检索结果-内蒙古大学图书馆

arXiv 2020年

作者： Gill, Sukhpal Singh Tuli, Shreshth Toosi, Adel Nadjaran Cuadrado, Felix Garraghan, Peter Bahsoon, Rami Lutfiyya, Hanan Sakellariou, Rizos Rana, Omer Dustdar, Schahram Buyya, Rajkumar School of Electronic Engineering and Computer Science Queen Mary University of London United Kingdom Delhi India Faculty of Information Technology Monash University ClaytonVIC Australia Spain School of Computing and Communications Lancaster University United Kingdom School of Computer Science University of Birmingham Birmingham United Kingdom Department of Computer Science University of Western Ontario LondonON Canada School of Computer Science University of Manchester Oxford Road ManchesterUK United Kingdom School of Computer Science and Informatics Cardiff University CardiffUK United Kingdom Distributed Systems Group Vienna University of Technology Vienna Austria Laboratory School of Computing and Information Systems University of Melbourne Australia

Current cloud computing frameworks host millions of physical servers that utilize cloud computing resources in the form of different virtual machines. Cloud Data Center (CDC) infrastructures require significant amounts of energy to deliver large scale computational services. Moreover, computing nodes generate large volumes of heat, requiring cooling units in turn to eliminate the effect of this heat. Thus, overall energy consumption of the CDC increases tremendously for servers as well as for cooling units. However, current workload allocation policies do not take into account effect on temperature and it is challenging to simulate the thermal behaviour of CDCs. There is a need for a thermal-aware framework to simulate and model the behaviour of nodes and measure the important performance parameters which can be affected by its temperature. In this paper, we propose a lightweight framework, ThermoSim, for modelling and simulation of thermal-aware resource management for cloud computing environments. This work presents a Recurrent Neural Network based deep learning temperature predictor for CDCs which is utilized by ThermoSim for lightweight resource management in constrained cloud environments. ThermoSim extends the CloudSim toolkit helping to analyse the performance of various key parameters such as energy consumption, service level agreement violation rate, number of virtual machine migrations and temperature during the management of cloud resources for execution of workloads. Further, different energy-aware and thermal-aware resource management techniques are tested using the proposed ThermoSim framework in order to validate it against the existing framework (Thas). The experimental results demonstrate the proposed framework is capable of modelling and simulating the thermal behaviour of a CDC and ThermoSim framework is better than Thas in terms of energy consumption, cost, time, memory usage and prediction accuracy. Copyright © 2020, The Authors. All rights rese

关键词： Cloud computing

来源：评论

学校读者我要写书评

暂无评论

Green Channel and Top Hat based Image Enhancement for Diabetic Retinopathy Screening

引用

Journal of Physics: Conference Series 2021年第1期1997卷

作者： N A M Sharif A S M Azhar N H Harun J A Bakar A A Abdullah Y F Chong Data Science Res Lab School of Computing Universiti Utara Malaysia Kedah Malaysia School of Computing Universiti Utara Malaysia Kedah Malaysia Faculty of Electronic Engineering Technology Universiti Malaysia Perlis (UniMAP) Arau Perlis Malaysia Sports Engineering Research Centre (SERC) Universiti Malaysia Perlis 02600 Arau Perlis Malaysia.

Diabetic Retinopathy remains one of the most feared diabetes complications that could lead to blindness. Image processing techniques have been widely used all around the world for early detection of diabetic retinopathy. However, most techniques used do not focus on the low visual quality problems in the fundus image. Low visual quality of fundus image may lead to difficulty in evaluation by ophthalmologist before reading it out to the patients. Hence, Automated Screening for Diabetic Retinopathy was created to focus on image enhancement of the fundus image. In this study, two main algorithms for image processing have been used which are green channel conversion and top-hat filters. Green channel in fundus image is selected due to better contrast of the features and background compared to the red and blue channel. While Top-hat filter used to details out small features in the fundus image. The evaluation result of the techniques is compared by using Mean-Squared Error (MSE), Peak Signal-to-Noise Ratio (PSNR) and Entropy calculations to measure quality of the enhanced fundus images. Results of image enhancement techniques implemented has proved that quality of the fundus image is improved.

关键词：

来源：评论

学校读者我要写书评

暂无评论

A comparison of deep learning-based techniques for solving partial differential equations

引用

AIP Conference Proceedings 2024年第1期2905卷

作者： Rabiu Bashir Yunus Nooraini Zainuddin Afza Shafie Muhammad Izzatullah Samsul Ariffin Abdul Karim Fundamental and Applied Sciences Department Universiti Teknologi PETRONAS Bandar Seri Iskandar 32610 Seri Iskandar Perak Darul Ridzuan Malaysia King Abdullah University of Science and Technology (KAUST) Division of Physical Sciences and Engineering Bldg. 1 Desk #0202-WS023 Thuwal 23955-6900 Kingdom of Saudi Arabia Software Engineering Programme Faculty of Computing and Informatics Universiti Malaysia Sabah Jalan UMS 88400 Kota Kinabalu Sabah Malaysia Data Technologies and Applications (DaTA) Research Group Universiti Malaysia Sabah Jalan UMS 88400 Kota Kinabalu Sabah Malaysia

Obtaining the solutions of high-dimensional partial differential equations (PDEs) seems to be difficult by utilizing the classical numerical methods. Recently, deep neural networks (DNNs) techniques have received special attentions in solving high–dimensional problems in PDEs. In this study, our quest is to investigate some newly introduced data-driven deep learning-based approaches and compare their performance in terms of their efficiency and faster training towards high-dimensional PDEs. However, the comparison is carried out based on different activation functions, number of layers and gradient based optimizers. We consider some benchmark problems in our numerical experiments which includes Burgers equation, Diffusion-reaction equation and Allen-Cahn Equations.

关键词：

来源：评论

学校读者我要写书评

暂无评论

Evaluation of IPv6 transition mechanisms using QoS service policies 11

Evaluation of IPv6 transition mechanisms using QoS service p...

引用

11th International Symposium on Communication Systems, Networks and Digital Signal Processing, CSNDSP 2018

作者： Smith, Luke Jacobi, Mark Al-Khayatt, Samir Faculty of Art Computing Engineering and Science Sheffield Hallam University SheffieldS1 1WB United Kingdom

ISBN: (纸本)9781538613351

IPv6 networks are rarely fully IPv6 from end to end particularly when interconnected over other providers core data networks, hence the need for IPv6 transition methods or mechanisms. Previous studies have identified some potential impact on performance where transitions occur. This study considered implementations where Quality of Service (QoS) service policies have been applied in the IPv6 network to determine if the transition mechanisms were able to guarantee the same performance and level of service when the IPv6 traffic transitions over a core IPv4 network. Using a test lab, traffic generators and data capture tools the study was able to fully test the transition mechanisms using data rates and link speeds that replicated equipment and traffic levels used in real world implementations. The study showed that the Quality of Service classes applied were maintained across the transition network providing the service guarantees required for a range of traffic classes. Results indicated that the transition implementations on the devices used (Cisco ISR 4351) translate the QoS settings from IPv6 to IPV4 and vice versa were highly effective and with negligible additional impact on performance occurring due to the additional processing required. © 2018 IEEE.

关键词： Quality of service

来源：评论

学校读者我要写书评

暂无评论

Iris Recognition for Authentication: Development on a Lighter computing Platform

Iris Recognition for Authentication: Development on a Lighte...

引用

2018 International Conference on Computational Approach in Smart Systems Design and Applications, ICASSDA 2018

作者： Nugroho, Hermawan Hassan Al-Absi, Hamada Rasheed Shan, Lee Pei Center for Digital Future Faculty of Engineering Computing and Science Swinburne University of Technology Sarawak Campus Kuching Malaysia ON Semiconductor Seremban Malaysia

ISBN: (纸本)9781538683699

With the advancement of cyber technologies, currently many applications are run on the cyberspace which make our life easier. Since it is an open space, we need tools to ensure the security. Authentication provides a mean to verify authenticity of an entity against illegal access to a device or an application in cyberspace. Recent smartphones have already equipped with biometrics system (fingerprint and face recognition) which can be used for authentication. Iris recognition is identified as the most reliable biometrics traits. It however needs a substantial computing power. In this paper, we develop an iris recognition algorithm based on Fisher algorithm which can be run in a lighter computing platform. Experiments conducted with CASIA database shows exciting results where the system achieves a very high accuracy. © 2018 IEEE.

关键词： Authentication

来源：评论

学校读者我要写书评

暂无评论

An AI-based approach to detect cognitive impairment from digital voice recordings of neuropsychological assessments

引用

Alzheimer's & Dementia 2022年第S7期18卷

作者： Samad Amini Boran Hao Lifu Zhang Mengting Song Aman Gupta Cody Karjadi Vijaya B. Kolachalama Rhoda Au Ioannis Paschalidis Division of Systems Engineering Boston University Boston MA USA Department of Electrical & Computer Engineering Boston University Boston MA USA The Framingham Heart Study Boston University School of Medicine Boston MA USA Hariri Institute for Computing and Computational Science & Engineering Boston University Boston MA USA Boston University School of Medicine Boston MA USA Faculty of Computing & Data Sciences Boston University Boston MA USA The Framingham Heart Study Boston University School of Medicine Boston University School of Public Health Boston MA USA Department of Biomedical Engineering Boston University Boston MA USA

Background Reliable cognitive impairment screening tools that are easy to administer and minimally time consuming are greatly needed. Given the high sensitivity of neuropsychological (NP) exams in detection of cognitive decline, we seek to develop an automated screening tool to detect dementia and mild cognitive impairment (MCI) based on digital voice recordings of NP assessments. This could enable wide-spread screening for dementia and accelerate preventative efforts. Method We used natural language processing methods to create a screening tool that identifies different stages of dementia based on automated transcription of digital voice recordings. The transcribed sentences were classified into 8 main sub-tests including memory assessment, naming and language skill, verbal fluency, general questions, etc. Using the idea of transfer learning, we encoded the participants' sentences into quantitative data. This data and the participants’ demographic variables such as age, sex, Apoe gene, and education were employed to train and test three binary classification tasks, (I) Normal cognition versus Dementia, (II) Normal/MCI versus Dementia, and (III) Normal versus MCI. Result We evaluated the performance of the classification tasks using the digital voice recordings of NP assessments, collected from the Framingham Heart Study, containing 410 cognitively intact subjects, 387 MCI, and 287 subjects with dementia. The average Area Under the Curve (AUC) on the held-out test data reached 92.6%, 88.0%, and 74.4% for differentiating Normal from Dementia, Normal or MCI from Dementia, and Normal from MCI, respectively. Looking at the importance of the sub-tests in differentiating MCI from Normal, we note that general questions can be more useful for assessment of MCI, whereas verbal fluency would not be as useful in this task. Conclusion The proposed approach offers a fully automated identification of MCI and dementia based on a recorded NP test, providing an opportunity to develo

关键词：

来源：评论

学校读者我要写书评

暂无评论

Framework Design for Secured Local Cloud Data Query Processing Analysis

Framework Design for Secured Local Cloud Data Query Processi...

引用

International Conference on Cybersecurity (ICoCSec)

作者： Azana Hafizah Mohd Aman Zainalabideen Ali Rahemm Al-Mayyah Rosilah Hassan Aisha Hassan Abdalla Hashim Amjed Sid Ahmed Mohamed Sid Ahmed Mahdi Jubair Cyber Security Center Faculty of Information Science and Technology National University of Malaysia Malaysia ECE Department Kuliyyah of Engineering International Islamic University Malaysia Kuala Lumpur Malaysia Computing Department Engineering Faculty Global College of Engineering and Technology Muscat Oman

Cloud computing is a vastly growing technology that enables more users and organizations to transfer their services to the cloud. With the exploitation of public cloud computing infrastructures, the usage of clouds to provide data query services is becoming an attractive solution due to its numerous benefits on scalability and cost-minimizing. The cloud services especially the database-as-a-service have tended to encrypt sensitive data before the migration over the cloud. Encrypting data would facilitate protecting private information from any violation by the service provider. Several studies have addressed the handling of cloud query processing by providing approaches to maintain the privacy of the data stored within the cloud. During their studies, researchers have proposed different types of encryption methods, each encryption method provides a specific level of security which comes with an opposite level of efficiency. This research is focused on framework design to evaluate cloud data query processing locally using two encryption methods namely AES and RSA. The parameters chosen are time consumption for encryption and decryption along with secrecy or the strength of the encryption and decryption.

关键词：

来源：评论

学校读者我要写书评

暂无评论

Synthesis of hydrochar from empty fruit bunches (EFB) and oil palm trunks (OPT) Via wet torrefaction: A parametric study

引用

AIP Conference Proceedings 2024年第1期3073卷

作者： Frederick Jit Fook Phang Megan Soh Jiuan Jing Chew Aqsha Aqsha Deni Shidqi Khaerudini Gerald Ensang Timuda Bing Shen How Soh Kheang Loh Suzana Yusup Jaka Sunarso Research Centre for Sustainable Technologies Faculty of Engineering Computing and Science Swinburne University of Technology Jalan Simpang Tiga 93350 Kuching Sarawak Malaysia Department of Bioenergy and Chemurgy Faculty of Industrial Technology Institut Teknologi Bandung Bandung 40132 Jawa Barat Indonesia Research Center for Advanced Materials National Research and Innovation Agency (BRIN) Bld. 440 Kawasan Puspiptek Serpong South Tangerang 15314 Banten Indonesia Energy and Environmental Unit Engineering and Processing Division Malaysian Palm Oil Board 6 Persiaran Institusi Bandar Baru Bangi 43000 Kajang Selangor Malaysia Generation Unit (Fuel and Combustion Section) TNB Research Sdn. Bhd. No. 1 Kawasan Institusi Penyelidikan Jalan Ayer Hitam 43000 Kajang Selangor Malaysia

With the rising popularity of the oil palm industry driven by global demand, an abundance of oil palm biomass waste is expected. Wet torrefaction is an appealing method for converting oil palm biomass waste into fuel. Empty fruit bunches (EFB) with a particle size range of 0.5 to 1.0 mm were treated via wet torrefaction at 180 to 220 °C with 20 °C intervals and 3 to 9 hours with 3 hours intervals. The wet torrefied EFB, termed EFB hydrochar, was evaluated for its mass yield and higher heating value (HHV). The energy yield of the hydrochar was assessed as well, as it represents the fuel potential of the hydrochar. Ensuring that the mass yield is relatively high whilst HHV is favourable is desirable. The HHV of hydrochar increased with increasing temperature and residence times whilst mass yield followed an opposite trend. For instance, hydrochar obtained at 220 °C and 9 hours had the highest HHV and the lowest mass yield, at 23.74 MJ kg−1 and 42.86 wt. %, respectively. Despite its highest HHV, its energy yield was relatively low, at only 60 %. On the contrary, hydrochar obtained at 180 °C and 3 hours had the lowest HHV (17.37 MJ kg−1) among all treated EFB; however, it had the highest energy yield, at 76 %. The higher value of energy yield was due to its relatively higher mass yield when compared to the hydrochar with the highest HHV (i.e. 73.78 wt. % to 42.86 wt. %, respectively). Similar trends were observed for OPT hydrochar subjected to the same wet torrefaction condition. Nevertheless, EFB hydrochar generally had a higher mass yield and HHV, offering a higher energy yield than OPT hydrochar. This disparity is likely attributed to the higher lignin content in EFB compared to OPT.

关键词：

来源：评论

学校读者我要写书评

暂无评论

Radio tomographic imaging (RTI) and privacy implications 18

Radio tomographic imaging (RTI) and privacy implications

引用

18th International Conference on Advances in ICT for Emerging Regions, ICTer 2018

作者： Dharmadasa, M.A.I.M. Gamage, C.D. Keppitiyagama, C.I. Department of Computer Science and Engineering Faculty of Engineering University of Moratuwa Katubedda Sri Lanka Department of Computer Science and Engineering Faculty of Engineering University of Moratuwa Katubedda Sri Lanka University of Colombo School of Computing University of Colombo No 35 Reid Avenue Colombo 07 Sri Lanka

ISBN: (纸本)9781538673515

Radio Tomographic Imaging (RTI) is generally known to be a privacy-preserving localization method. This is due to the reason that RTI does not identify individuals uniquely but only the presence and movements of a human or an object. Most of the existing research on RTI are based on the Zigbee (IEEE 802.15.4) transceivers, but in the present day environments it can be observed that there is a high density of Wi-Fi (IEEE 802.11) networks in public spaces, hence there is a significant improvement of applications for Wi-Fi based localization methods. There are number of Wi-Fi based localization methods in the current literature, but there are limited comprehensive studies on privacy violations that can happen due to the localization methods that are using publicly available data from wireless networks. This research focuses on identifying possibilities of privacy implications that can occur due to Wi-Fi based RTI methods using publicly available Wi-Fi (Received Signal Strength Indicator) RSSI data. In the initial experiments of this research, it has shown that presence of a human in a closed environment can be identified using logistic regression model with a 100% of accuracy level and unique human identification can be done using a multinomial logistic regression model with a 96.3% - 100% accuracy level. These results indicate that there is a possibility of happening privacy implications due to the aforementioned methods. © 2018 IEEE.

关键词： Wi-Fi

来源：评论

学校读者我要写书评

暂无评论

Visible and Infrared Image Fusion Framework based on RetinaNet for Marine Environment

Visible and Infrared Image Fusion Framework based on RetinaN...

引用

International Conference on Information Fusion

作者： Fahimeh Farahnakian Jussi Poikonen Markus Laurinen Dimitrios Makris Jukka Heikkonen University of Turku Turku Finland Kongsberg Maritime Oy Ab Turku Finland Dimitrios Makris is with Faculty of Science Engineering annd Computing Kingston University London

ISBN: (数字)9780996452786

ISBN: (纸本)9781728118406

Safety and security are critical issues in maritime environment. Automatic and reliable object detection based on multi-sensor data fusion is one of the efficient way for improving these issues in intelligent systems. In this paper, we propose an early fusion framework to achieve a robust object detection. The framework firstly utilizes a fusion strategy to combine both visible and infrared images and generates fused images. The resulting fused images are then processed by a simple dense convolutional neural network based detector, RetinaNet, to predict multiple 2D box hypotheses and the infrared confidences. To evaluate the proposed framework, we collected a real marine dataset using a sensor system onboard a vessel in the Finnish archipelago. This system is used for developing autonomous vessels, and records data in a range of operation and climatic and light conditions. The experimental results show that the proposed fusion framework able to identify the interest of objects surrounding the vessel substantially better compared with the baseline approaches.

关键词： Object detection Detectors Cameras Image fusion Feature extraction Navigation Training

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：