检索结果-内蒙古大学图书馆

IEEE International Conference on Microelectronic Test Structures

作者： Norihiro Miyazawa Naoto Usami Haibin Wang Takaya Kubo Hiroshi Segawa Yoshio Mita Akio Higo Department of Electrical Engineering and Information Systems The University of Tokyo Research Center for Advanced Science and Technology The University of Tokyo Systems Design Lab School of Engineering The University of Tokyo

ISBN: (数字)9781728140087

ISBN: (纸本)9781728140094

Characterizing junction is essential in new devices research. We are investigating a hybrid infrared-sensitive opto-electronic device on silicon-based large scale integrated circuits, among others, PbS colloidal quantum dots/ZnO/Si hybrid IR detector. To assess such new materials we propose a coaxial circular test structure. The structure is composed of common-central inner (circular) and outer (doughnut) electrodes. In addition to traditional Circular TLM (CTLM) test structure, which was used for ohmic contact, the junction type (ohmic or Schottky) can be identified and quantitatively be analyzed by measuring various gaps and surface areas of the proposed structure (Coaxial CTLM, CCTLM). The measurement using test structure on our n-type ZnO / n-type Si heterojunction sample revealed that the junction was Schottky type, which was opposite to our expectation.

关键词：

来源：评论

学校读者我要写书评

暂无评论

A Computer Aided Detection System for Breast Cancer using Lightweight CNN Models for Smart Healthcare

A Computer Aided Detection System for Breast Cancer using Li...

引用

International Conference on Biosignals, Images and Instrumentation (ICBSII)

作者： Varun C Ajay Kumar Reddy Poreddy Thunakala Balakrishna Priyanka Kokil Department of Electronics and Communication Engineering Advanced Signal and Image Processing (ASIP) Lab Indian Institute of Information Technology Design and Manufacturing Kancheepuram Chennai

ISBN: (数字)9798350350951

ISBN: (纸本)9798350350968

Breast cancer (BC) is a potentially life-threatening disease that occurs because of uncontrolled growth of abnormal corpuscles in the breast tissue. Pathologists analyze the tissue structures using histopathological whole slide images to identify cancerous anomalies. However, pathologists face severe challenges such as fatigue, subjectivity, and inter-observer variability in the early detection of BC. Understanding the intricacies of BC from molecular tissue structures is complex, and inexpertise leads to adverse outcomes. This paper proposes a computed aided detection (CAD) system that can assist histopathologists in the early detection of BC, potentially reducing the abnormalities and diagnostic time. Leveraging the power of convolutional neural networks (CNNs), a stacked ensemble-based model is developed to identify benign and malignant cancerous tissues using histopathological images. The ensemble models comprise three deep CNNs, namely MobileNetV2, ShuffleNet, and SqueezeNet, trained on the BreakHis dataset. Finally, individual CNNs predictions are fed to the average voting-based classifier to identify benign and malignant tissues. The stacked ensemble-based deep CNN model outperformed the individual CNN models in BC prediction, achieving superior accuracy and robustness.

关键词： Solid modeling Computational modeling Medical services Predictive models Breast cancer Robustness Quality assessment

来源：评论

学校读者我要写书评

暂无评论

Lightweight Deep Learning Model for Melanoma Classification in Dermoscopy Images for Smart Healthcare

Lightweight Deep Learning Model for Melanoma Classification ...

引用

International Conference on Wireless Communications, Signal Processing and Networking (WiSPNET)

作者： Pentapati Naga Sree Charan Teja Thunakala Bala Krishna Ajay Kumar Reddy Poreddy Priyanka Kokil Department of Electronics and Communication Engineering Advanced Signal and Image Processing (ASIP) Lab Indian Institute of Information Technology Design and Manufacturing Kancheepuram Chennai

ISBN: (数字)9798350350845

ISBN: (纸本)9798350350852

Among various types of skin cancers, melanoma is the most aggressive and deadly. There is a notable growth in the implementation of deep learning (DL) methods to identify skin malignancies in dermoscopy images. This paper introduces a lightweight DL-based approach designed for seamless integration into low-memory devices within healthcare applications. The proposed method incorporates three lightweight convolutional neural network (CNN) models: MobileNet-v2, SqueezeNet, and GoogLeNet. Initially, test features are computed from fine-tuned deep CNN models. Subsequently, probability scores for each class are derived by training and testing a random forest classifier with features extracted from the models. Then, the proposed method uses an average ensemble voting technique on the probability scores to enhance the classification performance compared to the individual models. The proposed of lightweight CNN model demonstrated an accuracy of 85.19 % which is better than existing works.

关键词： Deep learning Wireless communication Adaptation models Computational modeling Melanoma Medical services Feature extraction

来源：评论

学校读者我要写书评

暂无评论

Automated Anemia Classification and Hemoglobin Level Prediction using Deep CNN and GLCM Features of Palpebral Conjunctiva Images

Automated Anemia Classification and Hemoglobin Level Predict...

引用

Conference on Information and Communication Technology (CICT)

作者： Chandrasekhar Bhusham Ajay Kumar Reddy Poreddy Thunakala Bala Krishna Priyanka Kokil Department of Electronics and Communication Engineering Advanced Signal and Image Processing (ASIP) Lab Indian Institute of Information Technology Design and Manufacturing Kancheepuram Chennai

Anemia is a common medical condition affecting millions worldwide, particularly in developing countries. Early detection of anemia is crucial for prompt treatment and prevention of its potential complications. In recent years, deep learning (DL) has shown great potential in various medical applications, including medical image classification, anomaly detection, and segmentation. This study proposes a transfer learning-based approach using a pre-trained DL model to detect anemia from palpebral conjunctiva images. The proposed method utilizes a pre-trained DenseNet-201 model and fine-tuned it on a target dataset of palpebral conjunctiva images to detect anemia. Deep features of palpebral conjunctiva images computed from the fine-tuned DenseNet-201 are fed to MLP to identify anemia. The performance of the proposed method is evaluated on a publicly available anemia dataset, and the results show that the proposed method achieves an accuracy of 93.7 % in detecting anemia from palpebral conjunctiva images. In addition to anemia classification, we computed the hemoglobin level of palpebral conjunctiva images based on the gray-level co-occurrence matrix (GLCM) statistical properties. The statistical properties of GLCM are given to support vector and polynomial regressors, and the mean value of the predicted scores of both regressors is used to estimate the hemoglobin level. Experimental results show that the proposed model achieves an average root mean square error of 0.72 for conjunctiva images.

关键词：

来源：评论

学校读者我要写书评

暂无评论

Enhanced Focal Liver Lesion Classification using Channel Attention Technique

Enhanced Focal Liver Lesion Classification using Channel Att...

引用

Conference on Information and Communication Technology (CICT)

作者： Sunkanaboina Chandra Lingamaiah Thunakala Bala Krishna Ajay Kumar Reddy Poreddy Priyanka Kokil Department of Electronics and Communication Engineering Advanced Signal and Image Processing (ASIP) Lab Indian Institute of Information Technology Design and Manufacturing Kancheepuram Chennai

ISBN: (数字)9798331531539

ISBN: (纸本)9798331531546

Accurate and timely diagnosis of liver disorders such as fatty liver disease, chronic viral hepatitis, and excessive alcohol consumption is crucial for maintaining liver health. Traditional methods for liver screening are often subjective, time-consuming, and reliant on the expertise of the sonographer, which can impact diagnostic accuracy. To tackle these challenges, in this paper proposed a deep learning (DL) based framework to enhance the effective diagnosis of focal liver lesions. This approach leverages an channel attention mechanism integrated with the DarkNet-19 pre-trained model to improve feature extraction and boosts classification accuracy. By automating the diagnostic process, the proposed model addresses the limitations of traditional methods, providing a more efficient and reliable solution for liver disorder diagnosis. Experimental results with an ultrasound image dataset demonstrate that the proposed model significantly outperforms conventional DL methods, showcasing its advantages in performance and efficiency.

关键词： Frequency locked loops Deep learning Accuracy Ultrasonic imaging Attention mechanisms Liver diseases Feature extraction Information and communication technology Lesions Reliability

来源：评论

学校读者我要写书评

暂无评论

Noise and performance analysis on fundus images with CNN and transformer models

Noise and performance analysis on fundus images with CNN and...

引用

Conference on Information and Communication Technology (CICT)

作者： Niranjana Vannadil Priyanka Kokil Department of Electronics and Communication Engineering Advanced Signal and Image Processing (ASIP) Lab Indian Institute of Information Technology Design and Manufacturing Kancheepuram Chennai

Fundus imaging is a valuable diagnostic tool in ophthalmology, providing clinicians with detailed visualizations of the retina and aiding in the detection and monitoring of various eye diseases, including age-related macular degeneration (AMD), glaucoma, diabetic retinopathy (DR), and cataract. However, the quality of fundus images can be significantly affected by noise, mainly additive white Gaussian noise (AWGN), which is inherent in many imaging systems. The presence of noise in real-world data poses significant challenges for computer vision tasks. In the field of medical image classification, a wrong diagnoisis has heavy consequences. Understanding the impact of AWGN on fundus images is crucial for developing practical denoising algorithms and improving diagnostic accuracy. This work presents an analysis of AWGN noise in fundus images aims to characterize its effects on image quality and assess its impact on diagnostic tasks. The work also analyzes the performance of six models (3 each) of two popular deep learning architectures, Convolutional Neural Networks (CNN) and Vision Transformers (ViT) in the presence of AWGN. AWGN is first introduced to the clean image datasets to conduct the analysis. The CNN and ViT models are trained on the noisy datasets to evaluate the performance of the image classification task. The work also involves six denoising algorithms and a popular image enhancement algorithm- Contrast Limited Adaptive Histogram Equalization (CLAHE).

关键词：

来源：评论

学校读者我要写书评

暂无评论

Accelerating GPGPU architecture simulation

Accelerating GPGPU architecture simulation

引用

2013 ACM SIGMETRICS International Conference on Measurement and Modeling of Computer Systems, SIGMETRICS 2013

作者： Yu, Zhibin Eeckhout, Lieven Goswami, Nilanjan Li, Tao John, Lizy K. Jin, Hai Xu, Chengzhong Shenzhen Institute of Advanced Technology Chinese Academy of Sciences China ELIS Department Ghent University Belgium Intelligent Design of Efficient Architectures Lab. University of Florida Gainesville FL United States Department of Electrical and Computer Engineering University of Texas Austin TX United States Service Computing Technologies and System Lab./Cluster and Grid Computing Lab. HUST Wuhan China Department of Electrical and Computer Engineering Wayne State University MI United States

ISBN: (纸本)9781450319003

Recently, graphics processing units (GPUs) have opened up new opportunities for speeding up general-purpose parallel applications due to their massive computational power and up to hundreds of thousands of threads enabled by programming models such as CUDA. However, due to the serial nature of existing micro-architecture simulators, these massively parallel architectures and workloads need to be simulated sequentially. As a result, simulating GPGPU architectures with typical benchmarks and input data sets is extremely time-consuming. This paper addresses the GPGPU architecture simulation challenge by generating miniature, yet representative GPGPU kernels. We first summarize the static characteristics of an existing GPGPU kernel in a profile, and analyze its dynamic behavior using the novel concept of the divergence flow statistics graph (DFSG). We subsequently use a GPGPU kernel synthesizing framework to generate a miniature proxy of the original kernel, which can reduce simulation time significantly. The key idea is to reduce the number of simulated instructions by decreasing per-thread iteration counts of loops. Our experimental results show that our approach can accelerate GPGPU architecture simulation by a factor of 88X on average and up to 589X with an average IPC relative error of 5.6%.

关键词： Graphics processing unit

来源：评论

学校读者我要写书评

暂无评论

Effect of Mg Deoxidation on Inclusions, Microstructures and Toughness of Heat Affected Zones of Steel Plates after Large Heat Input Welding

Effect of Mg Deoxidation on Inclusions, Microstructures and ...

引用

2018年可持续发展炼钢技术国际研讨会

作者： Jian Yang Longyun Xu Yinhui Zhang Tao Zhang Joohyun Park Hideki Ono State Key Lab of Advanced Special Steel School of Materials Science and Engineering Shanghai University Department of Materials Engineering Hanyang University Faculty of Sustainable Design University of Toyama

The effect of Mg deoxidation on inclusions, microstructures and toughness of heat affected zone（HAZ） of steel plates after high heat input welding was experimentally investigated. The typical inclusions found in the steels were the complex inclusions comprising central oxide and peripheral MnS. With the increase of Mg content in the steel from 0 to 27, 38 and 99 ppm, the phase of central oxide inclusions changed sequentially from AlO to（Mg-Ti-O）, and MgO. The main microstructure constituents in HAZ were changed from the brittle constituents of ferrite side plate and upper bainite to the ductile constituents of intragranular acicular ferrite（IAF） and polygonal ferrite. It reveals that the IAFs are only observed in steel with Mg addition and the volume fraction of IAF is as high as 55.4 % in the steel containing 27 ppm Mg. It shows the MgO-AlO-TiO-MnS inclusions with size around 2 μm are effective nucleation sites for IAF. As a result, the HAZ toughness of steel plates after welding with the heat input of 400 kJ·cm has been greatly improved by inclusion control with Mg deoxidation.

关键词： large heat input welding heat affected zone toughness fine inclusions steel plate

来源：评论

学校读者我要写书评

暂无评论

Exponential Stability Criterion for State-space Interfered Discrete System through External Disturbance

Exponential Stability Criterion for State-space Interfered D...

引用

International Conference on Power, Control and Embedded Systems (ICPCES)

作者： Pulikonda Mounika Priyanka Kokil Department of Electronics and Communication Engineering Advanced Signal and Image Processing (ASIP) Lab Indian Institute of Information Technology Design and Manufacturing Kancheepuram Chennai India

ISBN: (数字)9798350310252

ISBN: (纸本)9798350310269

This article deals with stability investigation of state-space fixed-point discrete system using saturation nonlinearity and external interference. A criterion for exponential stability is proposed via a passivity-based approach for an externally interfered nonlinear discrete system. For the discrete system with saturation overflow nonlinearity, we have analyzed the passive behaviour under the effects of external interference and asymptotic stability with zero interference. A comparative study is made available with the previously reported result to highlight the worth and relaxed nature of the proposed work. The criterion developed is in linear matrix inequality (LMI) settings and therefore, numerically less complex. Numerical simulations are made available to showcase the productiveness of the obtained criterion.

关键词： Asymptotic stability Stability criteria Limit-cycles Interference Numerical simulation Control theory Linear matrix inequalities

来源：评论

学校读者我要写书评

暂无评论

Information Flow in Digital Twin for “Detection to Repair” of Defects Using Additive Manufacturing

引用

IFAC-PapersOnLine 2024年第19期58卷 736-741页

作者： Dylan Bender Jordan Anderson Mike Gilbert Ahmad Barari Advanced Digital Design Manufacturing and Metrology Laboratories (AD2MLabs) Department of Mechanical and Manufacturing Engineering Ontario Tech University Canada Innovation Development - X-Lab Ontario Power Generation

Digitalization in inspection and manufacturing results in a wide range of advantages including the reduction of cost, complexity, and operation time, and increasing the flexibility, level of automation, and the capabilities to gain intelligence. This paper discusses an attractive benefit of digitalization which allows the integration of the information flow and control for the two processes of digital inspection and additive manufacturing. A digital twin of the additive manufacturing process is dynamically updated based on the intermittent inspection data obtained from the workpiece to integrate the information of the digital model for planning and controlling additive manufacturing process. The ultimate objective is to repair highly expensive, and large components in industrial sectors. The developed digital twin for this integrated system includes eight activities are demonstrated through an industrial case study.

关键词： Digital Inspection Digital Manufacturing Additive Manufacturing Manufacturing Information Control Defect Detection Repair Integrated Inspection Systems

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：