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共 1333 条记录，以下是151-160 订阅

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Underwater Image Restoration Based on Marine Snow Removal Techniques 2

Underwater Image Restoration Based on Marine Snow Removal Te...

引用

2nd international conference on signal processing and Intelligent Computing, SPIC 2024

作者： Li, Yiping Niu, Ben Zhou, Tianwei Gao, Hongwei Shenyang Ligong University School of Automation and Electrical Engineering Shenyang China Shenzhen University Greater Bay Area International Institute for Innovation Shenzhen China

ISBN: (纸本)9798350368888

Image capture and analysis in underwater environments have applications in many fields, such as underwater archaeological exploration, deep-sea biological research, oil and gas resource exploration, and path navigation of intelligent underwater robots. Existing underwater image restoration methods usually face problems such as high model complexity and the inability to be deployed on actual underwater operating platforms. This paper proposes an efficient and fast underwater image restoration method called UIRF-Net to address these issues. This method can restore underwater images and remove marine snow phenomena. UIRF-Net makes the deep learning network more lightweight by using modules such as channel extraction prior information and RGB three-channel color enhancement, improving the image color while ensuring the image quality and processing speed. Through experiments on the UIEB-Withsnow dataset, it is verified that UIRF-Net can produce clear underwater images from photos containing complex marine snow noise. This study provides a comprehensive enhancement solution for the field of underwater image restoration, which significantly improves the clarity of underwater photos. © 2024 IEEE.

关键词： Underwater photography

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Advanced Deep learning for Emotion Recognition: A Hybrid Model Combining Spatial and Temporal Analysis 2

Advanced Deep Learning for Emotion Recognition: A Hybrid Mod...

引用

2nd international conference on Recent Trends in Microelectronics, Automation, Computing, and Communications Systems, ICMACC 2024

作者： Mehta, Shiva Bhalla, Anubhav Chitkara University Institute of Engineering and Technology Centre for Research Impact & Outcome Punjab Rajpura140401 India Chitkara University Chitkara Centre for Research and Development Himachal Pradesh 174103 India

ISBN: (纸本)9798350366570

It is necessary to distinguish that emotion recognition is among the significant tasks in various fields, including human-computer interaction, affective computing, and psychological research. This research presents a new deep learning model that incorporates two types of deep learning: CNNs to address the spatial aspect of face emotion detection and RNNs for the temporal aspect of face emotion detection. We have employed two widely used datasets, FER-2013 and AffectNet, to evaluate the proposed model. On FER-2013, the hybrid model achieved a classification score of 79.8 percent, while AffectNet achieved 85.3 percent, combining higher levels than traditional CNN-based models. As for the F1 scores reached 79.5% and 85.5%, showing an improved ability to categorize subtle differences in emotions. The confusion matrix analysis showed enhancement of the separation between similar feelings, for instance, fear and surprise, compared to the baseline CNN models. The model's generalizability was done with other datasets, with FER-2013 yielding an AUC of 0.88 and AffectNet giving an AUC of 90. This strong generalization makes the hybrid model suitable for applied applications where the facial expression data might have frequent significant changes. CNN and RNN integration helped the model to understand both static and temporal features of the emotion and made the model catch the complex changes of the emotions accurately. These results support this blended approach for advancing emotion recognition technology and providing reliable solutions for emotion-aware systems. © 2024 IEEE.

关键词： Contrastive learning

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Resource Allocation in IoT-based Edge Computing using Deep learning with Optimization Model 2

Resource Allocation in IoT-based Edge Computing using Deep L...

引用

2nd international conference on Electronics and Renewable Systems, ICEARS 2023

作者： Algani, Yousef Methkal Abd The Arab Academic College for Education in Israel-Haifa Department of Mathematics Israel

ISBN: (纸本)9798350346640

This study examines the problem of allocating resources for edge computing in IoT networks. In this model, each end device acts independently in deciding whether or not to offload compute chores to itself. As network states, the received signal from end nodes and the point of entry, the computation job column, and the leftover information processing source of the end nodes are considered in order to lessen the long-term summation cost, which encompasses the power requirements and the total completion latency. Cloud computing is the foundation of edge computing, which simply relocates processing, archiving, and connecting nodes closer to the data itself. The IoT architecture is akin to cloud computing in every respect. Limiting latency while making optimal use of energy is a major challenge in edge computing when processing activities provided by IoT devices. Deep learning technology is used to the issue of a chain of choices having to be made at the end devices. Starting with the challenges of resource management in cellular IoT and low-power IoT networks, the usual resource management solutions for these systems justify DL approaches. In future, deep learning research will be recommended for IoT network resource organization. © 2023 IEEE.

关键词： Resource allocation

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Comparative Analysis Between a Deep learning Model and a Region-Based Active Contour Model for Pulmonary Nodules Segmentation on CT 2

Comparative Analysis Between a Deep Learning Model and a Reg...

引用

2nd international conference on Electronics, Energy and Measurement, IC2EM 2023

作者： Beghoura, Imane Hamoudi, Riad Benssalah, Mustapha Sbargoud, Fazia Ecole Militaire Polytechnique Signal Processing Laboratory Algiers Algeria Ecole Militaire Polytechnique Comlplexe Systems Control Laboratory Algiers Algeria

ISBN: (纸本)9798350314243

Accurate segmentation of lung nodules on computed tomography (CT) scans plays a crucial role in assessing and managing suspected lung cancer patients. This study evaluates two approaches based on active contour and deep learning for pulmonary nodule segmentation on CT scans. The experiments were performed on a data set of 383 images from 111 patients with suspected lung nodules. The results showed that the method based on deep learning exceeded the conventional method of capturing complex patterns. The Jaccard and Dice coefficients obtained were 0.758 and 0.856, respectively, for the deep learning method, compared with 0.601 and 0.726 for the conventional method. A qualitative analysis of the segmentation results was conducted on juxta pleural, juxta-vascular, and well-circumscribed nodules to evaluate each approach's performance, strengths, and weaknesses in different scenarios. © 2023 IEEE.

关键词： Computerized tomography

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Generation of Netlist from a Hand drawn Circuit through Image processing and machine learning 2

Generation of Netlist from a Hand drawn Circuit through Imag...

引用

2nd international conference on Artificial Intelligence and signal processing, AISP 2022

作者： Mohan, Akshatha Mohan, Athulya Indushree, B. Malavikaa, M. Narendra, C.P. Electronics and Communication Bangalore Institute of Technology Bangalore India

ISBN: (纸本)9781665442909

Circuit diagrams are used to depict electronic or electrical circuits graphically. It is simple for everyone to put their thoughts on paper. However, in order to conduct simulations in the different available tools, the circuit model needs be in digital form. This project presents several image processing and machine learning approaches for the conversion of hand-drawn circuits to netlists. Rather than training the dataset for all components, a technique based on the length ratios of a few of lines was employed to identify elements such as a voltage source, ground, and capacitor. Various image processing techniques are used to eliminate noise and prepare pictures for further processing. HOG feature extraction is utilized throughout the training and segmentation stages to detect resistor, diode, and inductor components. The final stage is to construct a netlist from the detected elements, wires, and their locations, as well as the identified nodes. © 2022 IEEE.

关键词： Feature extraction

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Research on Semantic Segmentation Algorithm of Multispectral Remote Sensing Image Based on Deep learning 2

Research on Semantic Segmentation Algorithm of Multispectral...

引用

2nd international conference on signal Image processing and Communication, ICSIPC 2022

作者： Li, Zhang Yibei, Chen Yiyong, Lin Bingxi, Dong Xinglong, Ran Xichang Satellite Launch Center Sichuan Xichang615000 China

ISBN: (纸本)9781510657694

The semantic segmentation technology of remote sensing image refers to labeling the semantic information of pixel-level of the image to complete the classification, namely, terrain classification. It is widely used in intelligent maps, smart cities and other aspects. With the increase of satellite image resolution and the development video communication moonlet, its application and scenes are greatly broadened. The traditional remote sensing image semantic segmentation method mainly uses statistical machine learning methods, which cannot take into account the spectral features and the context semantic relationship of pixels, and has a bottleneck in improving the accuracy of classification. In deep learning method, Using the convolutional neural networks to extract features can achieve classification results of remote sensing images with higher classification accuracy. Aiming at the problems of existing deep learning methods in multi-spectral image semantic segmentation, to make full use of the information of multi-spectral images, this paper proposes a semantic segmentation algorithm for multispectral remote sensing images based on deep learning and verified the method on open data sets. © 2022 SPIE.

关键词： Semantic Segmentation

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machine learning for Intricate Detection in 6G Networks 2

Machine Learning for Intricate Detection in 6G Networks

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2nd IEEE international conference on IoT, Communication and Automation Technology, ICICAT 2024

作者： Tiwari, Annapurna Nand Revathi, N. Almusawi, Muntather Prabha, Sakthi Karpagam, J. Hafeena, Md. IES College of Technology Department of Electronics & Communication Engineering M. P Bhopal India Nehru Institute of Technology T.M Department of Electronics and Communication Engineering Tamil Nadu Palayam India The Islamic University College of Technical Engineering Department of Computers Techniques Engineering Najaf Iraq Prince Shri Venkateshwara Padmavathy Engineering College Department of Computer Science and Engineering TN India Academy of Higher Education Department of Electrical and Electronics Engineering Karpagam TN India CMR Technical Campus Medchal Hyderabad India

ISBN: (纸本)9798350368109

In the world of 6G networks, the merging of micro-services-oriented designing and intelligent network management demonstrates the central role of neural networks (AI), machine learning (ML), and supervised learning (DL). This study looks into strengthening the security of 6G communications networks through the development of a new anomaly recognition System for 6G Connections (AD6GNs) utilizing ensemble learning (EL). The EL-ADCN workflow comprises prior processing, feature selection utilizing the hybrid methodology CFS-RF, and intruder detection applying hybrid EL methods. The system shows impressive performance on datasets with names like NSL-KDD, UNSW-NB2015, CIC-IDS2017, when CICDDOS2019, getting accuracy of 99.5%, 99.9%, 99.8%, while 99.95426%, respectively, with low false alarm rates. The combination of assistance vector machines (SVM) particularly random forest computing (RF) as EL techniques further improves the efficiency of packing and adaboosting in guaranteeing network that self-sustainability and preventive threat prevention in the envisioned 6G model. © 2024 IEEE.

关键词： Network intrusion

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Traffic Sign Recognition for Automated Speed Control Using Deep learning 2

Traffic Sign Recognition for Automated Speed Control Using D...

引用

2nd international conference on Recent Trends in Microelectronics, Automation, Computing, and Communications Systems, ICMACC 2024

作者： Velpula, Vijaya Kumar Prasad, S.V.S. Vadlamudi, Jyothisri Yechuri, Sivaramakrishna Miriyala, Ganesh Kumar, Manoj MLR Institute of Technology Department of Electronics and Communication Engineering Hyderabad India Malla Reddy Engineering College Department of Electronics and Communication Engineering Hyderabad India

ISBN: (纸本)9798350366570

Today's newest and most popular topic is real-time driverless cars and their applications. To meet the requirements of these systems, complex algorithms must efficiently perform essential tasks. Autonomous driving technology presents one of the biggest challenges in speed control. We present a new method for speed instruction-based motor control that uses Convolutional Neural Networks (CNNs) to predict speed limits. CNNs extract crucial information from traffic sign signatures, and the performance of our system relies on its ability to accurately identify and classify objects in images. The Arduino microcontroller uses the output of the CNN to control the speed of the robot chassis. Experimental results, both in simulations and real-world tests, demonstrate the effectiveness of our approach. The proposed model achieves a high accuracy of 96% to 97% and outperforms existing models with a faster recognition time, maintaining the required speed with minimal error. These findings highlight the potential of CNN-based systems in enhancing autonomous driving technology. © 2024 IEEE.

关键词： Convolutional neural networks

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Acceleration method for target HRRP generation based on DDPM 2

Acceleration method for target HRRP generation based on DDPM

引用

2nd IEEE international conference on signal, Information and Data processing, ICSIDP 2024

作者： Shen, Xitai Zhou, Qiang Yu, Bingqian Zhang, Xin Wang, Yanhua Zhang, Liang Beijing Institute of Technology Zhengzhou Academy of Intelligent Technology Zhengzhou450000 China Beijing Institute of Technology Radar Technology Research Institute Beijing100081 China Beijing Key Laboratory of Embedded Real-Time Information: Processing Technology Beijing100081 China Beijing Institute of Technology Chongqing Innovation Center Chongqing401120 China

ISBN: (纸本)9798331515669

The target high resolution range profile (HRRP) is the sum of the vector projected on the radar line of sight of the target scatters, which can reflect the radial structure characteristics of the target and is one of the important means of radar target recognition. Existing HRRP-based target recognition methods adopt the idea of machine learning classification, which usually require sufficient training samples to obtain satisfactory recognition performance. To solve the dilemma of insufficient radar HRRP data caused by factors such as non-cooperative target and complexity of data acquisition process, this paper uses denoising diffusion probabilistic models (DDPM) to generate HRRP data. Furthermore, to solve the problem that the DDPM generation speed is too slow to meet the actual needs, this paper designs a fast DDPM HRRP (FDH) data generation method based on progressive knowledge distillation. Finally, the experimental results show that the generation speed is increased by about 25 times compared with the initial model under the premise of losing 7.6% similarity. © 2024 IEEE.

关键词： Radar target recognition

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Improved deep residual network for chirp signal recovery in microwave photonic receiving systems 2

Improved deep residual network for chirp signal recovery in ...

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2nd international Academic conference on Optics and Photonics, IACOP 2024

作者： Wang, Chenlu Li, Yi Jiang, Jing Bai, Chaoyu Chen, Ye Wang, Li Chen, Shilin Nantong Branch of State Grid Corporation of China Nantong226006 China Computer Department Nanjing University of Science and Technology ZiJin College Nanjing210029 China Communication Department Nanjing Vocational College of Information Technology Nanjing210023 China College of Communications Engineering Army Engineering University of PLA Nanjing210007 China

ISBN: (数字)9781510688827

ISBN: (纸本)9781510688810

We demonstrate an improved deep residual network model that can recover the distorted chirp signals of the microwave photonic receiving systems. The method can achieve high-precision recovery of distorted signals by learning the time-frequency characteristics of broadband chirp signals. The simulation results show that the peak-to-floor ratio (PFR) of signal correlation function is improved by about 20.3dB compared with that without deep learning processing. The residual network model can improve the performance of the microwave photonic receiving systems and is expected to reduce the hardware requirements of the receiving systems. © 2025 SPIE.

关键词： Chirp modulation

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