检索结果-内蒙古大学图书馆

International Conference on Microelectronics, ICM

作者： Eman Hassan Meriem Bettayeb Baker Mohammad Yahya Zweiri Hani Saleh Electrical Engineering and Computer Science Department System-on-Chip (SoC) Lab Khalifa University Abu Dhabi UAE Department of Aerospace Engineering and Advanced Research and Innovation Center (ARIC) Khalifa University Abu Dhabi UAE

The interest in brain-inspired computing architectures has been growing, particularly in the context of edge devices with constrained resources for executing cognitive functions. One such approach is hyperdimensional computing (HDC), a novel concept that draws inspiration from the large representation of human neuronal activity. HDC has demonstrated effectiveness in one-dimensional tasks, like text identification and activity recognition, offering advantages in power consumption and response time over convolutional neural networks (CNNs). This paper compares HDC and CNN regarding architecture, accuracy, and hardware complexity, explicitly focusing on image classification tasks. Our findings indicate that CNNs generally outperform HDC in two-dimensional tasks but require significantly more computational resources. In contrast, HDC offers adequate results using just 16% of the data needed for training. Additionally, experiments conducted using a Raspberry Pi 4 show that HDC can enhance inference speed and energy efficiency by approximately 2.5 times relative to CNNs.

关键词：

来源：评论

学校读者我要写书评

暂无评论

Mfagan: A Compression Framework for Memory-Efficient On-Device Super-Resolution Gan

SSRN

引用

SSRN 2023年

作者： Cheng, Wenlong Ye, Zhiling Kong, Han Zhao, Mingbo Gu, Shuhang Department of Electronic Engineering City University of Hong Kong Hong Kong Tencent Computer System Co. Ltd. Shenzhen China College of Information Science and Technology Donghua University Shanghai China School of Computer Science and Engineering University of Electronic Science and Technology of China Chengdu China

Generative adversarial networks (GANs) have promoted remarkable advances in single-image super-resolution (SR) by recovering photo-realistic images. However, high memory consumption of GAN-based SR (usually generators) causes performance degradation and more energy consumption, hindering the deployment of GAN-based SR into resource-constricted mobile devices. In this paper, we propose a novel compression framework Multi-scale Feature Aggregation Net based GAN (MFAGAN) for reducing the memory access cost of the generator. First, to overcome the memory explosion of dense connections, we utilize a memory-efficient multi-scale feature aggregation net as the generator. Second, for faster and more stable training, our method introduces the PatchGAN discriminator. Third, to balance the student discriminator and the compressed generator, we distill both the generator and the discriminator. Finally, we perform a hardware-aware neural architecture search (NAS) to find a specialized SubGenerator for the target mobile phone. Benefiting from these improvements, the proposed MFAGAN achieves up to 8.3× memory saving and 42.9× computation reduction, with only minor visual quality degradation, compared with ESRGAN. Empirical studies also show ∼70 milliseconds latency on Qualcomm Snapdragon 865 chipset. © 2023, The Authors. All rights reserved.

关键词： Generative adversarial networks

来源：评论

学校读者我要写书评

暂无评论

Identity-domain Removal for Robust EEG-based Emotion Recognition 25

Identity-domain Removal for Robust EEG-based Emotion Recogni...

引用

Proceedings of the 2025 International Conference on Multimedia Retrieval

作者： Wenchang Deng Shenghua Zhong Rongrong Lu Yi Wang College of Computer Science and Software Engineering Shenzhen University Shenzhen China Department of Rehabilitation Huashan Hospital Fudan University Shanghai China Laboratory for Big Data System Computing Technology Shenzhen University Shenzhen China

ISBN: (纸本)9798400718779

Although deep learning models have shown promise in Electroencephalogram (EEG) - based emotion recognition, their precision and robustness are often limited by domain-specific bias and low signal-to-noise ratio of EEG. Among the challenges encountered, the disparities in EEG data between subjects have been observed to potentially exceed those observed between different states of the task. The integration of identity domain information with task domain information may cause the model's learning of subject-dependent information, thereby hindering the ability to discern differences between tasks. This paper proposes a novel deep learning framework that improves the performance of EEG-based emotion classification models through the removal of the identity domain. Specifically, the proposed framework utilizes Auto-encoder (AE) based model as denoising module to extract globally informative features from the EEG signals. The domain information extraction module extracts subject-specific features from EEG samples by minimizing the differences between the network's output and the subject's identity labels while ensuring consistency of intermediate features across samples from the same subject. By combining domain-specific and globally informative features, an additional domain removal network is introduced before the final classification network to mitigate domain-specific biases by stripping away identity domain information from globally informative features through pairwise contrastive learning. The proposed EEG-based emotion recognition framework, supported by extensive experimental evidence, shows that incorporating the suggested domain removal technique significantly improves the robustness of the classification model and achieves state-of-the-art results in emotion classification.

关键词： domain removal

来源：评论

学校读者我要写书评

暂无评论

Significance of Internet-of-Things Edge and Fog Computing in Education Sector

Significance of Internet-of-Things Edge and Fog Computing in...

引用

Smart Computing and Application (ICSCA), International Conference on

作者： Shams Tabrez Siddiqui Mohammad Rafeek Khan Zeba Khan Nadim Rana Haneef Khan Md Imran Alam Department of Computer Science College of CS & IT Jazan University Jazan Saudi Arabia Department of Computer and Network Engineering College of CS & IT Jazan University Jazan Saudi Arabia Computer and Information System Applied College Jazan University Jazan Saudi Arabia College of Computer Science and Information Technology Jazan University Jazan Saudi Arabia

IoT gadgets include smart lights, ovens, and industrial data-collecting equipment. IDC expects 41.6 billion connected IoT devices to generate 79.4 ZB of data by 2025. Early IoT devices sent data to the cloud for analysis. When sending trillions of gigabytes to the cloud, the data pipeline clogs. Edge computing enables IoT devices to handle data locally instead of sending it to the cloud. Data is processed at the “edge” of your local network, rather than transferred away. Technology's versatility makes it valuable in education. IoT edge computing has different requirements than non-IoT edge computing. IoT devices have limited data processing and storage capabilities, thus processing must occur off the device. The edge offers an environment to manage huge numbers of IoT devices and data. Many functions can be off-loaded to the edge, reducing device cost. In education, IoT edge computing architecture has gained importance due to real-time applications. In order to facilitate the analysis of educational system architecture, this paper seeks to provide an IoT Edge computing framework in the field of education. The framework will identify IoT-related issues and concerns. This study discusses IoT Edge computing in education, identifies limits, and makes recommendations. As well as the paper evaluates IoT Edge computing's performance in educational systems.

关键词： Productivity Cloud computing Privacy systematics Education systems architecture Real-time systems

来源：评论

学校读者我要写书评

暂无评论

Smart Home Light Based Service Oriented Architecture and IoT 1

Smart Home Light Based Service Oriented Architecture and IoT

引用

1st International Conference on Industrial Automation, Smart Grid and its Application, ICIASGA 2020

作者： Irawan, Yuda Chrismondari Yolnasdi Linarta, Ari Muhardi Febriani, Anita Department of Information System STMIK Hang Tuah Pekanbaru Pekanbaru Indonesia Department of Electrical Engineering STT Pekanbaru Pekanbaru Indonesia Department of Information System STMIK Dumai Indonesia Department of Computer Science STMIK Hang Tuah Pekanbaru Pekanbaru Indonesia

The development of technology is very rapid in everyday life, so that it provides benefits for each. One of the advantages is that with technology, users can control electronic equipment which can be done with the smart home application (Smart Home) and from anywhere. This smart home application can help users control household electronic equipment such as lights, air conditioners, TVs, dispensers and others so as to reduce the waste of electricity when users forget to turn off household electronic equipment when traveling outside the house or anywhere. users are located. This research applies the concept of Service Oriented Architecture (SOA) and Internet of Things (IoT) on smart home light by integrating Ras-berry Pi with Python programming, web server (database) with PHP and An-droid programming with Java programming, and in experimental research that will be controlled. is the house light. The results of this research are in the form of a prototype of a home light controller using the IoT concept, web service and the Android SOA Smart home application with 7 buttons to control / monitor lights and this system is integrated with the concept of Service Oriented Architecture which is equipped with a token as system security. Tests have been carried out by controlling house lights remotely using a smartphone via the internet net-work and the results can be controlled properly. © Published under licence by IOP Publishing Ltd.

关键词： Internet of things

来源：评论

学校读者我要写书评

暂无评论

DDPS: Dynamic Differential Pricing-Based Edge Offloading system With Energy Harvesting Devices

引用

IEEE Transactions on Network science and engineering 2025年第4期12卷 2549-2565页

作者： Xue, Hai Xia, Yun Xiong, Neal N. Zhang, Di Pei, Songwen University of Shanghai for Science and Technology School of Optical-Electrical and Computer Engineering Shanghai200093 China Sul Ross State University Department of Computer Science and Mathematics AlpineTX79830 United States Zhengzhou University School of Electrical and Information Engineering Zhengzhou450001 China Zhengzhou University Henan International Joint Laboratory of Intelligent Health Information System Zhengzhou450001 China Zhengzhou University National Engineering Laboratory for Internet Medical Systems and Applications Zhengzhou450001 China

Mobile edge computing (MEC) mitigates the energy and computation burdens on mobile users (MUs) by offloading tasks to the network edge. To optimize MEC server utilization through effective resource allocation, a well-designed pricing strategy is indispensable. In this paper, we propose a dynamic differential pricing scheme (DDPS) for an edge offloading scenario with energy harvesting devices, which determines prices based on computing resource usage to enhance edge server (ES) utilization. First, an offloading decision algorithm is proposed to balance harvested and consumed energy, determining whether and how much data to offload. Second, a Stackelberg game-based differential pricing algorithm is proposed to optimize computing resource allocation for MUs and reallocate surplus resources to delay-sensitive devices. Extensive simulations are conducted to demonstrate the effectiveness of the proposed DDPS scheme. Specifically, in comparison to the existing best-performing pricing scheme, for different task arrival rates, DDPS can achieve a 5.3% decrease in average execution delay, a 1.7% increase in ES utility (Userver, which represents the payment from MUs minus penalties for discarded tasks), and a 2.1% increase in the average ratio of service for MUs. In addition, DDPS also improves 2.8% Userver on average with different ES computation capacities. © 2025 IEEE. All rights reserved.

关键词： Mobile edge computing

来源：评论

学校读者我要写书评

暂无评论

Explainable deep stacking ensemble model for accurate and transparent brain tumor diagnosis

引用

computers in Biology and Medicine 2025年 191卷 110166-110166页

作者： Haque, Rezaul Khan, Mahbub Alam Rahman, Hamdadur Khan, Shakil Siddiqui, Md Ismail Hossain Limon, Zishad Hossain Swapno, S M Masfequier Rahman Appaji, Abhishek Department of Computer Science and Engineering East West University A 2 Jahurul Islam Ave Dhaka1212 Bangladesh Department of Management Information System Pacific State University 3424 Wilshire Blvd. 12th Floor Los Angeles CA90010 United States Department of Management of Information System International American University 3440 Wilshire Blvd STE 1000 Los AngelesCA90010 United States Department of Business Analytics International American University 3440 Wilshire Blvd STE 1000 Los AngelesCA90010 United States Department of Engineering/Industrial Management Westcliff University IrvineCA92614 United States Department of Computer Science Westcliff University IrvineCA92614 United States Department Of Computer Science and Engineering Bangladesh University of Business and Technology Mirpur-2 Dhaka1216 Bangladesh Department of Medical Electronics Engineering B.M.S. College of Engineering Bangalore560019 India Maastricht University University Eye Clinic Maastricht Maastricht Netherlands

Early detection of brain tumors in MRI images is vital for improving treatment results. However, deep learning models face challenges like limited dataset diversity, class imbalance, and insufficient interpretability. Most studies rely on small, single-source datasets and do not combine different feature extraction techniques for better classification. To address these challenges, we propose a robust and explainable stacking ensemble model for multiclass brain tumor classification. To address these challenges, we propose a stacking ensemble model that combines EfficientNetB0, MobileNetV2, GoogleNet, and Multi-level CapsuleNet, using CatBoost as the meta-learner for improved feature aggregation and classification accuracy. This ensemble approach captures complex tumor characteristics while enhancing robustness and interpretability. The proposed model integrates EfficientNetB0, MobileNetV2, GoogleNet, and a Multi-level CapsuleNet within a stacking framework, utilizing CatBoost as the meta-learner to improve feature aggregation and classification accuracy. We created two large MRI datasets by merging data from four sources: BraTS, Msoud, Br35H, and SARTAJ. To tackle class imbalance, we applied Borderline-SMOTE and data augmentation. We also utilized feature extraction methods, along with PCA and Gray Wolf Optimization (GWO). Our model was validated through confidence interval analysis and statistical tests, demonstrating superior performance. Error analysis revealed misclassification trends, and we assessed computational efficiency regarding inference speed and resource usage. The proposed ensemble achieved 97.81% F1 score and 98.75% PR AUC on M1, and 98.32% F1 score with 99.34% PR AUC on M2. Moreover, the model consistently surpassed state-of-the-art CNNs, Vision Transformers, and other ensemble methods in classifying brain tumors across individual four datasets. Finally, we developed a web-based diagnostic tool that enables clinicians to interact with the proposed mo

关键词： Magnetic resonance imaging

来源：评论

学校读者我要写书评

暂无评论

Identifying Trustworthiness Challenges in Deep Learning Models for Continental-Scale Water Quality Prediction

arXiv

引用

arXiv 2025年

作者： Xia, Xiaobo Liu, Xiaofeng Liu, Jiale Fang, Kuai Lu, Lu Oymak, Samet Currie, William S. Liu, Tongliang School of Computer Science University of Sydney Australia Department of Statistics and Data Science Yale University United States Michigan Institute for Data and AI in Society University of Michigan United States School for Environment and Sustainability University of Michigan United States College of Information Science and Technology Penn State University United States Department of Earth System Science Stanford University United States Department of Electrical Engineering and Computer Science University of Michigan United States

Water quality is foundational to environmental sustainability, ecosystem resilience, and public health. Deep learning models, particularly Long Short-Term Memory (LSTM) networks, offer trans-formative potential for large-scale water quality prediction and scientific insights generation. However, their widespread adoption in high-stakes decision-making, such as pollution mitigation and equitable resource allocation, is prevented by unresolved trustworthiness challenges including fairness, uncertainty, interpretability, robustness, generalizability, and reproducibility. In this work, we present the first comprehensive evaluation of trustworthiness in a continental-scale multi-task LSTM model predicting 20 water quality variables (encompassing physical/chemical processes, geochemical weathering, and nutrient cycling) across 482 U.S. basins. Our investigation uncovers systematic patterns of model performance disparities linked to basin characteristics, the inherent complexity of biogeochemical processes, and variable predictability, emphasizing critical performance fairness concerns. We further propose methodological frameworks for quantitatively evaluating critical aspects of trustworthiness, including uncertainty, interpretability, and robustness, identifying key limitations that could challenge reliable real-world deployment. This work serves as a timely call to action for advancing trustworthy data-driven methods for water resources management and provides a pathway to offering critical insights for researchers, decision-makers, and practitioners seeking to leverage artificial intelligence (AI) responsibly in environmental management. Copyright © 2025, The Authors. All rights reserved.

关键词： Resource allocation

来源：评论

学校读者我要写书评

暂无评论

Robust Spectral Efficiency Maximization in 5G Networks Using Non-Linear Stochastic Precoding

Robust Spectral Efficiency Maximization in 5G Networks Using...

引用

Disruptive Technologies (ICDT), International Conference on

作者： Geetha Ponnaian Jayanthi. N R. Sarasu Sajitha. L.P R. Ramalakshmi Manimegalai L Department of Computer Science and Engineering Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences (SIMATS) Thandalam Chennai India School of Management Studies Vels Institute of Science Technology and Advanced Studies (VISTAS) Pallavaram Chennai Tamil Nadu India Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Department of Computer Science and business system R. M. K Engineering College Kavaraipettai Tamilnadu India Department of Electronics and Communication Engineering Ramco Institute of Technology Rajapalayam Department of Electronics and Communication Engineering R.M.K. College of Engineering and Technology Chennai India

This research focuses on enhancing the spectral efficiency of 5G networks through the utilization of nonlinear stochastic precoding techniques. The proposed approach aims to mitigate the detrimental effects of channel impairments and noise by employing advanced precoding schemes that exploit the statistical properties of the wireless channel. By incorporating nonlinear precoding algorithms and stochastic optimization methods, the system can dynamically adapt to varying channel conditions, resulting in improved data transmission rates and overall spectral efficiency. Through extensive simulations and analysis, the effectiveness of the proposed method is demonstrated, showcasing its potential to significantly enhance the performance of 5G networks in real-world scenarios.

关键词：

来源：评论

学校读者我要写书评

暂无评论

Explainable Artificial Intelligence for Antenna Sensor Modeling

Explainable Artificial Intelligence for Antenna Sensor Model...

引用

Communications and Networking (MeditCom), IEEE International Mediterranean Conference on

作者： Wenkai Tan Jayaprakash B. Shivakumar Rishikesh Srinivasaraghavan Govindarajan Nicholas Reed Daewon Kim Eduardo Rojas Yongxin Liu Houbing Song Department of Information System University of Maryland Baltimore County Maryland USA Department of Electrical Engineering & Computer Science Embry-Riddle Aeronautical University Florida USA Department of Aerospace Engineering Embry-Riddle Aeronautical University Florida USA Department of Mathematics Embry Riddle Aeronautical University Florida USA

ISBN: (数字)9798350309485

ISBN: (纸本)9798350309492

The extensive application of Artificial Intelligence and Machine Learning is notable in additive manufacturing realms such as manufacturing supervision and error identification. Nonetheless, the escalating employment of AI in vital processes during production necessitates its decision-making and prediction abilities to display interpretability. Consequently, the need for explainable Artificial Intelligence (xAI) in such manufacturing procedures becomes *** this paper, we develop an explainable AI framework for antenna sensor modeling during additive manufacturing. An explainable machine learning method is used in this framework for modeling based on the frequency response data from the antenna sensor. In this approach, the explainable dimension reduction method can interpret the chosen components using projection matrix. The decision tree regressor exhibits proficiency in efficiently handling small datasets and retaining commendable *** demonstrate that the S-parameter prediction generated by our method are more accurate and reliable. This method can be generalized to a new *** work could provide a useful tool for antenna sensor modeling and signal processing fields during additive manufacturing.

关键词： Production Learning (artificial intelligence) Signal processing Three-dimensional printing Frequency response Manufacturing Scattering parameters

来源：评论

学校读者我要写书评

暂无评论

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案：

请选择收藏分类：

通借通还

建议与咨询 留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案： 新增检索档案 确定 取消

请选择收藏分类： 新增自定义分类 确定 取消

通借通还

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

请选择保存的检索档案：

请选择收藏分类：