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检索条件"主题词=Signal processing algorithms"

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Implementation of Full Adder Logic using XOR-XNORLogic

Implementation of Full Adder Logic using XOR-XNORLogic

引用

Software, Systems and Information Technology (SSITCON), International Conference on

作者： Kondakinda Vanitha K. Lokesh Krishna Hariprasad Tarigonda Sunkesula Chanu Department of ECE Sri Venkateswara College of Engineering Tirupati India Department of ME SoE Mohan Babu University

ISBN: (数字)9798350352931

ISBN: (纸本)9798350352948

Advances in Complementary Metal Oxide Semiconductor (CMOS) scaling techniques have led to the proliferate usage of multimedia devices such as laptops, mobiles, graphic cards, personal digital assistants etc. In all these applications, the low power, low die size and high speed fundamental adder architectures are essential. Various algorithms such as fast-fourier, discrete cosine transforms and inverse discrete cosine transform are used. To implement these algorithms, adders with enhanced performance features such as low area and high speed computation are vital and necessary. Several adder architectures are available for different applications. In this paper, a ten transistor using CMOS Exclusive OR and Exclusive NOR gates in combination is proposed. The full adder logic circuit is designed and simulated in cadence virtuoso too environment and operated a dc voltage of 1.5 V in 0.09 micrometer process technology. The proposed ten transistor full adder logic circuit on an average operates at 14 percent less power when compared to the conventional CMOS logic full adder circuit. The propagation delay performance was also slightly better when compared to previous circuit. Both the full adder logic simulations have been carried for multiple dc voltages and performance analysis (i.e. power delay product) has been carried out at the end of the paper.

关键词： Semiconductor device modeling Logic circuits Software algorithms signal processing algorithms Voltage Logic gates Power dissipation Transistors Adders Propagation delay

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Sparse Array Design for ISAC in the Presence of Beam-Squint

Sparse Array Design for ISAC in the Presence of Beam-Squint

引用

International Conference on Communications, signal processing, and their Applications (ICCSPA)

作者： Ahmet M. Elbir Asmaa Abdallah Abdulkadir Celik Ahmed M. Eltawil King Abdullah University of Science and Technology Saudi Arabia

ISBN: (数字)9798350384819

ISBN: (纸本)9798350384826

This paper presents an efficient approach for joint antenna selection and hybrid beamforming for integrated sensing and communications (ISAC) in the presence of beamsquint. We proposed a manifold optimization-based technique that incorporates beam-squint compensation for hybrid beamformer design. To reduce computational and memory costs for antenna selection, we propose novel algorithms leveraging grouped subarrays, wherein the entire array is divided into non-overlapping groups. Numerical simulations show that the proposed approach exhibits up to 95% lower complexity for large antenna arrays while maintaining satisfactory communications with approximately 6% loss in the achievable rate.

关键词： Manifolds Costs Array signal processing signal processing algorithms Integrated sensing and communication Numerical simulation Approximation algorithms Complexity theory Antenna arrays

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GastroNet FusionAI: Artificial Intelligence Diagnosis of Clinical Gastric Cancer Tumors

GastroNet FusionAI: Artificial Intelligence Diagnosis of Cli...

引用

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

作者： Jialun Xu Xiayang Wu Zhenyuan Xu School of Statistics Nanjing Audit University Nanjing China School of Statistics Nanjing University of Posts and Telecommunications Nanjing China

ISBN: (数字)9798350355895

ISBN: (纸本)9798350355901

The significant advancements in natural image processing have sparked widespread interest in applying similar deep learning techniques to medical imaging. Unlike natural images, detecting details in medical images requires capturing finer details, necessitating the use of specially designed network architectures rather than standard ones developed for large-scale, downscaled natural images. Additionally, medical imaging often requires the fusion of images from multiple perspectives to form an accurate diagnosis. Therefore, we propose a novel AI algorithm called GastroNet FusionAI, an advanced algorithm specifically designed for gastric cancer diagnosis. It employs an innovative image fusion strategy that effectively integrates images from four different imaging perspectives in gastric cancer detection. This approach not only provides a more comprehensive view but also significantly enhances the accuracy of disease detection by enriching the image content. The development of GastroNet FusionAI aims to improve the early detection rate of gastric cancer, which is crucial for increasing patient survival rates. Gastric cancer is one of the most common types of cancer globally and has a high incidence in certain regions. Despite the enormous potential of AI technology in gastric cancer detection, research on its effectiveness and reliability remains relatively scarce. Existing studies are often limited by small sample sizes, homogeneous datasets, or a lack of rare gastric cancer subtypes. To overcome these challenges, we employed Real-Time Test-Time Augmentation (R-TTA) to expand the original dataset (consisting of 54,706 images) to 273,530 images. This method not only improved the model's learning efficiency but also effectively reduced the risk of overfitting. Our model was trained on a preprocessed dataset from three different hospitals and four different imaging perspectives, and its performance was evaluated using cross-validation to ensure the broad applicability of

关键词： Accuracy signal processing algorithms Cancer detection Real-time systems Artificial intelligence Medical diagnostic imaging Image fusion Cancer Overfitting Tumors

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Extended Kalman Filtering Using Quaternion Normalization for Attitude Measurement While Drilling

Extended Kalman Filtering Using Quaternion Normalization for...

引用

Intelligent Control, Measurement and signal processing and Intelligent Oil Field (ICMSP), International Conference on

作者： Tianjie Jiang Minghao Gao Guangxiao Jian China Oilfield Services Limited Beijing China

ISBN: (数字)9798331516550

ISBN: (纸本)9798331516567

Extended Kalman filtering (EKF) has extensively applied in attitude measurement while drilling, in which quaternion is commonly used as the state vector. However, for the dynamic model, the quaternion usually does not meet the requirement of the unit norm as time goes, which leads to the invalidity of attitude calculation. For this, this paper studies the EKF algorithm using normalized quaternion for attitude measurement while drilling. It establishes the state and measurement equations using quaternion according to specific rotation model of the drilling tool. Then, normalization is applied in each estimation step. Illustrative examples show the performance superiority of the method considered, compared with the method free of quaternion normalization and static calculation method.

关键词： Drilling Quaternions signal processing algorithms signal processing Prediction algorithms Mathematical models Vectors Kalman filters Rotation measurement Intelligent control

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Line Laser Vision-Guided Weld Seam Tracking System Research

Line Laser Vision-Guided Weld Seam Tracking System Research

引用

signal processing and Intelligent Computing (SPIC), International Conference on

作者： Hailong Yin Qiang Jiang School of Automation and Electrical Engineering Shenyang Ligong University Shenyang China

ISBN: (数字)9798350368888

ISBN: (纸本)9798350368895

Traditional welding robot processing relies heavily on demonstration teaching or offline programming, resulting in low flexibility and poor adaptability in the production process. Under the influence of complex and changing welding environment, there are still difficulties in weld seam recognition; when the position of the weldment is shifted or deformed by the high temperature of the welding torch, it will cause the error between the actual welding trajectory and the preset trajectory. This paper mainly focuses on the optimization and innovation of weld seam image recognition, after completing the calibration of each part of the welding system, using convolutional neural network to detect and recognize the weld seam, and carrying out a series of processing for the collected images, on this basis, it is proposed to establish an improved Faster Region-based Convolutional Neural Networks model, adjust and optimize the model structure, and at the same time, use the Feature Pyramid Networks algorithm for target detection. Through the comparison of the detection performance of different networks, it is proved that the improved Faster-RCNN network is more accurate in weld seam detection.

关键词： Image recognition Accuracy Welding signal processing algorithms Object detection Feature extraction Laser modes Trajectory Convolutional neural networks Optimization

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The application of Gaussian-Rayleigh mixture model in remote sensing image segmentation

The application of Gaussian-Rayleigh mixture model in remote...

引用

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

作者： Xiaoxue Sun Yue Yang Yanhao Li Guoren Gao Mechanical Engineering Department Jilin Agriculture Science and Technology University Jilin Jilin China State Grid Liaoning Extra High Voltage Company Liaoning Anshan China

ISBN: (数字)9798350376548

ISBN: (纸本)9798350376555

This paper employs the Entropy-Max method to determine the optimal number of classes in an image, utilizes the Markov Random Field (MRF) method to complete the image segmentation, and addresses the computation of maximum a posteriori probability during the segmentation process using the Iterative Conditional Model (ICM). Three remote sensing images are used in the experiments, comparing the segmentation and fitting results using Gaussian Mixture Mode (GMM) and Gaussian-Rayleigh Mixture Model (GRMM).

关键词： Maximum a posteriori estimation Image segmentation Technological innovation Computational modeling Fitting signal processing algorithms Mixture models signal processing Entropy Remote sensing

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Crack Image Classification: Performance Comparison Among Different Machine Learning algorithms

Crack Image Classification: Performance Comparison Among Dif...

引用

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

作者： Benelmir Okba Dhiabi Fathi Electrical Engeneering Departement Faculty of Sciences and Technology University Mohamed Khider Biskra Algeria

ISBN: (数字)9798350309249

ISBN: (纸本)9798350309256

Classification of surface crack images is an ever-evolving area of research that aims to develop machine learning models capable of identifying and categorizing cracks in materials. This study concentrates on evaluating and comparing the effectiveness of various machine learning algorithms when employed in the classification of crack images. The objective is to identify the most suitable classification technique for this purpose. To conduct this comparative study, several machine learning algorithms are selected, such as k-nearest neighbors (KNN), decision trees, support vector machines (SVM), random forests and neural networks (NN). Each of these algorithms is trained and evaluated on a dataset of previously annotated crack images.

关键词： Support vector machines Machine learning algorithms signal processing algorithms Prediction algorithms Vectors Classification algorithms Data mining

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FPGA Implementation of Short Message Authentication Based on the SM4 Algorithm

FPGA Implementation of Short Message Authentication Based on...

引用

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

作者： Kuangjie Jiang Ziming Wang Guangzu Liu Qian Sun School of Electronic Engineering an Optical Engineering Nanjing University of Science and Technology Nanjing Jiangsu School of Electronic Engineering and Optical Engineering Nanjing University of Science and Technology Nanjing Jiangsu Digital Signal Processing Research and Development Center Jiangsu YEE-Space Technology GO. LTD. Nanjing Jiangsu

ISBN: (数字)9798350355895

ISBN: (纸本)9798350355901

This paper presents a FPGA implementation of SM4 Encryption and Decryption which has been used in the short message service (SMS) to solve the security risks of information leakage and illegal tampering in the short message service by converting the plaintext into ciphertext for transmission within short message communication system. This paper expands the key bit width based on the traditional SM4 so that the algorithm supports 256-bit keys. The extension of Key Size significantly complicates cryptanalysis efforts, making it exceedingly difficult for analysts to deduce algorithm characteristics from ciphertext, thereby substantially enhancing the security of the algorithm, but throughput (blocks processed per second) is unaffected. Additionally, this paper also introduces enhancements to the operational mode of the encryption algorithm, allowing the authentication module to encrypt and decrypt data of any length. This paper implements an authentication module based on FPGA for encryption and decryption, providing effective security for the transmission of short message data.

关键词： signal processing algorithms Authentication signal processing Message services Throughput Information leakage Encryption Security Field programmable gate arrays Message authentication

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Evaluating the Impact of Point Cloud Colorization on Semantic Segmentation Accuracy

Evaluating the Impact of Point Cloud Colorization on Semanti...

引用

International Conference on Vision, Image and signal processing (ICVISP)

作者： Qinfeng Zhu Jiaze Cao Yuanzhi Cai Lei Fan Department of Civil Engineering Xi'an Jiaotong-Liverpool University Suzhou China CSIRO Mineral Resources CSIRO Kensington Australia

ISBN: (数字)9798331541460

ISBN: (纸本)9798331541477

Point cloud semantic segmentation, the process of classifying each point into predefined categories, is essential for 3D scene understanding. While image-based segmentation is widely adopted due to its maturity, methods relying solely on RGB information often suffer from degraded performance due to color inaccuracies. Recent advancements have incorporated additional features such as intensity and geometric information, yet RGB channels continue to negatively impact segmentation accuracy when errors in colorization occur. Despite this, previous studies have not rigorously quantified the effects of erroneous colorization on segmentation performance. In this paper, we propose a novel statistical approach to evaluate the impact of inaccurate RGB information on image-based point cloud segmentation. We categorize RGB inaccuracies into two types: incorrect color information and similar color information. Our results demonstrate that both types of color inaccuracies significantly degrade segmentation accuracy, with similar color errors particularly affecting the extraction of geometric features. These findings highlight the critical need to reassess the role of RGB information in point cloud segmentation and its implications for future algorithm design.

关键词： Point cloud compression Accuracy Three-dimensional displays Image color analysis Semantic segmentation Image edge detection signal processing algorithms signal processing Feature extraction

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Scanning Radar Super-Resolution Based on Fast Iterative Shrinkage Thresholding Network

Scanning Radar Super-Resolution Based on Fast Iterative Shri...

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IEEE International Symposium on Geoscience and Remote Sensing (IGARSS)

作者： Ding Yuan Juezhu Lai Jifang Pei Wenjing Wang Yin Zhang Yulin Huang School of Information and Communication Engineering University of Electronic Science and Technology of China Chengdu P.R. China

ISBN: (数字)9798350360325

ISBN: (纸本)9798350360332

Airborne scanning radar imaging is widely used both in military and civilian fields. However, the azimuth resolution of the imaging system is constrained by the size of the antenna. Iterative optimization super-resolution algorithms based on regularization can be used to overcome this limitation. But these methods demand manual tuning of parameters, which can be laborious. Deep unfolding network is a method of unfolding iterative optimization algorithms to deep learning networks, which combines the interpretability of iterative algorithms and the advantages of deep learning. Considering the excellent performance of the deep unfolding network in other signal processing tasks, this paper introduces the deep unfolding network based on fast iterative shrinkage-thresholding algorithm (FISTA) called FISTA-Net into the field of scanning radar imaging. For the task of scanning radar azimuth superresolution, we improve the model to fully learn the characteristics of radar azimuth data. Simulation results verify the effectiveness of the proposed method.

关键词： Azimuth Superresolution Neural networks signal processing algorithms Radar Radar imaging Iterative algorithms

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