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检索条件"主题词=Signal Processing Algorithms"

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Research and Development of Wireless Sensor Network Detection Software

Research and Development of Wireless Sensor Network Detectio...

引用

signal processing and Intelligent Computing (SPIC), International Conference on

作者： Yue Zhou Siming Huang Hong Wang Xiaotong Zhou Shenyang Ligong University Shenyang China New South Wales University Sydney Australia

ISBN: (数字)9798350368888

ISBN: (纸本)9798350368895

This paper investigates the technology of sensor network node parameter monitoring software and designs a complete monitoring software solution in conjunction with a specific application scenario of greenhouse monitoring. The communication data verification algorithm is thoroughly studied in this study, and the discussion and research on the nonlinear calibration algorithm of sensor parameters in data processing are conducted. The CRC cyclic redundancy algorithm and the least squares method are employed in the subsequent software design for relevant data *** real-time monitoring system is based on sensor network technology, integrating measurement and control to achieve remote monitoring. The system formats the data and uploads it to the host computer via COM serial communication according to the set transmission mode and communication protocol. Upon receiving a series of data packets, the system analyzes the data, dynamically displays it in real-time, issues normal or alarm messages, and notifies the system through different means. The software of this system demonstrates good operational performance, meeting the basic requirements of the application scenario. It possesses excellent monitoring real-time capability, openness, and compatibility, providing comprehensive information and decision support for sensor network node parameter monitoring.

关键词： Wireless sensor networks Software design Software algorithms signal processing algorithms Termination of employment Software Real-time systems Calibration Software measurement Monitoring

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SRAM: Spectral Residual Attention Module for Enhancing Shallow Features

SRAM: Spectral Residual Attention Module for Enhancing Shall...

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6th International Conference on Intelligent Computing and signal processing (ICSP)

作者： Chenhu Xiao Yanzi Li Yongsheng Liang Qingyu Mao College of Electronics and Information Engineering Shenzhen University Shenzhen China College of Big Data And Internet Shenzhen Technology University Shenzhen China

ISBN: (数字)9798350376548

ISBN: (纸本)9798350376555

Convolutional neural networks (CNNs) have achieved significant success in the field of computer vision by effectively identifying complex features within images. However, the shallow layers of CNNs are particularly susceptible to the influence of background noise, thereby compromising the quality of convolutional features. Integrating attention mechanisms into models can effectively enhance the recognition of crucial convolutional features. However, enhancing the entire feature map may inadvertently diminish the significance of certain features, potentially compromising the model's ability to accurately extract key information. To address this issue, we propose the Spectral Residual Attention Module (SRAM), which consists of the feature separation reconstruction unit (FSRU) and the salient feature detection unit (SFDU). The former separates features in the channel dimension and highlights foreground features, while the latter enhances salient features of important channels in the spatial dimension. Unlike attention mechanisms that rely on spatial domain for local assessment, SRAM achieves more effective global feature enhancement by identifying salient features in the frequency domain. In addition, SRAM incorporates traditional saliency detection algorithms, characterized by low computational costs and fewer parameters. The experimental results demonstrate that SRAM can effectively enhance shallow features, leading to superior performance in visual saliency visualization, computational complexity, and image classification compared to SIMAM.

关键词： Visualization Attention mechanisms Random access memory signal processing algorithms signal processing Feature extraction Classification algorithms Convolutional neural networks Saliency detection Residual neural networks

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Optimal OAM Transmission Techniques Using Vogel Spiral-enhanced Laguerre-Gaussian Beams in Non-Kolmogorov Turbulence

Optimal OAM Transmission Techniques Using Vogel Spiral-enhan...

引用

IEEE International Symposium on Intelligent signal processing and Communication Systems (ISPACS)

作者： Young Jae Moon Yeon Ho Chung Dept. of Artificial Intelligence Convergence Pukyong National University Busan Republic of Korea Dept. of Information and Communications Engineering Pukyong National University Busan Republic of Korea

ISBN: (数字)9798350389210

ISBN: (纸本)9798350389227

Orbital angular momentum is an intrinsic feature of all electromagnetic beams, which transmit data bits up to a THz speed with high capacity. As OAM topological charges or modes are independent, the potential of enhancing the data transmission system can easily be demonstrated. However, when the structured beam using the OAM feature passes a highly turbulent channel, the data encoded in the beam are susceptible to distortion and decoding errors. In this paper, we investigate Laguerre-Gaussian (LG) beam behaviors with OAM characteristics and reconstruction of intensity and phase in non-Kolmogorov atmospheric turbulence and also Gaussian random screen-induced turbulence environments. Specifically, we employ an adaptive U-Network and Vogel’s spiral padding, which is controllable by a topological charge added to the golden ratio. The proposed OAM transmission shows a peak signal-to-noise reconstruction ratio of 32.5 dB in mobile environments.

关键词： Spirals Atmospheric modeling signal processing algorithms Reconstruction algorithms Topology System identification Laser beams Phase distortion Terahertz communications signal to noise ratio

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Robust Decision Making for Autonomous Vehicles at Lane-Merging Scenarios: A Multi-Objective Deep Reinforcement Learning Approach

Robust Decision Making for Autonomous Vehicles at Lane-Mergi...

引用

Intelligent Control, Measurement and signal processing and Intelligent Oil Field (ICMSP), International Conference on

作者： Jun Gu Jing Ma Xiulei Zheng Yang Wang Shengfei Li Senqi Tan Shuai Chen China North Artificial Intelligence & Innovation Research Institute Beijing China China North Vehicle Research Institute Beijing China Department Collective Intelligence Collaboration Laboratory Beijing China

ISBN: (数字)9798331516550

ISBN: (纸本)9798331516567

Deep reinforcement learning (DRL) has demonstrated advanced ability to deal with complex decision making problems in autonomous driving. However, the driving demands (preferences) of passengers are hardly considered. In addition, the preferences usually change over time, how to balance these preferences still remain challenge for traditional DRL. In this paper, we present a multi-objective DRL approach considering dynamic preferences. Specifically, we model the sequential decision making problem with dynamic preferences for lane merging as markov decision process. Additionally, the random sampling with prior knowledge is advanced to reduce the sampling space and promote convergence rate. Finally, the homotopy optimization is introduced to avoid overfitting and balance the multiple preferences. We test the effectiveness of our algorithm in a lane-merging scenario. The results show that our method out performs the baseline method.

关键词： Markov decision processes Decision making signal processing algorithms signal processing Deep reinforcement learning Vehicle dynamics Optimization Autonomous vehicles Convergence Overfitting

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A Network Security Event Detection for Power Monitoring System Based on Matrix Factorization

A Network Security Event Detection for Power Monitoring Syst...

引用

6th International Conference on Intelligent Computing and signal processing (ICSP)

作者： Ye Liang Xuhui Lv Jing Wang Hang Zhao Beijing Kedong Power Control System Co. Ltd. Beijing China

ISBN: (数字)9798350376548

ISBN: (纸本)9798350376555

In the network environment of the power monitoring system, the security protection capabilities of the network boundary have been relatively complete. However, the power monitoring system has limited protection capabilities within the local area network and lacks the ability to resist attacks from internal network devices. In order to enhance the power monitoring system's ability to aware network security events within the local area network, this paper proposes a network security event detection scheme based on matrix factorization for the power monitoring system. A matrix factorization algorithm is introduced to construct a normal traffic model within the power monitoring system network, and a whitelist detection mechanism is used to identify abnormal network traffic. Experiments prove that the scheme proposed has good detection performance and can identify network security events in the local area network of the power monitoring system.

关键词： Adaptation models Accesslists Event detection signal processing algorithms Telecommunication traffic Network security Data models Protection Monitoring Local area networks

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Research on Adaptive SCMA System Based on User Communication Requirements

Research on Adaptive SCMA System Based on User Communication...

引用

6th International Conference on Intelligent Computing and signal processing (ICSP)

作者： Yongli Chen Xiaotian Guo Qihua Huang Zhihua Bai Haiqian Yin Beijing Smart-chip Microelectronics Technology Co. Ltd Beijing China Beijing University of Posts and Telecommunications Beijing China

ISBN: (数字)9798350376548

ISBN: (纸本)9798350376555

As an important and critical non-orthogonal multiple access (NOMA) technology, Sparse Code Multiple Access (SCMA) is proposed to support the massive connectivity in 5G networks. In most current SCMA studies, the user's codebook is generated from the same mother constellation. However, the existing schemes with predesigned SCMA codebooks cannot fully cope with the actual massive random access scenarios, in which different users have different requirements for transmission reliability and transmission rate. Although there have been a few studies on adaptive SCMA codebooks, the codebook can still be optimized. In this paper, we propose an adaptive SCMA system based on the requirement of user communication and optimize the design of SCMA codebook based on the Star-QAM mother constellation for different scenarios. The simulation results demonstrate that the adaptive SCMA codebook proposed in this paper exhibits improved BER performance compared to both the common non-adaptive SCMA codebooks and the existing adaptive SCMA codebooks presented in recent research for the scenarios of massive connectivity.

关键词： NOMA Adaptive systems Codes Simulation signal processing algorithms Modulation Reliability Optimization Genetic algorithms signal to noise ratio

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Microseismic Data Augmentation Based on Improved Generative Adversarial Network and Policy Gradient

Microseismic Data Augmentation Based on Improved Generative ...

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New Trends in Computational Intelligence (NTCI), International Conference on

作者： Chen Yang Li Sheng Weibo Wang Chunyu Li College of Control Science and Engineering China University of Petroleum (East China) Shandong China

ISBN: (数字)9798331517021

ISBN: (纸本)9798331517038

Microseismic data plays a crucial role in seismic exploration and oil/gas exploitation. However, its high acquisition cost leads to scarcity, making effective augmentation of microseis-mic datasets a critical issue. In this paper, a new approach based on policy gradient and generative adversarial network (GAN) is proposed, which aims to improve the scale and variety of micro-seismic data sets. We integrate GANs with policy gradient algorithms, employing a tailored policy network to better capture the distribution characteristics of microseismic data. Through adversarial training, the generator network continually refines synthesized microseismic data, while the discriminator network assesses the authenticity of generated samples. This adversarial approach effectively enhances the quality of generated data, aligning it more closely with the distribution characteristics of real microseismic data. Incorporating self-attention mechanisms within the generator facilitates improved learning of long-term temporal features in microseismic signals, thereby generating synthetic samples that closely resemble real micro-seismic signals. Experimental validation on real microseismic datasets demonstrates that the proposed method based on policy gradient and GANs effectively augments microseismic datasets. The synthesized data exhibits statistical properties and geological significance comparable to real data, providing richer and more diverse data resources for subsequent geological analysis and exploration efforts.

关键词： Training Geology Time series analysis signal processing algorithms Generative adversarial networks Feature extraction Data augmentation Generators Data models Synthetic data

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Attention-Guided Underground Coal Mine Pedestrian Re-Identification Network

Attention-Guided Underground Coal Mine Pedestrian Re-Identif...

引用

IEEE International Conference on signal processing, Communications and Computing (ICSPCC)

作者： Tian Ma Dandan Yu Jiayi Yang Weilu Shi Jiehui Zhang College of Computer Science and Technology Xi'an University of Science and Technology Xi'an China

ISBN: (数字)9798350366556

ISBN: (纸本)9798350366563

Pedestrian re-identification algorithms are crucial in personnel localization tasks in underground coal mines. The high similarity in attire among personnel in this environment renders general pedestrian re-identification algorithms unsuitable for personnel identification tasks in underground coal mines. This paper introduces an attention-guided feature fusion network to address the issue of poor identification accuracy arising from high similarity among personnel in coal mine scenarios. Initially, a ResNet network is utilized to extract detailed information about the target personnel. Subsequently, an attention-induced cross-level fusion module establishes a new feature fusion branch, enhancing cross-level learning and the representation of inter-class comparable subjects. Finally, contrastive global features are used to generate a powerful feature representation, reducing the difficulty of distinguishing between similar personnel. Experiments conducted on the proposed method in-house MineData dataset and the public Market-1501 dataset show that the proposed method outperforms current advanced methods, achieving mAP scores of 88.32 and 65.63, respectively.

关键词： Location awareness Pedestrians Accuracy signal processing algorithms signal processing Feature extraction Coal mining Personnel Data mining

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Robust and Secure Watermarking Algorithm Based on High Frequencies of Integer Wavelet Transform

Robust and Secure Watermarking Algorithm Based on High Frequ...

引用

International Conference on Telecommunications and signal processing (TSP)

作者： Ersin Elbasi Ahmet E. Topcu Yehia Alzoubi Ahmed A. Mohammed College of Engineering and Technology American University of the Middle East. Kuwait College of Business Administration American University of the Middle East Kuwait Computer and Information Department Ninevah University Iraq

ISBN: (数字)9798350365597

ISBN: (纸本)9798350365603

Nowadays, copyright owners, movie studios, photographers, software developers, and other content owners are concerned about copyright protection because of the ease of dissemination with internet technology. Watermarking and cryptography are two critical techniques for copyright protection, authentication, and broadcasting. Encryption protects multimedia content during transmission; however, after decryption, there is no protection. Frequency-based watermarking is an essential method to hide a logo, stamp, image, or text from the protected element. Discrete wavelet transformation and discrete cosine transformation give secure and robust results. However, these methods are not robust for some of the geometric attacks. In this work, we applied the integer wavelet transformation to hide binary watermark into the medical cover images. Cover images are first divided into blocks ( $4\times 4$ ), and then a binary logo is embedded using a secret key. Peak signal-to-noise ratio and similarity ratio values show that the integer wavelet transform based proposed algorithm is more robust, secure, and resistant to attacks than other frequency-based algorithms.

关键词： PSNR signal processing algorithms Watermarking Copyright protection signal processing Motion pictures Software

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Soldering Defect Identification Using Deep Learning and Machine Learning Approach

Soldering Defect Identification Using Deep Learning and Mach...

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Electronics, Communication and signal processing (ICECSP), International Conference on

作者： Swati Shilaskar Sai Singar Abhijit Sonawane Shobit Shinde Dept. of Electronics and Telecommunication Engineering Vishwakarma Institute of Technology Pune Maharashtra India

ISBN: (数字)9798350364590

ISBN: (纸本)9798350375381

Electronic device functionality and dependability can be severely compromised by soldering flaws in printed circuit boards (PCBs). Effective quality control in manufacturing processes depends on automated flaw identification. Using a bespoke dataset of 500 photos of defective PCBs, this research proposes a novel approach for soldering defect recognition and summary creation on printed circuit boards (PCBs) using a combination of Deep Learning (DL) and Machine Learning (ML) techniques. A framework for creating computer vision models is used to carefully classify these pictures according to six different defect categories: solder-bridge, solder-skip, overheated, offtrack-solder, overwetted, and underwetted. Total 300 images are classified into mentioned categories. To improve the model's performance and expand the dataset, the authors implemented augmentation methods, which led to the creation of a comprehensive 30% of total image dataset. The proposed system uses the Google Collab platform to train our unique dataset using the YOLOv5s model architecture, a deep learning model that helped us optimize the model to identify and categorize soldering faults properly. To ensure smooth implementation and reproducibility, we carefully recorded and downloaded the necessary dependencies for YOLOv5s. Preprocessing techniques for image enhancement were carried out on SVM algorithm to deploy the machine learning approach. To enable effective quality control measures in PCB manufacturing processes, our suggested methodology shows promising results in precisely identifying soldering faults.

关键词： Fault diagnosis Deep learning YOLO Manufacturing processes Machine learning algorithms Computational modeling Printed circuits signal processing algorithms Quality control Soldering

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