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Robust RGB-T tracking via Bimodal Constrained Correlation Filtering

Robust RGB-T tracking via Bimodal Constrained Correlation Fi...

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symposium of image, signal processing, and Artificial Vision (STSIVA)

作者： Xin Li Jun Cai Wan Ding Zhi Fang School of Internet of Things Nanjing University of Posts and Telecommunications Nanjing China School of Artificial Intelligence and Information Technology Nanjing University Of Chinese Medicine Nanjing China School of Telecommunication and Information Engineering Nanjing University of Posts and Telecommunications Nanjing China

ISBN: (数字)9781728170862

ISBN: (纸本)9781728170879

With the rapid development of Internet of Things, thermal camera has been widely applied in the intelligence surveillance. Thus, RGB-T tracker has become a hot topic, because it can effectively use the complement of RGB and thermal target information to achieve full-time visual tracking. To effectively enhance the RGB-T tracking speed, we propose a bimodal constrained correlation filter based method. The key point of our method is to integrate bimodal regularization into the traditional correlation filter tracking framework. In addition, to enhance the robustness of tracking performance, we mine the multi-source oriented correlation filter coefficients to make the regression parameters of RGB and thermal images avoid negative effect when obtaining complementary advantages. The experiment results demonstrate that the proposed method can improve the tracking speed without decreasing the tracking accuracy.

关键词： Visualization Correlation Target tracking Surveillance Information filters Robustness Internet of Things

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GPU-based Hybrid Parallel Logic Simulation for Scan Patterns

GPU-based Hybrid Parallel Logic Simulation for Scan Patterns

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International Test Conference in Asia (ITC-Asia)

作者： Liyang Lai Qiting Zhang Hans Tsai Wu-Tung Cheng Guangdong Provincial Key Laboratory of Digital Signal and Image Processing Key Laboratory of Intelligent Manufacturing Technology Shantou University Shantou Guangdong China Mentor a Siemens Business Wilsonville OR US

ISBN: (数字)9781728189444

ISBN: (纸本)9781728189451

GPGPU, general-purpose computing on graphics processing units, has been witnessed growing from a niche to a mainstream computing paradigm in the last decade. It is widely deployed in machine learning, artificial intelligence, and many scientific applications. In this article, we study gate-level logic simulation for scan test patterns, one of the key algorithmic components for test generation, fault grading and design rule check. We are exploring if GPGPU can deliver scalable performance speedup as promised by its massive parallelism. We discuss the limitations of the state of art work. With GPUs' architectural features in mind, a novel algorithm of hybrid race-tolerant parallel logic simulation is proposed to unleash its immense power of parallelization. Its unique integration of oblivious simulation and event-driven simulation leads to a scalable realization of parallel logic simulation for scan patterns. For the first time in the literature, it is demonstrated that a modest GPU can handle an industrial design of over twenty million gates with excellent performance scalability.

关键词： Logic gates Integrated circuit modeling computational modeling Graphics processing units Data models Instruction sets Circuit faults

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Low-Bitwidth Floating Point Quantization for Efficient High-Quality Diffusion Models

Low-Bitwidth Floating Point Quantization for Efficient High-...

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ieee International Workshop/symposium on Workload Characterization

作者： Cheng Chen Christina Giannoula Andreas Moshovos Electrical and Computer Engineering University of Toronto Toronto Canada Computer Science University of Toronto & Vector Institute Toronto Canada Electrical and Computer Engineering University of Toronto & Vector Institute Toronto Canada

ISBN: (数字)9798350356038

ISBN: (纸本)9798350356045

Diffusion models are emerging models that generate images by iteratively denoising random Gaussian noise using deep neural networks. These models typically exhibit high computational and memory demands, necessitating effective post-training quantization for high-performance inference. Recent works propose low-bitwidth (e.g., 8-bit or 4-bit) quantization for diffusion models, however 4-bit integer quantization typically results in low-quality images. We observe that on several widely used hardware platforms, there is little or no difference in compute capability between floating-point and integer arithmetic operations of the same bitwidth (e.g., 8-bit or 4-bit). Therefore, we propose an effective floating-point quantization method for diffusion models that provides better image quality compared to integer quantization methods. We employ a floating-point quantization method that was effective for other processing tasks, specifically computer vision and natural language tasks, and tailor it for diffusion models by integrating weight rounding learning during the mapping of the full-precision values to the quantized values in the quantization process. We comprehensively study integer and floating-point quantization methods in state-of-the-art diffusion models. Our floating-point quantization method not only generates higher-quality images than that of integer quantization methods, but also shows no noticeable degradation compared to full-precision models (32-bit floating-point), when both weights and activations are quantized to 8-bit floating-point values, while has minimal degradation with 4-bit weights and 8-bit activations. Additionally, we introduce a better methodology to evaluate quantization effects, highlighting shortcomings with existing output quality metrics and experimental methodologies. Finally, as an additional potential benefit, our floating-point quantization method increases model sparsity by an order of magnitude, enabling further optimization opportu

关键词： Degradation Measurement image quality Quantization (signal) computational modeling Noise reduction Natural languages Text to image Diffusion models Optimization

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Diff-FFT: Synthetic Lumbar Magnetic images From Frequency Domain-Based

Diff-FFT: Synthetic Lumbar Magnetic Images From Frequency Do...

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International Conference on computational intelligence and Communication Networks (CICN)

作者： Jie Cao Qingxuan Jiang Boshuo Li School of Computer Science Northeast Electric Power University Jilin China

ISBN: (数字)9798331505264

ISBN: (纸本)9798331505271

Magnetic resonance imaging is playing an increasingly important role in the diagnosis of lumbar disc disease. It can provide high-resolution soft tissue contrast, helping doctors accurately identify disc lesions, inflammation and changes in other related structures. However, the use of MRI is limited due to its high cost and significant operating and processing times. More importantly, MRI examinations are not allowed for people with pacemakers, other metal objects in the body such as cochlear implants and metal teeth, or people in critical condition or claustrophobic, as it may cause injury. For this reason, this paper proposes a frequency domain-based method for synthesizing lumbar magnetic images. First, we introduce a multi-scale frequency channel attention module to model global features, which helps the model more effectively process wide-area information and improve its accuracy and detail performance in generating lumbar magnetic resonance images. Then, a dual-resolution frequency domain-based FFN module is proposed. This module selectively retains important frequency information through a learnable quantization matrix W. The inverse method of JPEG compression. Experimental results show that this method is accurate and can construct MR images that are very close to the reference MR images, and it also outperforms the other three state-of-the-art methods.

关键词： Quantization (signal) image coding Inverse problems Magnetic resonance imaging Frequency-domain analysis Spine Magnetic resonance Transform coding Metals Magnetic domains

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Assessment of the Binding Characteristics of Human Immunodeficiency Virus Type 1 Glycoprotein120 and Host Cluster of Differentiation4 Using Digital signal processing

Assessment of the Binding Characteristics of Human Immunodef...

引用

ieee symposium on Bioinformatics and Bioengineering (BIBE)

作者： Norbert Nwankwo Huseyin Seker Centre of Computational Intelligence Department of Informatics Faculty of Technology De Montfort University Leicester UK

Protein residues which contribute to bio-recognition and binding interaction between HIV Surface protein also called Glycoprotein120 (gp120) and Cluster of Differentiation4 (CD4) have been identified. However, this was with limited number of isolates. Notwithstanding, the particular HIV isolate that harbors the gp120 with the greatest binding force to the CD4 has not been investigated. In this paper, protein sequences of gp120 from 43 HIV-1 isolates, 5 isolates each from the HIV-2 and SIV as well as the CD4 of 25 HIV host organisms were analyzed using Resonant Recognition Method (RMM). The results re-confirmed that protein sequences of the HIV and CD4 share common spectral features in relation to bio-recognition and binding. From the large dataset of the HIV and SIV isolates used, MFA group M subtype B (HIV-1) isolate was found to have the greatest affinity for the CD4. Furthermore, the CD4 of the human and chimpanzee were established to possess about same level of binding force to the HIV gp120. Also the CD4 of other species offered more attractive force to another protein in such a manner that the approach taken in this study has also shown to be a useful and reliable tool for clear categorization of species. Finally, clinically experimented results were found to correlate with the computationally obtained results as the gp120 of the HIV-2 and SIV which were recognized to circumvent the CD4 during infection were found to have low peak amplitude. This low peak amplitude observed in the HIV-2 and SIV implies that they have weak affinity or attraction for the host CD4.

关键词： Human immunodeficiency virus Digital signal processing Proteins Sequences Amino acids Organisms Resonance Informatics Peptides Drugs

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Fast simulation of ultrasound images

Fast simulation of ultrasound images

引用

ieee symposium (IUS) Ultrasonics

作者： J.A. Jensen I. Nikolov Center for Fast Ultrasound ImagingDepartment of Information Technology Technical University of Denmark Denmark

ISBN: (纸本)0780363655

Realistic B-mode and flow images can be simulated with scattering maps based on optical, CT, or MR images or parametric flow models. The image simulation often includes using 200,000 to 1 million point scatterers. One image line typically takes 1800 seconds to compute on a state-of-the-art PC, and a whole image can take a full day. Simulating 3D images and 3D flow takes even more time. A 3D image of 64 by 64 lines can take 21 days, which is not practical for iterative work. This paper presents a new fast simulation method based on the Field II program. In imaging the same spatial impulse response is calculated for each of the image lines, and making 100 lines, thus, gives 100 calculations of the same impulse response delayed differently for the different lines. Doing the focusing after this point in the simulation can make the calculation faster. This corresponds to full synthetic aperture imaging. The received response from each element is calculated, when emitting with each of the elements in the aperture, and then the responses are subsequently focused. This is the approach taken in this paper using a modified version of the Field II program. A 64 element array, thus, gives 4096 responses. For a 7 MHz 64 element linear array the simulation time for one image line is 471 seconds for 200,000 scatterers on a 800 MHz AMD Athlon PC, corresponding to 17 hours for one image with 128 lines. Using the new approach, the computation time is 10,963 seconds, and the beamforming time is 9 seconds, which makes the approach 5.5 times faster. For 3D images with 64 by 64 lines, the total conventional simulation time for one volume is 517 hours, whereas the new approach makes the simulation in 6,810 seconds. The time for beamforming is 288 seconds, and the new approach is, thus, 262 times faster. The simulation can also be split among a number of PCs for speeding up the simulation. A full 3D one second volume simulation then takes 7,500 seconds on a 32 CPU 600 MHz Pentium III PC cl

关键词： Ultrasonic imaging computational modeling Optical scattering image motion analysis Optical imaging Focusing Array signal processing Adaptive optics Nonlinear optics Computed tomography

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ADVANCING DEEP LEARNING FOR EARTH SCIENCES: FROM HYBRID MODELING TO INTERPRETABILITY

ADVANCING DEEP LEARNING FOR EARTH SCIENCES: FROM HYBRID MODE...

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ieee International symposium on Geoscience and Remote Sensing (IGARSS)

作者： Gustau Camps-Valls Markus Reichstein Xiaoxiang Zhu Devis Tuia Image Processing Laboratory (IPL) Universitat de València Max Planck Institute for Biogeochemistry Germany Technical University Munich (TUM) & German Aerospace Center (DLR) Munich Germany Wageningen University and Research (WUR) The Netherlands

ISBN: (数字)9781728163741

ISBN: (纸本)9781728163758

Machine learning and deep learning in particular have made a huge impact in many fields of science and engineering. In the last decade, advanced deep learning methods have been developed and applied to remote sensing and geoscientific data problems extensively. Applications on classification and parameter retrieval are making a difference: methods are very accurate, can handle large amounts of data, and can deal with spatial and temporal data structures efficiently. Nevertheless, several important challenges need still to be addressed. First, current standard deep architectures cannot deal with long-range dependencies so distant driving processes (in space or time) are not captured, and they cannot cope with non-Euclidean spaces efficiently. Second, as other data-driven techniques, deep learning models do not necessarily respect physical or causal relations. Finally, deep learning models are still obscure and resistant to interpretability. Advances are needed to cope with arbitrary signal structures and data relations, physical plausibility and interpretability. This paper discusses about ways forward to develop new DL methods for the Earth sciences in all three directions.

关键词： Earth Deep learning Remote sensing computational modeling Machine learning Predictive models Data models

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Evaluating Tourism Service Quality Using Large Language Models: A Sentiment Analysis Approach

Evaluating Tourism Service Quality Using Large Language Mode...

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symposium of image, signal processing, and Artificial Vision (STSIVA)

作者： Yate Feng Ying Zhao Junyang Pan Yimin Wen School of Tourism Data Guilin Tourism University Guilin China School of Computer Science and Information Security Guilin University of Electronic Technology Guilin China

ISBN: (数字)9798350363203

ISBN: (纸本)9798350363210

With the continuous expansion of the tourism industry, understanding and enhancing tourism service quality has become increasingly critical. This study introduces an innovative method for evaluating tourism service quality using large language models (LLMs) and aspect-based sentiment analysis of travelogue data. By leveraging the advanced capabilities of LLMs, this method enables nuanced sentiment recognition at the aspect level, which significantly enhances the understanding of customer sentiments towards various tourism services. Analyzing over 1,000 travelogues sourced from an online platform, this research reveals the strengths of using LLMs in processing large volumes of unstructured data, resulting in a richer, data-driven understanding of customer experiences. The insights derived from this study provide valuable information for industry stakeholders, empowering them to tailor their services more effectively and ultimately enhance overall tourist satisfaction.

关键词： Sentiment analysis Analytical models Large language models computational modeling User-generated content Tourism industry Transportation Data models Stakeholders Informatics

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Joint Optimization Offloading and Resource Allocation in Vehicular Edge Cloud Computing Networks with Delay Constraints

Joint Optimization Offloading and Resource Allocation in Veh...

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symposium of image, signal processing, and Artificial Vision (STSIVA)

作者： Xueying Liu Guanglin Zhang College of Information Science and Technology Donghua University Shanghai China

ISBN: (数字)9781728170862

ISBN: (纸本)9781728170879

The development of 5G technology has led to the rapid construction of the Internet of Vehicles (IOV). The emergences of various IOV applications like autonomous driving require unlimited low-latency computation resources, which can be provided by Mobile edge computing (MEC) technique. In this paper, we study a framework of IOV with a cloud computing center that supports edge computing, aiming at improving the quality of service (QoS) for vehicles. We consider delay-sensitive tasks and non-delay-sensitive tasks, which are classified according to their task processing delay constraints. We formulate the optimization problem as a system utility maximization problem, searching for the optimal QoS. Based on the distribution method and the ant colony algorithm (ACO), we propose an optimization approach with joint offloading selection strategy and computing resource distribution strategy. Lastly, we show the simulation results, which demonstrates the effectiveness and high-efficiency of the proposed algorithm.

关键词： Cloud computing computational modeling Quality of service Delays Resource management Task analysis Optimization

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An Efficient Seizure Prediction Method Based on Multi-scale Feature Fusion with Reduced Channels

An Efficient Seizure Prediction Method Based on Multi-scale ...

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symposium of image, signal processing, and Artificial Vision (STSIVA)

作者： Shunxian Gu Xinning Song College of Computer and Information Hohai University Nanjing China

ISBN: (纸本)9781665426565

Epilepsy is one of the most common neurological diseases worldwide as a common mental disorder. Seizure prediction plays a vital role in improving a patient’s quality of life. This paper proposes a patient-specific seizure prediction method based on multi-scale feature fusion. This study aims at developing an efficient and automatic seizure prediction technique by raw scalp EEG signals with reduced channels. The proposed approach utilizes the deep convolutional neural network in noise handling and the recurrent neural network in establishing contextual correlation. Not any manual feature engineering is performed on the raw EEG data. A multi-scale fusion approach based on the downsampling technique is introduced to compensate for the performance degradation problem caused by reduced channels. 2 is proven to be the best view number. Our proposed C-Bi-LSTM model with multi-views provides the highest overall accuracy of 99.597% and the lowest false positive rate of 0.004 per hour by comparing the classification results.

关键词： Degradation Correlation Sensitivity Recurrent neural networks computational modeling Scalp Predictive models

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