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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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Comparative Study of Image processing Neural Networks using Face Mask Dataset

Comparative Study of Image Processing Neural Networks using ...

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Advances in Electronics, Communication, Computing and Intelligent information systems (ICAECIS), International conference on

作者： Sunny Rajni Jindal Department of Computer Science & Engineering Delhi Technological University Delhi India

Over the years there has been huge improvements in the performance of Image processing algorithms due to increase in computation power of devices as well as use of Neural Networks. This paper focuses on comparison of size and performance of different Image Classification networks used widely. Each one of the machine learning models studied presents a new technique to process the input images that might be useful in different scenarios. Our main focus is to identify the framework with low latency, high accuracy as they can be used practically in surveillance equipment’s that need to be fast and accurate.

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Research on Resource Allocation Method of Unmanned Aerial Vehicle Ad Hoc Network Based on Deep Reinforcement Learning

Research on Resource Allocation Method of Unmanned Aerial Ve...

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

作者： Tao Zhang Zijia Wang Yupeng Wang Zhao Jiang School of Information Science and Engineering Southeast University Nanjing China Aviation Key Laboratory of Science and Technology on Electromagnetic Environmental Effects National Key Laboratory of Electromagnetic Information Control and Effects Shenyang China College of Electronic and Information Engineering Shenyang Aerospace University Shenyang City Liaoning Province China

ISBN: (数字)9798350376548

ISBN: (纸本)9798350376555

With the rapid and continuous development of Unmanned Aerial Vehicle(UAV) and communication technology, UAV cluster have become a preferred solution for long-distance mission execution. Additionally, Unmanned Aerial Vehicles(UAVs) are no longer confined to individual or single-task combat operations. Instead, UAVs are coordinated to form an Ad Hoc network to accomplish multiple mission objectives. As the number of missions and electronic communication devices increases, the complexity of spectrum resource allocation becomes more and more complex. In order to address the spectrum allocation challenges faced by multiple onboard systems within the UAV Ad Hoc network during mobility flights, this paper proposes an implicit spectrum resource allocation algorithm based on deep reinforcement learning, where the ground station and UAVs are viewed as intelligent agents in the deep reinforcement learning, addressing communication issues among different onboard systems. By leveraging the past experience through Q-learning and utilizing deep learning to train the spectrum allocation strategies, the proposed algorithm aims to achieve a better system performance. Simulation results demonstrate that the proposed spectrum resource allocation algorithm based on deep reinforcement learning can reasonably allocate spectrum resources according to various onboard systems and communication environment with benefits on system data rate.

关键词： Q-learning Simulation System performance Signal processing algorithms Clustering algorithms Signal processing Autonomous aerial vehicles Deep reinforcement learning Ad hoc networks Resource management

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Improved YOLOv5 Algorithm In Quadcopter UAV Target Detection

Improved YOLOv5 Algorithm In Quadcopter UAV Target Detection

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2024 International conference on Artificial Intelligence and Machine Learning Research, CAIMLR 2024

作者： Hikmatullah, Amini Amr, Ahmed S.S. Matiullah, Aminy School of Information and communication Engineering University of Electronic Science and Technology of China Sichuan Chengdu 611731 China School of Computer Science Engineering Shikh Zayed University Khost Khost 2557 Afghanistan

ISBN: (纸本)9781510691797

The rapid advancement of Unmanned Aerial Vehicles (UAVs), intelligent monitoring systems, and related technologies has heightened the need for efficient small target detection, emphasizing the importance of developing more effective algorithms. To address the difficulties in accurately detecting small and medium-sized targets in video, this paper introduces an algorithm specifically for detecting slow-moving small targets. Traditional image processing techniques are used for identifying slow-moving small targets. The paper proposes a deep learning-based algorithm that enhances feature extraction by incorporating a CBAM attention module into the YOLOv5 backbone network. The BiFPN structure replaces the PANet structure in the neck network to better utilize underlying features. Additionally, a high-resolution detection head is integrated into the detection head to improve the ability to detect small, slow-moving targets. The algorithm is trained on the VisDrone2019 UAV dataset, and experimental comparisons demonstrate that the proposed slow-moving small target detection algorithm outperforms the standard YOLOv5, achieving a 13% improvement in mAP for small target detection in video. © 2025 SPIE.

关键词： BiFPN Deep learning small target detection UAV

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Lightweight YOLOv4 Algorithm for Remote Sensing Image Detection 14

Lightweight YOLOv4 Algorithm for Remote Sensing Image Detect...

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14th International conference on Signal processing systems, ICSPS 2022

作者： Ma, Li He, Tong Sun, Yong Hu, Bin-Bin College of Information Liaoning University Shenyang China

ISBN: (纸本)9798350336313

Remote sensing images have the characteristics of complex backgrounds, high resolution, and small targets. Although the existing object detection algorithms can improve the detection accuracy, there are generally problems such as a large number of model parameters, high computational cost, and poor real-time performance. Aiming at the above problems, this paper designs a lightweight object detection algorithm GSC-YOLO based on YOLOv4 to achieve fast and accurate detection of remote sensing images. First, Ghostnet is used as the feature extraction network of GSC-YOLO to reduce the number of parameters and improve the detection speed;Secondly, the improved shuffle attention mechanism is introduced in the prediction head to make the model pay attention to important information and improve the detection accuracy;Finally, the Confidence Propagation Cluster algorithm CP-Cluster is used to post-process the prediction frame to improve the object recognition. Taking the preprocessed DOTA dataset as the experimental object, the experimental results show that the GSC-YOLO algorithm has a detection accuracy of 93.44%, a detection speed of 58 frames per second, and a model size of 43.65MB. Compared with the remote sensing image object detection algorithm based on YOLOv4, the detection accuracy is increased by 3.93%, the detection speed is increased by 1.87 times, and the model size is reduced by 5.62 times, which is more suitable for deployment on devices with limited resources. © 2022 IEEE.

关键词： Object recognition

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Towards a Lightweight Stress Prediction Model: A Study on Dimension Reduction and Individual Models in HRV Analysis 29

Towards a Lightweight Stress Prediction Model: A Study on Di...

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29th IEEE International conference on Parallel and Distributed systems, ICPADS 2023

作者： Cui, Zeyang Ma, Yanbo Ma, Muxin Huang, Runhe Du, Bowen The Hong Kong Polytechnic University Hong Kong Hong Kong Pontosense Inc. Canada Hosei University Faculty of Computer and Information Sciences Japan Zhongguancun Laboratory Beijing China Beihang University Beijing China

ISBN: (纸本)9798350330717

Occupational stress has emerged as an undeniable concern. Fortunately, leveraging IoT and AI technologies allows us to gather vital sign data and assess cardiovascular health, individual stress levels, physiological resilience, and emotional states. This study highlights the potential of Heart Rate Variability (HRV) analysis in constructing stress prediction models, with a specific focus on developing an efficient model with minimal data requirements. Convolutional Neural Networks (CNN) have been employed to process raw waveform data for feature extraction. Simultaneously, R-R Interval (RRI) analysis was conducted to derive a set of statistical features. Various dimension reduction algorithms Principal Component Analysis (PCA), Independent Component Analysis (ICA), and Random Projection (RP) were tested, and PCA demonstrated its significance in reducing dataset complexity, enabling swift model training without compromising accuracy excessively. This research aims to explore the feasibility of predicting an individual's stress levels using minimal and simpler data. The objective is to pave the way for predictive models to be integrated onto lightweight platforms such as millimeter-wave chips. Our approach emphasizes non-contact monitoring of heartbeat variations, particularly beat-to-beat intervals (BBI), offering a novel method for non-invasive stress detection suitable for real-time applications on compact devices. © 2023 IEEE.

关键词： Dimension Reduction Heart Rate Variability Principal Component Analysis Stress Prediction

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Wireless Broadband Ad Hoc Network Communication Algorithm Based on IGS Technology

Wireless Broadband Ad Hoc Network Communication Algorithm Ba...

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Mobile Networks and Wireless Communications (ICMNWC), IEEE International conference on

作者： Haigang Wang Liming Ren Chen Dai Jiafeng Jiang School of Information Science and Technology Tsinghua University Beijing China School of College of Electronic Information and Electrical Engineering Hefei University Hefei Anhui China Department of Computer Engineering Nanjing University of Engineering Nanjing Jiangsu China School of Information Engineering Inner Mongolia University of Science and Technology Baotou Inner Mongolia China

ISBN: (数字)9798331528348

ISBN: (纸本)9798331528355

In response to the problems faced by traditional wireless broadband ad hoc network communication algorithms in dealing with complex and changing network environments, this paper introduces the IGS (Imperfer Gaussian Signaling) technology based on wide linear new waveforms, aiming to improve the transmission efficiency and anti-interference ability of devices. Firstly, the study aims to optimize communication requirements in multiple scenarios of power business; then, the use of IGS technology is studied to convert non-circular signals into Gaussian distribution signals; finally, the study is conducted on intelligent routing algorithms based on wide linear new waveform to optimize path selection between nodes and dynamically adjust topology connections, thereby enhancing the system's self-organization capability and network throughput. As the number of nodes continues to increase to 500, the latency of traditional systems has risen to 33 milliseconds. Wireless broadband self-organizing network communication devices based on IGS technology provide efficient and stable solutions for large-scale self-organizing network applications in the power industry.

关键词： Wireless communication Heuristic algorithms Signal processing algorithms Self-organizing networks Routing Ad hoc networks Power industry Broadband communication Optimization Business

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Prediction of enhancer-promoter interaction based on ResNeXt

Prediction of enhancer-promoter interaction based on ResNeXt

引用

2022 International conference on Advanced algorithms and Neural Networks, AANN 2022

作者： Gong, KeCun Zhou, MengLin Rui, AiSha The Key Laboratory for Computer Systems of State Ethnic Affairs Commission Southwest Minzu University Sichuan Chengdu610041 China School of Computer Science and Engineering Southwest Minzu University Sichuan Chengdu610041 China School of Software Engineering Chengdu University of Information Technology Sichuan Chengdu610225 China

ISBN: (纸本)9781510655751

Aiming at the low accuracy of traditional algorithms in predicting Enhancer-Promoter Interactions (EPIs) in the human genome, an EPIs prediction network based on ResNeXt and attention mechanism was proposed. In the data processing stage, the gene sequence data of a small number of positive samples in the data set is expanded to be consistent with the number of negative samples;then an EPIRNX model is constructed for feature selection and extraction for a given gene sequence, and long-distance features are mined for use in This cell line prediction;the transfer learning model EPIRNX-Transfer was also trained for cross cell line prediction. Using AUROC and AUPRC as evaluation indicators, EPIRNX can better predict EPIs in this cell line than traditional models, and EPIRNX-Transfer can better predict EPIs across cell lines. © 2022 SPIE.

关键词： Forecasting

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optical computing with spatiotemporal fiber nonlinearities

Optical computing with spatiotemporal fiber nonlinearities

引用

conference on Lasers and Electro-Optics Europe / European Quantum Electronics conference (CLEO/Europe-EQEC)

作者： Tegin, Ugur Yildirim, Mustafa Oguz, Ilker Moser, Christophe Psaltis, Demetri Ecole Polytech Fed Lausanne Lab Appl Photon Devices Lausanne Switzerland Ecole Polytech Fed Lausanne Opt Lab Lausanne Switzerland

ISBN: (纸本)9781665418768

Optics offers significant advantages for information processing and artificial intelligence in terms of information density, speed, and energy efficiency compared to electronics. Starting with the Hopfield model’s implementation, the initial optical computing studies focused on mimicking trainable artificial neural network (ANN) architectures [1] . Including memory storage methods with holography, various methods were proposed, yet the controllability level of the training optical networks still challenging [2] . In the last decade, brain-inspired ANN designs operating based on projection of information to high order dimension with a fixed parameter ANN structure followed by a trainable decision layer were introduced [3] , [4] . The fixed network structure makes fixed-parameter ANN designs easy to implement with physical systems and various optical implementations were demonstrated [5] . However, optical nonlinear activation implementation was challenging to realize, and generally digital signal processing tools are employed [6] .

关键词： optical fibers Training Artificial neural networks optical computing optical fiber networks Holography Tools

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Designing a Medical Fuzzy Expert System Using the JFuzzyLogic Library

Designing a Medical Fuzzy Expert System Using the JFuzzyLogi...

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Annual Siberian Russian Workshop on Electron devices and Materials (EDM)

作者： Rustam A. Burnashev Ruslan M. Bagymanov Adelya I. Enikeeva Rustam R. Farahov Marina V. Bolsunovskaya Institute of Computational Mathematics and Information Technologies Kazan Federal University Kazan Russia Institute of Computer Science and Technology Peter the Great St. Petersburg Polytechnic University St. Petersburg Russia

ISBN: (数字)9798350389234

ISBN: (纸本)9798350353051

The article presents the processes of designing a medical expert system using software tools that work with fuzzy logic. Using a simplified fuzzy inference algorithm, it was possible to achieve the fuzziness of the system by creating a knowledge base of fuzzy rules that are adjusted to take into account changes in input data (fuzzification) and output data (defuzzification). The knowledge base was formed using the open source software library JFuzzyLogic (Java programming language). The study developed a UML activity diagram and identified the steps that researchers can use to create their own intelligent systems. A unified system that combines the components of a medical information system with the created knowledge base will contribute to more efficient data processing and analysis in scientific research. Filling and processing of the knowledge base of the expert system is available to an expert of the subject area. The created knowledge base includes all the necessary functions for processing fuzzy data, setting the necessary weights for input, output and data processing.

关键词： Fuzzy logic Java Software libraries Software algorithms Unified modeling language Medical information systems Software systems

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Exploring the trade-off between deep-learning and explainable models for brain-machine interfaces 38

Exploring the trade-off between deep-learning and explainabl...

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38th conference on Neural information processing systems, NeurIPS 2024

作者： Cubillos, Luis H. Revach, Guy Mender, Matthew J. Costello, Joseph T. Temmar, Hisham Hite, Aren Zutshi, Diksha Wallace, Dylan M. Ni, Xiaoyong Kelberman, Madison M. Willsey, Matthew S. van Sloun, Ruud J.G. Shlezinger, Nir Patil, Parag Draelos, Anne Chestek, Cynthia A. Departments of Electrical & Computer Engineering Biomedical Engineering Robotics Computational Medicine & Bioinformatics and Neurosurgery University of Michigan United States Biointerfaces Institute Neuroscience Institute University of Michigan United States Department of Information Technology and Electrical Engineering ETH Zürich Switzerland Department of Electrical Engineering Eindhoven University of Technology Netherlands School of Electrical and Computer Engineering Ben-Gurion University Israel

People with brain or spinal cord-related paralysis often need to rely on others for basic tasks, limiting their independence. A potential solution is brain-machine interfaces (BMIs), which could allow them to voluntarily control external devices (e.g., robotic arm) by decoding brain activity to movement commands. In the past decade, deep-learning decoders have achieved state-of-the-art results in most BMI applications, ranging from speech production to finger control. However, the'black-box' nature of deep-learning decoders could lead to unexpected behaviors, resulting in major safety concerns in real-world physical control scenarios. In these applications, explainable but lower-performing decoders, such as the Kalman filter (KF), remain the norm. In this study, we designed a BMI decoder based on KalmanNet, an extension of the KF that augments its operation with recurrent neural networks to compute the Kalman gain. This results in a varying "trust" that shifts between inputs and dynamics. We used this algorithm to predict finger movements from the brain activity of two monkeys. We compared KalmanNet results offline (pre-recorded data, n = 13 days) and online (real-time predictions, n = 5 days) with a simple KF and two recent deep-learning algorithms: tcFNN (non-ReFIT version) and LSTM. KalmanNet achieved comparable or better results than other deep learning models in offline and online modes, relying on the dynamical model for stopping while depending more on neural inputs for initiating movements. We further validated this mechanism by implementing a heteroscedastic KF that used the same strategy, and it also approached state-of-the-art performance while remaining in the explainable domain of standard KFs. However, we also see two downsides to KalmanNet. KalmanNet shares the limited generalization ability of existing deep-learning decoders, and its usage of the KF as an inductive bias limits its performance in the presence of unseen noise distributions. Despite thi

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