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An Open-Source GPU-Based Acoustic Simulator for Fast and Accurate Modeling of Acoustic Scattering

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An Open-Source GPU-Based Acoustic Simulator for Fast and Acc...

IEEE International Symposium on Applications of Ferroelectrics (ISAF)

作者： Zixuan Tian Yun Jing Aiguo Han Department of Electrical and Computer Engineering University of Illinois Urbana-Champaign Urbana USA Graduate Program in Acoustics Pennsylvania State University USA Department of Biomedical Engineering and Mechanics Virginia Polytechnic Institute and State University Blacksburg USA

ISBN: (数字)9798350371901

ISBN: (纸本)9798350371918

Fast, accurate, and realistic simulations of ultrasonic scattering in biological tissues play an important role in biomedical ultrasound research. This study introduces UltraWave, a new open-source ultrasound simulation tool we developed for accurately modeling acoustic and elastic wave propagation in two- and three-dimensional heterogeneous media. UltraWave allows the utilization of multiple graphics processing units (GPUs) to deliver faster and more accurate full-wave simulations. The perfectly matched layer was integrated into the simulator. UltraWave was validated against well-established scattering theories in a variety of basic scattering scenarios, and achieved similar or higher accuracy than the widely used k-Wave toolbox. Additionally, UltraWave outperformed k-Wave in terms of computational efficiency with both central processing units (CPUs) and GPUs, particularly when using GPU acceleration for elastic wave simulations. These results demonstrated the potential of UltraWave as a promising tool for biomedical ultrasound simulations.

关键词： Solid modeling Accuracy Ultrasonic imaging Biological system modeling Computational modeling Graphics processing units Media Acoustics Computational efficiency Perfectly matched layers

Channel Modeling for 6G Communications: A Survey

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Channel Modeling for 6G Communications: A Survey

2021 International Conference on computer System, Information Technology, and electrical engineering, COSITE 2021

作者： Walidainy, Hubbul Adriman, Ramzi Away, Yuwaldi Nasaruddin, Nasaruddin Univerisity of Syiah Kuala Doctoral Program School of Engineering Banda Aceh Indonesia Univerisity of Syiah Kuala Dept. Electrical and Computer Engineering Banda Aceh Indonesia

ISBN: (纸本)9781665425094

The development of communications technology is overgrowing, making disseminating information so fast, widespread, and massive. A network of communication systems starting from the first generation to the current sixth-generation (6G). Fifth-generation communication systems have been implemented in several places in the world today. However, research continues to develop the sixth-generation communication system. This paper aims to survey and design a channel modeling for a 6G communication system. Moreover, the channel model has also simulated the system in Banda Aceh city as a case study, using the NYUSIM simulator. In this paper, the channel model is considered in the form of Urban Microcell (Umi) and Line of Sight (LOS) with a frequency of 90 GHz. Based on the simulation, this paper found that the path loss value is 124.7 dB, and the received power is -45.5 dBm. The simulation result has also shown that the maximum distance from the transmitter to the receiver for 6G communication is 123.5 m with a delay of 6.4 ns on directional power delay profile (PDP). While on omnidirectional PDP, the delay is 21.9 ns. Thus, the resulting attenuation is relatively high for 6G communication systems due to high rainfall in Banda Aceh. © 2021 IEEE.

关键词： Surveys

Battling the Non-stationarity in Time Series Forecasting via Test-time Adaptation

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arXiv 2025年

作者： Kim, HyunGi Kim, Siwon Mok, Jisoo Yoon, Sungroh Department of Electrical and Computer Engineering Seoul National University Korea Republic of Interdisciplinary Program in Artificial Intelligence Seoul National University Korea Republic of AIIS ASRI INMC Seoul National University Korea Republic of

Deep Neural Networks have spearheaded remarkable advancements in time series forecasting (TSF), one of the major tasks in time series modeling. Nonetheless, the non-stationarity of time series undermines the reliability of pre-trained source time series forecasters in mission-critical deployment settings. In this study, we introduce a pioneering test-time adaptation framework tailored for TSF (TSF-TTA). TAFAS, the proposed approach to TSF-TTA, flexibly adapts source forecasters to continuously shifting test distributions while preserving the core semantic information learned during pre-training. The novel utilization of partially-observed ground truth and gated calibration module enables proactive, robust, and model-agnostic adaptation of source forecasters. Experiments on diverse benchmark datasets and cutting-edge architectures demonstrate the efficacy and generality of TAFAS, especially in long-term forecasting scenarios that suffer from significant distribution shifts. The code is available at https://***/kimanki/TAFAS. © 2025, CC BY-NC-SA.

关键词： Deep neural networks

Impact of CLAHE-based image enhancement for diabetic retinopathy classification through deep learning

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Procedia computer Science 2023年 216卷 57-66页

作者： Mira Hayati Kahlil Muchtar Roslidar Novi Maulina Irfan Syamsuddin Gregorius Natanael Elwirehardja Bens Pardamean Department of Electrical and Computer Engineering Universitas Syiah Kuala Banda Aceh 23111 Indonesia Telematics Research Center (TRC) Universitas Syiah Kuala Banda Aceh 23111 Indonesia Faculty of Medicine Universitas Syiah Kuala Banda Aceh 23111 Indonesia Department of Computer and Networking Engineering Politeknik Negeri Ujung Pandang Makassar 90245 Indonesia Bioinformatics and Data Science Research Center Bina Nusantara University Jakarta 11480 Indonesia Computer Science Department BINUS Graduate Program – Master of Computer Science Program Bina Nusantara University Jakarta 11480 Indonesia

Diabetic retinopathy (DR) is a type of diabetes mellitus that attacks the retina of the eye. DR will cause patients to experience blindness slowly. Generally, DR can be detected by using a special instrument called an ophthalmoscope to view the inside of the eyeball. However, in conditions where there is a very small difference between the normal image and the DR image, computer-based assistance is needed for maximizing image reading value. In this research, a method of image quality improvement will be carried out which will then be integrated with a classification algorithm based on deep learning. The results of image improvement using Contrast Limited Adaptive Histogram Equalization (CLAHE) shows that the average accuracy of the method on several models is very competitive, 91% for the VGG16 model, 95% for InceptionV3, and 97% for EfficientNet compared to the results original image which only has an accuracy of 87% for VGG16 model, 90% for InceptionV3 model, and 95% for EfficientNet. However, in ResNet34 better accuracy is obtained in the original image with an accuracy of 95% while in the CLAHE image the accuracy value is only 84%. The results of this comprehensive evaluation and recommendation of famous backbone networks can be useful in the computer-aided diagnosis of diabetic retinopathy.

关键词： Diabetic retinopathy Deep Learning CNN Image Enhancement CLAHE

Reduced KPCA based Ensemble Learning Approach for Fault Diagnosis of Grid-Connected PV Systems 19

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Reduced KPCA based Ensemble Learning Approach for Fault Diag...

19th IEEE International Multi-Conference on Systems, Signals and Devices, SSD 2022

作者： Dhibi, Khaled Mansouri, Majdi Bouzrara, Kais Nounou, Hazem Nounou, Mohamed Research Laboratory of Automation Signal Processing and Image National Engineering School of Monastir 5019 Tunisia Electrical and Computer Engineering Program Texas A&M University at Qatar Doha23874 Qatar Department of Mathematics and Sciences Prince Sultan University Riyadh11586 Saudi Arabia Chemical Engineering Program Texas A&M University at Qatar Doha23874 Qatar

ISBN: (数字)9781665471084

ISBN: (纸本)9781665471084

The main objective of this work is to develop novel fault diagnosis techniques using ensemble learning and multivariate statistical techniques. The proposed methods are capable of identifying and classifying PV faults. First, an ensemble learning (EL) method that combines several base models is proposed. Next, kernel principal components analysis (KPCA) and reduced KPCA are proposed to extract and select the pertinent characteristics from raw data. Then, the extracted significant characteristics are transmitted to the EL model for classification purposes. The main idea behind these proposals is to provide the best accuracy and also improve the results in terms of computation time. The diagno-sis results demonstrated the efficiency of the proposed frameworks. © 2022 IEEE.

关键词： Multivariant analysis

Surface phase diagram of CsSnI3 from first-principles calculations

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arXiv 2024年

作者： Li, Kejia Khamdang, Chadawan Wang, Mengen Department of Electrical and Computer Engineering State University of New York at Binghamton BinghamtonNY13902 United States Materials Science and Engineering Program State University of New York at Binghamton BinghamtonNY13902 United States

CsSnI3 is widely studied as an environmentally friendly Pb-free perovskite material for optoelectronic device applications. To further improve material and device performance, it is important to understand the surface structures of CsSnI3. We generate surface structures with various stoichiometries, perform density functional theory calculations to create phase diagrams of the CsSnI3 (001), (110), and (100) surfaces, and determine the most stable surfaces under a wide range of Cs, Sn, and I chemical potentials. Under I-rich conditions, surfaces with Cs vacancies are stable, which lead to partially occupied surface states above the valence band maximum. Under I-poor conditions, we find the stoichiometric (100) surface to be stable under a wide region of the phase diagram, which does not have any surface states and can contribute to long charge carrier lifetimes. Consequently, the I-poor (Sn-rich) conditions will be more beneficial to improve the device performance. © 2024, CC BY-NC-SA.

关键词： Tin compounds

Probabilistic Concept Bottleneck Models

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arXiv 2023年

作者： Kim, Eunji Jung, Dahuin Park, Sangha Kim, Siwon Yoon, Sungroh Department of Electrical and Computer Engineering Seoul National University Seoul Korea Republic of Interdisciplinary Program in Artificial Intelligence Seoul National University Seoul Korea Republic of

Interpretable models are designed to make decisions in a human-interpretable manner. Representatively, Concept Bottleneck Models (CBM) follow a two-step process of concept prediction and class prediction based on the predicted concepts. CBM provides explanations with high-level concepts derived from concept predictions;thus, reliable concept predictions are important for trustworthiness. In this study, we address the ambiguity issue that can harm reliability. While the existence of a concept can often be ambiguous in the data, CBM predicts concepts deterministically without considering this ambiguity. To provide a reliable interpretation against this ambiguity, we propose Probabilistic Concept Bottleneck Models (ProbCBM). By leveraging probabilistic concept embeddings, ProbCBM models uncertainty in concept prediction and provides explanations based on the concept and its corresponding uncertainty. This uncertainty enhances the reliability of the explanations. Furthermore, as class uncertainty is derived from concept uncertainty in ProbCBM, we can explain class uncertainty by means of concept uncertainty. Code is publicly available at https://***/ejkim47/prob-cbm. © 2023, CC BY-NC-SA.

关键词： Forecasting

ControlDreamer: Blending Geometry and Style in Text-to-3D

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arXiv 2023年

作者： Oh, Yeongtak Choi, Jooyoung Kim, Yongsung Park, Minjun Shin, Chaehun Yoon, Sungroh Department of Electrical and Computer Engineering Seoul National University Seoul Korea Republic of Interdisciplinary Program in Artificial Intelligence Seoul National University Seoul Korea Republic of

Recent advancements in text-to-3D generation have significantly contributed to the automation and democratization of 3D content creation. Building upon these developments, we aim to address the limitations of current methods in blending geometries and styles in text-to-3D generation. We introduce multi-view ControlNet, a novel depth-aware multi-view diffusion model trained on generated datasets from a carefully curated text corpus. Our multi-view ControlNet is then integrated into our two-stage pipeline, ControlDreamer, enabling text-guided generation of stylized 3D models. Additionally, we present a comprehensive benchmark for 3D style editing, encompassing a broad range of subjects, including objects, animals, and characters, to further facilitate research on diverse 3D generation. Our comparative analysis reveals that this new pipeline outperforms existing text-to-3D methods as evidenced by human evaluations and CLIP score metrics. Project page: https://***. © 2023, CC BY.

关键词： 3D modeling

Contract-Inspired Contest Theory for Controllable Image Generation in Mobile Edge Metaverse

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IEEE Transactions on Mobile Computing 2025年

作者： Liu, Guangyuan Du, Hongyang Wang, Jiacheng Niyato, Dusit Kim, Dong In Nanyang Technological University College of Computing and Data Science Energy Research Institute @ NTU Interdisciplinary Graduate Program Singapore University of Hong Kong Department of Electrical and Electronic Engineering Hong Kong Hong Kong Nanyang Technological University College of Computing and Data Science Singapore Sungkyunkwan University Department of Electrical and Computer Engineering Korea Republic of

The rapid advancement of immersive technologies has propelled the development of the Metaverse, where the convergence of virtual and physical realities necessitates the generation of high-quality, photorealistic images to enhance user experience. However, generating these images, especially through Generative Diffusion Models (GDMs), in mobile edge computing environments presents significant challenges due to the limited computing resources of edge devices and the dynamic nature of wireless networks. This paper proposes a novel framework that integrates contract-inspired contest theory, Deep Reinforcement Learning (DRL), and GDMs to optimize image generation in these resource-constrained environments. The framework addresses the critical challenges of resource allocation and semantic data transmission quality by incentivizing edge devices to efficiently transmit high-quality semantic data, which is essential for creating realistic and immersive images. The use of contest and contract theory ensures that edge devices are motivated to allocate resources effectively, while DRL dynamically adjusts to network conditions, optimizing the overall image generation process. Experimental results demonstrate that the proposed approach not only improves the quality of generated images but also achieves superior convergence speed and stability compared to traditional methods. This makes the framework particularly effective for optimizing complex resource allocation tasks in mobile edge Metaverse applications, offering enhanced performance and efficiency in creating immersive virtual environments. © 2002-2012 IEEE.

关键词： Resource allocation