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Attention-Enhanced Rainbow DQN for Cell Reselection in Satellite Communication Networks

Attention-Enhanced Rainbow DQN for Cell Reselection in Satel...

引用

International Conference on Wireless Communications and signal processing (WCSP)

作者： Zijian Yang Ming Zeng Zesong Fei School of Information and Electronics Beijing Institute of Technology Beijing China Chongqing Innovation Center Beijing Institute of Technology Chongqing China

ISBN: (数字)9798350390643

ISBN: (纸本)9798350390650

In satellite communication networks, user devices can be in either active or idle states. In active states, devices actively communicate with specific cells, maintaining a continuous connection for data transmission. In idle states, however, user devices do not actively communicate with specific cells but need to monitor potential cells and perform cell reselection (CR) under appropriate conditions for future connections. Inefficient CR decision-making is a significant issue in these states, particularly in low Earth orbit (LEO) satellite networks characterized by large-scale and highly dynamic environments. To address the inefficiency in CR decision-making under these conditions, especially when dealing with a varying number of candidate satellites, we first model the reselection problem as a Markov decision process. By incorporating a self-attention mechanism for candidate satellite correlation modeling and adjusting the related network structure, we propose an Attention-Enhanced Rainbow DQN algorithm. This algorithm improves the model's learning ability and decision stability in tasks involving a varying number of candidate satellites. Simulations demonstrate that the proposed algorithm outperforms weighted decision algorithms and conventional Rainbow DQN algorithms in terms of load balancing, signal-to-noise ratio (SNR), and the number of reselections.

关键词： Wireless communication Satellites Decision making signal processing algorithms Low earth orbit satellites Transforms Load management Satellite communications signal to noise ratio Load modeling

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Echo Cancellation Based on Improved Wave-U-Net

Echo Cancellation Based on Improved Wave-U-Net

引用

International Conference on signal processing Proceedings (ICSP)

作者： Lei Zhou Xinhai Xu Xianyi Rui Yibiao Yu School of Electronic Information Soochow University Suzhou China

ISBN: (数字)9798350387384

ISBN: (纸本)9798350387391

Most current implementations of deep learning-based echo cancellation algorithms use the short-time Fourier transform domain, but this approach may be limited by frame size selection and lack of accurate phase information. To overcome these limitations, in this paper, we will use the Wave-U-Net temporal network model as the baseline model for single-channel echo cancellation. Wave-U-Net performs better than traditional methods and neural networks in speech processing tasks; however, in the echo cancellation domain, the model still underperforms in near-end microphone speech feature extraction, and its ability to low distinction between near-end and far-end speech features, which is easy to cause confusion. Based on this, this paper will optimize the structure of Wave-U-Net network, aiming to improve its efficiency and performance of echo cancellation.

关键词： Echo cancellers Fourier transforms Attention mechanisms Noise reduction Neural networks signal processing algorithms signal processing Feature extraction Speech processing Microphones

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Infant Crying Recognition Method of Limited Data Based on Self-supervised Learning

Infant Crying Recognition Method of Limited Data Based on Se...

引用

IEEE International Conference on Information Communication and signal processing (ICICSP)

作者： Bin Wang Zhezhe Chen Shiwei Ren Meng Sun College of Electronic and Information Engineering Nanjing University of Aeronautics and Astronautics Jiangsu China

ISBN: (数字)9798350355895

ISBN: (纸本)9798350355901

Infant cry recognition and interpretation are crucial for understanding and addressing infant needs. However, current supervised learning methods suffer from limited annotated infant cry data, leading to performance degradation in classification. In this paper, we propose a self-supervised learning approach for infant cry analysis. The proposed method involves masking the speech input within the latent space and solving a contrastive task defined over quantized latent representations which are jointly learned. By leveraging a substantial volume of unlabeled data, the model discerns crucial audio contrast features. Subsequently, fine-tuning is performed by using a small set of labeled data. The unlabeled data comes from the 960h audio data of the Librispeech dataset. The labeled infant crying data consists of 926 audio tracks (hungry (565), discomfortable (195), angry (82), disturbed (84)), sourced from major hospitals and postpartum clubs, hich achieves accuracies of 98.67%, 95.60%, and 95.18% in binary, ternary, and quaternary classifications, respectively. Compared to traditional supervised learning models (CNN, TDNNS, Res2Net), there is a significant improvement in accuracy.

关键词： Training Pediatrics Visualization Solid modeling Accuracy Supervised learning signal processing algorithms Self-supervised learning Data models Reliability

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Music Melody Extraction Algorithm Based on Multi-Layer Perceptron Neural Network

Music Melody Extraction Algorithm Based on Multi-Layer Perce...

引用

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

作者： Mengyuan Sun School of Music Shenyang Normal University Shenyang China

ISBN: (数字)9798331528348

ISBN: (纸本)9798331528355

Current melody extraction methods face challenges from the complexity of audio signal processing and multi-track interference. This study proposes a music melody extraction algorithm based on a multilayer perceptron neural network. The algorithm uses a multilayer perceptron to build a model. The input layer processes the audio signal features through short-time Fourier transform (STFT), taking into account spectral and time domain information. The model is designed with three hidden layers, each containing a large number of neurons, and nonlinear transformation is performed through the ReLU activation function. At the same time, the dropout technology is used to control overfitting. The output layer uses the soft max activation function to generate the probability distribution of the melody signal. The highest accuracy of the melody extraction task reached 95%, and the precision and recall rates were 87%. The melody extraction algorithm based on the multilayer perceptron performs well in processing complex audio signal tasks, providing an effective solution for the field of melody extraction.

关键词： Training Wireless communication Adaptation models Accuracy signal processing algorithms Multilayer perceptrons Feature extraction Data models Multiple signal classification Biological neural networks

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Research on Enterprise Innovation Interaction Model Based on Complex Network and Big Data Analysis

Research on Enterprise Innovation Interaction Model Based on...

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signal processing, Communications and Engineering Management Conference (ISPCEM), International

作者： Lili Zhang Guangxi Vocational Normal University Guangxi China

ISBN: (数字)9798331528676

ISBN: (纸本)9798331528683

This paper proposes an enterprise innovation interaction model based on complex network and big data analysis. The model uses PageRank algorithm to analyze the importance of each node in the enterprise innovation network, identify the core enterprises in the innovation activities and their key roles in the cooperation network. By constructing a complex network, the cooperation relationship in the enterprise innovation system is visualized, and valuable interaction patterns are extracted by combining big data analysis. The system design includes modules such as data preprocessing, complex network construction and PageRank algorithm implementation, which can effectively improve the accuracy of enterprise innovation interaction. During the simulation process, the paper used a real data set containing 500 enterprises and 2000 cooperation projects. The results show that the system can significantly improve the efficiency of identifying key nodes in the cooperation network between enterprises. The node importance identification accuracy of the PageRank algorithm reaches 93.2%, which is about 10% higher than the traditional method. In addition, the simulation results show that the prediction accuracy of core node identification and innovation interaction patterns in complex networks has also been significantly improved.

关键词： Technological innovation Analytical models Accuracy Data preprocessing signal processing algorithms Data visualization Complex networks Big Data Prediction algorithms Data models

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Joint SIM Configuration and Power Allocation for Stacked Intelligent Metasurface-assisted MU-MISO Systems with TD3

Joint SIM Configuration and Power Allocation for Stacked Int...

引用

IEEE Conference on Global Communications (GLOBECOM)

作者： Xiaolei Yang Jiayi Zhang Enyu Shi Ziheng Liu Jun Liu Kang Zheng Bo Ai School of Electronic and Information Engineering Beijing Jiaotong University Beijing China China Mobile Zijin Innovation Institute Co. Ltd Nanjing China National Mobile Communications Research Laboratory Southeast University Nanjing China

ISBN: (数字)9798350351255

ISBN: (纸本)9798350351262

The stacked intelligent metasurface (SIM) emerges as an innovative technology with the ability to directly manipulate electromagnetic (EM) wave signals, drawing parallels to the operational principles of artificial neural networks (ANN). Leveraging its structure for direct EM signal processing alongside its low-power consumption, SIM holds promise for enhancing system performance within wireless communication systems. In this paper, we focus on SIM-assisted multi-user multi-input and single-output (MU-MISO) system downlink scenarios in the transmitter. We proposed a joint optimization method for SIM phase shift configuration and antenna power allocation based on the twin delayed deep deterministic policy gradient (TD3) algorithm to efficiently improve the sum rate. The results show that the proposed algorithm outperforms both deep deterministic policy gradient (DDPG) and alternating optimization (AO) algorithms. Furthermore, increasing the number of meta-atoms per layer of the SIM is always beneficial. However, continuously increasing the number of layers of SIM does not lead to sustained performance improvement.

关键词： Wireless communication Transmitters System performance signal processing algorithms Optimization methods Artificial neural networks Massive MIMO signal processing Metasurfaces Resource management

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Blending the Powers of BERT and Neural Style Transfer for Artistic Text Generation in Poetry

Blending the Powers of BERT and Neural Style Transfer for Ar...

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Innovations in High Speed Communication and signal processing (IHCSP), International Conference on

作者： Manish Dakhore Muktha Eti Mandar Diwakar A. Sivanantham Lokesh Verma M. Shyam Mechanical Engineering Department Yeshwantrao Chavan College of Engineering Nagpur Maharashtra India School of Engineering and Technology Electrical and Electronics Engineering JAIN (Deemed to be University) Bangalore Karnataka India AI & Ds Vishwakarma Institute of Technology Pune India Department of Mechanical Engineering Karpagam Academy of Higher Education Coimbatore India Centre of Research Impact and Outcome Chitkara University Rajpura Punjab India R.M.K. Engineering College Tamilnadu India

ISBN: (数字)9798350368949

ISBN: (纸本)9798350368956

Developing original content that prioritizes both semantic elegance and truth requires the collaboration of two individuals. The visually distinctive appearance of this piece is achieved through the integration of BERT (Bidirectional Encoder Representations from Transformers) capabilities with the sophisticated aesthetics of Neural Style Transfer (NST). Three distinct methods are merged in the proposed method: decoding and generation (D&G), semantic encoding (BERT), and style embedding (NST). The initial methodology, "Semantic Encoding with BERT," guarantees that the resultant poem remains faithful to its initial intent through the utilization of contextual embeddings in BERT. Second, you may create a unique poetry by inserting style components into an existing reference text using Style Embedding with NST. The third algorithm deciphers the style embeddings using an RNN-based generative model. Poetry that is both original and contextualized is the result. Scientific evaluation and comparison of the proposed technique with more conventional approaches is based on a number of criteria. Among these considerations are creative variety, originality, continuity, diversity, meaning accuracy, cohesiveness, and style preservation. The results demonstrate that the proposed approach outperforms conventional ones in generating aesthetically original and easily comprehensible prose.

关键词： Technological innovation Accuracy Semantics signal processing algorithms Bidirectional control Writing signal processing Transformers Encoding Neural style transfer

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Creative Research on Automatic Detection of Flying Objects

Creative Research on Automatic Detection of Flying Objects

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

作者： Lu Liu Yiyang Lu Binrui Xu Heyang Peng Yuhan Ren College of International Education SiChuan Normal University Chengdu China Faculty of Computing Harbin Institute of Technology Harbin China School of Information Science and Engineering Harbin Institute of Technology Harbin China Law School SiChuan Normal University Chengdu China

ISBN: (数字)9798350376548

ISBN: (纸本)9798350376555

With the increasing development of China's modernisation and the growing demand for national defence security, it is very important to detect unidentified flying objects in the air and eliminate potential threats to ensure national security. In order to solve the dual problems of high cost of manual inspection and poor performance of the detection model with low accuracy, this paper proposes a way to automate the detection of flying objects by using the YOLOv5 target detection model, improving the loss function CIOU_Loss to EIOU_Loss by default to speed up the convergence speed of the model, and at the same time adopting a variety of novel data enhancement methods to improve the accuracy. This paper also compares the performance of the YOLOv5 model before and after the improvement in the training and flying objects detection process to demonstrate the effectiveness of the improvement.

关键词： YOLO Training Accuracy Atmospheric modeling Computational modeling signal processing algorithms signal processing Data models Security Convergence

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Benefits of Hypergraphs for Density-Based Clustering

Benefits of Hypergraphs for Density-Based Clustering

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European signal processing Conference (EUSIPCO)

作者： Louis Hauseux Konstantin Avrachenkov Josiane Zerubia Inria Neo team & Ayana team Université Côte d'Azur Sophia Antipolis France Inria Neo team Université Côte d'Azur Sophia Antipolis France Inria Ayana team Université Côte d'Azur Sophia Antipolis France

ISBN: (数字)9789464593617

ISBN: (纸本)9798331519773

Many of clustering algorithms are based on density estimates in ${\mathbb{R}^{d}}$ . Building geometric graphs on the dataset $\mathcal{X}$ is an elegant way of doing this. In fact, the connected components of a geometric graph match exactly with the high-density clusters of the 1-Nearest Neighbor density estimator. In this paper, We show that the natural way to generalize geometric graphs is to use hypergraphs with a more restrictive notion of connected component called $K$ -Polyhedron. Herein, we prove that $K$ -polyhedra correspond to high-density clusters of K-Nearest Neighbors density estimator. Furthermore, the percolation phenomenon is omnipresent behind the family of clustering algorithms we look at in this paper.

关键词： Buildings signal processing algorithms Clustering algorithms Europe signal processing Nearest neighbor methods

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Catalyzing Near-field Localization Through RIS Assistance: Optimization of Hybrid Operational Paradigms

Catalyzing Near-field Localization Through RIS Assistance: O...

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IEEE Conference on Global Communications (GLOBECOM)

作者： Xu Fang Lixiang Lian School of Information Science and Technology ShanghaiTech University Shanghai China

ISBN: (数字)9798350351255

ISBN: (纸本)9798350351262

The utilization of reconfigurable intelligent meta-surface (RIS) for target localization has emerged as a prominent technology in sixth-generation (6G) wireless networks. Due to the deployment of large-scale RIS and the application of high-frequency signaling in 6G, future localization scenarios are anticipated to predominantly occur within the near-field region. In this paper, we investigate the scenario of near-field target localization supported by large-scale RIS with hybrid operational paradigms, wherein elements capable of passive or active signal processing (SP) are employed. Through a series of complex technical calculations, we derive the closed-form of Cramer-Rao bound (CRB) for near-field localization assisted by RIS with hybrid operational paradigms. By analyzing the Cramer-Rao bound (CRB) of near-field localization, we aim to optimize the ratio of active and passive SP elements of RIS to determine the optimal hybrid operational paradigms. To achieve this, we utilize a spatial angle approximation technique to convert the complex combinatorial optimization problem into a tractable optimization problem, which is amenable to efficient solutions using existing optimization algorithms. Through simulations, we show that the proposed technique can effectively find the optimal ratio of active and passive SP elements that minimizes the localization error.

关键词： Location awareness 6G mobile communication Lower bound Cramer-Rao bounds Wireless networks Simulation signal processing algorithms Reconfigurable intelligent surfaces signal processing Optimization

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