electronics and Communication Jaypee Institute of Information Technology Noida UP India

ISBN: (数字)9798331511890

ISBN: (纸本)9798331511906

The integration of electronics engineering into molecular refractivity prediction is transforming drug toxicology studies. By combining biosensors, machine learning algorithms, and high-speed data systems, scientists can better predict toxicological profiles of drugs. This interdisciplinary approach improves safety and efficacy in drug development. Electronics engineering enables real-time monitoring of drug metabolism through wearable devices and predictive models, identifying toxic metabolites early and reducing adverse reactions. These technologies enhance the assessment of metabolic processes, guiding safer drug development. Machine learning algorithms further refine predictions by analyzing large datasets, identifying toxicity patterns, and improving personalized treatment plans, ultimately reducing time and costs in toxicology testing. In this research work, machine learning classifier accuracy has been achieved 85% to identify toxic drugs and toxicity prediction accuracy by using the Bayesian Neural Network was 95.8%.

关键词： Drugs Toxicology Machine learning algorithms Accuracy Refractive index Machine learning Predictive models Real-time systems Safety Metabolism

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A Vehicle Attitudes Measurement Method Based on MEMS IMU and Integrating Road Smoothness

A Vehicle Attitudes Measurement Method Based on MEMS IMU and...

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6th International conference on Intelligent Computing and Signal processing (ICSP)

作者： Haozhe Liu Yufan Wu Sujing Song Kunpeng Li Jianping Lan Sheng Tang College of Information Science and Technology Northwest University Xi'an Shaanxi China

ISBN: (数字)9798350376548

ISBN: (纸本)9798350376555

Obtaining the accurate attitudes of vehicle is of great importance for safe operation and automatic driving. This paper researches a vehicle attitudes measurement method where the low-cost MEMS IMU devices are used, and proposes an improved attitudes measurement algorithm based on Mahony Complementary Filter. According to IRI (International Roughness Index) measured in real-time, the complementary filter coefficients are adjusted adaptively to achieve the purpose of high-precision measurement under different road conditions. The experimental verification on a self-developed prototype vehicle model indicates that the proposed method has better performance than other traditional methods on moving vehicle attitudes measurement.

关键词： Micromechanical devices Semiconductor device measurement Accuracy Roads Signal processing algorithms Filtering algorithms Signal processing Real-time systems Rough surfaces Synchronization

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