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Cleo: Smart Glasses to Monitor Consumption of Alcohol and Cigarettes

SN computer science 2023年第1期4卷 53页

作者： Sethuraman, Sibi Chakkaravarthy Kompally, Pranav Mohanty, Saraju P. Raju, Bksp Kumar Cruz, Meenalosini Vimal School of Computer Science and Engineering and Center of Excellence in Artificial Intelligence and Robotics (AIR) VIT-AP University Amaravati India Department of Computer Science and Engineering University of North Texas Denton 76207 TX United States Department of Information Technology Allen E. Paulson College of Engineering and Computing Georgia Southern University Statesboro GA United States

It is estimated that over 60% of people around the globe consume alcohol and cigars daily. Many people use them beyond the permitted limit, which causes lung cancer, liver and kidney failure. If there is a system that could monitor their intake level, it will alert them in case of excess consumption, which could help them control the intake. To help the users monitor their consumption, we introduce Cleo Eyeglasses in this paper. Cleo is a wearable spectacle device with a mounted camera and single onboard computer that performs custom trained object recognition to identify alcoholic beverages and cigarettes. Upon recognition, a log is automatically maintained in the corresponding mobile application. If the intake exceeds the user threshold, then our system generates an alert to both the user and concerned medical personnel. Cigarette/alcohol consumption can immediately affect the body without noticeable symptoms. Cleo addresses this with keen monitoring of vital body parameters and generates an alert when abnormalities are detected. © 2022, The Author(s), under exclusive licence to Springer Nature Singapore Pte Ltd.

关键词： Alcohol monitoring CNN computer vision Deep learning Smart glasses Smoke monitoring

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基于Seed-PCG法的列车-轨道-地基土三维随机振动GPU并行计算方法

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Journal of Central South University 2024年第1期31卷 302-316页

作者：朱志辉冯杨杨啸李昊邹有 National Engineering Research Center of High-speed Railway Construction Technology Changsha 410075China School of Civil Engineering Central South UniversityChangsha 410075China School of Computer Science and Engineering Center South UniversityChangsha 410083China Information and Network Center Central South UniversityChangsha 410083China

为了解决列车-轨道-地基土三维有限元模型随机多样本计算效率低的问题,本文提出了一种基于Seed-PCG法的高效并行计算方法。基于有限元法和虚拟激励法建立轨道不平顺激励下的三维列车-轨道-地基土耦合随机振动分析模型;针对车致地基土随... 详细信息

为了解决列车-轨道-地基土三维有限元模型随机多样本计算效率低的问题,本文提出了一种基于Seed-PCG法的高效并行计算方法。基于有限元法和虚拟激励法建立轨道不平顺激励下的三维列车-轨道-地基土耦合随机振动分析模型;针对车致地基土随机振动分析产生的多右端项线性方程组求解问题,采用Seed-PCG方法进行求解。通过PCG方法求解种子系统得到的Krylov子空间进行投影,以改进其余线性方程组的初始解和对应的初始残量,有效提高了PCG法的收敛速度,最后,在MATLABCUDA混合平台上开发了并行计算程序。数值算例表明:相同计算平台下的该方法相比多点同步算法获得了104.2倍的加速;相比PCG法逐个求解方案减少了18%的迭代次数,获得了1.21倍的加速。

关键词： Seed-PCG法多右端项线性方程组随机振动 GPU并行计算列车-轨道-地基土耦合模型

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An Enhanced Authentication Protocol Suitable for Constrained RFID Systems

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IEEE Access 2024年 12卷 61610-61628页

作者： Hosseinzadeh, Mehdi Servati, Mohammad Reza Rahmani, Amir Masoud Safkhani, Masoumeh Lansky, Jan Janoscova, Renata Ahmed, Omed Hassan Tanveer, Jawad Lee, Sang-Woong Duy Tan University Institute of Research and Development Da Nang550000 Viet Nam Duy Tan University School of Medicine and Pharmacy Da Nang550000 Viet Nam Shahid Rajaee Teacher Training University Department of Computer Engineering Tehran16788-15811 Iran National Yunlin University of Science and Technology Future Technology Research Center Douliou Yunlin64002 Taiwan School of Computer Science Tehran19395-5746 Iran University of Finance and Administration Faculty of Economic Studies Department of Computer Science and Mathematics Prague101 00 Czech Republic University of Human Development Department of Information Technology Sulaymaniyah0778-6 Iraq Sejong University Department of Computer Science and Engineering Seoul05006 Korea Republic of Gachon University Pattern Recognition and Machine Learning Laboratory Department of Ai Software Seongnam-si13557 Korea Republic of

RFID technology offers an affordable and user-friendly solution for contactless identification of objects and individuals. However, the widespread adoption of RFID systems raises concerns regarding security and privacy. Vulnerabilities such as message tampering, interception, and eavesdropping pose significant risks to the integrity of the system. This study examines the effectiveness of two recently proposed ultra-lightweight RFID authentication protocols, URASP and KUAJB, in addressing these security challenges. Surprisingly, our findings reveal that both URASP and KUAJB protocols are susceptible to secret disclosure attacks, despite their claims of providing robust security. Consequently, the development of a truly ultra-lightweight protocol that ensures adequate security becomes a formidable task. As a potential solution, the enhancement of the URASP protocol through the integration of a simple cryptographic primitive is suggested to bolster its security measures. © 2013 IEEE.

关键词： Hash functions

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Smartphone-based colorimetric detection of formaldehyde in the air

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Building Simulation 2024年第11期17卷 2007-2015页

作者： Meng Yang Jin Ye Tao Yu Ying Song Hua Qian Tianyi Liu Yang Chen Junqi Wang Shi-jie Cao Cong Liu School of Energy and Environment Southeast UniversityNanjing210096China School of Energy and Power Jiangsu University of Science and TechnologyZhenjiangJiangsu212100China Wuhan Second Ship Design and Research Institute Wuhan430205China Hubei Provincial Engineering and Technology Research Center for Food Quality and Safety Test Hubei Provincial Institute for Food Supervision and TestWuhan430075China Laboratory of Image Science and Technology the School of Computer Science and EngineeringSoutheast UniversityNanjingChina Jiangsu Provincial Joint International Research Laboratory of Medical Information Processing Southeast UniversityNanjingChina School of Cyber Science and Engineering Southeast UniversityNanjingChina Key Laboratory of Computer Network and Information Integration Southeast UniversityMinistry of EducationNanjingChina School of Architecture Southeast UniversityNanjingChina Jiangsu Province Engineering Research Center of Urban Heat and Pollution Control Southeast UniversityNanjingChina Global Centre for Clean Air Research(GCARE) University of SurreyGuildfordUK

Adverse impacts of exposure to formaldehyde on human health significantly increases attention in monitoring formaldehyde concentrations in the *** formaldehyde detection methods typically rely on large and costly instruments and requires high skills of expertise,preventing it from being widely accessible to *** study introduced a novel approach utilizing smartphone-based colorimetric *** of green channel signals of digital images by a smartphone successfully capture variation of purple color of 4-amino-3-hydrazino-5-mercapto-1,2,4-triazol solution,which is proportional to formaldehyde *** is because that green and purple are complimentary color pairs.A calibration curve was established between green channel signals and formaldehyde concentrations,with a correlation coefficient of *** limit of the smartphone-based method is 0.008 mg/m^(3).Measurement errors decrease as formaldehyde concentrations increase,with median relative errors of 34%,17%,and 6%for concentration ranges of 0–0.06 mg/m^(3),0.06–0.12 mg/m^(3),and 0.12–0.35 mg/m^(3),*** method replaced scientific instrumentation with ordinary items,greatly reducing cost and operation *** would provide an opportunity to realize onsite measurements for formaldehyde by occupants themselves and increase awareness of air quality for better health protection.

关键词： indoor air quality VOC RGB color identification machine vision

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Continuous Authorization Architecture for Dynamic Trust Evaluation 1

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14th IFIP WG 11.11 International Conference on Trust Management, IFIPTM 2023

作者： Joumaa, Hussein Petrovska, Ana Hariri, Ali Dimitrakos, Theo Crispo, Bruno German Research Center Huawei Technologies Duesseldorf GmbH Munich Germany Department of Information Engineering and Computer Science University of Trento Trento Italy School of Computing University of Kent Canterbury United Kingdom

ISBN: (数字)9783031767142

ISBN: (纸本)9783031767135

The modern security paradigms emphasizing dynamic trust assessment and resilient access controls form the fundamental principles of Zero Trust Architecture. Within this architecture, the relationship between stakeholders is always untrusted and requires continuous assessment. This principle gains special attention in contemporary systems and applications, like Internet of Things (IoT) or cyber-physical systems, that are omnipresent and embedded in every aspect of our lives, which also, at the same time, are highly dynamic and uncertain. In response, users’ trust in the embedded devices fluctuates over time, necessitating dynamic adaptive mechanisms. Traditional access control models lack continuous monitoring, increasing interest in usage access control models that evaluate access in response to evolving attributes. This paper presents an innovative integration of a lifecycle-oriented Usage Control with a Trust Level Evaluation Engine (TLEE) within a Zero-Trust application. Continuously monitoring trust levels as a dynamic attribute is at the core of our strategy. Relying solely on this monitoring process, we facilitate a transition that enables access management, encompassing the possibilities of granting, withholding, or revoking access based on real-time trust evaluations. The proposed architecture implements a distinct separation between TLEE and the authorization engine, resulting in an adaptable and policy-independent framework. Through this integration, we aim to enhance the effectiveness of authorization mechanisms in evolving IoT landscapes like Smart Homes. Lastly, our approach presents a workflow featuring an example of a subjective logic-based TLEE. © IFIP International Federation for information Processing 2024.

关键词： Authorization

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Automatic Recognition of Construction Worker Activities Using Deep Learning Approaches and Wearable Inertial Sensors

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Intelligent Automation & Soft Computing 2023年第5期36卷 2111-2128页

作者： Sakorn Mekruksavanich Anuchit Jitpattanakul Department of Computer Engineering School of Information and Communication TechnologyUniversity of PhayaoPhayao56000Thailand Department of Mathematics Intelligent and Nonlinear Dynamic Innovations Research CenterFaculty of Applied ScienceKing Mongkut’s University of Technology North BangkokBangkok10800Thailand

The automated evaluation and analysis of employee behavior in an Industry 4.0-compliant manufacturingﬁrm are vital for the rapid and accurate diagnosis of work performance,particularly during the training of a new *** techniques for identifying and detecting worker performance in industrial applications are based on computer vision *** widespread com-puter vision-based approaches,it is challenging to develop technologies that assist the automated monitoring of worker actions at external working sites where cam-era deployment is *** the use of wearable inertial sensors,we propose a deep learning method for automatically recognizing the activities of construction *** suggested method incorporates a convolutional neural network,residual connection blocks,and multi-branch aggregate transformation modules for high-performance recognition of complicated activities such as con-struction worker *** proposed approach has been evaluated using standard performance measures,such as precision,F1-score,and AUC,using a publicly available benchmark dataset known as VTT-ConIoT,which contains genuine con-struction work *** addition,standard deep learning models(CNNs,RNNs,and hybrid models)were developed in different empirical circumstances to compare them to the proposed *** an average accuracy of 99.71%and an average F1-score of 99.71%,the experimentalﬁndings revealed that the suggested model could accurately recognize the actions of construction ***,we examined the impact of window size and sensor position on the identiﬁcation efﬁciency of the proposed method.

关键词： Complex human activity recognition wearable inertial sensors deep learning construction workers automatic recognition

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Deep CNN based brain tumor detection in intelligent systems

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International Journal of Intelligent networks 2024年第1期5卷 30-37页

作者： Gupta, Brij B. Gaurav, Akshat Arya, Varsha International Center for AI and Cyber Security Research and Innovations & Department of Computer Science and Information Engineering Asia University Taiwan Taichung413 Taiwan Ronin Institute MontclairNJ United States Department of Electrical and Computer Engineering Lebanese American University Beirut1102 Lebanon Symbiosis International University Pune India School of Computing Skyline University College P.O. Box 1797 Sharjah United Arab Emirates Department of Business Administration Asia University Taiwan Taiwan Dehradun India

The early detection of brain tumor is crucial for effective treatment and improved patient prognosis in Industrial information Systems. This research introduces a novel computational model employing a three-layer Convolutional Neural network (CNN) for the identification of brain tumors in Industrial information Systems. Leveraging advanced computational techniques, this proposed model can autonomously detect intricate patterns and features from medical imaging data, resulting in more accurate and expedited diagnoses. With an impressive 90 % precision rate, our model demonstrates the potential to serve as a valuable tool for medical professionals working in the field of neuroimaging. By presenting a dependable and precise computational model, this study contributes to the advancement of brain tumor identification within the domain of medical imaging. We anticipate that our methodology will aid healthcare providers in making more accurate diagnoses, thereby leading to enhanced patient outcomes. Potential avenues for future research encompass refining the model's fundamental architecture and exploring real-time therapeutic applications. © 2024 The Authors

关键词： Deep learning

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Human Blood Cell (Haematologicalimages) Recognition by Applying Deep Convolution Methods 9

Human Blood Cell (Haematologicalimages) Recognition by Apply...

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9th IEEE International Conference for Convergence in Technology, I2CT 2024

作者： Krishna Kumar, N. Sarada, Ch. Mallika, S. Suguna Subhashini, P. Sai Kumar, K. Zearah, Sajad Ali Gnitc Department of Cse Hyderabad India Cvr College of Engineering Department of Cse Telangana Hyderabad India Mlr Institute of Technology Department of Computer Science And Information Technology Telangana Hyderabad India Malla Reddy University School of Engineering Computer Science Dulapally Maisammaguda Telangana Hyderabad500100 India Al-Ayen University Scientific Research Center Thi-Qar Iraq

ISBN: (纸本)9798350394474

Utilizing Deep Convolution Strategies to Perceive Human Platelets (Hematological Pictures): Deep Learning is rapidly becoming seen as a preferable method for learning over conventional ML models. This is on the grounds that it has previously been demonstrated to have the option to manage continuous issues in numerous application regions. Convolutional neural networks (CNNs), a sort of deep learning calculation, have a few advantages in the field of well-being, where a ton of pictures should be handled and inspected. The objective of the review is to make a deep learning model to take care of the issue of placing platelets into gatherings. It is one of the hardest things to sort out while checking blood out. A CNN-based framework has been made that consequently sorts pictures of platelets into various kinds of cells, like MONOCYTE, LYMPHOCYTE, NEUTROPHIL, and EOSINOPHIL. To further develop CNN innovation, specialists have investigated thoughts like the utilization of numerous initiation and misfortune capabilities, regularization, boundary advancement, and plan development. Be that as it may, changes to CNN's design have made it feasible for it to show much more. A block of layers can likewise be utilized as a structure block. This concentrates first glances at the inward scientific classifications of recently declared deep CNN frameworks. This is done so that new CNN configuration patterns can be placed into classes. The seven regions are channel gain, utilization of room, utilization of element maps, multipath, profundity, width, and consideration. Overall information on CNN's parts, its ongoing issues, and how it tends to be utilized is likewise given. © 2024 IEEE.

关键词： Platelets

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Icon-to-Icon Translation using Pixel-Level Weight Adversarial networks

Journal of Network Intelligence

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Journal of network Intelligence 2022年第1期7卷 147-160页

作者： Pan, Yao-Ming Lin, Kai-Biao Chen, Chin-Ling School of Computer and Information Engineering Xiamen University of Technology Xiamen361024 China Engineering Research Center of Big Data Application in Private Health Medicine Fujian Provincial University Putian351100 China School of Information Engineering Changchun Sci-Tech University Changchun130600 China Department of Computer Science and Information Engineering Chaoyang University of Technology Taichung41349 Taiwan

We investigate image-to-image translation as a solution to icon-to-icon translation problems. Icon-to-icon translation aims to translate the input icon to appear like the target domain’s icons and simultaneously preserve the original icon’s information (e.g., application logo, text and background color). We present a novel approach to trans-lating icons to the version with the target style intelligently. By observing many icons with various styles, we propose a pixel-level weight matrix to calculating the weighted losses. These losses guide the generator to focus on the critical regions of the icon. People can clearly get the original icon’s information from the proposed method’s results, while the existing state-of-the-art image-to-image translation methods may fail to do this. © 2022, Taiwan Ubiquitous information CO LTD. All rights reserved.

关键词： Pixels

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Characterizing Memory Access Patterns of Various Convolutional Neural networks for Utilizing Processing-in-Memory

Characterizing Memory Access Patterns of Various Convolution...

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2023 International Conference on Electronics, information, and Communication, ICEIC 2023

作者： Jang, Jihoon Kim, Hyun Lee, Hyokeun Research Center for Electrical and Information Technology Seoul National University of Science and Technology Department of Electrical and Information Engineering Seoul Korea Republic of Inter-University Semiconductor Research Center Seoul National University Department of Electrical and Computer Engineering Seoul Korea Republic of

ISBN: (纸本)9798350320213

Convolutional neural network (CNN) models require deeper networks and more training data for better performance, which in turn results in greater computational and memory requirements. In this paper, we analyze the memory access patterns that occur in main memory during the training processes of various CNN models. CNN training is a linear procedure consisting of a forward pass (FP) and a backward pass (BP). As a result of the analysis, we found that BP accounted for 83.4% of the total main memory accesses on average. Therefore, CNN training including FP and BP is much more memory-intensive than CNN inference using only FP. This demonstrates that CNN training is a suitable application for near-data processing to reduce memory bottlenecks and conserve computational resources. © 2023 IEEE.

关键词： Convolutional Neural networks Memory access pattern Near data processing network training Processing-in-Memory

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