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检索条件"任意字段=Conference on Image Processing - Algorithms and Systems IV"

共 13664 条记录，以下是1341-1350 订阅

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Analysis of Optimization on Sparse Matrix Vector Multiplication Model Application in Digital Signal processing

Analysis of Optimization on Sparse Matrix Vector Multiplicat...

引用

International conference on Electrical Power and Energy systems (ICEPES)

作者： Alden C Shaji Kavita Khare Electronics & Communication Engineering Department Maulana Azad National Institute of Technology Bhopal India

ISBN: (数字)9798350390728

ISBN: (纸本)9798350390735

Conventional digital signal processing (DSP) technology, utilizing DSP and FPGA, excels in real-time signal processing but faces limitations in handling large-scale data and achieving high frequency resolution. This restricts its effectiveness for tasks involving offline data processing, analysis, and mining in applications with extensive data volumes. Some of the SpMV applications in image editing operations like linear filtering, resizing and mixing are reformulated in the SpMV multiplication form. The growing complexity of applications and the demand for top-notch digital data necessitate a boost in processing power. With energy efficiency becoming a critical concern, there is research on designing SpMV algorithms that minimize energy consumption. This is particularly relevant for embedded DSP systems and applications where power efficiency is a key consideration. A comparative study of High-Level Synthesis (HLS) techniques to reduce the power consumption and resource utilization conducted on Xilinx-Zynq 7000 FPGA is presented. This paper explores various HLS pragmas techniques using OpenCL framework applied on Sparse Matrix Vector Multiplication (SpMV) kernel for optimizing power efficiency and resource utilization. HLS has emerged has a pivotal technology for simplifying the design process of complex digital systems by allowing developers to work at higher levels of abstraction, using high-level programming languages. From the analysis, identified the best optimization technique for the low power and the minimum resource utilization for the SpMV implementation on FPGA hence it can be more useful for applications in the field of CNN.

关键词： Energy consumption Signal processing algorithms Digital signal processing Vectors Energy efficiency Resource management Sparse matrices

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Comparing MTCNN and Viola-Jones Algorithm in Face Recognition

Comparing MTCNN and Viola-Jones Algorithm in Face Recognitio...

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Iranian conference on Intelligent systems (ICIS)

作者： Anahita Bahmeie Maryam Shakiba dept. Electrical Engineering Jundi-Shapur University of Technology Dezful Iran

ISBN: (数字)9798331507565

ISBN: (纸本)9798331507572

Taking into account the importance of human face recognition in numerous applications such as identity authentication, determining gender and age, analyzing facial expressions, assessing head pose, evaluating lighting conditions, and overcoming obstacles, the aim of this study is to investigate and contrast the effectiveness of neural networks and the Viola-Jones algorithm in recognizing human faces. To achieve this specific goal, the Multi-Task Cascade Convolutional Neural Network (MTCNN) was used to evaluate the accuracy and processing time. In addition, the two algorithms MTCNN and Viola-Jones were compared in terms of accuracy and processing time. The results show that MTCNN can recognize the human face more accurately. At the same time, the processing time for face recognition with MTCNN is shorter than 71.63% compared to the processing time required by the Viola-Jones algorithm. The results clearly show that MTCNN works much faster, with a 71.63 % faster than the Viola-Jones algorithm.

关键词： Accuracy Head Face recognition image edge detection Lighting Authentication Multitasking Convolutional neural networks Intelligent systems

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Designing a User-Friendly and Responsive AI based image Generation Website and Performing Diversity Assessment of the Generated images

Designing a User-Friendly and Responsive AI based Image Gene...

引用

Electronics and Sustainable Communication systems (ICESC), 2020 International conference on

作者： Harshil T. Kanakia Suraj Prakkash Nair Master of Computer Application Department Sardar Patel Institute of Technology Mumbai India

This research paper presents a website that leverages the power of the image GPT engine for image generation. The website allows users to input a textual prompt and generate a corresponding image using image GPT’s natural language processing and machine learning capabilities. The paper details the architecture of the website, the image GPT API integration, and the algorithms used for image generation. Additionally, we present the results and evaluate its quality and discuss potential applications of this technology in various industries such as advertising, art and design. We also discuss how the performance of image generation can be potentially improved. Overall, the website demonstrates the potential of combining natural language processing and machine learning for image generation and opens up new avenues for future research in this field.

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Development of Lane and Zebra Crossing Marker Detection algorithms

Development of Lane and Zebra Crossing Marker Detection Algo...

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Device Intelligence, Computing and Communication Technologies, (DICCT), International conference on

作者： Vivek Chamoli R Gowri V P Dubey Jyoti Kandpal Jyoti Joshi Divesh Kumar Dept. of Electronics & Communication Engineering Graphic Era Hill University Dehradun India Dept. of Comupter Science & Engineering Graphic Era Hill University Dehradun India

This research involves using an object recognition system and an ancillary image sensor to detect lane markers and zebra crossings on a roadway in a vehicle. To perform this detection, these steps have been followed: take a picture of the scene, digitize it, normalize it, define a search area within it, look for lane and zebra markers within it. The Lane Tracking for Driving System was created to assist drivers in making lane departure decisions, to lessen concentration breaks, and to avoid accidents while driving. To offer a way to detect a specific traffic violation—namely, stopping on a zebra crossing at a traffic signal rather than following behind it—and to offer a workaround. In the proposed work, systems are implemented and improved using adaptive algorithms. MATLAB’s image processing toolbox is used to design and implement the proposed algorithm.

关键词： image sensors image processing Adaptive algorithms Communications technology Object recognition Detection algorithms Vehicles

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Radon-Fourier Transform for Timing Correction in Uncoupled Digital Radar Networks

Radon-Fourier Transform for Timing Correction in Uncoupled D...

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German Microwave conference

作者： Julian Aguilar Lukas Paulus David Werbunat Alexander Grathwohl Christian Waldschmidt Institut of Microwave Engineering Ulm University Germany

ISBN: (数字)9783982039749

ISBN: (纸本)9798331521790

Uncoupled radar networks offer many desired features such as large virtual apertures and flexibility in sensor placement without costly radio frequency (RF) cables. However, they also introduce novel challenges, especially the recovery of coherency between individual radar sensors. The correction of timing errors due to sampling frequency offsets (SFO) between a transmitter’s digital-to-analog converter (DAC) and receiver’s analog-to-digital converter (ADC) is essential for the coherent processing of uncoupled digital radar network measurements. systems with different error estimation and correction approaches have been proposed recently, which utilize resampling-based algorithms. However, they are costly to implement, i.e. on an FPGA, and require additional signal processing steps. The herein presented Radon-Fourier Transform (RFT)-based algorithm is capable of directly calculating the corrected radar image without any resampling, negating the downside of additional signal processing steps required after resampling. It is shown that the RFT approach can almost completely restore erroneous radar data, and even outperforms the resampling-based algorithm. In this work, an experimental orthogonal frequency-division multiplexing (OFDM) demonstrator is utilized to verify the concepts, which are applicable to other modulation types typically used for digital radar.

关键词： OFDM Computational modeling Signal processing algorithms Radar Transforms Radar imaging Data models image restoration Frequency measurement Frequency division multiplexing

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Pavement Distress Detection Using image processing from Unmanned Aerial Vehicle Data

Pavement Distress Detection Using Image Processing from Unma...

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International conference on Innovative Methods and Practical Applications for Cognizant Transportation systems, IMPACTS 2023

作者： Manjusha, M. Rudhra, A. Sunitha, V. Department of Civil Engineering National Institute of Technology Tiruchirappalli India

ISBN: (纸本)9789819772995

The scale of public roads, particularly highways, has rapidly increased in many nations over the last few decades. To increase the road’s usability and thus its social and economic benefits, road maintenance has received more attention lately. Road inspections were done manually, the old-fashioned way takes a lot of time, and work, and is subjective. For proper road maintenance, deteriorated roads must be repaired as quickly as possible. Therefore, there is an urgent need for high-performance, low-cost technology to identify road surface distress. Unmanned aerial vehicles (UAVs), on the other hand, are an exciting option for monitoring the status of roads. They have the benefits of great flexibility, cheap cost, and simple agility. The data was collected in a 5 km road stretch of NH-336, Tamil Nadu. In this study, some of the computer vision tools including OpenCV, CUDA, etc. are tried to detect and localize the distress. It includes the detection and classification of pavement distress by using Object detection algorithms like Faster RCNN, Yolov3, and Yolov4 were used for detection and classification of road distresses. The main focus is on the detection and classification of potholes and cracks. Local road authorities and communities can benefit from this system to evaluate the state of their road network pavement in real-time. The application of deep learning techniques in transportation engineering problems is very rare. This study makes an effort to use deep learning techniques in the classification of the distresses and localizing them. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Unmanned aerial vehicles (UAV)

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Overcoming Hardware Limitations in Massive MIMO: A Generative AI Take

Overcoming Hardware Limitations in Massive MIMO: A Generativ...

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2025 IEEE Wireless Communications and Networking conference, WCNC 2025

作者： Banerjee, Bitan Nimr, Ahmad Fettweis, Gerhard Technische Universität Vodafone Mobile Communications Systems Dresden Germany

ISBN: (纸本)9798350368369

Recent transition in mobile communication standards suggests massive multiple-input multiple-output (MIMO) to be an integral part of the foreseeable future. However, as antenna elements increase to hundreds in the fifth-generation (5G) and beyond, traditional signal processing methods become prone to significant hardware impairments compound from multiple chains, leading to a substantial performance degradation. This paper explores the effectiveness of generative artificial intelligence (AI) techniques in addressing these challenges within massive MIMO systems. For this purpose, the conditional generative adversarial network (CGAN), a special class of generative AI algorithms, is employed to enhance the accuracy of channel state information (CSI) estimation in a hardware-impaired transceiver setup. This problem is treated as an image-denoising task, where the noise is introduced by the hardware impairments and LS estimation error. Through simulations conducted across various antenna array sizes, the potential of generative AI to improve CSI estimation accuracy under hardware impairments is demon-strated. This highlights its capacity to address critical signal processing challenges in the next-generation wireless systems. © 2025 IEEE.

关键词： image denoising

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image processing Based Intelligent Mini Robotic Face Recognition System

Image Processing Based Intelligent Mini Robotic Face Recogni...

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2023 IEEE World conference on Communication and Computing, WCONF 2023

作者： Srivastava, Anurag Kumar Kumar, Mayank Mahur, Chandramani Tiwari, Vivek Kumar Tiwari, Shivam Srivastava, Durgesh GL Bajaj Institute of Technology and Management Department of Computer Science and Engineering Greater Noida India Chitkara Unversity Institute of Engineering and Technology Chitkara University Punjab Rajpura India

ISBN: (纸本)9798350311204

The number of devices connecting to the Internet is increasing rapidly along with the exchange of large amounts of data. Many types of problems used to come in managing that data, but today's new techniques coming in this time have helped a lot in managing big data. Among those techniques, machine learning is a very advanced and precise technique. With the help of which we can manage and categorize the data very easily in the coming times. Machine learning models were not found to be very complex. Such techniques, technology and algorithms are used in these to make it easier and more accurate for provide the result. In this paper have used some algorithm to increase the processing and best result like MX Net with 89%, Convolutional Neural Networks with 85%, ALEX NET with 83% and Support Vector Machine with 75% results. In such models some other techniques are used to make the hardware work continuously with the systems. Like as NLP, Open CV and other techniques which can analyze the data and show it in a systematic manner. And in the last of all this network, rare hardware units are used to analyze the data from it to make the model. The purpose of this paper is just to understand the purpose of technologies like network and machine learning in the future. And by doing it properly, life should be made easier. Now if the same human again comes to that hospital, then he calls from that unique ID by matching the previous data through this model. © 2023 IEEE.

关键词： Computer vision

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A Comprehensive Survey on INTELLICCTV: An AI based CCTV Camera Surveillance System

A Comprehensive Survey on INTELLICCTV: An AI based CCTV Came...

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Data Intelligence and Cognitive Informatics (ICDICI), International conference on

作者： Prithvi More Mayuresh Joyashi Sonali Rangdale Soham Bhalerao Laxman Khandagle Samruddhi Sathe Department Of Information Technology JSPM’s Rajarshi Shahu College of Engineering Pune India

ISBN: (数字)9798350389609

ISBN: (纸本)9798350389616

Upon day to day observation as well as crime rates chart analysis we can conclude that crime rates are increasing day by day with increase in population. Criminals are finding multiple ways to accomplish their monetary needs like for e.g Child Trafficking, Kidnapping, Murdering, trading of morphine and content that can damage teenagers and adults severely. Police agencies and crime control organisations are finding multiple ways to stop these things. One of them is by using CCTV camera surveillance and some of its drawbacks are: No alert and notification, no parallel image processing , chances of human error. Also, the traditional CCTV camera surveillance systems often fail to detect crime patterns successfully. So we are contributing by developing AI Based CCTV camera surveillance. This system involves enhancing accurate detection and conclusion of crime and people involved at the spot by using most advanced image processing techniques with the most suitable machine learning classifier. Also one of the advantages is , instead of manual monitoring this system involves automated real time detection and response. We will be using the VGG-16 Neural network for image processing which allows image processing to be done more accurately. The model is currently trained with weapons and action dataset to monitor the crimes in real time more *** database we will be using is MongoDB.

关键词： Surveys Machine learning algorithms Surveillance image processing Weapons Neural networks Manuals Cameras Real-time systems Testing

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DNA Computing: A Breakthrough in image Cryptography - A Comprehensive Review

DNA Computing: A Breakthrough in Image Cryptography - A Comp...

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International CSI Computer conference, CSICC

作者： Hesam Noorpoor Asadollah Shahbahrami Department of Computer Engineering University of Guilan Rasht Iran

ISBN: (数字)9798331523114

ISBN: (纸本)9798331523121

Deoxyribonucleic acid-DNA computing, an interdisciplinary field at the frontier between molecular biology and computer science, has become a revolutionary field. DNA computation provides a new approach to data computation and cryptography. The inherent properties of DNA include high parallelism, excellent data density, and stability; it can hence be considered one of the ideal candidates in cryptographic applications, especially in image encryption. This review provides recent advances in DNA-based cryptographic algorithms, their fusion with chaotic systems, and also their applications to practical scenarios that enhance data security. Different algorithms are studied that exploit the encoding capability of DNA, such as substitution, transposition, and hybrid models including crossover and mutation. It also discusses DNA for secure image transmission in applications like healthcare, industrial IoT, and cloud environments. The latter presents scalability challenges and problems regarding key generation along with certain implementation constraints, while at the same time pointing future research toward a perspective of opening up for stronger cryptography.

关键词： Reviews Scalability DNA Medical services Parallel processing Stability analysis Encryption Molecular biology Computer security DNA computing

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