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Consumer Technology (ICCT-Pacific), International Conference on

作者： Yu-Hsi Chuang Shin-Lun Chen Tsun-Kuang Chi He-Sheng Chou Ya-Yun Huang Department of Electronic Engineering Chung Yuan Christian University Taoyuan City Taiwan Program on Semiconductor Manufacturing Technology Academy of Innovative Semiconductor and Sustainable Manufacturing National Cheng Kung University Tainan City Taiwan

ISBN: (数字)9798331504120

ISBN: (纸本)9798331504137

Point cloud is a three-dimensional data representation based on points and is widely applied in the field of 3D vision technology to describe the geometry of objects or scenes. They serve as a crucial data storage format. However, due to the large volume of point cloud data, compression techniques are essential for reducing its size. To address this, the Moving Picture Experts Group (MPEG) developed the Video-based Point Cloud Compression (V-PCC) process specifically for dynamic point cloud data, utilizing High-Efficiency Video Coding (HEVC) as the encoder. Despite these advancements, point cloud compression often results in the loss of edge points, which leads to the formation of holes, the appearance of compression artifacts, and a subsequent degradation in visual quality. This study employs the AOMedia Video 1 (AV1) encoder for point cloud compression and introduces an effective method to mitigate compression artifacts. By leveraging the characteristics of occupancy maps generated during the V-PCC process, this study identify the edges between patches in 2D projected point cloud images from different projection angles by a distance transform algorithm. Along these edges, distortion values in the geometry maps before and after compression are recorded, and pixels with significant distortion are compensated. The optimized geometry map is then re-encoded by AV1 and further processed through the V-PCC decoding workflow, ultimately producing a higher-quality reconstructed point cloud. Experimental results demonstrate that the proposed method achieves an average improvement of 2.63% and 2.12% in point-to-point and point-to-plane MSE, respectively. Furthermore, in terms of BD-rate results, the compression efficiency for point-to-point and point-to-plane PSNR improves by an average of -3.0% and -2.2%, respectively, thereby further enhancing the subjective visual quality of the reconstructed point cloud.

关键词： Point cloud compression Geometry Visualization Image coding Image edge detection Transform coding Transforms Distortion Image reconstruction High efficiency video coding

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Reducing Errors and Powers in LPDDR for DNN Inference: A Compression and IECC-Based Approach

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Journal of Systems Architecture 2025年 163卷

作者： Hong, Jae-Youn Jang, Je-Woo Cho, Sung-Hyuk Kong, Youngbae Kim, Sungkyu Kang, Youngjung Ko, Jaehyung Chung, Jaeyong Yang, Joon-Sung Department of Electrical and Electronic Engineering Yonsei University Seoul Korea Republic of Memory Division Samsung Electronics Hwaseong Korea Republic of Department of System Semiconductor Engineering Yonsei University Seoul Korea Republic of BK21 Graduate Program in Intelligent Semiconductor Technology Yonsei University Seoul Korea Republic of

In modern edge systems, the demand for data processing, especially for complex DNN tasks, is rapidly increasing. To address this, various compression schemes have been proposed to enable on-device AI while meeting the strict power and storage constraints of edge devices. However, despite these advancements, the compatibility of the compression methods with edge device memory, such as LPDDR, has not been thoroughly investigated. LPDDR operates at low voltage and faces reliability challenges like cell leakage, which is particularly concerning for applications where accuracy is critical, such as Advanced Driver Assistance Systems (ADAS) or medical devices. To address these reliability concerns, an ECC engine, known as IECC, is employed within each LPDDR bank. While IECC improves reliability, it also incurs performance penalties due to Read-Modify-Write (RMW) operations and parity storage overheads. This paper introduces RELIA, a DNN weight compression scheme with three-stage protection, aimed at enabling power-efficient and reliable DNN operations in mobile environments. RELIA reduces the operation granularity of the IECC engine to eliminate RMW overhead. Additionally, it proposes a SEC-FOEC(72,64) scheme (Single Error Correction-Frequently Occurring Error Correction) that can correct 99.97% of LPDDR errors. To mitigate the added storage overhead, a compression scheme based on DNN weight characteristics is introduced. Experimental results show RELIA outperforms traditional IECC schemes, reducing power by 16.12%, cycles by 12.6%, energy by 30.62%, and storage by 22.78%, while offering superior reliability in DNN inference. © 2025

关键词： Advanced driver assistance systems

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Efficient synthesis method of CsPbBr3/Al2O3 composite powders for scalable production: Augmented green emission and thermal stability

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Journal of Industrial and engineering Chemistry 2025年

作者： Kim, Seok-Hun Shin, Weon Ho Oh, Jong-Min Kim, Sang-Wook Kim, Sunghoon Center for Brain Busan 21 Plus Program Dong-Eui University Busan47340 Korea Republic of Department of Electronic Materials Engineering Kwangwoon University Seoul01897 Korea Republic of Department of Molecular Science and Technology Ajou University Suwon16499 Korea Republic of

Metal halide perovskites (MHPs) have gained significant attention for their exceptional optoelectronic properties, particularly in displays. However, their practical utility is severely limited by their intrinsic instability, including poor thermal stability, photostability, and susceptibility to moisture-induced degradation. This study introduces scalable synthesis of CsPbBr3/Al2O3 green-emitting phosphors, using submicron Al2O3 as a support, comparing mechanochemical reaction (MRM) and dissolution-evaporation (DEM) methods. DEM is the optimal route, enabling the formation of monoclinic CsPbBr3/Al2O3 with high photoluminescence quantum yield (PLQY) and enhanced stability. The influence of the CsPbBr3 to Al2O3 mass ratio on PLQY was quantitatively analyzed using the Halder-Wagner method, which indirectly calculated crystallite size and its correlation with PLQY. The composite demonstrated exceptional thermal stability, maintaining over 90 % of its initial PL intensity after 200 °C exposure for 15 days and showed excellent photostability, maintaining nearly its initial luminous efficiency under the currents of 20 mA and 70 mA for 10000-seconds. When integrated as a green phosphor in a white LED package with K2SiF6:Mn+4 as a red phosphor, it achieved a luminous efficiency of 25 lm W−1 and a color gamut of 112.4 % NTSC. These results highlight the potential of CsPbBr3/Al2O3 composites for cost-effective, stable, and high-performance optoelectronic applications. © 2025 The Korean Society of Industrial and engineering Chemistry

关键词： Perovskite

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Electrocatalysis for sustainable nitrogen management: materials innovation for sensing, removal and upcycling technologies

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Science China Chemistry 2025年第6期 2322-2342页

作者： Mei Yi Hongmei Li Minghao Xie Panpan Li Zhaoyu Jin Guihua Yu Institute of Fundamental and Frontier Sciences University of Electronic Science and Technology of China College of Materials Science and Engineering and College of Chemistry Sichuan University Materials Science and Engineering Program and Walker Department of Mechanical Engineering The University of Texas at Austin

The global nitrogen cycle holds immense importance due to its crucial role in supporting life, supplying vital nutrients for plant growth, preserving environmental balance, and enabling the proper functioning of ecosystems. However, human activities frequently disrupt this cycle, leading to the accumulation of nitrates and nitrites in water bodies. This accumulation causes environmental pollution and health risks. Traditional methods for treating nitrogen pollution, including biological, physical, and chemical approaches, have inherent limitations. In recent years, electrocatalysis has emerged as a promising and sustainable approach for nitrogen management. This technology offers superior efficiency, high selectivity, and environmental *** not only enables accurate detection of nitrogen pollutants in the environment but also facilitates their conversion into harmless nitrogen gas. Moreover, recent advancements have focused on the upcycling of nitrogen pollutants into valuable compounds,such as ammonia and urea. In this comprehensive review, we showcase the applications of electrocatalysis in sustainable nitrogen management. Specifically, we highlight its use in the sensing, removal, and upcycling of major nitrogen pollutants,including nitrate（NO3-）, nitrite（NO2-）, and nitric oxide（NO）. We discuss the use of catalysts, such as Pd alloys, Cu-based, and Fe-based materials, in electrochemical sensing and catalysis. Additionally, we explore recent advancements in the conversion of nitrogen pollutants into valuable compounds like ammonia and urea. The review also addresses current challenges and future opportunities in the field, including innovations in sensor and catalyst design, as well as large-scale treatment strategies. We anticipate that these perspectives will provide profound insights for effective nitrogen pollution control and sustainable utilization of nitrogen resources.

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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

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Label Space-Induced Pseudo Label Refinement for Multi-Source Black-Box Domain Adaptation

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IEEE Transactions on Image Processing 2025年 34卷 3181-3193页

作者： Yoo, Chaehwa Liu, Xiaofeng Xing, Fangxu Woo, Jonghye Kang, Je-Won Chungbuk National University School of Electrical Engineering Cheongju Korea Republic of Massachusetts General Hospital Harvard Medical School Gordon Center for Medical Imaging Dept. of Radiology BostonMA United States Yale University New Haven United States Ewha W. University Department of Electronic and Electrical Engineering Graduate Program in Smart Factory Seoul Korea Republic of

Conventional unsupervised domain adaptation (UDA) requires access to source data and/or source model parameters, prohibiting its practical application in terms of privacy, security, and intellectual property. Recent black-box UDA (BDA) reduces such constraints by defining a pseudo label from a single encapsulated source application programming interface (API) prediction, which allows for self-training of the target model. Nonetheless, existing methods have limited consideration for multi-source settings, in which multiple source domain APIs are available to generate pseudo labels. In this work, we introduce a novel training framework for multi-source BDA (MSBDA), dubbed Label Space-Induced Pseudo Label Refinement (LPR). Specifically, LPR incorporates a Pseudo label Refinery Network (PRN) that learns the relationship among source domains conditioned by the target domain only utilizing source API’s prediction. The target model is adapted by our dual phases PRN. First, a warm-up phase targets to avoid failure due to noisy samples and provide an initial pseudo-label, which is followed by a label refinement phase with domain relationship exploration. We provide theoretical support for the mechanism of the LPR. Experimental results on four benchmark datasets demonstrate that MSBDA using LPR achieves competitive performance compared to state-of-the-art approaches with different DA settings. © 1992-2012 IEEE.

关键词： Adaptation models Labeling Data models Predictive models Training Closed box Application programming interfaces Upper bound Sports Smoothing methods

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Drought propagation in an Andean basin in South America: a spatio-temporal analysis approach with monitoring indices

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Arabian Journal of Geosciences 2025年第3期18卷 1-21页

作者： Ocampo-Marulanda, Camilo Mera-Franco, Julián David Lizcano, Víctor Augusto Carvajal-Escobar, Yesid Canchala, Teresita Zorrilla-Millan, Daniel Felipe Caicedo-Bravo, Eduardo Water Resources Engineering and Soil Research Group (IREHISA) School of Natural Resources and Environmental Engineering Universidad del Valle Cali Colombia Graduate Program in Biometrics and Applied Statistics Department of Statistics and Informatics Federal Rural University of Pernambuco Recife Brazil School of Exact Sciences and Engineering Universidad Sergio Arboleda Bogotá Colombia Programa de Ingeniería Ambiental Facultad de Ingeniería Universidad Mariana Pasto Colombia School of Electrical and Electronic Engineering Universidad del Valle Cali Colombia

Drought is a climatic phenomenon that has increased in severity over time. This study analyzed drought spread in the Alto Patía basin (13,047 km2) in South America, applying the Standardized Precipitation Index (SPI), Vegetation Health Index (VHI), and Streamflow Drought Index (SDI) to assess meteorological, agricultural, and hydrological drought, respectively. Synchronous and asynchronous correlations were estimated, and Wavelet analysis was conducted to verify relationships among drought types. Results show that the lag between drought types varies based on the intensity, temporal continuity, and spatial extent of meteorological drought. A lag of zero months (1–2 months) was observed between meteorological and hydrological drought when moderate (severe) meteorological drought partially (fully) covers the basin. Agricultural drought, however, does not always correspond to meteorological drought during isolated rainfall events. As the first study in Colombia to systematically connect these drought types, this research addresses a gap in understanding how drought impacts progress across systems in regions with complex rainfall patterns. These findings offer critical insights into the interdependencies among drought types, supporting enhanced drought monitoring and early warning systems in similar climatic regions. By identifying specific lags between meteorological and hydrological droughts, this study provides practical guidance for land management, water conservation, and crop planning, offering a foundation for future research on drought resilience strategies.

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Quantum Kernel-Based Long Short-term Memory

Quantum Kernel-Based Long Short-term Memory

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Acoustics, Speech, and Signal Processing Workshops (ICASSPW), IEEE International Conference on

作者： Yu-Chao Hsu Tai-Yu Li Kuan-Cheng Chen National Center for High-Performance Computing NARlabs Hsinchu Taiwan Cross College Elite Program National Cheng Kung University Tainan Taiwan Department of Electrical and Electronic Engineering Imperial College London London UK Centre for Quantum Engineering Science and Technology (QuEST) Imperial College London London UK

ISBN: (数字)9798331519315

ISBN: (纸本)9798331519322

The integration of quantum computing into classical machine learning architectures has emerged as a promising approach to enhance model efficiency and computational capacity. In this work, we introduce the Quantum Kernel-Based Long Short-Term Memory (QK-LSTM) network, which utilizes quantum kernel functions within the classical LSTM framework to capture complex, non-linear patterns in sequential data. By embedding input data into a high-dimensional quantum feature space, the QK-LSTM model reduces the reliance on large parameter sets, achieving effective compression while maintaining accuracy in sequence modeling tasks. This quantum-enhanced architecture demonstrates efficient convergence, robust loss minimization, and model compactness, making it suitable for deployment in edge computing environments and resource-limited quantum devices (especially in the NISQ era). Benchmark comparisons reveal that QK-LSTM achieves performance on par with classical LSTM models, yet with fewer parameters, underscoring its potential to advance quantum machine learning applications in natural language processing and other domains requiring efficient temporal data processing.

关键词： Performance evaluation Quantum computing Computational modeling Computer architecture Signal processing Minimization Data models Natural language processing Speech processing Long short term memory

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Reducing Errors and Powers in Lpddr for Dnn Inference: A Compression and Iecc-Based Approach

SSRN

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SSRN 2025年

In modern edge systems, the demand for data processing for complex DNN tasks, is rapidly increasing. To address this, various compression schemes have been proposed to enable on-device AI while meeting the strictpower and storage constraints. However, despite these advancements, the compatibility of the compression methods with edge device memory(LPDDR), has not been thoroughly investigated. LPDDR operates at low voltage and faces reliability challenges, which is particularly concerning for applications where accuracy is critical, such as ADAS or medical devices. To address these concerns, ECC engine, known as IECC, is employed within each LPDDR bank. While IECC improves reliability, it also incurs performance penalties due to Read-Modify-Write(RMW) operations and parity storage overheads. This paper introduces RELIA, a DNN weight compression scheme with three-stage protection, aimed at enabling power-efficient and reliable DNN operations in mobile environments. RELIA reduces the operation granularity of the IECC engine from SEC(136,128) to SEC(72,64), eliminating RMW overhead. Additionally, it proposes SEC-FOEC(72,64) scheme that can correct 99.97% of LPDDR errors. To mitigate the added storage overhead, compression scheme based on DNN weight characteristics is introduced. Experimental results show RELIA reduces power, cycle, energy, and storage by 17.35%, 13.2%, 32.7%, and 23.6%, respectively, while offering superior reliability. © 2025, The Authors. All rights reserved.

关键词： Biomedical equipment

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An Agent-Based Model of Microgrids with Distributed Renewable Energy Sources

An Agent-Based Model of Microgrids with Distributed Renewabl...

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Power, Energy and Electrical engineering (CPEEE), International Conference on

作者： Gyu Yeon Kim Danyao Peng Sangyub Lee Seon Han Choi Graduate Program in Smart Factory Ewha Womans University Seoul South Korea Department of Electronic and Electrical Engineering Ewha Womans University Seoul South Korea Energy IT Convergence Research Center Korea Electronics Technology Institute Seongnam South Korea

ISBN: (数字)9798331520090

ISBN: (纸本)9798331520106

The high penetration of renewable energy sources poses a challenge to stability of power systems, particularly microgrids, which are more vulnerable due to their small capacity and low inertia. To address instability, system components and their interactions need to be represented to more efficiently understand and optimize complex systems. Agent-based modeling enables autonomous operation and collaborative interaction, making the system adaptable to changes efficiently, making it ideal for microgrid management. In this paper, an agent-based microgrid simulation model is proposed. By independently representing microgrid components such as generators, energy storage systems (ESS), loads, environments and controllers, models can dynamically simulate the behavior of power systems, respond to system changes in real time. To validate the proposed model, experiments are conducted based on IEEE 13 bus system, and the results show that with the use of ESS, the frequency peak is reduced from over 62 Hz to around 60 Hz. In the case of sudden demand fluctuations, the frequency remains relatively stable, staying close to 60 Hz. The model demonstrates flexibility and robustness in managing renewable energy fluctuations and sudden load changes.

关键词： Renewable energy sources Adaptation models Fluctuations Microgrids Power system stability Stability analysis Real-time systems Smart grids Thermal stability Load modeling

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