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检索条件"任意字段=16th Image Processing: Algorithms and Systems Symposium 2018"

共 650 条记录，以下是31-40 订阅

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CHARACTERIZATION OF WETTING PROPERTIES OF MAGNETOACTIVE ELASTOMER SURFACES 16

CHARACTERIZATION OF WETTING PROPERTIES OF MAGNETOACTIVE ELAS...

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ASME 16th Annual Conference of the Smart Materials, Adaptive Structures and Intelligent systems (SMASIS)

作者： Kriegl, Raphael Kravanja, Gaia Hribar, Luka Jezersek, Matija Drevensek-Olenik, Irena Shamonin, Mikhail Ostbayer Tech Hsch Regensburg Regensburg Germany Univ Ljubljana Fac Mech Engn Ljubljana Slovenia Univ Ljubljana Fac Math & Phys Ljubljana Slovenia J Stefan Inst Ljubljana Slovenia

ISBN: (纸本)9780791887523

Commercially available contact angle (CA) measuring devices usually do not allow for the application of magnetic fields to the sample under test. A setup for measuring the CA of liquids on magnetosensitive surfaces has been developed specifically for investigating the surfaces of magnetoactive elastomers (MAEs). the addition of a programmable linear stage, which moves a permanent magnet, allows for fine control of the magnetic field applied to the MAE without the need for large and power-consuming electromagnets. Paired with a custom control and evaluation software, this measurement setup operates semiautomatically, limiting operator error and increasing precision, speed, as well as repeatability of static and dynamic CA measurements for different magnetoactive materials. the software is equipped with robust droplet fitting algorithms to avoid experimental challenges arising with soft magnetoactive materials, such as the curling of sample edges or diffuse non-reflective surfaces. Several application examples on MAE surfaces, both processed and unprocessed, are presented.

关键词： magnetoactive elastomers image processing droplet contour fitting contact angle

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Exploring Parallelism in ZynqNet AI Engine to Accelerate image Classification in Aerospace 16

Exploring Parallelism in ZynqNet AI Engine to Accelerate Ima...

引用

16th IEEE Latin American symposium on Circuits and systems, LASCAS 2025

作者： Aguilar, Eduardo Maranon Benevenuti, Fabio Kastensmidt, Fernanda Lima Porto Alegre Brazil

ISBN: (纸本)9798331522124

this research investigates various configurations of the Zynqnet AI Engine to accelerate image classification tasks using the SAT-6 dataset and a 4-layer CNN. the C++ code for the hardware accelerator was translated into RTL using Vivado HLS and integrated with the Zynq processing System on a ZedBoard Zynq-7000 ARM/FPGA SoC. the AI engine was configured to work with 32 -bit floating point, 8bit fixed point and 8 -bit integer type operations, with different processing elements for parallelizing CNN tasks. Given a 64-bit data memory bus in communication with the accelerator and an 8-bit integer quantization, the variation in the number of processing elements does not result in a significant performance improvement. To address this, 8 values were grouped for reading and writing in 64-bit parallel mode, and different communication architectures were designed to improve data transfer between the accelerator and DDR memory, with optional enablement of L1D and L2 cache. the research analyzes CNN computation performance and FPGA resource usage (LUTs, FFs, BRAMs, DSPs) across configurations, providing insights into the trade-offs in FPGA-based CNN implementations, aiding the development of efficient, high-performance hardware accelerators for Deep Learning applications. © 2025 IEEE.

关键词： C++ (programming language)

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Performance Analysis of Machine Learning-based Face Detection algorithms in Face image Transmission over AWGN and Fading Channels 16

Performance Analysis of Machine Learning-based Face Detectio...

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16th IEEE International symposium on Broadband Multimedia systems and Broadcasting (IEEE BMSB)

作者： Kim, Junghwan Wei, Lan Univ Toledo EECS Dept 2801 W Bancroft St Toledo OH 43606 USA

ISBN: (纸本)9781665449083

Although machine learning and artificial intelligence have been widely applied, noise and interference are still major disturbances to degrade the quality of image transmission and processing efficiency in multimedia data transmission. Based on the face detection system utilizing the machine learning algorithms and artificial intelligence, this paper examines, analyzes and compares the performances of the AdaBoost machine learning algorithm and Convolutional Neural Network (CNN)-based algorithm in processing face information, which is disturbed by channel noise and fading effect encountered in the transmission of face image. the face detection system used is based on HAAR feature extraction. the extracted HAAR features are subjected to classification training and learning of the cascade classifier. then the face detection is performed on the picture information outside the database. Results of computer simulation show that for the image data affected by the fading and AWGN, the face recognition system still marked the positions of the eyes and mouth with high accuracy. However for fading and higher AWGN, using a machine learning algorithm with a convolutional neural network is better than the ADABOOST algorithm. It can be concluded that the machine learning algorithm can effectively reduce the adverse effect of multimedia data transmission without increasing the SNR and use of higher level of modulation scheme.

关键词： Machine learning Artificial intelligence-based multimedia coding and processing Advanced signal processing Face detection system BPSK/QPSK Fading Channel

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Tweeting from Left to Right: Tracing the Dynamics of Emotional Messaging and Public Engagement During the 2018 vs. 2022 US Campaigns 21

Tweeting from Left to Right: Tracing the Dynamics of Emotion...

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21st IEEE International symposium on Parallel and Distributed processing with Applications, 13th IEEE International Conference on Big Data and Cloud Computing, 16th IEEE International Conference on Social Computing and Networking and 13th International Conference on Sustainable Computing and Communications, ISPA/BDCloud/SocialCom/SustainCom 2023

作者： Wang, Meng-Jie University of Canterbury School of Psychology Speech and Hearing Christchurch New Zealand

ISBN: (纸本)9798350329223

Despite numerous studies delving into social media politicking, there is, thus far, little understanding of how distinct emotions spread online. Much of the prior work has focused on positive vs. negative diffusion or measured emotional rhetoric without considering public engagement on such sites. Since elected officials increasingly turn to social media, even less is known about the impact of distinct emotional messaging on mobilizing users, attracting supporters, or disseminating political speeches. Moving beyond merely a valence-based approach, the study here seeks to address this gap by analyzing over 23,000 tweets posted by Senate candidates during the 2018 and 2022 US midterm elections through natural language processing. Specifically, leveraging the IBM Tone Analyzer, the present work implements a cross-sectional examination to explore: a) whether certain emotions - joy, confidence, anger, fear, and sadness - conveyed in candidates' tweets might be more likely to garner public attention;and b) if so, does this pattern vary across the political spectrum, illuminating the ways in which Democratic vs. Republican candidates adopt to expand their online reach over time? © 2023 IEEE.

关键词： Natural language processing systems

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Evaluation of an AI system designed for breast cancer detection 16

Evaluation of an AI system designed for breast cancer detect...

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16th International Workshop on Breast Imaging, IWBI 2022

作者： Riveira-Martin, Mercedes Rodríguez-Ruiz, Alejandro Chevalier, Margarita Medical Physics. Radiology Rehabilitation and Physiotherapy Department Medicine School Complutense University of Madrid Spain Medical Physics and RP Department Meixoeiro Hospital Spain Department of Image Guided Therapy Systems Philips Healthcare Netherlands

ISBN: (纸本)9781510655843

Screening programs for the early detection of breast cancer have significantly reduced mortality in women. the limitations of these programmes are primarily due to the use of 2D techniques and the high number of mammograms to be read by radiologists. Artificial Intelligence (AI) systems may lead to new tools to help radiologists read mammograms and classify the examination based on the malignancy of the detected lesions. Several factors related to breast characteristics (thickness and density), technical factors of image acquisition, X-ray system performance and image processing algorithms can influence the outcome of a mammogram and thus also the detection capability of an AI system. the aim of this work is to analyze the robustness of an AI system for breast cancer detection and its dependence on breast characteristics and technical factors. For this purpose, mammograms from a population-based screening program were scored with the AI system. the AUC (area under the ROC curve) index generated from the scoring ROC curve was 0.92 (CI(95%) = 0.89 - 0.95), demonstrating the robust performance of the AI system. Moreover, the statistical analysis performed showed that the AUC index was independent of breast characteristics, the type of mammographic system and most of the technical parameters considered, demonstrating the effectiveness of the AI system. © 2022 SPIE.

关键词： Mammography

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Toward Full GPU Acceleration of Agile Homomorphic Encryption Frameworks

Toward Full GPU Acceleration of Agile Homomorphic Encryption...

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IEEE Latin American symposium on Circuits and systems (LASCAS)

作者： Emilio Quaggiotto Shiva Nejati Alexander J. Leigh Mitra Mirhassani Electrical and Computer Engineering University of Windsor Windsor Canada

ISBN: (数字)9798331522124

ISBN: (纸本)9798331522131

CHIMERA and PEGASUS introduced new categories of Fully Homomorphic Encryption schemes, offering methods for converting between schemes and improving repacking algorithms for agile encryption frameworks. Using PEGASUS as a starting point, we aimed to speed up their novel repacking algorithm through the use of additional GPU computation. this work achieved a 50x speedup and 25% reduction in the overall repacking algorithm compared to CPU computation, focusing on unexplored parts and Homomorphic functions that are already GPU-amenable. In addition, We integrated our acceleration function into the Open-Source PEGASUS framework, enhancing its compatibility with complementary works on Homomorphic Encryption acceleration.

关键词： Privacy Machine learning algorithms Software algorithms Graphics processing units Seals Machine learning Medical services Software Homomorphic encryption Protection

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In-Memory-Computing (IMC) Technique in Local Difference Decision Block of an On-Board Satellite Hyperspectral Data Compression Algorithm 66

In-Memory-Computing (IMC) Technique in Local Difference Deci...

引用

2023 IEEE 66th International Midwest symposium on Circuits and systems, MWSCAS 2023

作者： Joshi, Vijay Rani, Sheeba J.

ISBN: (纸本)9798350302103

Due to high data acquisition rates and large data volumes, on-board hyperspectral imagery (HSI) demands data compression before transmission. the major bottleneck in on-board implementation of algorithms is the limited availability of power and resources along with the need to be less error prone while accessing and processing the data. the recently proposed simple lossless algorithm (SLA) for on-board satellite hyperspectral data compression, performs 80% calculations by utilizing previous neighborhood pixels, which in turn requires majority of memory access. this may lead to an increase in overall power dissipation and probability of error due to bit flips in the von-Neumann architectures of SLA. thus, to reduce the probability of error and to improve power efficiency, this work adopts in-memory-computing (IMC) technique for the local difference (LD) decision block of the SLA for non-overlapping case. the LD decision block calculates absolute difference between the neighboring pixels and requires XOR operation for the same. To perform IMC, the SRAM cell from literature is modified to get XOR between two pixels. A non-overlapping (6*16) bit memory cell is implemented using von-Neumann and IMC based architecture for LD decision block. the proposed architecture allows reduced memory overheads and instead of using more number of transistors in a cell, consumes comparable power when compared to the von-Neumann architecture. the error resilience of SLA is expected to improve, as the total memory access for processing a pixel is reduced by 21.42%. © 2023 IEEE.

关键词： hyperspectral data IMC on-board data compression satellite image compression XOR based SRAM cell

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No-reference Panoramic image Quality Assessment based on Adjacent Pixels Correlation 16

No-reference Panoramic Image Quality Assessment based on Adj...

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16th IEEE International symposium on Broadband Multimedia systems and Broadcasting (IEEE BMSB)

作者： Ding, Wenxin An, Ping Liu, Xin Yang, Chao Huang, Xinpeng Shanghai Univ Sch Commun & Informat Engn Shanghai Inst Adv Commun & Data Sci Shanghai 200444 Peoples R China

ISBN: (纸本)9781665449083

With the popularity of virtual reality, the distortion measurement plays an important role in the processing of panoramic image. the distortion of the panoramic image can make the statistical characteristics of the image change. However, most existing panoramic image quality assessment (IQA) methods ignore the statistical characteristics of panoramic image. therefore, we propose to utilize adjacent pixels correlation (APC) feature to calculate the statistical characteristics and blindly assess panoramic image quality. Specifically, we find that the distortion can change the proportion of low-frequency information in the panoramic image. We calculate the probability distribution of adjacent pixel difference map using Markov chains to build APC feature, which captures the change in statistical characteristics. Ultimately, the APC feature are fed into support vector regression (SVR) for training and predicting the image quality score. Experiments show that our proposed algorithm is more accurate than the existing algorithms.

关键词： Panoramic image Quality Assessment Statistical Characteristics Markov Chain

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Reliability Estimation of ML for image Perception: A Lightweight Nonlinear Transformation Approach Based on Full Reference image Quality Metrics

Reliability Estimation of ML for Image Perception: A Lightwe...

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IEEE International symposium on Embedded Multicore Socs (MCSoC)

作者： João-Vitor Zacchi Francesco Carella Priyank Upadhya Shanza Ali Zafar John Molloy Lisa Jöckel Janek Groß Núria Mata Nguyen Anh Vu Doan Fraunhofer Institute for Cognitive Systems IKS Munich Germany University of York York United Kingdom Fraunhofer Institute for Experimental Software Engineering IESE Kaiserslautern Germany

As machine learning (ML) models for image perception continue to advance, ensuring their robustness and reliability under various real-world scenarios remains a significant challenge. image quality factors, such as blur, brightness, and other environmental conditions, can significantly affect the performance of these algorithms, leading to inaccurate detection and potential failures in critical applications. In this paper, we propose a comprehensive diagnosis framework that leverages image quality metrics to assess and enhance the performance of these algorithms. To accomplish this goal, we deliberately introduce disturbances in parameters such as brightness, saturation, and other relevant factors. Subsequently, we compute a set of full-reference image quality metrics to evaluate the image quality after the perturbations. Once we have obtained the metrics, we apply a nonlinear transformation to these values. Based on the transformed metrics, we create a regression model that predicts the detection Intersection over Union (IOU). To validate our framework, we conducted experiments using three state-of-the-art machine learning models for object detection and instance segmentation. the models were subjected to various scenarios with different levels of image quality perturbations. Our experimental results clearly demonstrate the possibility of establishing a strong correlation between image quality metrics and the performance of the algorithms.

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Skin temperature classification by image processing 16

Skin temperature classification by Image Processing

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16th Iberian Conference on Information systems and Technologies (CISTI)

作者： Caridade, Cristina M. R. Roseiro, Luis ISEC Polytech Coimbra Coimbra Portugal ISEC Appl Biomech Lab Coimbra Portugal CICGE Ctr Invest Ciencias Geoespaciais Vila Nova De Gaia Portugal

ISBN: (纸本)9789895465910

Currently, body map temperature is one of the most commonly used indicators of human health status. the use of portable thermal camera equipment for temperature measurements in the human skin is nowadays a reality. this work aims to present an automatic method to skin temperature classification using image processing, investigating the potential role of temperature changes in skin inflammation. Infrared thermography (IRT) was used to assess the thermogenic response of the hand skin. Characteristics of the map temperature distribution in some anatomical regions of the hand were collected. the tests were realized to demonstrate the potential of using image processing to identify and define specific regions of temperatures and their characteristics. the thermal information provided by these imaging systems can be used for monitoring purposes, as some previous research demonstrated. However, to develop more accurate algorithms, larger training and testing sets must be used.

关键词： thermographic image image processing Skin inammation Monitoring temperature map

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