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检索条件"任意字段=Conference on Advanced Signal Processing Algorithms"

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A New optical diagnostics tools for grading additive manufactured parts 21

A New optical diagnostics tools for grading additive manufac...

引用

conference on Interferometry XXI

作者： Trolinger, James Davis Gao, Jian Hess, Cecil F. Lal, Amit K. Metrolaser Inc Laguna Hills CA 92653 USA Opt Measurement Syst Corp Laguna Hills CA USA

ISBN: (纸本)9781510654310;9781510654303

In previous papers, we described a diagnostics tool for additive manufacturing products that is based on the concept that the acoustical/vibrational spectrum of an object can be used as a unique signature that characterizes the material and geometry of a product sufficiently to enable its comparison with a perfect reference to identify anomalies. This enables a user to identify rogue parts, such as defective, counterfeit, suspicious or problem parts such as defective or failing to meet specifications. The instrument produces a signature by measuring, with a laser Doppler vibrometer, the vibration of surface points on the part while it is energized by a swept frequency, piezo-electric exciter. Since its first introduction, additional research and development has enhanced, automated, and moved the instrument to a new level in terms of capability and ease of use. This paper describes the latest enhancements, including improved procedures and automation to enable use by an unsophisticated user with minimum training. The latest version provides automatic signal processing and comparison of acoustical signatures of reference and test objects that provide a quantitative grade for the object in terms of material properties, geometrical anomalies, and defects. This is achieved with specially developed algorithms that employ unique types of correlation of vibrational spectra taken from different objects or spectra produced by a simulation of a perfect object. The instrument can quickly produce an optimized correlation coefficient that serves as a numeric grade for a part for identifying suspicious or problem parts such as counterfeits, defective, or failing to meet specifications. The user can set acceptance criteria and initiate a pass or fail signal that can be used for large quantity inspections. Such an instrument is expected to find widespread applications in the AM industry.

关键词： Composites Additive manufacturing Inspection Finite element methods Amplitude modulation Laser spectroscopy Optical diagnostics Optics manufacturing signal processing Nondestructive evaluation

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Optimizing Wavelength Division Multiplexing Free-Space Optical Systems: A Comparative Analysis of Predictive Modeling Techniques

Optimizing Wavelength Division Multiplexing Free-Space Optic...

引用

International conference on advanced Electrical Engineering (ICAEE)

作者： Kebaili Rima Driz Samia Fassi Benattou Telecommunications and Digital Signal Processing Laboratory Faculty of Electrical Engineering University of Djillali Liabes Sidi Bel Abbes Algeria

ISBN: (数字)9798350349351

ISBN: (纸本)9798350349368

In the ever-evolving landscape of telecommunications, Free-Space Optical (FSO) communication systems are increasingly positioned as promising candidates for next-generation networks. Their immunity to interference and high bandwidth perfectly align with the surging demand for high-speed data transmission. This research dives deep into the world of cutting edge predictive modeling techniques, which are essential for optimizing Wavelength Division Multiplexing FSO systems and addressing fading effects in atmospheric conditions. To rigorously assess the predictive power of various models, we utilize cutting edge OptiSystem simulations. Four Machine Learning (ML) models, including Fine Trees, linear Support Vector Machines (SVMs), boosted trees, and linear regression models incorporating interaction terms, are evaluated to identify the most effective model for accurate performance prediction of the WDM- FSO system, specifically in terms of key metrics like Q-Factor, signal- to Noise Ratio (SNR) and Bit Error Rate (BER). A comprehensive approach is employed. Extensive data is generated through meticulous simulations of a wide range of atmospheric conditions. Further, leveraging rigorous evaluation techniques like Root Mean Squared Error (RMSE), R-squared, Mean Squared Error (MSE), and Mean Absolute Error (MAE), an in depth comparison of the four aforementioned algorithms is performed. This thorough analysis provides valuable insights into the strengths and weaknesses of each model, ultimately guiding the selection of the optimal model for next-generation WDM-FSO systems.

关键词： Q-factor Analytical models Free-space optical communication Atmospheric modeling Bit error rate Predictive models Wavelength division multiplexing Prediction algorithms Next generation networking signal to noise ratio

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Automated Detection and Segmentation of Breast Cancer in Ultrasound Images with Deep Learning

Automated Detection and Segmentation of Breast Cancer in Ult...

引用

International conference on Image and signal processing and their Applications (ISPA)

作者： Meryem Ketfi Mebarka Belahcene Salah Bourennane RB_IAIM LI3C M.Khider Biskra University Algeria Ecole Centrale Marseille and GSM Institut Fresnel France

ISBN: (数字)9798350309249

ISBN: (纸本)9798350309256

Breast cancer, characterized by uncontrolled cell division in breast tissue, affects individuals of all genders. Risk factors encompass age, genetics, hormones, and lifestyle. Symptoms comprise lumps, breast and nipple changes, and skin abnormalities. Early detection via self-exams, clinical exams, and mammograms is crucial. Artificial Intelligence (AI) in breast cancer detection systems utilizes advanced algorithms and image analysis techniques to aid radiologists in identifying potential cancerous abnormalities, enhancing accuracy, and facilitating earlier diagnosis. In this paper, various feature fusion methods were proposed for binary classification of breast cancer. The first technique involves concatenating the flattened layers of VGG19 and Xception models. The second technique removes the fully connected layers of both VGG19 and Xception models, concatenates their outputs, and applies an Encoder to reduce spatial dimensions and extract hierarchical features. Both Flatten and GlobalAveragePooling2D (GAP2D) are utilized to convert a 2D feature map into a 1D vector and choose the best ones. Our approach achieved the best results with an accuracy of 94.61% for classification and a dice score of 72% for segmentation task.

关键词： Image segmentation Ultrasonic imaging signal processing Feature extraction Breast cancer Vectors Skin

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IoT-Enabled Automotive Software Development through Integration of Engine Control Unit signals for Adaptive Autonomous Emergency Braking

IoT-Enabled Automotive Software Development through Integrat...

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Innovation in Technology (ASIANCON), Asian conference on

作者： Chinmayee Khondekar Priti Gade Department Of Electronics Engineering G. H. Raisoni College Of Engineering Nagpur (An Autonomous Educational Institute Affiliated to the Rashtrasant Tukdoji Maharaj Nagpur University)

ISBN: (数字)9798350354218

ISBN: (纸本)9798350354225

This paper explores the integration of engine control unit (ECU) signals within an IoT-enabled automotive software development framework. The study focuses on leveraging ECU data for real-time decision-making in adaptive systems, enhancing automotive safety and performance through advanced software development and testing methodologies. It presents the design and implementation of an adaptive autonomous emergency braking (AEB) system for automotive safety applications, focusing on the integration of engine control unit (ECU) signals within an IoT-driven software framework. The system employs a network of sensors and cameras to continuously monitor the vehicle’s surroundings and detect potential obstacles. When the vehicle’s battery and ignition are switched on, the sensors/cameras send detected obstacle signals to the ECU, which in turn initiates signal processing via Controller Area Network (CAN) communication. The signal processing algorithm analyzes the obstacle’s distance and the vehicle’s speed to assess collision risks in real-time. Upon detecting a potential collision, the system triggers a warning on the Human-Machine Interface (HMI) to alert the driver. If the driver fails to respond promptly, the AEB system automatically activates the brakes to prevent a collision.

关键词： Technological innovation Vehicle safety signal processing algorithms Sensor systems Real-time systems Sensors Engines Software development management Vehicles Testing

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Enhancing Efficiency in Computational Intensive Domains via Redundant Residue Number Systems

Enhancing Efficiency in Computational Intensive Domains via ...

引用

International SoC Design conference, ISOCC

作者： Soudabeh Mousavi Dara Rahmati Saeid Gorgin Jeong-A Lee Department of Computer Science and Engineering Shahid Beheshti University Tehran Iran Department of Computer Engineering Chosun University Gwangju Republic of Korea

ISBN: (数字)9798350377088

ISBN: (纸本)9798350377095

In computation-intensive domains such as digital signal processing, encryption, and neural networks, the performance of arithmetic units, including adders and multipliers, is pivotal. Conventional numerical systems often fall short of meeting the efficiency requirements of these applications concerning area, time, and power consumption. Innovative approaches like residue number systems (RNS) and redundant number systems have been introduced to surmount this challenge, markedly elevating computational efficiency. This paper examines from multiple perspectives how the fusion of redundant number systems with RNS (termed R-RNS) can diminish latency and enhance circuit implementation, yielding substantial benefits in practical scenarios. We conduct a comparative analysis of four systems— RNS, redundant number system, Binary Number System (BNS), and Signed-Digit Redundant Residue Number System (SD-RNS)—and appraise SD-RNS through an advanced Deep Neural Network (DNN) utilizing the CIFAR-10 dataset. Our findings are encouraging, demonstrating that SD-RNS attains computational speedups of 1.27 times and 2.25 times over RNS and BNS, respectively, and reduces energy consumption by 60% compared to BNS during sequential addition and multiplication tasks.

关键词： Performance evaluation Power demand signal processing algorithms Artificial neural networks Digital signal processing Computer architecture Energy efficiency Computational efficiency Encryption Arithmetic

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KEPS-NET: Robust Parking slot Detection based Keypoint estimation for High Localization Accuracy

KEPS-NET: Robust Parking slot Detection based Keypoint estim...

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International conference on Acoustics, Speech, and signal processing (ICASSP)

作者： Jaewoo Lee Kapje Sung Daeul Park Younghan Jeon Samsung Advanced Institute of Technology (SAIT)

In this paper, we study parking slot detection problem for automated parking function. Main contribution is to propose a neural network to detect parking slot based on keypoint estimation, which is called KEPS-NET. The proposed network detects three kinds of parking slot (perpendicular, parallel, and slanted) with two entrance corner points. We first analyze how precise localization of the entrance points in pixel level is important for real automated parking scenario. Then we construct parking slot detector with four heads using a common feature extraction backbone network to estimate center point, entrance points, slot type, and slot angle. And separate key-point detector with two heads is added to find entrance points more accurately by leveraging richer local features. We also design a slope loss helping the network minimize the orientation errors of the entrance line, which allows the network to obtain greatly improved localization accuracy. Through the experimental results, we show that the proposed algorithm achieves state-of-the-art performance compared to the previous works.

关键词： Location awareness Head Neural networks Estimation signal processing algorithms Detectors signal processing

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Surface Defect Detection of Cigarette Packs Based on Improved YOLOv8

Surface Defect Detection of Cigarette Packs Based on Improve...

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

作者： Peng Dong Di Wang Yongsheng Wang Guohao Zong Henan Institute of Advanced Technology Zhengzhou University Zhengzhou China Zhengzhou Tobacco Research of CNTC China National Tobacco Corporation Zhengzhou China

ISBN: (数字)9798350376548

ISBN: (纸本)9798350376555

The surface quality of cigarette packets is an important indicator to ensure that cigarette products comply with quality standards, which is crucial for the smooth entry of cigarette products into the market and the reduction of material waste. To address the challenges posed by complex defects on cigarette pack surfaces and improve detection performance, this paper proposes a lightweight method-based YOLOv8 defect detection model, DHG-YOLO. Firstly, depth-wise separable convolution (DWConv) is used in the backbone network to replace the original convolution modules, thereby reducing model parameters. Secondly, HGNetV2 is employed to replace the feature extraction network of YOLOv8, allowing the model to better extract complex defect information. Finally, the Ghost module optimizes the feature extraction module of HGNetV2, enhancing model accuracy by reducing redundant parameters and improving detection efficiency. Experimental results on the cigarette box defect dataset demonstrate that DHG-YOLO achieves the best performance, with an mAP@0.5 of 91.1%, representing an increase of 5.0%, and an mAP@0.5:0.95 of 73.1%, reflecting an increase of 7.5%.

关键词： Training Accuracy Convolution signal processing algorithms Production Feature extraction Data mining Surface treatment Standards Defect detection

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Leveraging UAV Technology for Enhanced Disaster Management: Innovations and Applications

Leveraging UAV Technology for Enhanced Disaster Management: ...

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

作者： Govind Murari Upadhyay Prashant Vats Surabhi Shanker Saloni Bhushan Sonu Mittal Manju Mandot Dept. of Computer Application Manipal University Jaipur Jaipur Rajasthan India Dept. of CSE School of CSE Manipal University Jaipur Jaipur Rajasthan India Dept. of CSE School of Engineering & Technology K.R. Mangalam University Sohna Road Gurugram Haryana India Dept. of Computer Science/IT Chhatrapati Shivaji Maharaj University Panvel Maharashtra India Dept. of CSE Amity Institute of Information Technology Amity University Noida Uttar Pradesh India Dept. of Computer Science/IT J. R. N. Rajasthan Vidyapeeth University Udaipur Rajasthan India

ISBN: (数字)9798350385205

ISBN: (纸本)9798350385212

Unmanned Aerial Vehicles (UAVs) have substantially advanced the sector by providing improved real-time monitoring, data collecting, and situational awareness capabilities when integrated into disaster management techniques. This study examines the creative uses of unmanned aerial vehicles (UAVs) in disaster relief, emphasising how they may speed up reaction times and increase operational effectiveness in emergency situations. With an emphasis on their usefulness in human detection, damage assessment, and coordination efforts, we propose a thorough framework that describes the design, deployment, and evaluation of UAV systems customised for catastrophe situations. The study shows how UAVs may be used to enhance processes of decision-making and enable more efficient recovery and catastrophe response through instances and quantitative evaluation. The results demonstrate how UAV technology may revolutionise conventional disaster management procedures and provide insightful information for future studies and real-world applications in the field.

关键词： Technological innovation signal processing algorithms Disaster management Transforms Medical services signal processing Autonomous aerial vehicles Regulation Real-time systems Relays

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Designing 256-QAM for High Data Rate Optical Wireless Communication

Designing 256-QAM for High Data Rate Optical Wireless Commun...

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Space, Aerospace and Defence conference (SPACE), IEEE

作者： Sree Harsha Prada Peesapaty Chinchu Joseph Eguri Samson Vineeth Kumar Anjali Askhedkar A. A. Bazil Raj Department of Electrical and Electronics Engineering Dr. Vishwanath Karad MIT World Peace University Pune India Department of Electronics Engineering RF Photonics Lab Defence Institute of Advanced Technology Pune India

ISBN: (数字)9798350367386

ISBN: (纸本)9798350367393

The evolving field of wireless optical communication finds its applications in various fields of space, defence, underwater, and last-mile connectivity too. To achieve the aspect of higher data rates and an optimal bandwidth it is necessary to incorporate advanced modulation techniques. This research work focuses on designing a higher data rate link using a 256-QAM scheme. The design of system is done using Mach-Zehnder Modulator and was carried out in Optisystem software. This research work focuses on the designing aspect and simulations are carried out which acts as a further foundation for hardware implementation in real-time as well.

关键词： Wireless communication Prevention and mitigation Noise signal processing algorithms Symbols Optical distortion Real-time systems

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Hyperspectral imaging using deep learning in wheat diseases: (Review)

Hyperspectral imaging using deep learning in wheat diseases:...

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International conference on Image and signal processing and their Applications (ISPA)

作者： Fadi Abd Eladhim zidi Ouafi Abdelkrim Dept. Electrical Engineering University of Biskra Biskra Algeria

ISBN: (数字)9798350309249

ISBN: (纸本)9798350309256

Wheat is a significant staple crop worldwide, but its cultivation is challenged by various factors such as pests, diseases, climate change, lack of water, and yield enhancement. To prevent wheat infections, nondestructive and self-sufficient remote detection methods are necessary, which are sensitive and reliable. Hyperspectral imaging with deep learning is a powerful method for identifying diseases early, mapping them accurately, and evaluating their severity in plants in an unbiased way. This method involves data collection, preprocessing, and analysis of Hyperspectral images to identify wheat diseases. The article also presents several Hyperspectral imaging datasets and relevant research on using this technology for detecting wheat illnesses. The integration of spectroscopy and imaging in Hyperspectral imaging represents a novel approach to achieving an in-depth and comprehensive understanding of plant life. Moreover, deep learning, a subset of artificial intelligence, has gained considerable attention due to its ability to process vast and intricate data sets. By synergistically merging these two advanced technologies, researchers and scientists can significantly enhance the analysis of hyperspectral imagery, thereby enabling improved crop monitoring, disease identification, and yield projection in wheat farming This article summarizes the use of deep learning techniques in Hyperspectral imaging for wheat crops. Hyperspectral imaging is a non-destructive way to study crops but generates a lot of data that can be difficult to analyze. Deep learning automates the analysis process and improves accuracy. However, it requires large and diverse datasets, complex model development, and computational resources. This report provides information on the benefits and challenges of using deep learning in Hyperspectral imaging for wheat crops.

关键词： Deep learning Plant diseases Spectroscopy Crops signal processing algorithms signal processing Reliability Climate change Hyperspectral imaging Deep learning

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