检索结果-内蒙古大学图书馆

29th IEEE International Conference on Parallel and Distributed Systems, ICPADS 2023

作者： Lee, Chao-Yu Hong, Ding-Yong Liu, Pangfeng Wu, Jan-Jan Academia Sinica Institute of Information Science Taipei Taiwan National Taiwan University Department of Computer Science and Information Engineering Taipei Taiwan National Taiwan University The Graduate Institute of Networking and Multimedia Taipei Taiwan

ISBN: (纸本)9798350330717

In recent years, container technology has gained significant attention in the software industry, with many businesses opting for its elasticity, cost-effectiveness, and ease of implementation. The most common way to ensure a seamless user experience is to keep a substantial number of containers active throughout the day, which causes resource over-provision. Conversely, closing the container right after handling the requests can reduce memory consumption but generate a cold start whenever the request arrives. Cold start occurrence and resource usage is a trade-off and presents a significant challenge on the container *** address this challenge, we observe that serving consecutive requests with the same container can notably decrease the number of cold starts. We propose TAC, a Temporal Adjacency Function Clustering algorithm, to meet the challenge. TAC selects the functions with time adjacency requests into a cluster from the historical data. TAC packs functions serving time adjacency requests into a cluster to reduce cold starts and enable efficient resource utilization. The experiment result shows that TAC reduces 8% cold start occurrences and 53% memory usage with the real-world traces compared to the state-of-the-art methods, e.g., Defuse [1] and Hybrid histogram policy [2]. © 2023 IEEE.

关键词： cold-start container platform Function-as-a-Service serverless

来源：评论

学校读者我要写书评

暂无评论

Investigating Positive and Negative Qualities of Human-in-the-Loop Optimization for Designing Interaction Techniques 22

Investigating Positive and Negative Qualities of Human-in-th...

引用

2022 CHI Conference on Human Factors in Computing Systems, CHI 2022

作者： Chan, Liwei Liao, Yi-Chi Mo, George B Dudley, John J Cheng, Chun-Lien Kristensson, Per Ola Oulasvirta, Antti Department of Computer Science National Yang Ming Chiao Tung University Taiwan Department of Communications and Networking Aalto University Finland Department of Engineering University of Cambridge United Kingdom

ISBN: (纸本)9781450391573

Designers reportedly struggle with design optimization tasks where they are asked to find a combination of design parameters that maximizes a given set of objectives. In HCI, design optimization problems are often exceedingly complex, involving multiple objectives and expensive empirical evaluations. Model-based computational design algorithms assist designers by generating design examples during design, however they assume a model of the interaction domain. Black box methods for assistance, on the other hand, can work with any design problem. However, virtually all empirical studies of this human-in-the-loop approach have been carried out by either researchers or end-users. The question stands out if such methods can help designers in realistic tasks. In this paper, we study Bayesian optimization as an algorithmic method to guide the design optimization process. It operates by proposing to a designer which design candidate to try next, given previous observations. We report observations from a comparative study with 40 novice designers who were tasked to optimize a complex 3D touch interaction technique. The optimizer helped designers explore larger proportions of the design space and arrive at a better solution, however they reported lower agency and expressiveness. Designers guided by an optimizer reported lower mental effort but also felt less creative and less in charge of the progress. We conclude that human-in-the-loop optimization can support novice designers in cases where agency is not critical. © 2022 ACM.

关键词： Multiobjective optimization

来源：评论

学校读者我要写书评

暂无评论

Improved Security for Cloud Storage Using Elgamal Algorithms Authentication Key Validation 2

Improved Security for Cloud Storage Using Elgamal Algorithms...

引用

2nd International Conference for Advancement in Technology, ICONAT 2023

作者： Narayanan, K. Lakshmi Naresh, R. SRM Institute of Science and Technology Kattankulathur Department of Computer Science and Engineering Chennai Tamilnadu Chengalpattu603 203 India SRM Institute of Science and Technology Kattankulathur Department of Networking and Communications Chennai Tamilnadu Chengalpattu603 203 India

ISBN: (纸本)9781665475174

Cloud computing is a newly developing concept in the area of computer technology. In one data centre, the data has traditionally been kept and managed. However, cloud computing nowadays offers computation as a service. It has the benefits of optimization, dependability, superior efficiency, and being a comparatively low-cost feasible solution compared to dedicated infrastructures. It expected to grow in the future. In this case, the system administrator will first encrypt the usable index using the ElGamal cryptosystem, which will then use to protect the data. Therefore, depending on the service, extensive data transferred over the network and this enormous amount of data retained in cloud service centers. The result introduces a new method of encrypting data known as batch key generation, which is a unique idea since it enables certain operations to done on the encrypted files, it is used to analysis of security and in which two parties exchange a large number of keys to enhance security. ElGamal variants divided into two kinds based on the method of creating signatures used in producing them. Afterwards, we build a general process that converts a collection of ElGamal variations into their threshold versions, and we demonstrate that the schemes we construct are unforgeable. In this article, we present a key encryption method for enhancing the privacy and security of information kept in the cloud while also reducing storage costs. © 2023 IEEE.

关键词： Cloud storage

来源：评论

学校读者我要写书评

暂无评论

Design of an Air Pollution Monitoring System Based on a Low-Cost Sensor Node

Design of an Air Pollution Monitoring System Based on a Low-...

引用

2024 IEEE International Workshop on Metrology for Living Environment, MetroLivEnv 2024

作者： Guerron, Rocio-Alejandra Carni, Domenico-Luca Lanuzza, Marco D'Amore, Francesco Bencardino, Mariantonia Lamonaca, Francesco Taco, Ramiro University of Calabria Department of Computer Engineering Modeling Electronics and Systems Rende Italy Institute for High Performance Computing and Networking Italian National Research Council Rende Italy Institute of Atmospheric Pollution Research Italian National Research Council Rende Italy

ISBN: (纸本)9798350385014

Air quality influences the life of the living beings that inhabit planet Earth. High levels of air pollutants in ambient air are in fact able to affect human health and also ecosystem integrity. Their monitoring allows for regulating the most appropriate air quality indices and verifying whether they are below the established harmful levels. However, this is a task that requires many physical and economic resources. For this reason, the research activity herein presented proposes a low-cost system, which is able to qualitatively evaluate the meteorological conditions and the main pollutants that affect a certain area. Through this proposal, the behavior of Alphasense's electrochemical gas low-cost sensors is studied. Their response to the concentrations of the main air pollutants regulated by the relevant European Directive (2008/50/CE), such as Carbon Monoxide, Nitrogen Dioxide, Ozone, and Sulfur Dioxide, is characterized and analyzed under various environmental conditions of Temperature and Relative Humidity and as a function of the electrical current signal obtained by the chemistry of the sensor. © 2024 IEEE.

关键词： Air quality

来源：评论

学校读者我要写书评

暂无评论

QoS-aware Resource Allocation for Healthcare Data Transmission using D2D Communication in NB-IoT

QoS-aware Resource Allocation for Healthcare Data Transmissi...

引用

International Conference on Advanced Communication Technology (ICACT)

作者： Nahar Sultana Farhana Huq Md. Abdur Razzaque Md. Mustafizur Rahman Department of Computer Science and Engineering Green Networking Research Group University of Dhaka Bangladesh

The deployment of Narrowband Internet of Things (NB-IoT) for the transmission of healthcare data has recently gained special attention. Existing works in the literature are limited by handling heterogeneous data in an inefficient way due to lack of prioritization of health events. In this work, we investigate the use of Device to Device (D2D) integrated NB- IoT to ensure the Quality of service (QoS) while allocating radio resources to maximize the data rate of an application’s urgency in smart healthcare. We specifically create a novel system with a two-tier structure for prioritized health data transfer across cellular user equipment (CUE), D2D, and eNB to increase throughput and develop an optimization framework using multi- objective non-integer linear programming (MNILP) for fair resource distribution among the participants. Performance analysis results demonstrate that the suggested resource allocation results in a higher percentage of data delivery ratio and offers a significantly decreased data delivery delay.

关键词： Performance evaluation Simulation Medical services Quality of service Throughput Device-to-device communication Delays

来源：评论

学校读者我要写书评

暂无评论

Smart Waste Collecting Robot Integration With IoT and Machine Learning 2

Smart Waste Collecting Robot Integration With IoT and Machin...

引用

2nd International Conference on Smart Technologies for Smart Nation, SmartTechCon 2023

作者： Srinivasan, S. Jambulingam, Umamagewaran Purushothaman, K.E. Jegan Amarnath, J. Department of Biomedical Engineering Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Saveetha University Tamil Nadu Chennai India Department of Networking and Communications School of Computing College of Engineering and Technology SRM Institute of Science and Technology Kattankulathur Tamil Nadu Chengalpattu India College of Computer Science King Khalid University Saudi Arabia Department of Computer Science and Engineering Kammavari Sangham Institute of Technology Karnataka Bangalore India

ISBN: (纸本)9798350305418

Non-biodegradable waste collecting from a wide area is a time-consuming process. The study aims to create an intelligent robot that can independently collect waste utilizing innovative technology. The study uses the Internet of Things (IoT) to connect and manage the various robot parts. A Raspberry Pi organizes many functions as the system's central processing unit. Real-time visual data, essential for waste detection and identification, is captured by a camera module. Ultrasonic sensors identify obstacles and measure distances so the robot can navigate its environment. The sensor helps the robot avoid collisions and navigate the predetermined space effectively. The robot's robotic arm, which enables it to pick up and collect waste materials physically, is one of its primary features. The Raspberry Pi controls the arm, allowing for accurate and synchronized motions for waste collection. They use machine learning methods to facilitate waste identification and categorization, especially a CNN model. To reliably identify various forms of waste, the CNN model is trained using a collection of waste images. These technologies may be combined to enable the automated waste-collected robot to function independently while effectively locating and collecting rubbish. By seamlessly integrating IoT, machine learning, and robots, this solution helps to save human labor and enhance waste management procedures. © 2023 IEEE.

关键词： Convolutional Neural Network (CNN) Environmental sustainability Image processing Internet of Things (IoT) Waste Collecting Robot

来源：评论

学校读者我要写书评

暂无评论

Combined optimization strategy for IoT resource allocation with workload prediction

引用

Sustainable Computing: Informatics and Systems 2025年 47卷

作者： Sasikala, B. Kalaiselvi, K. Senthil Murugan, V. Research Scholar Department of Networking and Communication SRM Institute of Science and Technology Kattankulathur Tamil Nadu Chennai603203 India Department of Networking and Communication Faculty of Engineering and Technology SRM Institute of Science and Technology Kattankulathur Tamil Nadu Chennai603203 India Department of Computer Science and Engineering School of Engineering and Technology CMR University Karnataka Bengaluru India

A significant limitation in IoT technology is the challenge of handling the diverse and dynamic nature of IoT workloads, which complicates accurate workload prediction and efficient resource allocation. IoT devices generate vast amounts of heterogeneous data with varying speeds, volumes, and varieties, making traditional methods inadequate for managing this variability and leading to inefficient resource management, suboptimal performance, and increased operational costs. To address these issues, this research proposes a novel hybrid optimization algorithm known as the Lyrebird-Adapted Kookaburra Optimization Algorithm-Improved Analytic Hierarchy Process (LAKO-IAHP) for work load prediction and resource allocation. This approach includes two main phases: the Improved Analytic Hierarchy Process (IAHP) for workload prediction and the LAKO algorithm for resource allocation. The IAHP phase enhances conventional Analytic Hierarchy Process (AHP) techniques by incorporating the Improved k-means clustering (IKMC) process and Euclidean distance calculations to improve the accuracy of workload predictions by considering specific Load Balancing (LB) parameters such as server load and response time. Following this, the LAKO algorithm- an advanced hybrid method combining Kookaburra Optimization Algorithm (KOA) and Lyrebird Optimization Algorithm (LOA)- performs the resource allocation phase, that considers the Quality of Service (QoS) parameters including degree of imbalance, execution time, reliability, and resource utilization. The effectiveness of the LAKO-IAHP approach is demonstrated through various performance metrics and comparisons with existing methods, proving its capability to enhance resource management and maintain high performance and reliability in IoT environments. © 2025

关键词： Resource allocation

来源：评论

学校读者我要写书评

暂无评论

Monitoring Oceanographic and Cryosphere Changes: A Remote Sensing Approach to Climate-Induced Marine and Polar Dynamics

引用

Remote Sensing in Earth Systems Sciences 2025年第2期8卷 496-509页

作者： Shaik, Reddi Khasim Jeslet, D. Santhi Aroulanandam, Vijay Vasanth Siva, B.A. Painam, Ranjith Kumar Thiagarajan, R. Department of Electrical and Electronics Engineering Vishnu Institute of Technology Bhimavaram India Department of Computer Science M.G.R. College Hosur India Department of Networking and Communications School of Computing SRM Institute of Science and Technology Chennai Kattakalathur India Department of ECE Chennai Institute of Tech Chennai India Department of Electronics and Communication Engineering Kallam Haranadhareddy Institute of Technology (Autonomous) Guntur Chowdavaram India Department of Information Technology Vel Tech Multi Tech Dr.Rangarajan Dr.Sakunthala Engineering College Chennai-62 Avadi India

Climate change has accelerated the dynamics of cryospheric and oceanographic systems, leading to rising sea levels, increased ice melt, and significant shifts in polar regions. Monitoring these changes is crucial, yet traditional observational methods are often impractical in remote, harsh environments. This study presents a novel approach to monitoring oceanographic and cryospheric changes induced by climate dynamics, using a cascaded long short-term memory (LSTM) and recurrent neural network (RNN) model with ICESat and supplementary remote sensing data. The model leverages ICESat’s high-resolution laser altimetry for precise measurements of ice thickness and ice sheet elevation, combined with data from MODIS, Sentinel-1, and CryoSat-2 for comprehensive insights. Our results indicate that the cascaded LSTM-RNN model achieves high predictive accuracy, with root mean square error (RMSE) values as low as 0.08 m for ice thickness and a mean absolute error (MAE) of 0.05 m. Additionally, it attains a correlation coefficient of 0.96, significantly outperforming other models like standalone LSTM (RMSE 0.12 m, MAE 0.09 m) and CNN-LSTM hybrid (RMSE 0.10 m, MAE 0.08 m). This model captures both seasonal and long-term trends in ice dynamics, highlighting its utility for robust climate monitoring. The successful integration of ICESat and other remote sensing data demonstrates the potential for advanced machine learning techniques to enhance predictions of polar and oceanographic changes, supporting climate adaptation efforts. © The Author(s), under exclusive licence to Springer Nature Switzerland AG 2025.

关键词： Climate change Cryosphere Ice thickness ICESat LSTM-RNN Machine learning Oceanographic changes Predictive modeling Remote sensing Sea ice extent

来源：评论

学校读者我要写书评

暂无评论

Automatic Detection and Characterization of Human Veins Using Infra-Red Image Processing

引用

Journal of computer and Communications 2024年第9期12卷 141-159页

作者： Jean Ndoumbe Brice Ekobo Akoa Gaelle Patricia Talotsing Frederic Franck Kounga Samuel Kaissassou Bertin Chouanmo Njo Laboratory of Computer Engineering Data Science and Artificial Intelligence Department of Computer & Telecommunications Engineering National Higher Polytechnic School Douala Cameroon Laboratory of Electrical Engineering Mechatronics and Signal Processing Department of Electrical & Telecommunications Engineering National Advanced School of Engineering Yaound Cameroon Mobile Computing and Networking Research Laboratory Department of Computer and Software Engineering Polytechnique Montreal Montreal Canada DigiPlus SARL Bonamoussadi Douala Cameroon

The detection and characterization of human veins using infrared (IR) image processing have gained significant attention due to its potential applications in biometric identification, medical diagnostics, and vein-based authentication systems. This paper presents a low-cost approach for automatic detection and characterization of human veins from IR images. The proposed method uses image processing techniques including segmentation, feature extraction, and, pattern recognition algorithms. Initially, the IR images are preprocessed to enhance vein structures and reduce noise. Subsequently, a CLAHE algorithm is employed to extract vein regions based on their unique IR absorption properties. Features such as vein thickness, orientation, and branching patterns are extracted using mathematical morphology and directional filters. Finally, a classification framework is implemented to categorize veins and distinguish them from surrounding tissues or artifacts. A setup based on Raspberry Pi was used. Experimental results of IR images demonstrate the effectiveness and robustness of the proposed approach in accurately detecting and characterizing human. The developed system shows promising for integration into applications requiring reliable and secure identification based on vein patterns. Our work provides an effective and low-cost solution for nursing staff in low and middle-income countries to perform a safe and accurate venipuncture.

关键词： Vein Detection Blood Radiation Infrared Image CLAHE Algorithm Raspberry Pi

来源：评论

学校读者我要写书评

暂无评论

Denoising Techniques in Healthcare Data for Improving Accuracy in Medical Applications - A Survey 7

Denoising Techniques in Healthcare Data for Improving Accura...

引用

7th International Conference on Communication and Electronics Systems, ICCES 2022

作者： Karthikram, A. Saravanan, M. Srm Institute of Science and Technology Department of Computer Science and Engineering Chennai Kattankulathur India Srm Institute of Science and Technology Department of Networking and Communications Chennai Kattankulathur India

ISBN: (数字)9781665496346

ISBN: (纸本)9781665496346

Bigdata alludes to a huge assortment of information that keeps on expanding dramatically over the long run. Big data sources in health care incorporate patient medical records, clinic records, clinical test results, and data gathered by medical services testing machines. The main challenge associated with the health care data is the lack of accuracy, medical errors. This can be overcome by using big data so that the health care data can be denoised and accurate data can be obtained. This helps to enhance the efficiency of diagnosis of diseases. This review paper deals with various algorithms and denoising techniques which are used to enhance the accuracy of obtained health care data. © 2022 IEEE.

关键词： Big data

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：