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arXiv 2024年

作者： Shen, Tianqi Liu, Shaohua Feng, Jiaqi Ma, Ziye An, Ning Department of Computer Science City University of Hong Kong Hong Kong Image Processing Center Beihang University China Shen Yuan Honors College Beihang University China Research Institute of Mine Artificial Intelligence China Coal Research Institute China State Key Laboratory of Intelligent Coal Mining and Strata Control China

Gaussian Splatting (GS) has emerged as a crucial technique for representing discrete volumetric radiance fields. It leverages unique parametrization to mitigate computational demands in scene optimization. This work introduces Topology-Aware 3D Gaussian Splatting (Topology-GS), which addresses two key limitations in current approaches: compromised pixel-level structural integrity due to incomplete initial geometric coverage, and inadequate feature-level integrity from insufficient topological constraints during optimization. To overcome these limitations, Topology-GS incorporates a novel interpolation strategy, Local Persistent Voronoi Interpolation (LPVI), and a topology-focused regularization term based on persistent barcodes, named PersLoss. LPVI utilizes persistent homology to guide adaptive interpolation, enhancing point coverage in low-curvature areas while preserving topological structure. PersLoss aligns the visual perceptual similarity of rendered images with ground truth by constraining distances between their topological features. Comprehensive experiments on three novel-view synthesis benchmarks demonstrate that Topology-GS outperforms existing methods in terms of PSNR, SSIM, and LPIPS metrics, while maintaining efficient memory usage. This study pioneers the integration of topology with 3D-GS, laying the groundwork for future research in this area. Copyright © 2024, The Authors. All rights reserved.

关键词： Interpolation

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On-chip cell separation and manipulation using traveling wave dielectrophoretic force controlled by four-phase signal generator

On-chip cell separation and manipulation using traveling wav...

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Biomedical Engineering International Conference (BMEiCON)

作者： Kata Jaruwongrangsee Thitima Maturos Sakshin Bunthawin Anurat Wisitsoraat Manas Sangworasil Adisorn Tuantranont Biomedical signal and image processing laboratory King Mongkut's Institute of Technology Ladkrabang Bangkok Thailand Nanoelectronics and MEMS laboratory National electronics and computer technology center Pathumthani Thailand Biotechnology of Electromechanics Research Unit Prince of Songkla University Phuket Thailand

In this work, we present a device for cell manipulation and separation using travelling wave dielectophoretic (twDEP) force. The device consists of microchamber and 16 parallel electrode array controlled by four-phase signal. The dielectrophoretic PDMS chamber and Cr/Au parallel electrode array were fabricated by standard microfabrication techniques. The driving signals for the twDEP system are produced by a specially designed four-phase signal generator. The signal generator can be operated in a fixed or ±90° phase shift mode with varying frequency, applied voltage and shift interval. In addition, the signal conditions can be store to and retrieved from a built-in database. The twDEP system was tested with polystyrene microspheres suspension in de-ionized water and red blood cells in D-mannitol solution. Cells responded to the electric field in various mechanisms depending on the applied signals conditions. The results showed that the twDEP force occurred when the applied signals were 10 V (50 kHz-700 kHz) and 7 V (30 kHz-1 MHz) for 4.5 μm and 10 μm microspheres respectively. The mixed solution containing equal amount of 4.5 and 10 μm microspheres were used for separation test. Under the signal conditions for 10 μm microspheres, the microspheres were moved under twDEP force while the smaller microspheres were attached to the electrodes. Therefore, the twDEP device can successfully manipulate and separate the microspheres of different sizes, and it can be further applied for cells selection.

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GEM: Context-Aware Gaze EstiMation with Visual Search Behavior Matching for Chest Radiograph

arXiv

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arXiv 2024年

作者： Liu, Shaonan Chen, Wenting Liu, Jie Luo, Xiaoling Shen, Linlin Computer Vision Institute College of Computer Science and Software Engineering Shenzhen University China Department of Electrical Engineering City University of Hong Kong Hong Kong AI Research Center for Medical Image Analysis and Diagnosis Shenzhen University China Guangdong Provincial Key Laboratory of Intelligent Information Processing China

Gaze estimation is pivotal in human scene comprehension tasks, particularly in medical diagnostic analysis. Eye-tracking technology facilitates the recording of physicians’ ocular movements during image interpretation, thereby elucidating their visual attention patterns and information-processing strategies. In this paper, we initially define the context-aware gaze estimation problem in medical radiology report settings. To understand the attention allocation and cognitive behavior of radiologists during the medical image interpretation process, we propose a context-aware Gaze EstiMation (GEM) network that utilizes eye gaze data collected from radiologists to simulate their visual search behavior patterns throughout the image interpretation process. It consists of a context-awareness module, visual behavior graph construction, and visual behavior matching. Within the context-awareness module, we achieve intricate multimodal registration by establishing connections between medical reports and images. Subsequently, for a more accurate simulation of genuine visual search behavior patterns, we introduce a visual behavior graph structure, capturing such behavior through high-order relationships (edges) between gaze points (nodes). To maintain the authenticity of visual behavior, we devise a visual behavior-matching approach, adjusting the high-order relationships between them by matching the graph constructed from real and estimated gaze points. Extensive experiments on four publicly available datasets demonstrate the superiority of GEM over existing methods and its strong generalizability, which also provides a new direction for the effective utilization of diverse modalities in medical image interpretation and enhances the interpretability of models in the field of medical imaging. https://***/Tiger-SN/GEM. Copyright © 2024, The Authors. All rights reserved.

关键词： Eye movements

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Raspberry Pi-Based Monitoring System for Grid-Connected PV Systems using Deep Learning Technique*

Raspberry Pi-Based Monitoring System for Grid-Connected PV S...

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International Conference on Control, Decision and Information Technologies (CoDIT)

作者： Zahra Yahyaoui Wajdi Saadaoui Mansour Hajji Majdi Mansouri Mohamed Nounou Kais Bouzrara Research Unit Advanced Materials and Nanotechnologies Higher Institute of Applied Sciences and Technology of Kasserine Kairouan University Tunisia Department of Electrical and Computer Engineering Program Texas A&M University at Qatar Doha Qatar Research Laboratory of Automation Signal Processing and Image National Engineering School of Monastir Tunisia

ISBN: (数字)9798350373974

ISBN: (纸本)9798350373981

The evolution of embedded systems has demonstrated their reliability as a solution for monitoring and controlling industrial systems, particularly in renewable energy conversion systems like photovoltaic (PV) energy. The increasing adoption of PV systems highlights the critical need for effective fault diagnosis to ensure their reliable operation. In this paper, we present a novel fault diagnosis approach utilizing Long Short-Term Memory (LSTM) networks optimized through Bayesian optimization techniques. Our methodology is implemented on a Raspberry Pi platform, demonstrating the feasibility of deploying sophisticated fault diagnosis algorithms in resource-constrained environments. Through extensive experiments, we demonstrate the effectiveness of our approach to accurately diagnose faults in grid-connected photovoltaic systems, thereby improving the reliability and efficiency of integrated environmental monitoring *** obtained results highlight the potential of combining advanced deep learning techniques with embedded systems to address complex diagnostic challenges, as demonstrated by achieving a 100% accuracy rate.

关键词： Fault diagnosis Deep learning Photovoltaic systems Embedded systems Accuracy Economic indicators Bayes methods Environmental monitoring Long short term memory Optimization

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A joint source-channel coding approach to network transport on digital video

A joint source-channel coding approach to network transport ...

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IEEE Annual Joint Conference: INFOCOM, IEEE computer and Communications Societies

作者： B. Kurceren J.W. Modestino Dept. of Electr. Comput. & Syst. Eng. Rensselaer Polytech. Inst. Troy NY USA Electrical Computer and Systems Engineering Department Center for Image Processing Research Rensselaer Polytechnic Institute Troy NY USA

The use of forward error-control (FEC) coding, possibly in conjunction with passive-error recovery techniques, has emerged as a promising approach for real-time video transport over ATM networks for cell loss recovery and/or bit error correction, such as might be required for wireless links. Although FEC provides cell-loss recovery, through its erasure correcting capabilities, it also introduces transmission overhead which can possibly cause additional cell losses. A joint source-channel coding methodology is described to maximize the number of video sources multiplexed at a given quality of service (QoS), measured in terms of overall reproduced video quality. The transport channel is modeled as a block interference channel (BIC) and the multiplexer as a single server, deterministic service, finite buffer supporting N users. Based upon an information-theoretic characterization of the BIC and large deviation bounds on the buffer overflow probability, we describe a methodology that provides theoretically achievable upper limits on the number of sources multiplexed at a given level of performance. Performance of a specific coding technique using an MPEG-2 source encoder and interlaced non-binary Reed-Solomon (RS) channel codes is illustrated and shown to approach the information-theoretic predictions with increasing levels of complexity.

关键词： Forward error correction Quality of service Error correction Asynchronous transfer mode Error correction codes Propagation losses Interference channels Multiplexing Buffer overflow Reed-Solomon codes

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Transfer Learning of Fuzzy Spatio-Temporal Rules in the NeuCube Brain-Inspired Spiking Neural Network: A Case Study on EEG Spatio-temporal Data

TechRxiv

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TechRxiv 2022年

作者： Kasabov, Nikola K. Tan, Yongyao Doborjeh, Maryam Tu, Enmei Yang, Jie School of Engineering Computer and Mathematical Science KEDRI Auckland University of Technology New Zealand Intelligent Systems Research Centre University of Ulster United Kingdom Bulgarian Academy of Sciences Bulgaria Institute of Image Processing and Pattern Recognition Shanghai Jiao Tiong University China

The paper demonstrates for the first time that a brain-inspired spiking neural network (SNN) architecture can be used not only to learn spatio-temporal data, but also to extract fuzzy spatio-temporal rules from such data and to update these rules incrementally in a transfer learning mode. We propose a method, where a SNN model learns incrementally new time-space data related to new classes/tasks/categories, always utilising some previously learned knowledge, and presents the evolved knowledge as fuzzy spatio-temporal rules. Similarly, to how the brain manifests transfer learning, these SNN models do not need to be restricted in number of layers, neurons in each layer, etc. as they adopt self-organising learning principles. The continuously evolved fuzzy rules from spatio-temporal data are interpretable for a better understanding of the processes that generate the data. The proposed method is based on a brain-inspired SNN architecture NeuCube and is illustrated on tasks of incremental and transfer learning and knowledge transfer using spatio-temporal EEG data measuring brain activity, when persons performing tasks in space and time. The method is a general one and opens the field to create new types of adaptable and explainable AI systems for applications across domain areas, e.g. brain-machine interfaces, robotics, speech and sound processing, cognitive studies and the creation of lifelong-learning cognitive machines. © 2022, CC BY.

关键词： Brain

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Forward-backward sequential Monte Carlo smoothing for joint target detection and tracking

Forward-backward sequential Monte Carlo smoothing for joint ...

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International Conference on Information Fusion

作者： Daniel E. Clark Ba Tuong Vo Ba Ngu Vo Joint Research Institute in Signal & Image Processing School of Engineering & Physical Sciences Heriot-Watt University UK School of Electrical Electronic & Computer Engineering University of Western Australia Crawley WA Australia Department of Electrical & Electronic Engineering University of Melbourne Parkville VIC Australia

ISBN: (纸本)9780982443804

The problem of jointly detecting whether a target is present in a scene and, if there is, estimating its state can be viewed as a multi-object estimation problem where there is a maximum of one target. This joint detection and estimation problem can be solved using a special case of the multi-object Bayes filter. In this paper we investigate the joint target detection and estimation problem with forward-backward smoothing and propose a sequential Monte Carlo implementation. Finite Set Statistics not only facilitates the development of appropriate joint detection and estimation filters, but also the direct extension of these filtering solutions to their related smoothing counterparts. Preliminary results indicate that using the smoothing has two distinct advantages over just using filtering: Firstly, we are able to more accurately identify the appearance and disappearance of a target in the scene and secondly, we can provide improved state estimates when the target exists.

关键词： Monte Carlo methods Smoothing methods Object detection Target tracking Nonlinear filters Filtering State estimation Layout Current measurement Delay estimation

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FPGA based emulated-digital CNN-UM implementation with GAPU

FPGA based emulated-digital CNN-UM implementation with GAPU

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IEEE International Workshop on Cellular Nanoscale Networks and their Applications, (CNNA)

作者： Zsolt Voroshazi Andras Kiss Zoltan Nagy Peter Szolgay Department of Image Processing and Neurocomputing University of Pannonia Veszprem Hungary Faculty of Information Technology Pázmány Péter Catholic University Hungary Cellular Sensory and Wave Computing Laboratory Computer and Automation Research Institute Hungarian Academy of Sciences (ATOMKI) Budapest Hungary

The paper addresses the issue of implementing a GAPU (global analogic programming unit) on the emulated-digital CNN-UM (cellular neural/nonlinear networks universal machine) architecture. It has been embedded with a flexible Xilinx MicroBlaze soft-core processor to take full advantage of the joint computing power of high-speed arithmetics and programmability. The GAPU implementation accelerates the processing performance of the Falcon architecture by decreasing the I/O communication and it also provides a stand-alone operation, which is capable of controlling complex sophisticated analogic CNN algorithms similar to various visual microprocessors, such as the ACE16k and the Q-Eye in the Bi-i, and eye-RIS cellular vision systems. Our primary aim was to implement an FPGA-based emulated-digital CNN-UM with embedded GAPU for computation intensive, real image-processing tasks.

关键词： Field programmable gate arrays computer architecture Process control Random access memory Vector processors Mathematical model Computational modeling

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A standalone FPGA based emulated-digital CNN-UM system

A standalone FPGA based emulated-digital CNN-UM system

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IEEE International Workshop on Cellular Nanoscale Networks and their Applications, (CNNA)

作者： Zsolt Voroshazi Andras Kiss Zoltan Nagy Peter Szolgay Department of Image Processing and Neurocomputing University of Pannonia Veszprem Hungary Faculty of Information Technology Pázmáany Péter Catholic University Hungary Cellular Sensory and Wave Computing Laboratory Computer and Automation Research Institute Hungarian Academy of Sciences (ATOMKI) Budapest Hungary

The Falcon emulated-digital CNN-UM (cellular neural/nonlinear networks universal machine) architecture has been extended by an embedded GAPU (global analogic programming unit) using the flexible Xilinx MicroBlaze soft-core processor to take full advantage of the joint computing power of high-speed distributed arithmetics and programmability. The implemented GAPU provides a stand-alone operation, which is capable of controlling complex sophisticated CNN analogic algorithms similar to various visual microprocessors, such as the ACE4k, ACE16k, and Bi-i vision systems. The quality of the embedded GAPU implementation is demonstrated by analogic algorithms, mainly in which sequences of template operations are required.

关键词： Field programmable gate arrays computer architecture image processing Mathematical model Organizing computers Cellular neural networks

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A Pavement Crack Detection Method Based on Multiscale Attention and HFS

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Computational Intelligence and Neuroscience 2022年 2022卷 1822585页

作者： Li, Chun Wen, Yu Shi, Qingxuan Yang, Fang Ma, Hongyan Tian, Xuedong School of Cyber Security and Computer Hebei University Baoding071002 China Hebei Machine Vision Engineering Research Center Hebei University Baoding071002 China Institute of Intelligent Image and Document Information Processing Hebei University Baoding071002 China School of Mathematics and Information Science Hebei University Baoding071002 China

To solve the problem of low detection accuracy due to the loss of detailed information when extracting pavement crack features in traditional U-shaped networks, a pavement crack detection method based on multiscale attention and hesitant fuzzy set (HFS) is proposed. First, the encoding-decoding structure is used to construct a pavement crack segmentation network, ResNeXt50 is used to extract features in the encoding stage, and a multiscale feature fusion module (MFF) is designed to obtain multiscale context information. Second, in the decoding stage, a high-efficiency dual attention module (EDA) is used to enhance the ability of capturing details of the cracks while suppressing background noise. Finally, the membership degree of the crack is calculated based on the advantages of the HFS in multiattribute decision-making to obtain the similarity of the crack, and the binary image after segmentation is judged by the hesitation fuzzy measure. The experiment was conducted on the public road crack dataset Crack500. In terms of segmentation performance, the evaluation indexes Intersection over Union (IoU), Precision, and Dice coefficients of the proposed network reached 55.56%, 74.26%, and 67.43%, respectively;in terms of classification performance, for transversal and longitudinal cracks, the classification accuracy was 84% ± 0.5%, while the block and the alligator were both 78% ± 0.5%. The experimental results prove that the crack details detected by the proposed method are more abundant, and the image detection effect of complex topological structures and small cracks are better. © 2022 Chun Li et al.

关键词： Binary images

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