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Efficient Co-Approximate Parallel Compressive Depth Reconstruction on FPGA

Efficient Co-Approximate Parallel Compressive Depth Reconstr...

引用

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

作者： Yun Wu John McAllister School of Electrical Electronic and Computer Engineering Queen’s University Belfast

ISBN: (数字)9798350368741

ISBN: (纸本)9798350368758

Efficient depth image reconstruction from sparse samples is crucial for machine perception applications, such as robotics, vehicle assistance and autonomy. It demands fast processing speed with low power consumption for sensing quality and safety, as well as cost reduction for FPGA and solid state implementations, within constrained resource budgets on edge devices. A new co-approximate framework of parallel approximate compressive depth reconstruction engine on FPGA is proposed using ℓ 1 solvers, proximal gradient decent (PGD), with instrumented frequency and voltage scaling during the iterative optimization process. By evaluating various number of parallel approximate processing units for the depth image reconstruction engine, up to 51% further power saving is achieved, and 421× speed up of parallel processing compared to the baseline, henceforth the efficiency is elevated over 43×.

关键词： image coding Power demand Instruments Voltage Robot sensing systems Throughput Sensors image reconstruction Field programmable gate arrays Engines

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Applying Feature Detection to xPCS image processing 14

Applying Feature Detection to XPCS Image Processing

引用

14th International conference on Synchrotron Radiation Instrumentation, SRI 2021

作者： xu, Yihui Tong, Yajun Hu, Menglu Fan, Jiadong Jiang, Huaidong Center for Transformative Science ShanghaiTech University 393 Middle Huaxia Road Shanghai201210 China 393 Middle Huaxia Road Shanghai201210 China

Sequential x-ray photon correlation spectroscopy (xPCS) reveals sample dynamics by analyzing a series of coherent scattering images, which is often time-consuming. For applications like real-time xPCS analysis, high efficiency is desired. Pixel binning is a straightforward strategy to reduce the processing time, but over-binning may result in an insufficient signal-to-noise ratio. In this work, feature detection is applied to obtain the optimal binning factor for the xPCS image processing. Results show that under optimal binning, the processing time is reduced by more than one order of magnitude. In addition, it is illustrated that feature detection could potentially be applied to other coherent imaging and scattering techniques such as coherent diffraction imaging (CDI). © Published under licence by IOP Publishing Ltd.

关键词： signal to noise ratio

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Continuous Action Learning Automata Game Optimizer Applied to Convolutional Neural Networks

Continuous Action Learning Automata Game Optimizer Applied t...

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International conference on Communications, signal processing, and their applications (ICCSPA)

作者： James Lindsay Sidney Givigi Department of Computer and Electrical Engineering Royal Military College of Canada Kingston Canada School of Computing Queen's University Kingston Canada

ISBN: (数字)9798350384819

ISBN: (纸本)9798350384826

This paper explores the application of the Continuous Action Learning Automata (CALA) game optimizer to Convolutional Neural Networks (CNNs) for image classification tasks. The CALA game optimizer, initially developed for training Artificial Neural Networks (ANNs), offers a non-gradient descent-based optimization approach that can adapt to different network architectures and activation functions. Leveraging the versatility of the CALA game optimizer, we investigate its performance on CNNs, specifically targeting image recognition within the MNIST dataset. The paper discusses the rationale behind using the CALA game optimizer for CNNs, including its ability to accommodate various activation functions and deeper network architectures. Experimental results demonstrate the efficacy of CALA in training CNNs, showcasing its flexibility and effectiveness in optimizing network parameters for image classification tasks.

关键词： Training image recognition Learning automata Games Network architecture signal processing Convolutional neural networks Optimization image classification

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Comparison of Multi-Modal Large Language Models with Deep Learning Models for Medical image Classification

Comparison of Multi-Modal Large Language Models with Deep Le...

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IEEE International conference on signal and image processing applications (ICSIPA)

作者： Joel Chia Ming Than Wan Tze Vong Kelvin Sheng Chek Yong School of Information Computing and Technology Swinburne University of Technology Sarawak Campus Kuching Malaysia

ISBN: (数字)9798350352368

ISBN: (纸本)9798350352375

In recent years, the advancement of large language models (LLMs) such as GPT-4 and Gemini has opened new avenues for artificial intelligence applications in various domains, including medical image classification. This study aims to compare the performance of multi-modal LLMs with state-of-the-art deep learning networks in classifying tumour and non-tumour images. The performance of four multi-modal LLMs and four conventional deep learning methods were evaluated using several performance measures. The results demonstrate the strengths and limitations of both approaches, providing insights into their applicability and potential integration in clinical practice. Gemini 1.5 Pro performs the best out of the eight models evaluated. This comparison underscores the evolving role of AI in enhancing diagnostic accuracy and supporting medical professionals in disease detection especially when training data is scarce.

关键词： Deep learning Heating systems Accuracy Large language models image processing Training data Internet

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Dual-Path Consistency Unsupervised Domain Adaptation for Nighttime Semantic Segmentation

Dual-Path Consistency Unsupervised Domain Adaptation for Nig...

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

作者： Yuwu Lu Jicong Lang Meirong Ding School of Artificial Intelligence South China Normal University Foshan China

ISBN: (数字)9798350368741

ISBN: (纸本)9798350368758

Nighttime semantic segmentation is an indispensable component in practical applications, such as automated vehicles. However, it is often hindered by the lack of annotations due to interference caused by inadequate lighting or exposure. To overcome these difficulties, we propose a Dual-Path Consistency (DPC) unsupervised domain adaptation (UDA) approach. One path is image Darkening Path (IDP), in which feature representations of original images and darkened images extracted from the feature encoder are leveraged to maintain cross-domain style consistency. Another path is image Masking Path (IMP), in which the masked images are reconstructed under the guidance of pseudo-labels, aiming to maintain content consistency in an entirely identical scenario. Extensive experiments on four bench-marks demonstrate the superior performance of the proposed DPC for nighttime semantic segmentation.

关键词： Semantic segmentation Semantics Lighting Interference signal processing Benchmark testing Feature extraction image restoration Speech processing image reconstruction

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Infrared APD Detectors for High Bandwidth applications 9

Infrared APD Detectors for High Bandwidth Applications

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conference on image Sensing Technologies - Materials, Devices, Systems, and applications Ix

作者： D'Souza, A., I Khalap, V Baker, I Zemaityte, E. Prigozhin, I Bellotti, E. Leonardo DRS 10600 Valley View St Cypress CA 92808 USA Leonardo UK First AveMillbrook Ind Estate Southampton S015 0LG Hants England Boston Univ Elect & Comp Engn Dept 8 St Marys St Boston MA 02215 USA

ISBN: (纸本)9781510650596;9781510650589

HgCdTe material has been grown on GaAs substrates using Metal Organic Vapour Phase Epitaxy (MOVPE) and 64 x 64 arrays were subsequently manufactured. HgCdTe was grown on 12 wafers and 6 wafers continued through processing. Companion 320 x 256, 24 mu m pitch arrays were also manufactured on the same wafer. These 320 x 256 arrays are hybridized to an existing imaging ROIC. signal and noise data are collected as a function of bias to determine Gain vs Bias and operability of the companion detector arrays. The existing 320 x 256 ROIC was designed for astronomy applications and precludes measurements at bias values > similar to 10 V since the amplified signal from the detector saturates the well of the ROIC. Gain was measured for bias values up to similar to 10 V and extrapolated to determine gain at higher bias values. This ROIC also does not permit fast pulse measurements. An alternate ROIC has been designed for fast pulse measurements but will not be presented here. Based on the 320 x 256 array signal, noise, Gain vs Bias and morphology data all 6 processed wafers yielded 64 x 64 detector arrays that are available for hybridization to ROICs. 320 x 256 arrays had operability > 99.9% based on the signal and noise data. Response and noise histograms have mean and median values within 1% of each other. The noise histogram is near Gaussian in shape. APD arrays hybridized to fanout chips are in assembly and APD gain vs bias, noise and transient response measurements are being measured directly without going through a ROIC.

关键词： Sensors Avalanche photodetectors Semiconducting wafers Monte Carlo methods Mercury cadmium telluride signal detection Readout integrated circuits Diffusion Particles Manufacturing

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Vision-Based Estimation of Cable Slab Forces in Precision Motion applications 63

Vision-Based Estimation of Cable Slab Forces in Precision Mo...

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63rd IEEE conference on Decision and Control, CDC 2024

作者： Al-Rawashdeh, Yazan M. Saaideh, Mohammad Al Pumphrey, Michael Alatawneh, Natheer Janaideh, Mohammad Al Al-Zaytoonah University of Jordan Department of Electrical Eng Amman130 11733 Jordan Memorial University Department of Mechanical and Mechatronics Engineering St. John'sNL A1B 3X5 Canada University of Guelph School of Engineering GuelphONN1G 2W1 Canada Czech Technical University Department of Applied Mathematics Prague Czech Republic

ISBN: (纸本)9798350316339

In this study, a novel method for estimating the cable slab internal and reaction forces is presented. Using visual feedback comprising a high-speed camera, the cable slab responses due to cyclic and acyclic motion profiles are recorded, and the positions of a sufficient finite set of markers attached to the cable slab are extracted off-line using image processing. The kinematics of the markers are obtained via numerical differentiation and signal processing. Adopting the Voigt viscoelastic model, the cable slab is segmented into several lumped mass-spring-damper elements whose linear and angular motions are expressed analytically. Using the strain and its rate of change during motion, estimates of internal and reaction forces are written as functions of the cable slab composite material properties that are identified by manual tuning. The link between these forces and the motion system kinematics reveals the tuning process of a proposed fixed-parameter feedforward/feedback controller/compensator that can be used to enhance the precision of the motion system despite its simplicity. The experimental results reveal the effectiveness of the proposed approach. © 2024 IEEE.

关键词： High speed cameras

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Identical Human Preference Alignment Paradigm for Text-to-image Models

Identical Human Preference Alignment Paradigm for Text-to-Im...

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

作者： Haoyuan Sun Bo xia Yifei Zhao Yongzhe Chang xueqian Wang Shenzhen International Graduate School Tsinghua University Shenzhen China

ISBN: (数字)9798350368741

ISBN: (纸本)9798350368758

Implicit reward mechanism of Direct Preference Optimization (DPO) has facilitated its recent applications beyond large language models (LLMs), notably in aligning text-to-image models with human preferences. While promising results have been achieved with algorithms such as Diffusion-DPO, their reliance on the assumptions of the Bradley-Terry model could potentially lead to significant overfitting. In this paper, we propose the Step Identical Preference Alignment (SIPA) method, departing text-to-image alignment from the assumptions of Bradley-Terry preference model. We assess the performance of four models, Diffusion-DPO, SPO, and SIPA, alongside the original model, on the HPS-V2 test set, which focus on three key aspects: text-image alignment, human value alignment, and generation diversity. Experimental results show that SIPA matches or outperforms existing SOTA alignment methods, and even exceeds the original model in terms of generation diversity, which compellingly demonstrates SIPA’s superiority in mitigating alignment overfitting.

关键词： Large language models Text to image signal processing algorithms signal processing Acoustics Speech processing Optimization Overfitting

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Integral image based Fast Convolution Operations via Separable Kernels 29

Integral Image based Fast Convolution Operations via Separab...

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29th IEEE conference on signal processing and Communications applications (SIU)

作者： Tuncel, Tolga Akbulut, Orhan Istanbul Esenyurt Univ Bilgisayar Muhendisligi Bolumu Istanbul Turkey Kocaeli Univ Bilgisayar Muhendisligi Bolumu Kocaeli Turkey

ISBN: (纸本)9781665436496

In this paper a method for accelerating the convolution processes, which is performed with different size of kernel filters, is proposed. The proposed method is basically based on the integral image representation approach. Integral image approaches are not directly usable with kernel filters. The convolution process is performed over sub-matrices by defining kernel filters as linear combinations of sub-matrices. Comparing the running times between standard convolution and the proposed method under different sizes of kernel filters and different resolutions of images, it has been observed that the convolution processing times for 19 x 19 and larger kernel matrices are in favor of the proposed method.

关键词： Integral image convolution

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image based Land Cover Classification for Remote Sensing applications-A review 3

Image based Land Cover Classification for Remote Sensing App...

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3rd International conference on signal processing and Communication (ICPSC)

作者： Manikandan, Mishaa M. Andrushia, A. Diana Neebha, T. Mary Karunya Inst Technol & Sci Dept Elect & Commun Engn Coimbatore Tamil Nadu India

ISBN: (纸本)9781665428644

Land cover is a material present in the earth surface. It consists of forest, farming land, fertile, infertile lands, desert, water bodies and agricultural areas etc., The basic need for capturing the information of the land cover is to classify the land resources according to their soil, water, climatic condition and to utilize it in the proper manner. Land use gives the knowledge about the uses and the benefits of the land used for man like mining, industrial and cultivation land. The aim of this article is to perform a detailed review of image-based land cover classification with respect to remote sensing applications.

关键词： land cover classification remote sensing image processing urban development

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