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检索条件"主题词=Signal Processing Algorithms"

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Enhancing Financial Trading Strategies: A Comprehensive Sentiment Analysis Pipeline

Enhancing Financial Trading Strategies: A Comprehensive Sent...

引用

signal processing and Advance Research in Computing (SPARC), International Conference on

作者： Baljinder Singh Mandeep Kaur Gurbinder Singh Brar Computer Science and Engineering CT University Ludhiana Punjab India School of Computer Science and Engineering Lovely Professional University Phagwara Punjab India

ISBN: (数字)9798350385205

ISBN: (纸本)9798350385212

Sentiment analysis plays a crucial role in understanding market dynamics and making informed decisions in financial trading. This paper proposes a comprehensive sentiment analysis pipeline tailored to financial markets, integrating machine learning techniques and swarm intelligence for feature selection. The pipeline encompasses pre-processing, feature extraction, swarm-based feature selection, training, classification, and evaluation stages. Pre-processing involves text normalization techniques such as tokenization, lowercasing, and stop words removal. Feature extraction utilizes TF-IDF values and similarity measures to represent text data. Swarm intelligence algorithms are employed for feature selection, optimizing the subset of features based on classifier performance. Machine learning classifiers are trained on the selected features to predict sentiment labels, and evaluation metrics assess the pipeline’s performance. The effectiveness of the pipeline is demonstrated through experiments on financial datasets, highlighting the importance of parameter tuning and algorithmic optimization. This research contributes to advancing sentiment analysis in financial markets, providing a robust framework for decision-making in trading and investment.

关键词： Sentiment analysis Machine learning algorithms Pipelines signal processing algorithms Machine learning Feature extraction Prediction algorithms Classification algorithms Particle swarm optimization Tuning

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Data Augmentation by Hybrid Data Resampling: Towards Enhanced Performance in Automatic Cardiac Arrhythmia Detection

Data Augmentation by Hybrid Data Resampling: Towards Enhance...

引用

IEEE Calcutta Conference (CALCON)

作者： Debasmita Pal Sumitra Mukhopadhyay Rajarshi Gupta Radio Physics and Electronics University of Calcutta Kolkata India Electrical Engg. Applied Physics University of Calcutta Kolkata India

ISBN: (数字)9798331516666

ISBN: (纸本)9798331516673

Real-time automatic cardiac arrhythmia detection (CAD) can enhance timely medical intervention thereby reducing mortality caused by it. One of the most concerning challenges in automatic CAD using machine learning approaches is inherent data imbalance between different types of abnormal beat classes, which results in the biasing of the classifier towards majority classes during training of the classifier. In this paper, an offline data balancing technique is proposed which is a combination of graded boundary-sensitive majority class undersampling (GBMU) and adaptive scattering-based minority class oversampling (ASMO). The algorithm was evaluated with MIT BIH arrhythmia dataset with 15 types of heartbeats. This hybrid data resampling increases the overall F1- score by 15.28% and reduces the false positive rate (FPR) and false negative rate (FNR) by ${1 1. 3 \%}$ and ${3 9. 4 1 \%}$ respectively using a simple classification flow which would otherwise result in compromised detection accuracy for minority classes.

关键词： Training Solid modeling Accuracy Heart beat Arrhythmia signal processing algorithms signal processing Feature extraction Real-time systems Classification algorithms

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Joint Singular Value Decomposition: A New Algorithm for Complex Matrices

Joint Singular Value Decomposition: A New Algorithm for Comp...

引用

European signal processing Conference (EUSIPCO)

作者： Ammar MESLOUB Tayeb TOUHAMI Karim ABED MERAIM Adel BELOUCHRANI Mustapha DJEDDOU Lab. Antenne et Dispositifs Micro-Ondes Ecole Militaire Polytechnique Bordj El Bahri Algeria Lab. Traitement du Signal Ecole Militaire Polytechnique Bordj El Bahri Algeria PRISME Lab. Université d'Orléans Orléans France Electrical Engineering Department LDCCP Ecole Nationale Polytechnique Algiers Algeria

ISBN: (数字)9789464593617

ISBN: (纸本)9798331519773

The Joint Singular Value Decomposition (JSVD) aims to find a pair of unitary matrices allowing a simultaneous diagonalization of a set of matrices. Based on Givens rotations, this paper proposes two approaches dealing with the JSVD problem in the complex domain. A new criterion is defined to set all diagonal entries to real and non-negative values. An indirect method based on complex to real transformation and a direct method exploiting justified steps of the first one are developed. Numerical simulations show the performance achieved by our algorithm in terms of JSVD quality and convergence rate.

关键词： Source separation Accuracy Simulation signal processing algorithms Europe Numerical simulation Iterative algorithms Matrix decomposition Singular value decomposition Convergence

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Super-resolution Underwater Acoustic Imaging Based on Sparse Bayesian Learning

Super-resolution Underwater Acoustic Imaging Based on Sparse...

引用

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

作者： Peihui Liang Wujie Wang Wusheng Tang Pan Xu Fengjian Mao Jun Wang College of Meteorology and Oceanography National University of Defense Technology Changsha China Tianjin Institute of Advanced Technology Tianjin China

ISBN: (数字)9798350355895

ISBN: (纸本)9798350355901

In this paper, the sparse Bayesian learning (SBL) theory and underwater acoustic imaging model are the core, and the underwater acoustic imaging model and algorithm based on sparse Bayesian learning are introduced for the purpose of improving the resolution of underwater acoustic imaging. Focusing on the underwater acoustic imaging model, firstly, the echo signal model based on the bright spot model is established, and the observation matrices of the near-field and far-field targets are established according to the discriminant conditions of the near-field and far-field respectively. Secondly, the sparse Bayesian learning theory is introduced, and the obtained SBL algorithm is combined with the underwater acoustic imaging system to explain the sparsity in the system, and it can be seen that the accuracy of the SBL algorithm is theoretically better than that of the traditional algorithm. Finally, the derived SBL algorithm is combined with the specific underwater acoustic imaging parameters for simulation. According to the simulation results, the SBL algorithm can significantly improve the imaging resolution, and because it uses probabilistic information to quickly update the hyperparameters, it can complete the signal reconstruction with high performance, and has excellent noise immunity and universality.

关键词： Simulation Superresolution Imaging signal processing algorithms Sonar signal reconstruction Bayes methods Sparse matrices Underwater acoustics signal resolution

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A Novel DA-Based Parallel Architecture for Inner-Product of Variable Vectors

A Novel DA-Based Parallel Architecture for Inner-Product of ...

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IEEE International Symposium on Circuits and Systems (ISCAS)

作者： Anil Kali Samrat L. Sabat Pramod K. Meher CASEST University of Hyderabad Hyderabad India Department of Computer Science & Engineering C. V. Raman Global University Bhubaneswar India

ISBN: (数字)9798350330991

ISBN: (纸本)9798350331004

Computation of the inner products is frequently used in machine learning (ML) algorithms apart from signal processing and communication applications. Distributed arithmetic (DA) has been frequently employed for area-time efficient inner-product implementations. In conventional DA-based architectures, one of the vectors is constant and known a priori. Hence, the traditional DA architectures are not suitable when both vectors are variable. However, computing the inner product of a pair of variable vectors is frequently used for matrix multiplication of various forms and convolutional neural networks. In this paper, we present a novel DA-based architecture for computing the inner product of variable vectors. To derive the proposed architecture, the inner product of any given length is decomposed into a set of short-length inner products, such that the inner product could be computed by successive accumulation of the results of short-length inner products. We have designed a DA-based architecture for the computation of the short-length inner-product of variable vectors and used that in successive clock cycles to compute the whole inner-product by successive accumulation. The post-layout synthesis results using Cadence Innovus with a GPDK 90nm technology library show that the proposed DA-based parallel architecture offers significant advantages in area-delay product and energy consumption over the bit-serial DA architecture.

关键词： Energy consumption Machine learning algorithms Architecture signal processing algorithms Machine learning signal processing Vectors

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Adaptive Beamforming based on Convolutional U-net

Adaptive Beamforming based on Convolutional U-net

引用

Electronics, Circuits and Information Engineering (ECIE), International Conference on

作者： Yu Zhang Yang Chu Pingping Li College of information Science and electronic engineering University Zhejiang Zhejiang China

ISBN: (数字)9798350388312

ISBN: (纸本)9798350388329

In the current field of array signal processing, traditional beamforming algorithms face challenges such as limited signal enhancement capability and poor adaptability to high signal-to-noise ratio environments. To address these issues, in this study, we innovatively propose an adaptive beamforming strategy based on the U-net convolutional neural network. The aim of this method is to optimize the array receiver’s enhancement capability in the direction of the target signal while effectively suppressing interference signals and noise from other non-desired directions. By employing deep learning techniques, we accurately estimate the necessary parameters of the Minimum Variance Distortionless Response (MVDR) beamformer. Simulation experiments have validated that compared to traditional beamforming techniques, our proposed method not only demonstrates outstanding performance in both high and low signal-to-noise ratio scenarios but also significantly enhances the array signal processing performance while maintaining lower algorithm complexity. The research findings of this paper highlight the potential application of deep learning in the field of signal processing, particularly in signal enhancement and interference suppression, providing new perspectives and methodological foundations for future research endeavors.

关键词： Deep learning Adaptive systems Array signal processing signal processing algorithms Adaptive arrays Parallel processing Vectors

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Research on Deep Learning Models Combining Transformer and BERT: Application in Network Intrusion Detection

Research on Deep Learning Models Combining Transformer and B...

引用

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

作者： Zhongqiu Fang Ziwei Hong School of Information and Electrical Engineering Heilongjiang Bayi Agricultural University Daqing China Mathematical School Renmin University of China Beijing China

ISBN: (数字)9798331528676

ISBN: (纸本)9798331528683

In response to the low recognition accuracy, complex preprocessing techniques, and unstable detection accuracy in network intrusion detection, this paper proposes a deep learning model based on the combination of Transformer and BERT. Firstly, based on the basic theories of Transformer and BERT, an intrusion detection model was established to obtain detailed information on network traffic characteristics, intrusion behavior types, and detection performance. Secondly, in order to eliminate the interference of these issues, a principal component analysis strategy was proposed. Furthermore, based on the characteristics of Transformer neural networks, an intrusion detection model was constructed that comprehensively considers the influence of network traffic characteristics and intrusion behavior types, providing a more accurate description of network intrusion behavior. Finally, an intrusion detection system based on Transformer and BERT was built, and the effectiveness of the proposed algorithm was verified through numerical examples.

关键词： Deep learning Accuracy Neural networks Network intrusion detection signal processing algorithms Telecommunication traffic signal processing Transformers Numerical models Principal component analysis

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A Minimum Value Determination Circuit Using a Neuron CMOS WTA with FGC Circuit

A Minimum Value Determination Circuit Using a Neuron CMOS WT...

引用

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

作者： Naruaki Hokari Hisaya Sawada Hirotaka Furukawa Tatsuya Hasegawa Daishi Nishiguchi Masaaki Fukuhara Graduate School of Information and Telecommunication Engineering Tokai University Tokyo Japan Research Institute of Science and Technology Tokai University Kumamoto Japan

ISBN: (数字)9798350389210

ISBN: (纸本)9798350389227

Given an unlabeled time series dataset, it is often required to cluster similar time series data. Dynamic Time Warping (DTW) is one of the useful methods to evaluate the similarity between time series data. However, the algorithm of DTW has a high computational cost and requires substantial computation time. To solve the issue, a DTW distance calculator has been proposed to reduce the computation time of DTW distance calculations. Additionally, a conventional circuit using neuron CMOS Winner-Take-All (vCMOS WTA) circuit has also been proposed for the minimum value determination circuit used in the DTW calculator. However, the proposed minimum value determination circuit was still unstable. In this paper, we propose a new minimum value determination circuit for use in the DTW distance calculator. In addition, this paper describes the circuit design, the circuit operation, and the results of HSPICE simulations.

关键词： Semiconductor device modeling Hands Calculators Heuristic algorithms Time series analysis Neurons Noise signal processing algorithms Timing Integrated circuit modeling

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Improved MTPA Acquisition for SynRM Based on Golden Section Searching Considering Magnetic Saturation Effect

Improved MTPA Acquisition for SynRM Based on Golden Section ...

引用

International Conference on Power Electronics and Motion Control (IPEMC)

作者： Shuo Wang Yuli Bao Dmytro Prystupa Vasyl Varvolik Giampaolo Buticchi He Zhang Key Laboratory of More Electric Aircraft Technology of Zhejiang Province University of Nottingham Ningbo China (UNNC) Ningbo China

ISBN: (数字)9798350351330

ISBN: (纸本)9798350351347

The main feature of synchronous reluctance machine (SynRM) is highly nonlinearity for flux-linkage, due to magnetic saturation and cross-saturation effect. Maximum torque per ampere (MTPA) control focuses on minimizing copper loss, which is a popular and important method for SynRM high-efficiency control. In this study, an improved golden section searching method (GSSM) is proposed for fast MTPA acquisition, taking into account nonlinear magnetic saturation and cross-saturation effect. Compared with traditional GSSM, the key feature is to use the MTPA optimal angle from the last iteration to optimize the initial search interval, thereby improving the system convergence speed and reducing computing resources. The proposed GSSM can be easily implemented with several iterations with a standard drive. The proposed algorithm has been implemented by a digital signal processor (DSP)-based 15-kW SynRM variable frequency converter.

关键词： Accuracy Torque signal processing algorithms Process control Optimization methods Reluctance machines Frequency conversion

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Hardware Based Real-time processing of Online Ultrasonic Data of Railway Wheel Cracks: Multi-Sampling Rate Algorithm

Hardware Based Real-time Processing of Online Ultrasonic Dat...

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Automation, Robotics and Computer Engineering (ICARCE), International Conference on

作者： Zhilin Sun Jie Sun ZhengYing Shi PAI Industry Research Institute Nanjing Pioneer Awareness Information Technology Company Ltd. Nanjing China School of Information Engineering Nanjing Xiaozhuang University Nanjing China Nanjing Pioneer Awareness Information Technology Company Ltd. Nanjing China

ISBN: (数字)9798331529505

ISBN: (纸本)9798331529512

In the piezoelectric ultrasonic online flaw detection of railway wheels, the transmission efficiency of ultrasonic data is critical to ensure the real-time detection of wheel flaws. Due to the large number of train wheels to be detected, the hardware sampling frequency of the online flaw detection equipment is high, the amount of ultrasonic echo data is large, and the data transmission and analysis processing time is extended. To improve the data processing efficiency, it is necessary to convert the sampling rate of the ultrasound signal to change the original data volume. According to the actual detection requirements, this paper studies the decimation and interpolation algorithm of ultrasonic digital signals and realizes the sampling rate conversion in the Field Programmable Gate Array (FPGA), which reduces data redundancy and improves system transmission and processing efficiency. The effectiveness and reliability of the method are verified in practical applications.

关键词： Interpolation Wheels signal processing algorithms Data processing Acoustics Rail transportation Real-time systems Logic design Hardware Field programmable gate arrays

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