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Triboelectric nanogenerator based on silanecoupled LTA/PDMS for physiological monitoring and biomechanical energy harvesting

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Microsystems & Nanoengineering 2024年第5期10卷 393-405页

作者： Muhammad Umair Khan Deepa Dumbre Yawar Abbas Moh'd Rezeq Anas Alazzam Nahla Alamoodi Maryam Khaleel Baker Mohammad Department of Computer and Information Engineering Khalifa University.Abu DhabiUAE System on Chip Lab Khalifa UniversityAbu DhabiUAE Department of Chemical Engineering Khalifa UniversityAbu DhabiUAE Department of Physics Khalifa UniversityAbu DhabiUAE Department of Mechanical Engineering Khalifa UniversityAbu DhabiUAE Center for Catalysis and Separations Khalifa UniversityAbu DhabiUAE Research and Innovation Center on CO_(2)and H_(2) Abu DhabiUAE

Energy harvesting from ambient sources present in the environment is essential to replace traditional energy *** strategies can diversify the energy sources,reduce maintenance,lower costs,and provide near-perpetual operation of the *** this work,a triboelectric nanogenerator(TENG)based on silane-coupled Linde type A/polydimethylsiloxane(LTA/PDMS)is developed for harsh environmental *** silane-coupled LTA/PDMS-based TENG can produce a high output power density of 42.6μW/cm^(2) at a load resistance of 10 MQ and operates at an open-circuit voltage of 120 V and a short-circuit current of 15μA under a damping frequency of 14 ***,the device shows ultra-robust and stable cyclic repeatability for more than 30 k *** fabricated TENG is used for the physiological monitoring and charging of commercial capacitors to drive low-power electronic ***,these results suggest that the silane-coupled LTA/PDMS approach can be used to fabricate ultra-robust TENGs for harsh environmental conditions and also provides an effective path toward wearable self-powered microelectronic deVices.

关键词： PDMS energy harvesting

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Warped-Compaction: Maximizing GPU Register File Bandwidth Utilization via Operand Compaction 31

Warped-Compaction: Maximizing GPU Register File Bandwidth Ut...

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31st IEEE International Symposium on High Performance computer Architecture, HPCA 2025

作者： Jeong, Eunbi Jeong, Ipoom Yoon, Myung Kuk Kim, Nam Sung Ewha Womans University Department of Computer Science and Engineering Seoul Korea Republic of Yonsei University Department of System Semiconductor Engineering Seoul Korea Republic of Yonsei University Department of Electrical and Electronic Engineering Seoul Korea Republic of University of Illinois Urbana-Champaign Urbana United States

ISBN: (纸本)9798331506476

The GPU has been successfully used for diverse emerging compute-intensive applications, including imaging, computer vision, and more recently, deep learning, to name a few. To offer high performance for such applications, it is provisioned with massive Register Files (RFs) to exploit high Thread-Level Parallelism (TLP). As RFs are designed to store thousands of contexts and provide very high bandwidth for uninterrupted supply of operands to hundreds of compute units for high TLP, they have become one of the most power-hungry components in the GPU. Meanwhile, faced with the end of Dennard scaling, it is more important than ever to innovate the microarchitecture of (power-constrained) GPUs to continuously improve the performance of future compute-intensive *** this work, we propose a GPU microarchitecture, WarpedCompaction, designed to utilize given RFs more efficiently, instead of relying on larger-capacity and/or higher-bandwidth RFs to further improve GPU performance. Specifically, first, we reverse-engineer the latest GPU's RF organization through microbenchmarking. This uncovers that each sub-core within a streaming multiprocessor contains only two dual-ported RF banks, and accesses to these banks are arbitrated solely based on register IDs. We also reveal that, despite the modest configurations, RF banks are largely underutilized, staying inactive for 33.5% of the time. Second, we observe that previously proposed RF optimization techniques, data forwarding and dead register elimination, cannot address this underutilization problem. This is mainly due to the (R1) insufficient RF access requests from a limited number of Operand Collector Units (OCUs) and (R2) inefficient operand distribution by the conventional RF bank arbitration. Third, we present two architectural solutions to tackle the observed inefficiency: (S1) OCU sharing and (S2) skewed arbitrator. Building on enhanced OCU early allocation for partially ready instructions and operand forwardin

关键词： Compaction

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BAV-DSS: Blockchain Assisted Verifiable Data Sharing Scheme with Fast Encryption and Outsourced Decryption for IoT 24th

BAV-DSS: Blockchain Assisted Verifiable Data Sharing Scheme ...

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24th International Conference on Algorithms and Architectures for Parallel Processing, ICA3PP 2024

作者： Huang, Yao Yu, Zhongyuan Wang, Guijuan Chai, Baobao Zhang, Hongliang Jinan250353 China Shandong Provincial Key Laboratory of Industrial Network and Information System Security Shandong Fundamental Research Center for Computer Science Jinan250353 China School of Information Science and Engineering Lanzhou University Lanzhou730000 China School of Computer Science and Engineering Shandong University of Science and Technology Qingdao266590 China

ISBN: (纸本)9789819615414

Many sophisticated cloud platforms are available to provide services such as storage and computation for Internet of Things (IoT) applications. However, with the concerns about the semi-trusted nature of cloud platforms, data security and privacy have been gaining broad attention. In this paper, we propose a blockchain-assisted verifiable data sharing scheme (BAV-DSS) for IoT. The BAV-DSS features fine-grained access control, fast encryption, and outsourced decryption with verifiability. Specifically, with the application of ciphertext-policy attribute-based encryption (CP-ABE), flexible access control is enabled. Blockchain is employed to store public keys, generate account addresses, and perform data verification. Furthermore, the selective replayable chosen ciphertext attacks (sRCCA) security of BAV-DSS is proved, and the simulation results indicate that BAV-DSS obtains high efficiency and practicality compared to other schemes. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Cloud platforms

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Classification And Prediction of Brain Tumors and its Types using Deep Learning 1

Classification And Prediction of Brain Tumors and its Types ...

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1st International Conference on Computational Intelligence, Communication Technology and Networking, CICTN 2023

作者： Bajaj, Madhvan Rawat, Priyanshu Bhatt, Aastha Sharma, Vikrant Jain, Ayushi Kumar, Navneet Graphic Era Hill University Computer Science and Engineering Dehradun India BSc Nursing Uttar Pradesh Lucknow India Jawaharlal Nehru University School of Computer & System Sciences New Delhi India

ISBN: (纸本)9798350338027

Brain tumors are a major source of illness and mortality globally, with about 11,700 persons diagnosed with one each year. Brain tumor diagnosis and categorization are critical for proper treatment planning, which can increase patients' life expectancy. Due to the intricacy of brain tumors and their attributes, the traditional approach of manual assessment of brain MRI images might be error-prone. Automated classification approaches based on Machine Learning (ML) and Artificial Intelligence (AI) have been shown to be more accurate than manual categorization. Deep Learning techniques including Convolutional Neural Networks (CNNs), Artificial Neural Networks (ANNs), and Transfer Learning (TL) have shown promising results for the automatic categorization of brain MRI images in this context. These automated procedures can save time and lessen the possibility of misdiagnosis, but they should always be used in tandem with clinical experience. The suggested method has the potential to significantly improve the accuracy and efficiency of brain tumor identification and categorization for doctors and radiologists globally. © 2023 IEEE.

关键词： Tumors

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Towards Temporal Dependency Identification based on Multivariate Time Series IIoT Data 14

Towards Temporal Dependency Identification based on Multivar...

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14th International Conference on Information and Communication Technology Convergence, ICTC 2023

作者： Isah, Abubakar Shin, Hyeju Hassan, Sani Ahmad Oh, Seugmin Kim, Jinsul Chonnam National University Department of Ict Convergence System Engineering Gwangju Korea Republic of Ahmadu Bello University Department of Computer Science Zaria Nigeria

ISBN: (纸本)9798350313277

In multivariate time series data, the analysis of temporal dependencies is crucial for comprehending complex relationships, forecasting trends, and streamlining diverse applications. In this study, we proposed a method for improving multivariate time series data using temporal dependence modeling. Our approach is based on utilizing cutting-edge machine learning methods, particularly Long Short-Term Memory (LSTM) networks, that are well known for their capacity to record complicated temporal correlations. We want to reveal hidden patterns and dependencies within multivariate time series data by employing LSTM networks. We use benchmark metrics Mean Absolute Percentage Error (MAPE), Mean Absolute Error (MAE), and Mean Squared Error (MSE) to assess the effectiveness of our strategy. We run comprehensive tests on various datasets to demonstrate the accuracy and robustness of our model. © 2023 IEEE.

关键词： LSTM multivariate temporal dependency

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Harnessing Light Field Angular Cues and Spatial Geometries for Semantic Segmentation

Harnessing Light Field Angular Cues and Spatial Geometries f...

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International Conference on Acoustics, Speech, and Signal Processing (ICASSP)

作者： Chen Jia Fan Shi Xu Cheng School of Computer Science and Engineering The Engineering Research Center of Learning-Based Intelligent System (Ministry of Education) The Key Laboratory of Computer Vision and System (Ministry of Education) Tianjin University of Technology Tianjin China

ISBN: (数字)9798350368741

ISBN: (纸本)9798350368758

4D light field imaging captures rich spatial-angular information, providing essential geometric cues for semantic segmentation tasks. In this paper, we introduce a novel backbone network called the Light Field Extraction Interaction Network (LFEI-Net). LFEI-Net excels in extracting global structures and multi-scale spatial-angular features, capturing feature dependencies through channel modeling and diverse feature interactions. Unlike traditional methods that depend on pyramid and dilated feature extraction, LFEI-Net pioneers an efficient method by integrating large-scale horizontal depth-wise convolution (HDWC) and vertical depth-wise convolution (VDWC) with interactive operations for comprehensive spatial multi-scale feature extraction. Furthermore, we present the Multi-Angular Modeling (MAM) module, which effectively captures scene angle variations from multiple perspectives and precisely delineates object boundaries, thereby improving model adaptability. Our experimental evaluations on two datasets demonstrate that LFEI-Net significantly outperforms state-ofthe-art (SOTA) 2D and 4D light field semantic segmentation methods, achieving mean Intersection over Union (mIoU) of 83.72% and 86.88%, respectively.

关键词： Geometry Adaptation models Convolution Semantic segmentation Imaging Feature extraction Light fields Acoustics Speech processing

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MMM-RS: A Multi-modal, Multi-GSD, Multi-scene Remote Sensing Dataset and Benchmark for Text-to-Image Generation 38

MMM-RS: A Multi-modal, Multi-GSD, Multi-scene Remote Sensing...

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38th Conference on Neural Information Processing systems, NeurIPS 2024

作者： Luo, Jialin Wang, Yuanzhi Gu, Ziqi Qiu, Yide Yao, Shuaizhen Wang, Fuyun Xu, Chunyan Zhang, Wenhua Wang, Dan Cui, Zhen PCA Lab Key Lab of Intelligent Perception and Systems for High-Dimensional Information of Ministry of Education School of Computer Science and Engineering Nanjing University of Science and Technology Nanjing China Beijing Institute of Spacecraft System Engineering Beijing China

Recently, the diffusion-based generative paradigm has achieved impressive general image generation capabilities with text prompts due to its accurate distribution modeling and stable training process. However, generating diverse remote sensing (RS) images that are tremendously different from general images in terms of scale and perspective remains a formidable challenge due to the lack of a comprehensive remote sensing image generation dataset with various modalities, ground sample distances (GSD), and scenes. In this paper, we propose a Multi-modal, MultiGSD, Multi-scene Remote Sensing (MMM-RS) dataset and benchmark for text-to-image generation in diverse remote sensing scenarios. Specifically, we first collect nine publicly available RS datasets and conduct standardization for all samples. To bridge RS images to textual semantic information, we utilize a large-scale pretrained vision-language model to automatically output text prompts and perform hand-crafted rectification, resulting in information-rich text-image pairs (including multi-modal images). In particular, we design some methods to obtain the images with different GSD and various environments (e.g., low-light, foggy) in a single sample. With extensive manual screening and refining annotations, we ultimately obtain a MMM-RS dataset that comprises approximately 2.1 million text-image pairs. Extensive experimental results verify that our proposed MMM-RS dataset allows off-the-shelf diffusion models to generate diverse RS images across various modalities, scenes, weather conditions, and GSD. The dataset is available at https://***/ljl5261/MMM-RS. © 2024 Neural information processing systems foundation. All rights reserved.

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Attention Mechanism Based Probabilistic Day-Ahead Net-Load Forecasting with Behind-the-Meter Solar 7

Attention Mechanism Based Probabilistic Day-Ahead Net-Load F...

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7th IEEE Conference on Energy Internet and Energy system Integration, EI2 2023

作者： Liang, Junkai Tang, Wenyuan Zhai, Suwei Yu, Kaiyuan Wang, Dongdong Yunnan Power Grid Department of System Operation Kunming China North Carolina State University Department of Electrical and Computer Engineering Raleigh United States

ISBN: (纸本)9798350345094

The increasing penetration of behind-the-meter solar photovoltaic is deteriorating the accuracy of legacy load forecasting algorithms, disrupting the operation and planning of power systems. Moreover, the specifications of the behind-the-meter photovoltaic systems are not well exposed to the grid operators, which is further complicated by the possible presence of energy storage systems. In this paper, an attention mechanism based graph transformer network, which utilizes the information from the neighboring zones and accounts for the temporal correlations within a time series, is proposed to perform probabilistic day-ahead net-load forecasting. The proposed architecture forecasts the net load directly without the need of knowing the capacity of solar-plus-storage or historical solar generation. Numerical results on the data from Open Power system Data show superior performance over six baselines. © 2023 IEEE.

关键词： Solar power generation

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Dataset of Large Gathering Images for Person Identification and Tracking

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computers, Materials & Continua 2023年第3期74卷 6065-6080页

作者： Adnan Nadeem Amir Mehmood Kashif Rizwan Muhammad Ashraf Nauman Qadeer Ali Alzahrani Qammer H.Abbasi Fazal Noor Majed Alhaisoni Nadeem Mahmood Faculty of Computer and Information System Islamic University of MadinahMadinah42351Saudi Arabia Department of Computer Science and Information Technology Sir Syed University of Engineering and TechnologyKarachi75300Pakistan Department of Computer Science Federal Urdu University of ArtsScience&TechnologyIslamabad45570Pakistan Department of Physics Federal Urdu University of ArtsScience&TechnologyKarachi75300Pakistan James Watt School of Engineering University of GlasgowGlasgowG128QQUK Computer Sciences Department College of Computer and Information SciencesPrincess Nourah bint Abdulrahman UniversityRiyadh11671Saudi Arabia Department of Computer Science University of KarachiKarachi75270Pakistan

This paper presents a large gathering dataset of images extracted from publicly filmed videos by 24 cameras installed on the premises of Masjid Al-Nabvi,Madinah,Saudi *** dataset consists of raw and processed images reflecting a highly challenging and unconstraint *** methodology for building the dataset consists of four core phases;that include acquisition of videos,extraction of frames,localization of face regions,and cropping and resizing of detected face *** raw images in the dataset consist of a total of 4613 frames obtained fromvideo *** processed images in the dataset consist of the face regions of 250 persons extracted from raw data images to ensure the authenticity of the presented *** dataset further consists of 8 images corresponding to each of the 250 subjects(persons)for a total of 2000 *** portrays a highly unconstrained and challenging environment with human faces of varying sizes and pixel quality(resolution).Since the face regions in video sequences are severely degraded due to various unavoidable factors,it can be used as a benchmark to test and evaluate face detection and recognition algorithms for research *** have also gathered and displayed records of the presence of subjects who appear in presented frames;in a temporal *** can also be used as a temporal benchmark for tracking,finding persons,activity monitoring,and crowd counting in large crowd scenarios.

关键词： Large crowd gatherings a dataset of large crowd images highly uncontrolled environment tracking missing persons face recognition activity monitoring

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Three-dimensional reconstruction based on three-dimensional phase mapping using single-pixel imaging

Three-dimensional reconstruction based on three-dimensional ...

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2024 International Conference on Optoelectronic Information and Optical engineering, OIOE 2024

作者： Liu, Yemei Li, Yanfei Yu, Jing Miao, Yupei Liu, Xiaoli Peng, Xiang Tang, Qijian Key Laboratory of Optoelectronic Devices and System of Ministry of Education and Guangdong Province College of Physics and Optoelectronic Engineering Shenzhen University Shenzhen518060 China School of Electrical Computer and Telecommunications Engineering University of Wollongong WollongongNSW2522 Australia

ISBN: (数字)9781510680470

ISBN: (纸本)9781510680463

The single-pixel imaging technique has garnered widespread attention and research due to its low requirements on detector resolution and equipment cost. In this study, a laser point scanning single-pixel imaging system is constructed. It utilizes a grating installed in front of the detector to capture a single-frame deformed fringe pattern, combining Fourier transform profilometry with three-dimensional phase mapping to ultimately achieve three-dimensional data acquisition. Following the calibration of the system, the positions of each light ray are calibrated using the intrinsic and extrinsic parameters obtained through camera calibration. Consequently, this paper proposes a single-pixel three-dimensional reconstruction method based on three-dimensional phase mapping, establishing a correlation between three-dimensional points and phases to realize three-dimensional phase mapping. Experimental results demonstrate that the model can achieve high reconstruction accuracy, presenting a novel solution for the field of single-pixel three-dimensional reconstruction. © 2024 SPIE.

关键词： Calibration

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