检索结果-内蒙古大学图书馆

Thermal Hall effect in insulating quantum materials

The Innovation 2022年第5期3卷 3-4页

作者： Shucheng Guo Youming Xu Ran Cheng Jianshi Zhou Xi Chen Department of Electrical and Computer Engineering University of CaliforniaRiversideRiversideCA 92521USA Department of Physics and Astronomy University of CaliforniaRiversideRiversideCA 92521USA Materials Science and Engineering Program Department of Mechanical EngineeringUniversity of Texas at AustinAustinTX 78712USA

The emerging field of quantum materials involves an exciting new class of materials in which charge,spin,orbital,and lattice degrees of freedom are intertwined,exhibiting a plethora of exotic physical *** materials include,but are not limited to,superconductors,topological quantum matter,and systems with frustrated spins,which enable a wide range of potential applications in biomedicine,energy transport and conversion,quantum sensing,and quantum information processing。

关键词： properties. quantum topological

来源：评论

学校读者我要写书评

暂无评论

Forward and Reverse Converters for the Moduli-Set {22q+1, 2q + 2q−1 ± 1}

arXiv

引用

arXiv 2024年

作者： Jaberipur, Ghassem Nadimi, Bardia Kazemi, R. Lee, Jeong-A The Brain Pool Program for Chosun University Gwangju Korea Republic of The University of South Florida TampaFL United States Computer Engineering The Department of Computer Science and Engineering Shahid Beheshti University Tehran Iran The Department of Computer Engineering Chosun University Korea Republic of

Modulo-(2q + 2q−1 ± 1) adders have recently been implemented using the regular parallel prefix (RPP) architecture, matching the speed of the widely used modulo-(2q ± 1) RPP adders. Consequently, we introduce a new moduli set τ+ = {22q+1, 2q + 2q−1 ± 1}, with over (2q+2)× dynamic range and adder speeds comparable to the conventional τ = {2q, 2q ± 1} set. However, to fully leverage τ+ in residue number system applications, a complete set of circuitries is necessary. This work focuses on the design and implementation of the forward and reverse converters for τ+. These converters consist of four and seven levels of carry-save addition units, culminating in a final modulo-(2q + 2q−1 ± 1) and modulo-(22q+1 + 22q−2 − 1) adder, respectively. Through analytical evaluations and circuit simulations, we demonstrate that the overall performance of a sequence of operations-including residue generation, k additions, and reverse conversion-using τ+ surpasses that of τ when k exceeds a certain practical threshold. Copyright © 2024, The Authors. All rights reserved.

关键词： Adders

来源：评论

学校读者我要写书评

暂无评论

Overlapped Error Correction Codes in Two-Dimensional Structures

Overlapped Error Correction Codes in Two-Dimensional Structu...

引用

IEEE Latin American Symposium on Circuits and Systems (LASCAS)

作者： Andrew Fritsch Wagner Savaris Adahil Muniz Giovanna Borba Rodrigo Girardi Jarbas Silveira César Marcon Graduate Program in Computer Science (PPGCC) Pontifical Catholic University of Rio Grande do Sul (PUCRS) Brazil Graduate Program in Teleinformatics Engineering (PPGETI) Federal University of Ceará (UFC) Brazil

ISBN: (数字)9798331522124

ISBN: (纸本)9798331522131

The continuous advancement of communication systems necessitates the development of algorithms capable of identifying and correcting errors that may arise during data transmission and storage. This pursuit of reliability is particularly crucial in critical systems and sectors that are challenging to access, such as space exploration, passenger transportation, and financial services. In this context, the Error Correction Code (ECC) is a fundamental tool for providing a certain degree of reliability to these systems. This research proposes a novel technique to enhance the error correction capacity of ECCs by leveraging region overlapping. Specifically, we propose correcting data areas protected by more than one ECC, which allows for the inference of logical correlations between ECCs, thereby augmenting their error detection and correction capability. Our focus is bidimensional codeword structures, commonly known as 2D-ECCs, which entail a hierarchical arrangement of ECCs. We evaluated the ECC proposal, comparing its error correction and detection capabilities. Through this evaluation, we aim to demonstrate the technique's efficacy in bolstering the reliability and resilience of communication systems, particularly in critical domains where precision and accuracy are paramount.

关键词： Fault tolerance Fault tolerant systems Transportation Error correction codes Error correction Space exploration Proposals Integrated circuit reliability Financial services Resilience

来源：评论

学校读者我要写书评

暂无评论

Email Spam Detection Using Logistic Regression and Explainable AI

Email Spam Detection Using Logistic Regression and Explainab...

引用

International Conference on Electrical, computer and Communication engineering (ECCE)

作者： Sumit Kanti Sarker Ratul Bhattacharjee Md Abu Sufian Md Solaiman Ahamed Md Abu Talha Faiza Tasnim K.M. Nazmul Islam Sharia Tasnim Adrita Department of Computer Science & Engineering (CSE) Faculty of Science & Technology (FST) American International University Bangladesh Dhaka Bangladesh Bachelor of Science in Information Technology (BSIT) Cybersecurity Program Concentrations Washington University of Science and Technology (WUST) VA USA Department of Electrical and Electronics Engineering (EEE) Faculty of Engineering (FE) American International University Bangladesh Dhaka Bangladesh Department of Computer Science and Engineering (CSE) Faculty of Science & Engineering Northern University Bangladesh-(NUB) Dhaka Bangladesh

ISBN: (数字)9798350357509

ISBN: (纸本)9798350357516

Email spam detection is crucial for ensuring a positive user experience and maintaining communication security. This study presents a novel spam detection approach leveraging Logistic Regression, optimized through hyperparameter tuning and enhanced with Explainable Artificial Intelligence. The proposed method is evaluated on a large-scale dataset of emails collected from a real-world spam detection system. Term Frequency-Inverse Document Frequency is utilized for feature extraction, converting email text into numerical representations. XAI introduces interpretability by highlighting critical features such as "pill," "PHP," and "Businessweek," which indicate spam classification patterns. Metrics such as precision, recall, and the confusion matrix are employed to assess the model’s performance, ensuring a balanced evaluation. Hyperparameter optimization using Grid Search CV achieves an optimal regularization parameter (C = 100) and maximum iterations (maxiter=100), resulting in an impressive F1 score of 98.76%, accuracy of 98.82%, precision of 98.62%, and recall of 98.90%.

关键词： Logistic regression Explainable AI Filtering Unsolicited e-mail Computational modeling Predictive models Transformers Frequency conversion Feature extraction Tuning

来源：评论

学校读者我要写书评

暂无评论

Human intracortical responses to varying electrical stimulation conditions are separable in low-dimensional subspaces

Human intracortical responses to varying electrical stimulat...

引用

2022 IEEE International Conference on Systems, Man, and Cybernetics, SMC 2022

作者： Sun, Samantha Levinson, Lila H. Paschall, Courtnie J. Herron, Jeffrey Weaver, Kurt Hauptman, Jason Ko, Andrew Ojemann, Jeffrey Rao, Rajesh P. N. University of Washington Paul G. Allen School of Computer Science & Engineering Department of Bioengineering SeattleWA98195 United States University of Washington Graduate Program in Neuroscience Paul G. Allen School of Computer Science & Engineering SeattleWA98195 United States University of Washington Paul G. Allen School of Computer Science & Engineering Department of Neurological Surgery SeattleWA98195 United States University of Washington Paul G. Allen School of Computer Science & Engineering Department of Radiology SeattleWA98195 United States

ISBN: (数字)9781665452588

ISBN: (纸本)9781665452588

Electrical stimulation is a powerful tool for targeted neurorehabilitation, and recent work in adaptive stimulation where stimulation can be adjusted in real-time has shown promise in improving stimulation outcomes and reducing stimulation-induced side effects. Mapping the relationship between electrical stimulation input and neural activity response can help reveal their interactions and can give us tools to iterate and improve on our stimulation protocols. Here, we introduce methods for identifying low-dimensional subspaces of human intracortical responses to electrical stimulation in invasive electroencephalography. In epilepsy patients (n=4) undergoing clinical monitoring, we applied a stimulation protocol of varying amplitude and frequency in 5-second intervals to capture a range of responses to different stimulation conditions. We characterized these responses using time-frequency spectral power, applied baseline subtraction and outlier removal procedures, and performed principal component analysis across frequencies. We identified that intracortical responses to different stimulation conditions can be represented in a 3-dimensional subspace, accounting for more than 95% of the variance. Using support vector machine classification, we demonstrated separability of intracortical responses in different stimulation conditions across subjects, where this separability was contingent on applying baseline subtraction and outlier removal. Our results represent a first step towards building a predictive model of neural response from stimulation input, an important prerequisite for adaptive closed-loop stimulation for targeted neurorehabilitation. © 2022 IEEE.

关键词： Electroencephalography

来源：评论

学校读者我要写书评

暂无评论

SODIUM-ION BATTERY CYCLE LIFE PREDICTION USING MACHINE LEARNING

SODIUM-ION BATTERY CYCLE LIFE PREDICTION USING MACHINE LEARN...

引用

International Conference on Green Energy, Computing and Intelligent Technology 2024, GEn-CITy 2024

作者： Liu, Linfeng Juwono, Filbert H. Wong, W.K. Purwanto, Erick Jin, Tingyan Zhen, Yuyang Sustainable Energy Technology Program Xi'an Jiaotong-Liverpool University Suzhou China Department of Electrical and Electronic Engineering Xi'an Jiaotong-Liverpool University Suzhou China Department of Electrical and Computer Engineering Curtin University Malaysia Miri Malaysia Department of Computing Xi'an Jiaotong-Liverpool University Suzhou China School of Mechanical and Electrical Engineering Beijing Information Science and Technology University Beijing China School of Instrument Science and Technology Beijing University of Technology Beijing China

ISBN: (纸本)9781837242689

In this study, we explore the classification and prediction capabilities of three models-Genetic programming (GP), Logistic Regression (LR), and the Kolmogorov-Arnold Network (KAN)-on the task of sodium-ion battery life prediction. By leveraging a dataset composed of multiple battery characteristics, we aim to determine the remaining power of sodium-ion batteries using these machine learning models. The KAN model, being a novel approach, demonstrates superior performance across various metrics, including accuracy, precision, recall, and F1 score, when compared to the other two models. This highlights the potential of KAN as a robust model for complex classification tasks in the field of battery life prediction. © The Institution of engineering & Technology 2024.

关键词： Genetic programming

来源：评论

学校读者我要写书评

暂无评论

PacECG-Net: A Multi-modal Approach Integrating LLMs and ECG for LVSD Classification in Pacemaker Patients 25

PacECG-Net: A Multi-modal Approach Integrating LLMs and ECG ...

引用

40th Annual ACM Symposium on Applied Computing, SAC 2025

作者： Shim, Wonkyeong Park, Namjun Ko, Donggeun Kim, San Gwag, Hye Bin Park, Young Jun Park, Seung-Jung Kim, Jaekwang Department of Computer Science and Engineering Sungkyunkwan University Suwon Korea Republic of Aim Future AI Seoul Korea Republic of Dep. of Computer Science and Eng. Sungkyunkwan University Suwon Korea Republic of Samsung Changwon Hospital Changwon Korea Republic of Wonju Severance Christian Hospital Wonju Korea Republic of Samsung Medical Center Seoul Korea Republic of School of Convergence/Convergence Program for Social Innovation Sungkyunkwan University Seoul Korea Republic of

ISBN: (纸本)9798400706295

This study presents an AI-based model using ECG signals to predict left ventricular systolic dysfunction (LVSD) in pacemaker patients. A 1D convolutional neural network (CNN) combined with large language models processed both sequential ECG data and nonsequential clinical metadata. The model achieved an AUROC of 0.97 on both general and pacemaker-specific datasets, demonstrating its high accuracy. This approach offers a fast, cost-effective alternative to traditional echocardiography, improving LVSD detection in patients with pacemakers. Copyright © 2025 held by the owner/author(s).

关键词： Convolutional neural networks

来源：评论

学校读者我要写书评

暂无评论

Diabetes Prediction Model for Better Clarification by using Machine Learning 6

Diabetes Prediction Model for Better Clarification by using ...

引用

6th International Conference on Inventive Computation Technologies, ICICT 2023

作者： Eben, J. Lysa Jayasudha, R. Ramya, S. Kaliappan, S. Aswal, Shobha Al-Salehi, Khalid Ali Salem Department of Computer Applications School of Computing Sciences Vels Institute of Science Technology and Advanced Studies Tamilnadu Chennai India Department of Mathematics Dr.N.G.P.Institute of Technology Coimbatore India Department of Computer Science and Engineering Kongu Engineering College Tamilnadu Erode India Division of Research and Development Lovely Professional University Punjab Phagwara144411 India School of Computing Graphic Era Hill University Dehradun India Biomedical Engineering Study Program Faculty of Science and Technology Airlangga University Surabaya Indonesia

ISBN: (纸本)9798350398496

Diabetes mellitus is one of the most pressing health concerns because so many people are afflicted by its disabling symptoms. Factors such as age, excess body fat, insufficient physical activity, a history of diabetes in one's family, a sedentary lifestyle, an unhealthy diet, hypertension, etc., all increase the likelihood of developing diabetes mellitus. Health complications are more common in people with diabetes, including cardiovascular disease, renal failure, stroke, blindness, and nerve injury. To validate a diagnosis of diabetes, hospitals typically perform a battery of procedures on the patient. Big data analytics has many vital applications in the healthcare sector. Numerous large computer systems are used in the healthcare sector. With the help of big data analytics, researchers can sift through mountains of data in search of previously unseen patterns and insights. Current techniques have a poor degree of precision in classification and forecast. While previous research has focused on factors such as glucose, body mass index, age, insulin, etc., the proposed model takes these into account and also the other factors that may be more relevant to the development of diabetes. The newer sample is superior to the older one based on categorization accuracy. A workflow algorithm for diabetes prognosis is also required to improve the accuracy. © 2023 IEEE.

关键词： Forecasting

来源：评论

学校读者我要写书评

暂无评论

Persistent Spiral Storage

Persistent Spiral Storage

引用

IEEE International Conference on computer Design: VLSI in computers and Processors, (ICCD)

作者： Wenyu Peng Tao Xie Paul H. Siegel Engineering Science Joint Doctoral Program San Diego State University San Diego USA Computer Science San Diego State University San Diego USA Electrical and Computer Engineering University of California San Diego La Jolla USA

ISBN: (数字)9798350380408

ISBN: (纸本)9798350380415

The advent of byte-addressable persistent memory (PM) has led to a resurgence of interest in adapting existing dynamic hashing schemes to PM. Compared with its two well-known peers (extendible hashing and linear hashing), spiral storage has received little attention due to its limitations. After an in-depth analysis, however, we discover that it has a good potential for PM. To show its strength, we develop a persistent spiral storage called PASS (Persistence-Aware Spiral Storage), which is facilitated by a group of new/existing techniques. Further, we conduct a comprehensive evaluation of PASS on a server equipped with Intel Optane DC Persistent Memory Modules (DCPMM). Experimental results demonstrate that compared with two state-of-the-art schemes it exhibits better performance.

关键词： Spirals Memory modules Data structures Servers

来源：评论

学校读者我要写书评

暂无评论

Dimensionality reduction in 3D causal deep learning for neuroimage generation: an evaluation study

引用

Journal of Medical Imaging 2025年第2期12卷

作者： Ohara, Erik Y. Vigneshwaran, Vibujithan Souza, Raissa Vamosi, Finn G. Wilms, Matthias Forkert, Nils D. University of Calgary Biomedical Engineering Graduate Program CalgaryAB Canada University of Calgary Department of Radiology CalgaryAB Canada University of Calgary Hotchkiss Brain Institute CalgaryAB Canada University of Calgary Alberta Children's Hospital Research Institute CalgaryAB Canada University of Calgary Department of Computer Science CalgaryAB Canada University of Calgary Departments of Pediatrics and Community Health Sciences CalgaryAB Canada

Purpose: Causal deep learning (DL) using normalizing flows allows the generation of true counterfactual images, which is relevant for many medical applications such as explainability of decisions, image harmonization, and in-silico studies. However, such models are computationally expensive when applied directly to high-resolution 3D images and, therefore, require image dimensionality reduction (DR) to efficiently process the data. The goal of this work was to compare how different DR methods affect counterfactual neuroimage generation. Approach: Five DR techniques [2D principal component analysis (PCA), 2.5D PCA, 3D PCA, autoencoder, and Vector Quantised-Variational AutoEncoder] were applied to 23,692 3D brain images to create low-dimensional representations for causal DL model training. Convolutional neural networks were used to quantitatively evaluate age and sex changes on the counterfactual neuroimages. Age alterations were measured using the mean absolute error (MAE), whereas sex changes were assessed via classification accuracy. Results: The 2.5D PCA technique achieved the lowest MAE of 4.16 when changing the age variable of an original image. When sex was changed, the autoencoder embedding led to the highest classification accuracy of 97.84% while also significantly impacting the age variable predictions, increasing the MAE to 5.24 years. Overall, 3D PCA provided the best balance, with an age prediction MAE of 4.57 years while maintaining 94.01% sex classification accuracy when altering the age variable and 94.73% sex classification accuracy and the lowest age prediction MAE (3.84 years) when altering the sex variable. Conclusions: 3D PCA appears to be the best-suited DR method for causal neuroimage analysis. © 2025 Society of Photo-Optical Instrumentation Engineers (SPIE).

关键词： Principal component analysis

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：