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检索条件"主题词=Signal Processing Algorithms"

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On Quantized Kernel Affine Projection Algorithm: Sparsification via Delayed Vector Quantization

On Quantized Kernel Affine Projection Algorithm: Sparsificat...

引用

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

作者： En-Ting Lin Ying-Ren Chien Department of Electrical Engineering National Ilan University Yilan Taiwan

ISBN: (数字)9798350389210

ISBN: (纸本)9798350389227

Quantization techniques compress data by reducing large datasets into smaller code vector sets. This study introduces a vector quantization method within the Kernel Affine Projection Algorithm (KAPA) framework, combining the simplicity and online capabilities of the Kernel Least Mean Squares (KLMS) algorithm with improved performance through reduced gradient noise via multiple feature space samples. The resulting Quantized Kernel Affine Projection Algorithm (Quantized-KAPA) outperforms existing sparsification methods. Simulations involving chaotic time-series forecasting and nonlinear communication channel equalization demonstrate its superior performance.

关键词： Adaptive systems Vector quantization Time series analysis Refining signal processing algorithms Vectors Timing Projection algorithms Kernel Convergence

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Research on exchange rate risk prediction model based on neural network

Research on exchange rate risk prediction model based on neu...

引用

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

作者： Chang Yifang Shanghai University Shanghai China

ISBN: (数字)9798331528676

ISBN: (纸本)9798331528683

Against the backdrop of the continuous appreciation of the renminbi, foreign exchange risk management is crucial. Therefore, it is necessary to adopt scientific and reasonable methods to predict and optimize the exchange rate risk prediction model. According to the specific characteristics of the exchange rate model, this paper selects the analysis method of fusing time-frequency signal analysis and neural network model, and preliminarily analyzes and preprocesses the price level of the RMB exchange rate. By using the ensemble empirical mode decomposition method, the signals are effectively screened and reconstructed, and then the neural network is used to predict the price level of the RMB exchange rate. The model accurately captures the trend of fluctuations, which verifies the effectiveness of the proposed prediction method. This is of far-reaching practical significance for the foreign exchange management department to avoid market risks and cope with various impacts brought about by interest rate and exchange rate fluctuations.

关键词： Exchange rates Analytical models Time-frequency analysis Fluctuations Neural networks signal processing algorithms Predictive models Prediction algorithms Market research signal analysis

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An Ensemble Learning Framework for Prediction of Diabetes Mellitus using Soft Voting and Hard Voting Framework

An Ensemble Learning Framework for Prediction of Diabetes Me...

引用

Artificial Intelligence and signal processing (AISP), INDIA, International Conference on

作者： Subasish Mohapatra Akanksha Keshari Subhadarshini Mohanty School of Computer Sciences Odisha University of technology and Research Bhubaneswar India

ISBN: (数字)9798350350654

ISBN: (纸本)9798350350661

Diabetes Mellitus (DM) is a collection of long-term illnesses that arise from excessively elevated blood sugar levels. It ranks among the leading seeds of mortality globally and affects vision loss, kidney issues, heart issues, and other conditions. Machine learning techniques help reduce the workload of medical professionals by improving disease diagnosis and treatment accuracy. Early diabetes prediction could result in better medications. Research in the area of diabetes prediction is mushrooming. Type-1 and Type-2 diabetes are the two fundamental groups into which Diabetes Mellitus divides. The International Diabetes Federation reported 537 million people aged 20–79 with diabetes in 2021, set to rise to 783 million by 2045. To improve the prediction model's efficiency, this research study proposed a diabetes prediction system that makes use of medical data and Ensemble machine learning (ML) algorithms like Naïve Bayes (NB), Random Forest (RF), Adaptive Boosting (AdaBoost), and Gradient Boosting Machine (GBM) with Voting Classification. All of the investigations in this literature were carried out using the Pima Indian Diabetes Dataset (PIDD) i.e. dataset is downloaded from Kaggle. The resultant shows better performance measures in accuracy, precision, recall, F1 score, and AUC with values being 0.8095, 0.7647, 0.6164, 0.6716, and 0.8741 respectively employing Soft Voting classification whereas the values 0.8008, 0.7377, 0.5342, 0.6290 and 0.7291 respectively utilizing Hard Voting Classification.

关键词： Radio frequency Accuracy signal processing algorithms signal processing Predictive models Boosting Prediction algorithms Diabetes Medical diagnostic imaging Random forests

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Exploitation of Symmetrical Non-Convexity for Symbol-Level DFRC signal Design

Exploitation of Symmetrical Non-Convexity for Symbol-Level D...

引用

IEEE International Conference on Communications (ICC)

作者： Ly V. Nguyen Rang Liu A. Lee Swindlehurst Center for Pervasive Communications and Computing University of California Irvine CA USA

ISBN: (数字)9781728190549

ISBN: (纸本)9781728190556

Constructive interference exploited by symbol-level (SL) signal processing is a promising solution for addressing the inherent interference problem in dual-functional radar-communication (DFRC) signal designs. This paper considers an SL-DFRC signal design problem which maximizes the radar performance under communication performance constraints. We exploit the symmetrical non-convexity property of the communication-independent radar sensing metric to develop low- complexity yet efficient algorithms. We first propose a radar-to- DFRC (R2DFRC) algorithm that relies on the non-convexity of the radar sensing metric to find a set of radar-only solutions. Based on these solutions, we further exploit the symmetrical property of the radar sensing metric to efficiently design the DFRC signal. Since the radar sensing metric is independent of the communication channel and data symbols, the set of radar-only solutions can be constructed offline, therefore reducing the computational complexity. We then develop an accelerated R2DFRC algorithm that further reduces the complexity. Finally, we demonstrate the superiority of the proposed algorithms compared to existing methods in terms of both radar sensing and communication performance as well as computational complexity.

关键词： Measurement signal processing algorithms Symbols Radar Interference Communication channels Sensors

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An Efficient Depth-tracking Method for Live Control of Laser Drilling Process with Optical Coherence Tomography

An Efficient Depth-tracking Method for Live Control of Laser...

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IEEE International Conference on Mechatronics and Automation

作者： Tianyi Zhao Yuxin Luo Jinhan Zhao Hongjie Ren Hai Xiang Yifei Liu Yaoyu Ding School of Automation Engineering University of Electronic Science and Technology of China Chengdu China Westa College Southwest University Chongqing China

ISBN: (数字)9798350388077

ISBN: (纸本)9798350388084

Laser drilling has been widely used for fabricating holes in the semiconductor industry due to high throughput and small heat-affected zone. However, it produces varying depths owing to uncertain external conditions and needs a live control at a few kHz rate to handle the fast material removal rate. Optical coherent tomography holds a capacity of in situ acquiring raw interferogram at a high rate (>80 kHz), but the depth extraction is slow (<0.5 kHz) due to the involved heavy Fast Fourier Transform (FFT). To address it, an efficient depth-tracking algorithm is proposed to save the FFT. It searches the depth in the raw interferogram locally with a known last depth given the two truths that only one depth exists and the adjacent depths would not change significantly. The proposed algorithm was proved to expedite the measuring rate to around 3 kHz with a sub-pixel tracking precision. To further secure the rate from the interrupt of operation system, the tracking process is parallelly implemented with a stabilized 50 kHz in a field programmable gate array. The tests were conducted on probe cards with depth variations introduced by offsetting laser focus. The proposed method exhibited the capacity of maintaining a uniform depth.

关键词： Drilling Fast Fourier transforms Optical feedback signal processing algorithms Process control Laser feedback Optical imaging

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Taekwondo action recognition model based on deep learning and key point matching technology

Taekwondo action recognition model based on deep learning an...

引用

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

作者： Jin Senbo Guangzhou Tianxing Experimental School Guangzhou China

ISBN: (数字)9798331528676

ISBN: (纸本)9798331528683

In order to solve the problems in Taekwondo action recognition and key point matching, this paper proposes a hybrid matching model that combines YOLOv8 and an improved dynamic time warping (DDTW) algorithm. The model uses YOLOv8 to detect key points in each frame of the video. When the feature points are lost due to occlusion, the model tries to use the information of adjacent frames to compensate. At the same time, in order to reduce jitter, the key point coordinate sequence is smoothed by a low-pass filter. The key frames are matched by the DDTW algorithm, and the action score is calculated according to the best path found by the algorithm. The scoring basis includes the cumulative cosine similarity, action fluency and accuracy. The experimental results show that by combining the images of the three perspectives, the model has achieved significant improvement in recognition accuracy, especially in the key frame combination extracted at the quartile position, the highest recognition accuracy of the front view and the right view reached 92.1% and 94.3% respectively. The method shows strong robustness in noise and speed change processing, and is suitable for motion analysis, rehabilitation training and sports science.

关键词： Training Accuracy Image recognition Heuristic algorithms signal processing algorithms Jitter Solids Robustness Real-time systems Sports

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Component-Wise Squared Factorization

Component-Wise Squared Factorization

引用

IEEE Workshop on Machine Learning for signal processing

作者： Joakim Lefebvre Arnaud Vandaele Nicolas Gillis University of Mons

ISBN: (数字)9798350372250

ISBN: (纸本)9798350372267

The component-wise squared factorization decomposes a matrix as the component-wise square of a low-rank matrix. It can be used to compute the so-called square root rank of a matrix, used in the compact representation of convex polytopes, but also to represent compactly nonnegative data including arithmetic circuits. This paper introduces a coordinate descent algorithm to solve this problem, along with an accelerated variant using extrapolation. We show that it compresses better dense and sparse nonnegative matrices than standard linear low-rank matrix factorization models such as the truncated singular value decomposition (TSVD) and nonnegative matrix factorization (NMF). Then, we explore its application on synthetic matrices generated for experimentation, and also on the slack matrices of regular n-gons and linear Euclidean distance matrices.

关键词： Extrapolation signal processing algorithms signal processing Minimization Polynomials Numerical models Matrix decomposition Sparse matrices Standards Singular value decomposition

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Exploiting CSI-RS Structure for Efficient Fronthaul Functional Split in 5G O-RAN and Low Complexity Channel Estimation

Exploiting CSI-RS Structure for Efficient Fronthaul Function...

引用

IEEE Conference on Wireless Communications and Networking

作者： Arunesh Anbukani Harivignesh A K. Giridhar Department of Electrical Engineering TelWise Research Group Indian Institute of Technology Madras Chennai India

ISBN: (数字)9798350303582

ISBN: (纸本)9798350303599

The limited bandwidth of the Fronthaul (FH) network is one of the main challenges faced by the 5G Open Radio Access Networks (O-RAN). The FH network connects the O-RAN Distributed Unit (O-DU) to one or more O-RAN Radio Units (O-RU). In Frequency Division Duplexing (FDD) Massive Multiple-Input and Multiple-Output (MIMO) systems, the FH load increases in proportion to the number of antennas during the transmission of Channel State Information - Reference signal (CSI-RS) for downlink channel sounding. However, the O-RAN functional split scales the FH capacity with the number of MIMO layers. This paper proposes an alternative to O-RAN functional split (7-2x) for CSI-RS in Downlink Channel Sounding systems. The proposed split distributes the signal processing functions for CSI-RS transmission into two stages. The first stage (CSI-RS base sequence mapping) is implemented in the O-DU, and the second stage (superimposing orthogonal weights) is implemented in the O-RU. Our results indicate that the proposed split significantly reduces the fronthaul CSI-RS transportation load while enabling much simpler processing at O-DU and O-RU compared to the existing O-RAN split. Additionally, the paper proposes a computationally efficient way to implement Least Squares (LS) channel estimation on the User Equipment (UE) side, again by exploiting the structure of the CSI-RS pilots.

关键词： 5G mobile communication System performance Transmitting antennas Channel estimation signal processing algorithms Transportation signal processing

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Parametric Modeling and Estimation of Photon Registrations for 3D Imaging

Parametric Modeling and Estimation of Photon Registrations f...

引用

IEEE Workshop on Multimedia signal processing

作者： Weijian Zhang Hashan K. Weerasooriya Prateek Chennuri Stanley H. Chan School of Electrical and Computer Engineering Purdue University West Lafayette USA

ISBN: (数字)9798350387254

ISBN: (纸本)9798350387261

In single-photon light detection and ranging (SP-LiDAR) systems, the histogram distortion due to hardware dead time fundamentally limits the precision of depth estimation. To compensate for the dead time effects, the photon registration distribution is typically modeled based on the Markov chain self-exciting process. However, this is a discrete process and it is computationally expensive, thus hindering potential neural network applications and fast simulations. In this paper, we overcome the modeling challenge by proposing a continuous parametric model. We introduce a Gaussian-uniform mixture model (GUMM) and periodic padding to address high noise floors and noise slopes respectively. By deriving and implementing a customized expectation maximization (EM) algorithm, we achieve accurate histogram matching in scenarios that were deemed difficult in the literature.

关键词： Histograms Three-dimensional displays Computational modeling Noise signal processing algorithms Mixture models Predictive models Distortion Parametric statistics Photonics

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Adaptive LMS Algorithm for Effective Noise Suppression

Adaptive LMS Algorithm for Effective Noise Suppression

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North Karnataka Subsection Flagship International Conference (NKCon), IEEE

作者： Rajani S. Pujar Chayalakshmi C. L. Mahabaleshwar S. K. Department of Electronics and Communication Engineering Basaveshwar Engineering College Bagalkote Karnataka India Department of Electrical and Electronics Engineering Basaveshwar Engineering College Bagalkote Karnataka India

ISBN: (数字)9798350364569

ISBN: (纸本)9798350364576

Noise cancellation aims to reduce or eliminate unwanted signals from the original signals, such as audio signals, thereby improving signal quality and clarity. A popular method for active noise cancellation is the adaptive filter, which uses an algorithm to dynamically adjust the filter parameters based on the input signal to effectively remove noise. An adaptive filter, a type of digital filter, adjusts its coefficients to reach an optimal state without needing prior information. The Least Mean Squares (LMS) algorithm is a well-known adaptive filtering technique utilized in signal processing and communication systems. This algorithm is a form of stochastic gradient descent used to minimize the mean square error between the desired signal and the filter's output. To assess the performance of the proposed LMS-based noise cancellation algorithm, both subjective and objective analyses were conducted. The subjective evaluation involved Mean Opinion Score (MOS) tests to gauge the perceived quality of the speech signal under various signal-to-Noise Ratio (SNR) conditions in noisy environments. Objective evaluation was performed using Segmental SNR. The results from the listening tests showed the following MOS scores for the processed speech "The burse can lead on a smooth plank": 3.3, 3.5, 4.3, 4.0, and 4.3 for SNR values of 0, 5, 10, 15, and 20 dB, respectively. In contrast, the MOS scores for unprocessed speech were significantly lower: 1.0, 1.5, 2.0, 2.0, and 2.8 for the same SNR values. Additionally, Segmental SNR values for the processed speech improved to 4.5712, 4.0248, 3.5847, 3.0045, and 2.547 for the corresponding SNR values. These findings indicate a noticeable improvement in speech quality under different SNR conditions due to the LMS algorithm. Furthermore, similar subjective and objective analyses were performed for various speech signals in the presence of car noise and babble noise, demonstrating the algorithm's effectiveness across different noise environ

关键词： Noise reduction signal processing algorithms Adaptive filters Filtering algorithms Speech enhancement Lead Noise cancellation Noise measurement Digital filters signal to noise ratio

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