检索结果-内蒙古大学图书馆

2023 International Conference on Advancement in Computation and computer Technologies, InCACCT 2023

作者： Gupta, Vaishali Patel, Ruchi Institute of Engineering and Science Department of Computer Science and Engineering IPS Academy MP Indore India Gyan Ganga Institute of Technology and Sciences Department of Computer Science and Engineering MP Jabalpur India

ISBN: (纸本)9798350396485

People all over the world are afflicted by lung disease, which is a prevalent illness. The earliest possible diagnosis of lung illness is necessary. Due to this, a number of deep learning models for processing image data evolved over time. Advances in deep learning have helped identify lung disorders and detect them in diagnostic images. Various types of modern deep learning techniques, including vanilla neural networks, convolutional neural networks (CNN), visual geometry group (VGG) dependent neural networks, and capsule networks, can be used to classify lung cancer. The basic CNN performs poorly when trying to handle rotated, curved, or other unusual image orientations. As a result, the proposed work explored using principal components analysis (PCA) and the VGG16 deep learning architecture. In order to extract significant features from an image dataset, PCA is generally used. The Chest X-ray of National Institutes of Health (NIH) is taken as dataset which contains 112,120 images of X-ray of 30,805 different patients. In the current work, accuracy is used to evaluate performance, and VGG 16's accuracy is 79.1%. The PCA approach has raised it by up to 96%. Additionally, the proposed architecture is contrasted with current work. © 2023 IEEE.

关键词： Principal component analysis

来源：评论

学校读者我要写书评

暂无评论

Review of Deep Speech Recognizer using Transcriber 6

Review of Deep Speech Recognizer using Transcriber

引用

6th IEEE International Conference on Advances in science and technology, ICAST 2023

作者： Tivarekar, Rahulkumar P. Khadye, Ruchita Mahesh Chavande, Saloni Ravindra Talkatkar, Pranita Sachin Mumbai University Finolex Academy of Management and Technology Computer Science and Engineering Department Ratnagiri India

ISBN: (纸本)9798350359817

This research presents the development of a 'Deep Speech Recognizer using Transcriber,' aimed at streamlining the process of capturing and converting live speech into text. The primary problem addressed by this system is the need for efficient and accurate transcription in various contexts, such as meetings, lectures, and interviews. The methodology employed in this project utilizes Jupyter widgets for user interaction, PyAudio for microphone audio capture, Vosk for speech recognition, and recasepunc for punctuation enhancement. Key findings of this research include the successful implementation of a user-friendly system capable of recording and transcribing live speech in real-time. The system exhibits commendable accuracy in speech recognition, making it a valuable tool for professionals and researchers in fields requiring transcription services. Furthermore, the integration of recasepunc significantly improves the readability and usability of the transcribed text. This research contributes to the automation of speech transcription, offering a practical solution to the time-consuming and error-prone process of manual transcription. The system's versatility and ease of use make it a promising tool for enhancing productivity and accessibility in various domains. Future work may involve further optimization and the exploration of additional features to expand the system's capabilities and applications. © 2023 IEEE.

关键词： Speech recognition

来源：评论

学校读者我要写书评

暂无评论

Temperature-dependent wakeup behavior in back-end-of-line compatible ultra-thin HfxZr1-xO2 ferroelectric film

引用

science China(Information sciences) 2025年第6期68卷 216-217页

作者： Xiaopeng LI Lu TAI Xiaoyu DOU Yang FENG Dong ZHANG Xuepeng ZHAN Jixuan WU Xiao GONG Jiezhi CHEN School of Information Science and Engineering Shandong University Beijing Superstring Academy of Memory Technology Department of Electrical and Computer Engineering National University of Singapore

HfO2-based ferroelectric（FE） devices have emerged as promising candidates for next-generation non-volatile memories（NVMs）, owing to their excellent CMOS compatibility, robust scalability, and low operating voltage requirements [1]. Among them, ultra-thin HfxZr1-xO2（HZO） FE films are particularly attractive for back-end-of-line（BEOL）integration in monolithic 3D memory architectures.

关键词：

来源：评论

学校读者我要写书评

暂无评论

Evaluating and Contrasting the Performance of Web Development Technologies 9

Evaluating and Contrasting the Performance of Web Developmen...

引用

9th International Conference on Communication and Electronics Systems, ICCES 2024

作者： Swedheetha, C. Shruthika, C. Preethi, Rubi Vedha Varshini, T.B.J. Maryam, Sabreen Manikandan, P. Velammal College of Engineering and Technology Department of Computer Science and Engineering Madurai India Kalasalingam Academy of Research and Education Department of Electronics and Communication Engineering Virudhunagar India

ISBN: (纸本)9798350377972

The next generation of webpages is heavily influenced by large scale, high concurrency, and massive amounts of data. Building data-intensive web apps has made *** popular and effective. We employed scenario testing and benchmark testing to examine and contrast the performance of ***, Python-Web, and PHP. Per the testing results, PHP and Python-Web handle a lot less requests in a given amount of time than ***, which is evident from some useful performance metrics. Our findings, taken together, show that *** is the most lightweight and efficient of the three, making it a good choice for I/O-intensive websites;PHP, on the other hand, is best suited for small- to medium-sized applications, while Python-Web is developer-friendly and excels at large web architectures. This research study includes the performance evaluation of web development technologies. © 2024 IEEE.

关键词： Python

来源：评论

学校读者我要写书评

暂无评论

A Novel Pressure Sensor with Composite Sensitive Film for High Precision Measurement at Broad Range 38

A Novel Pressure Sensor with Composite Sensitive Film for Hi...

引用

38th IEEE International Conference on Micro Electro Mechanical Systems, MEMS 2025

作者： Chen, Xiaopeng Ling, Sijia Tong, Hanyang Xiao, Yujing Zhang, Jin Yu, Zhengyin Jin, Qinghui Ningbo University The Faculty of Electrical Engineering and Computer Science China Chinese Academy of Sciences Shanghai Institute of Microsystem and Information Technology China

ISBN: (纸本)9798331508890

Microelectromechanical system (MEMS) based pressure sensors have been utilized for decades;however, new trends in pressure sensors have recently emerged, such as increased sensitivity, a broader range and reduced chip size. This paper presents a tiny 0.6mm×0.8mm piezoresistive pressure sensor that offers exceptional sensitivity (0.27mV/kPa/3.3V) at a wide range of pressures (0-1MPa). The innovative composite pressure-sensitive film of the sensor is composed of a 10μm "*"-shaped single-crystalline silicon structure and a remarkably thin thickness of only 2.5μm poly-silicon film. It is developed utilizing the thin-film under bulk-silicon technique. And simultaneous improvement of detection sensitivity and linearity is achieved by etching out the nonlinear deformation region of the film. © 2025 IEEE.

关键词： Pressure sensors

来源：评论

学校读者我要写书评

暂无评论

An Improved Deadlock Recovery Policy of Flexible Manufacturing Systems Based on Resource Flow Graphs

引用

IEEE Access 2024年 12卷 65202-65212页

作者： Tseng, Ching-Yun Chen, Ju-Chin Pan, Yen-Liang National Kaohsiung University of Science and Technology Department of Electronic Engineering Kaohsiung807618 Taiwan R.O.C. Air Force Academy Mathematics and Physics Division General Education Center Gangshan Kaohsiung820009 Taiwan National Kaohsiung University of Science and Technology Department of Computer Science and Information Engineering Kaohsiung807618 Taiwan Chaoyang University of Technology Department of Aeronautical Engineering Taichung413310 Taiwan

The system deadlock problem of the flexible manufacturing system (FMS) needs to be solved urgently. Deadlocks may occur when resources are repeatedly requested by multiple working processes, where such competition causes a lack of resources, and the system cannot get into subsequent processes smoothly. In this paper, we build FMS by using a particular class of Petri Net (PN) model called a system of simple sequential processes with resources (S3PR) net, which features a high utilization rate of resources. When parts of resources are held by related processes and these processes wait for others to release resources to move on, a circular waiting situation occurs. Circular waiting is one of the conditions for system deadlocks occurring, where the system under this situation has a high probability of causing system deadlocks. The resources being held have to be released to break the waiting states. This paper develops a novel control transition-based deadlock recovery policy by combining graph theory and reachability graph analysis technique. The proposed approach firstly builds a subnet of the PN model called resource flow graph (RFG) and finds out all its hold and request circuits (HRCs), which may lead to a circular waiting situation. In addition, we design a new algorithm to obtain the minimal set of control transitions further. We introduce three PN models of FMS to demonstrate the proposed method. Through experimental results and comparison between related works, it is known that the proposed deadlock recovery uses fewer controllers and has the same performance. © 2013 IEEE.

关键词： Frequency modulation

来源：评论

学校读者我要写书评

暂无评论

Effects of different scattering components in speckle autocorrelation imaging

引用

Optics Letters 2025年第3期50卷 880-883页

作者： Xie, Gengchen Zhang, Xuyu Zhang, Dawei Han, Shensheng Liu, Honglin School of Optical-Electrical and Computer Engineering University of Shanghai for Science and Technology Shanghai200093 China Shanghai Institute of Optics and Fine Mechanics Chinese Academy of Sciences Shanghai201800 China Center of Materials Science and Optoelectronics Engineering University of Chinese Academy of Sciences Beijing100049 China University of Chinese Academy of Sciences Hangzhou Institute for Advanced Study Hangzhou310024 China

Various methods of imaging through scattering media suffer a rapid degradation in image quality as medium thickness increases. To address this issue, based on speckle autocorrelation imaging we designed a double-layer diffuser experiment to study the effects of different scattering components on imaging. The results show that both singly scattered light and doubly scattered light can transmit information. However, the difference in memory effect range leads to diverse abilities in information extraction, thereby affecting image recovery. This study may provide insights into the mechanisms of information transmission within thick scattering media and methods to extract information from multiply scattered light to reconstruct images. © 2025 Optica Publishing Group. All rights, including for text and data mining (TDM), Artificial Intelligence (AI) training, and similar technologies, are reserved.

关键词： Speckle

来源：评论

学校读者我要写书评

暂无评论

A Deep Learning Model-Based Neural Network Framework for Diabetic Foot Ulcer Classification 5

A Deep Learning Model-Based Neural Network Framework for Dia...

引用

5th International Conference on Electronics and Sustainable Communication Systems, ICESC 2024

作者： Shanmugam, P. Kumar, P.K. Manoj Hussain, Mohammad Manzoor Revathi, R. Deepa, P. Sakthivelu, U. Rajalakshmi Engineering College Department of Computer Science and Engineering Chennai India Subbalakshmi Lakshmipathy College of Science Department of Computer Science Tamil Nadu Madurai India B v Raju Institute of Technology Department of Computer Science and Engineering Medak Telangana Narsapur India Karpagam Academy of Higher Education Department of Computer Science Coimbatore India Texcity Arts and Science College Department of Computer Science Madukkarai Market Coimbatore India College of Engineering and Technology Srm Institute of Science and Technology Department of Networking and Communications Tamil Nadu Kattankulathur India

ISBN: (纸本)9798350379945

Diabetic Foot Ulcer (DFU) is a severe complication of diabetes, frequently resulting in amputation and having a significant impact on patients' quality of life. Early and accurate DFU classification is essential for prompt and efficient therapy. This study introduces an innovative neural network framework that utilizes deep learning techniques to categorize DFU based on thermograms. The proposed framework employs the pre-trained EfficientNet-B0 deep learning model, well-known for its exceptional performance in image classification tasks. The framework underwent training and assessment using a dataset sourced from Kaggle, consisting of 1866 colored images. Among them, 890 images depict plantar thermograms with negative DFU, whereas 976 images depict foot thermograms with positive DFU. The proposed framework demonstrated a higher accuracy of 98.71, outperforming previous approaches by a wide margin. The findings demonstrate the ability of deep learning to categorize DFU, paving the way for the development of automated testing techniques to assist healthcare practitioners in the timely identification and treatment of this incapacitating ailment. © 2024 IEEE.

关键词： Ability testing

来源：评论

学校读者我要写书评

暂无评论

Neuromorphic-computing-based adaptive learning using ion dynamics in flexible energy storage devices

引用

National science Review 2022年第11期9卷 172-181页

作者： Shufang Zhao Wenhao Ran Zheng Lou Linlin Li Swapnadeep Poddar Lili Wang Zhiyong Fan Guozhen Shen State Key Laboratory for Superlattices and Microstructures Institute of SemiconductorsChinese Academy of Sciences and Center of Materials Science and Optoelectronic EngineeringUniversity of Chinese Academy of Sciences Department of Electronic and Computer Engineering The Hong Kong University of Science and Technology School of Integrated Circuits and Electronics Beijing Institute of Technology

High-accuracy neuromorphic devices with adaptive weight adjustment are crucial for high-performance computing. However, limited studies have been conducted on achieving selective and linear synaptic weight updates without changing electrical pulses. Herein, we propose high-accuracy and self-adaptive artificial synapses based on tunable and flexible MXene energy storage devices. These synapses can be adjusted adaptively depending on the stored weight value to mitigate time and energy loss resulting from recalculation. The resistance can be used to effectively regulate the accumulation and dissipation of ions in single devices, without changing the external pulse stimulation or preprogramming, to ensure selective and linear synaptic weight updates. The feasibility of the proposed neural network based on the synapses of flexible energy devices was investigated through training and machine learning. The results indicated that the device achieved a recognition accuracy of ～95% for various neural network calculation tasks such as numeric classification.

关键词： neuromorphic computing MXene flexible device high accuracy adaptability ion dynamics

来源：评论

学校读者我要写书评

暂无评论

CodGRU: Covariate gated attention GRU for short term runoff forecasting 24

CodGRU: Covariate gated attention GRU for short term runoff ...

引用

4th International Conference on Signal Processing and Communication technology, SPCT 2024

作者： Cao, Jiahao Xiao, Tong Cao, Zhe School of Computer Science and Engineering Chongqing University of Science and Technology Chongqing China Shenzhen Branch of China Academy of Urban Planning and Design Guangdong Shenzhen China

ISBN: (纸本)9798400710636

Runoff simulation and forecasting are critical for effective watershed management, yet their complexity and nonlinear characteristics pose significant challenges. The generation of runoff is the result of the interaction between multiple factors such as precipitation, soil moisture, topography, and human activities, making this dynamic process extremely complex and difficult to model accurately. Traditional models are often based on an understanding of these natural processes but may fall short when dealing with complex real-world scenarios. To address these issues, this study has developed a dynamic gated feature selection model with attention mechanisms (CodGRU), specifically designed for multi-watershed hourly runoff forecasting. By dynamically adjusting the weights of input variables and selecting the most important features, CodGRU can better capture the key factors influencing runoff, thereby improving the accuracy of forecasting. The test results of five watersheds on the real dataset show that the CodGRU model performs excellently in multi-watershed hourly-scale forecasting, outperforming the existing mainstream models and providing reliable support for water resource management and disaster forecasting and mitigation. © 2024 Copyright held by the owner/author(s).

关键词： Soil moisture

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：