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检索条件"主题词=Signal Processing Algorithms"

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Soldering Defect Identification Using Deep Learning and Machine Learning Approach

Soldering Defect Identification Using Deep Learning and Mach...

引用

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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HDR Imaging with One-Bit Quantization

HDR Imaging with One-Bit Quantization

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Sensor Array and Multichannel signal processing Workshop

作者： Arian Eamaz Farhang Yeganegi Mojtaba Soltanalian University of Illinois Chicago Chicago IL USA

ISBN: (数字)9798350344813

ISBN: (纸本)9798350344820

Modulo sampling and dithered one-bit quantization frame-works have emerged as promising solutions to overcome the limitations of traditional analog-to-digital converters (ADCs) and sensors. Modulo sampling, with its high-resolution approach utilizing modulo ADCs, offers an unlimited dynamic range, while dithered one-bit quantization offers cost-efficiency and reduced power consumption while operating at elevated sampling rates. Our goal is to explore the synergies between these two techniques, leveraging their unique advantages, and to apply them to non-bandlimited signals within spline spaces. One noteworthy application of these signals lies in High Dynamic Range (HDR) imaging. In this paper, we expand upon the Unlimited One-Bit (UNO) sampling framework, initially conceived for bandlimited signals as proposed in [1], to encompass non-bandlimited signals found in the context of HDR imaging. We present a novel algorithm rigorously examined for its ability to recover images from one-bit modulo samples. Additionally, we introduce a sufficient condition specifically designed for UNO sampling to properly recover non-bandlimited signals within spline spaces. Our numerical results vividly demonstrate the effectiveness of UNO sampling in the realm of HDR imaging.

关键词： Image sensors Sufficient conditions Quantization (signal) Power demand Heuristic algorithms signal processing algorithms Sensors

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Traffic flow models and statistical analysis using compressed data from acquisition module

Traffic flow models and statistical analysis using compresse...

引用

signal processing algorithms, Architectures, Arrangements and Applications (SPA)

作者： Alin Alexandru ŞERBAN Mădălin Frunzete Faculty of Electronics Telecommunications and Information Technology National University of Science and Technology Politehnica Bucharest Bucharest Romania

ISBN: (数字)9788362065486

ISBN: (纸本)9798350373806

Congested traffic in big cities represents one of the current problems of society because it has harmful effects on people’s quality of life by increasing the level of pollution, increasing fuel consumption of the cars or the increased time spent in traffic. That is why traffic analysis is necessary and automatic solutions based on traffic prediction and traffic flow models are needed for decongestion and its improvements. In this paper, a universal, portable, lightweight, and highautonomy module for acquisition of the relevant traffic data (GPS data) is created and proposed, based on a custom printed circuit board that can be placed in a personal car or in a vehicle from public transport. For the data acquired by testing the module, a compression algorithm is applied to extract the relevant data, and these will be used in the implementation of traffic flow models such as Greenshields, Greenberg, Underwood, and Pipes to analyze the relationship between speed, flow and density of the traffic. The results of the implementation of these traffic flow models are compared and the combination of them will be applied for multiple regime models.

关键词： Analytical models Statistical analysis Urban areas signal processing algorithms Predictive models Data models Automobiles Integrated circuit modeling Traffic congestion Testing

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Millimeter Wave Radar Gesture Recognition Based on ECC

Millimeter Wave Radar Gesture Recognition Based on ECC

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IEEE International Symposium on Geoscience and Remote Sensing (IGARSS)

作者： Junjie Huang Longxiang Jiao Chenfeng Zhou Han Li School of Information and Electronics Beijing Institute of Technology Beijing China Beijing Institute of Technology Chongqing Innovation Center Chongqing China

ISBN: (数字)9798350360325

ISBN: (纸本)9798350360332

Gesture recognition of millimeter-wave radar plays an important role in human-computer interaction within the current Internet of Everything context. Based on the signal pre-processing algorithm of MUSIC, this paper proposes a gesture recognition algorithm using error-correction code(ECC) module. Firstly, the gesture signals collected by millimeter-wave radar are processed into three sets of images of azimuth, elevation and depth by using signal processing algorithms such as MUSIC; then they are sent to the feature extraction network and output the discriminant code; finally, the discriminant codes are sent to ECC module to output the final verdict result. The addition of error correction code makes the whole decision mechanism more logical and robust, and it may also significantly enhance the real-time performance of gesture detection since it avoids the complicated network.

关键词： signal processing algorithms Gesture recognition Millimeter wave radar Robustness Real-time systems Error correction codes Multiple signal classification

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Research on Intelligent Estimation Model of Carbon Stock of Rocky Desertification Plantations Based on LiDAR Point Cloud Data

Research on Intelligent Estimation Model of Carbon Stock of ...

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

作者： Yin Beilei Liu Feng Wei Shengling Jiang Guijuan Zhang Gentao Guangxi Eco-Engineering Vocational and Technical College Liuzhou China Guangxi Laibin Forestry Survey and Design Institute Co. Ltd Laibin China

ISBN: (数字)9798331528676

ISBN: (纸本)9798331528683

This paper proposes an intelligent estimation model of carbon stock of rocky desertification plantations based on LiDAR point cloud data. The model uses an advanced point cloud segmentation algorithm to finely segment the forest data collected by LiDAR, so as to accurately distinguish the vegetation type and density. Through the processing and feature extraction of point cloud data, combined with the geographical characteristics of rocky desertification areas, this study designed a carbon stock estimation algorithm model suitable for rocky desertification plantations. The system model includes modules such as data acquisition, point cloud segmentation, and carbon stock estimation, which are optimized by combining machine learning methods. The simulation part verifies the accuracy of the model through a large amount of field measurement data, and accurately estimates the carbon stock of different rocky desertification levels through quantitative analysis. The results show that the error rate of the model in carbon stock estimation is less than 5%, which has high application potential and practicality. The research in this paper provides an effective technical means for the accurate assessment of carbon stocks in rocky desertification areas, which will help further promote the implementation of carbon sink mechanisms and the restoration of ecosystems.

关键词： Point cloud compression Laser radar Accuracy Estimation Plantations signal processing algorithms Vegetation mapping Desertification Data models Carbon

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Declouding of Satellite Images for Crop Growth Monitoring Via Unrolling of Gradient Graph Laplacian Regularizer

Declouding of Satellite Images for Crop Growth Monitoring Vi...

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IEEE International Conference on Image processing

作者： Parham Eftekhar Gene Cheung Tim Eadie York University Canada Growers Edge

ISBN: (数字)9798350349399

ISBN: (纸本)9798350349405

Spectral images periodically captured by satellites are often obscured by clouds. For crop monitoring, cloud removal—called “declouding”—in satellite images is important, so that crop growth at a field level can be estimated from restored images. In this paper, we adopt a graph signal processing (GSP) approach to satellite image declouding to capture neighboring pixel correlations that persist over time. We first assume an atmospherical scattering model (ASM) for image formation, where observation $\mathbf{y}$ is a product of piecewise constant (PWC) target image x and piecewise planar (PWP) transmission map t. To decompose y back into $\mathbf{x}$ and $\mathbf{t}$, we formulate respective quadratic programming (QP) problems, without / with box constraints, using — graph Laplacian regularizer (GLR) / gradient graph Laplacian regularizer (GGLR) as prior, to compute $\mathbf{x} / \mathbf{t}$ alternately. For efficient optimization, we compute $\mathbf{x}$ in an unconstrained QP via conjugate gradient (CG) without matrix inverse, while we compute t in a constrained QP via proximal gradient descent (PGD). We unroll iterations of our alternating algorithm into neural layers for end-to-end data-driven parameter optimization, resulting in an interpretable, algorithm-specific feed-forward network. Experimental results show that our unrolled network outperforms model-based and pure deeplearning schemes in declouded image quality, objectively and subjectively.

关键词： Image quality Laplace equations Satellites Clouds Crops signal processing algorithms signal processing

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Physics Informed Neural Networks(PINNs) for Burgers' equation

Physics Informed Neural Networks(PINNs) for Burgers' equatio...

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

作者： Indrajit Das Debjit Das Papiya Debnath Subhrapratim Nath Manash Chanda Department of Information Technology Meghnad Saha Institute of Technology Kolkata India Department of Basic Science & Humanities Techno International New Town Kolkata India Department of Computer Science & Engineering Meghnad Saha Institute of Technology Kolkata India Department of Electronics & Communication Engineering Meghnad Saha Institute of Technology Kolkata India

ISBN: (数字)9798350350654

ISBN: (纸本)9798350350661

PINNs are neural networks that undergo training to execute supervised learning tasks while adhering to certain laws of physics, which are typically defined by basic nonlinear partial differential equations. This paper concentrates on the advancements in addressing burger equation problems using data-driven approaches for partial differential equations. Here the algorithms are separated into continuous time designs. The neural networks that emerge from this process are part of a new class of data-efficient approximators for universal functions, which automatically include all fundamental physical principles as prior information. U sing these networks, this research demonstrates how to deduce solutions to partial differential equations.

关键词： Training Partial differential equations Computational modeling Neural networks signal processing algorithms Predictive models Approximation algorithms Mathematical models Computational efficiency Physics

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Computational Load Offloading Mechanism in a Converged SDN Control Plane in a 6G Network

Computational Load Offloading Mechanism in a Converged SDN C...

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International Symposium on Communication Systems, Networks and Digital signal processing, CNSDSP

作者： Irene Keramidi John Vardakas Ioannis Moscholios Michael Logothetis Christos Verikoukis Iquadrat Informatica S.L. Barcelona Spain Dept. Informatics & Telecommunications University of Peloponnese Greece Dept. Informatics UOWM Greece ECE Dept. University of Patras Greece University of Patras and ATHENA/ISI

ISBN: (数字)9798350348743

ISBN: (纸本)9798350348750

The evolution towards the emergence of the smart 6 th generation of telecommunication networks targets on un-precedentedly transforming the existing network systems. This unparallel transformation relies on being able to serve in zero-latency highly demanding applications and services hosted on a massive number of devices. This new era of telecommunication networks has designated the need to develop innovative solutions that can intelligently manage the systems' resources in order to deal with the intensified requirements. In this work, we propose a load offloading mechanism aiming to efficiently manage the computational resources of a cell free-based 6G network. We also present a novel traffic-engineering model that aims to evaluate the proposed offloading mechanism and we validate the analytical model with numerical simulation results in order to demonstrate its high accuracy. Finally, we examine the system's performance for different parameter values and it is shown that the offloading mechanism has a strong impact on improving the system's service provisioning.

关键词： 6G mobile communication Analytical models System performance signal processing algorithms Telecommunication traffic Optical fiber networks Numerical simulation

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Research on Path Planning System of UAV Remote Sensing under Second-Degree Flight Control Computer

Research on Path Planning System of UAV Remote Sensing under...

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Electronic Technology, Communication and Information (ICETCI), IEEE International Conference on

作者： Chuang Deng Haitao Cheng Guozhu Yang Maojie Tian Enhui Wei Zezhao Wang Chao Gao Shirui Sun State Grid Sichuan Electric Power Corporation Power Emergency Management Center Chengdu China State Grid Electric Power Space Technology Company Limited Changping China

ISBN: (数字)9798350361643

ISBN: (纸本)9798350361650

This article discusses the overall structure and specific implementation of the second-degree flight control computer in more detail. The digital signal processor with TMS320F2812 as the core is introduced. This greatly improves the reliability of the control system. The Tasmanian Devil algorithm (TDO) and weight algorithm are used to further reduce the problem that the algorithm is prone to local minima. The new optimization algorithm "threshold" is introduced, which enables the algorithm to take into account both global optimization and local optimization. Then an optimization method based on genetic algorithm is proposed. It was optimized using various algorithms such as particle swarm algorithm, artificial fish swarm algorithm and ant colony algorithm. Calculation examples prove that the algorithm proposed in this article converges quickly and has high accuracy. This method cannot only prevent falling into local minima, but also effectively improve the search effect. The improved Tasmanian devil method can accelerate the convergence and obtain high precision when solving the 3D optimal route.

关键词： Three-dimensional displays Fault tolerant systems signal processing algorithms Reliability engineering Software Hardware Software reliability

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A Low-complexity I/Q Imbalance Calibration and DC Offset Calibration Method in 5G Millimeter-Wave Communication

A Low-complexity I/Q Imbalance Calibration and DC Offset Cal...

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International Conference on Communication Technology (ICCT)

作者： He Lv Xueqin Zhu Qin Wei Qingsheng Hu School of Information Science and Engineering Southeast University Nanjing China State Grid Wulumuqi Electric Power Supply Company Xinshi District Urumqi China

ISBN: (数字)9798350363760

ISBN: (纸本)9798350363777

RF receiver has a wide range of application scenarios in 5G millimeter-wave communication, but its non-ideal characteristics of devices and circuits will affect the subsequent signal processing, of which I/Q mismatch and DC offset are the two most important influencing factors. The proposed method enables the completion of power-on calibration immediately after the antenna array receiver starts up. With the presented circuit, the impact of receiver DC offset can be reduced significantly, and the amplitude mismatch and phase mismatch parameters can be accurately determined, leading to effective suppression of the mirror signal. The I/Q mismatch utilizes a digital blind estimation calibration method, and the mirror image rejection ratio(IRR) after calibration can typically exceed 50dB.

关键词： 5G mobile communication Circuits signal processing algorithms Estimation Receiving antennas Receivers Calibration Mirrors Millimeter wave communication Antenna arrays

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