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作者： Huang, Xing Pan, Youlin Chen, Zhen Guo, Wenzhong Wang, Lu Li, Qingshan Wille, Robert Ho, Tsung-Yi Schlichtmann, Ulf Technical University of Munich Electronic Design Automation Munich80333 Germany Fuzhou University College of Computer and Data Science Fuzhou350116 China Xidian University School of Computer Science and Technology Xi'an710071 China Technical University of Munich Chair for Design Automation Munich80333 Germany Software Competence Center Hagenberg GmbH Hagenberg4232 Austria The Chinese University of Hong Kong Department of Computer Science and Engineering Hong Kong

Owing to the high complexity of chip architecture and assay protocol, considerable effort has been directed toward the design automation of continuous-flow microfluidics over the past decade. Existing methods, however, perform the corresponding design tasks, including binding, scheduling, placement, and routing separately, leading to serious gaps between different steps and potentially even cause design failure. To overcome these drawbacks, in this article, we propose a one-pass design paradigm for continuous-flow microfluidic lab-on-a-chip systems, integrating all the design steps into an 'organic whole,' which has never been considered in prior work. With the proposed paradigm, all the design tasks can be synchronized seamlessly and performed in a combined manner, thereby eliminating the gaps between design steps. Consequently, optimized biochip architectures can be generated without any design adjustments and modifications. The experimental results demonstrate the effectiveness of the proposed automation flows. © 1982-2012 IEEE.

关键词： Microfluidics

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Strategies for Data Security and Privacy Protection in Cloud Infrastructure

Strategies for Data Security and Privacy Protection in Cloud...

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Blockchain and Distributed Systems Security (ICBDS), IEEE International Conference on

作者： Namrata Naikwade Shafi Pathan Dept. of Computer Science & Engineering MIT Art Design and Technology University Pune India

ISBN: (数字)9798350354348

ISBN: (纸本)9798350354355

In today's rapidly growing digital era, cloud computing has emerged as an important technology, providing the ability to scale resources efficiently, accessibility, and cost-effectiveness for storing and processing huge amounts of data. However, alongside its numerous benefits, the adoption of cloud computing has also introduced deep concerns regarding data protection and privacy. The current scenario of cloud computing is characterized by increase in usage of cloud services and an exponential growth of data. While organizations leverage the cloud to enhance agility and innovation, the data sharing ability of cloud and the outsourcing of data management raise significant challenges for ensuring the privacy, integrity, and availability of sensitive data. Moreover, the rising cyber threats, regulatory compliance requirements, and the possibility for unauthorized access bring out the critical importance of robust data protection mechanisms in the cloud *** paper presents a comprehensive survey of strategies for data protection and identity protection in the context of cloud based systems. It examines encryption techniques, access control mechanisms, identity management solutions, among other approaches, highlighting their strengths, limitations, and applicability in different cloud deployment models and use cases. Furthermore, the paper discusses emerging trends such as homomorphic encryption, Attributes Based Encryption (ABE) Schemes and Identity Based Encryption (IBE), which hold promise for enhancing data protection in the developing landscape of cloud technology.

关键词： Surveys Access control Cloud computing Technological innovation Data security Data protection Encryption Protection Usability Secure storage

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Differential Evolution-Boosted Sine Cosine Golden Eagle Optimizer with Lévy Flight

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Journal of Bionic engineering 2022年第6期19卷 1850-1885页

作者： Gang Hu Liuxin Chen Xupeng Wang Guo Wei Department of Applied Mathematics Xi’an University of TechnologyXi’an710054People’s Republic of China School of Computer Science and Engineering Xi’an University of TechnologyXi’an710048People’s Republic of China School of Art and Design Xi’an University of TechnologyXi’an710054China Department of Mathematics and Computer Science University of North Carolina at PembrokePembrokeNC28372USA

Golden eagle optimizer(GEO)is a recently introduced nature-inspired metaheuristic algorithm,which simulates the spiral hunting behavior of golden eagles in ***,the GEO suffers from the challenges of low diversity,slow iteration speed,and stagnation in local optimization when dealing with complicated optimization *** ameliorate these deficiencies,an improved hybrid GEO called IGEO,combined with Lévy flight,sine cosine algorithm and differential evolution(DE)strategy,is developed in this *** Lévy flight strategy is introduced into the initial stage to increase the diversity of the golden eagle population and make the initial population more abundant;meanwhile,the sine-cosine function can enhance the exploration ability of GEO and decrease the possibility of GEO falling into the local ***,the DE strategy is used in the exploration and exploitation stage to improve accuracy and convergence speed of ***,the superiority of the presented IGEO are comprehensively verified by comparing GEO and several state-of-the-art algorithms using(1)the CEC 2017 and CEC 2019 benchmark functions and(2)5 real-world engineering problems *** comparison results demonstrate that the proposed IGEO is a powerful and attractive alternative for solving engineering optimization problems.

关键词： Golden eagle optimizer Lévy flight Sine cosine algorithm Differential evolution strategy engineering design Bionic model

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A System for Real-Time Shoulder Angle Measurement Using computer Vision 4

A System for Real-Time Shoulder Angle Measurement Using Comp...

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4th International Conference on Ubiquitous Computing and Intelligent Information Systems, ICUIS 2024

作者： Langote, Meher Saratkar, Saniya Puri, Chetan Datta Meghe Institute of Higher Education and Research Faculty of Engineering and Technology Department of Artificial Intelligence and Data Science Maharashtra Wardha India Datta Meghe Higher Education and Research Faculty of Engineering and Technology Department of Artificial Intelligence and Data Science Maharashtra Wardha India Datta Meghe Higher Education and Research Faculty of Engineering and Technology Department of Computer Science Design Maharashtra Wardha India

ISBN: (纸本)9798331529635

Recently, angle measurements have been performed using a goniometer, but the complex motion of shoulder movement has made these measurements intricate. The angle of rotation of the shoulder is particularly difficult to measure from an upright position because of the complicated base and moving axes. This article presents a real-time shoulder angle measurement system utilizing computer vision and machine learning techniques, specifically designed for the rehabilitation of frozen shoulder and related conditions. The system leverages MediaPipe, an open-source framework, to capture and analyze shoulder movements accurately. With its modular architecture, the system processes video feeds in real time, detecting key body points and calculating shoulder angles, thereby providing immediate feedback to patients and therapists. The study evaluates the system's effectiveness in clinical and home-based environments, highlighting its potential to enhance physiotherapy by enabling precise, data-driven treatment plans. This research study also identifies current research gaps, proposes solutions, and explores future enhancements, including environmental adaptation, mobile integration, and the extension to other joints. This study has attempted to estimate the shoulder joint internal/external rotation angle using the combination of pose estimation artificial intelligence (AI), computer vision, and a machine learning mode. © 2024 IEEE.

关键词： Goniometers

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I-Card: A Generative AI-Supported Intelligent design Method Card Deck 25

I-Card: A Generative AI-Supported Intelligent Design Method ...

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2025 CHI Conference on Human Factors in Computing Systems, CHI 2025

作者： Chen, Liuqing Cheang, Wengteng Jiang, Zhaojun Xu, Yuan Cai, Zebin Sun, Lingyun Childs, Peter Han, Ji Hansen, Preben Zuo, Haoyu College of Computer Science and Technology Zhejiang University Zhejiang Hangzhou China School of Computer Science and Engineering Central South University Hunan Changsha China International Design Institute Zhejiang University Zhejiang Hangzhou China Dyson School of Design Engineering Imperial College London London United Kingdom Department of Innovation Technology and Entrepreneurship University of Exeter Exeter United Kingdom Dept of Computer and Systems Sciences Stockholm University Kista Sweden

ISBN: (纸本)9798400713941

A design method card deck helps designers understand and provoke thinking by presenting each method in a simple format and allow designers to switch between methods seamlessly by maintaining the same simple format across the deck. However, recent observations have shown designers hesitate to use a card deck due to the lack of support, while other tools have provided identified support with generative AI. Through a formative study, we identified the specific support designers need when applying the design method cards and intentions in integrating generative AI. Accordingly, we developed the intelligent design method card deck, I-Card, which integrates generative AI to provide applicable design methods, design knowledge and data support, and interactive and dynamic support. A user study demonstrates that I-Card improved the design efficiency and applicability by offering personalized guidance, enhanced decision-making with comprehensive data generation and provided more design inspiration via interactive support. © 2025 Copyright held by the owner/author(s).

关键词： Product design

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Supply Chain Management Using Internet of Things and Blockchain for Smart Meat Packaging

Supply Chain Management Using Internet of Things and Blockch...

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Data Intelligence and Cognitive Informatics (ICDICI), International Conference on

作者： KanwalPreet Kour S Kanwal Deep Singh Malvinder Singh Bali G S Pradeep Ghantasala Rajesh Sharma R U Ananthanagu School of Computer Science and Engineering Manipal University Jaipur India Department of Computer Science Federation University Australia Department of Data Science and Engineering Manipal University Jaipur Rajasthan India Department of Computer Science and Engineering Alliance College of Engineering and Design Alliance University Bengaluru India

ISBN: (数字)9798350389609

ISBN: (纸本)9798350389616

In recent times, blockchain has evolved the security of traditional supply chain systems. Different issues of supply chain management like flexibility and reliability can be easily addressed using blockchain. Meat Packing Industry involves the complete process of processing, packaging and supplying the meat and its products throughout the world for human consumption. The animals used for the meat supply chain include pigs, sheep, cattle etc. Due to the enormous increase in the number of meat-eaters worldwide, its packing and timely supply are of utmost importance. The meat packing industry has revolutionized a lot by using the latest supply chain methods based on new technologies like blockchain. These include adulteration, long-term delays leading to spoilage of the product, mixing of other similar products which are lower in price etc. All these issues can be easily handled by using blockchain and IoT for meat supply chain management. Using these technologies, the challenges ranging from timely transportation of good quality meat to the best quality meat delivered to the customer can be easily addressed. In this paper, an effective blockchain and IoT-based model has been proposed which will counter the above challenges and improve the reliability and security factor.

关键词： Industries Supply chain management Supply chains Transportation Packaging Blockchains Safety Security Reliability Internet of Things

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Fusion of Traditional and Deep Learning Models for Underwater Image Quality Assessment

Fusion of Traditional and Deep Learning Models for Underwate...

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Conference on Information and Communication Technology (CICT)

作者： Dhivya R M V Nagasai V. Masilamani Department of Computer Science and Engineering Indian Institute of Information Technology Design and Manufacturing Kancheepuram Chennai

ISBN: (数字)9798331531539

ISBN: (纸本)9798331531546

Underwater image quality is significantly affected by the unique challenges of the underwater environment, including light attenuation, color distortion, backscatter, and the presence of particulate matter. These factors reduce visibility, alter color perception, and introduce noise and artifacts that degrade the overall quality of captured images. Ensuring high-quality underwater images is essential for various applications, including marine biology, environmental monitoring, underwater navigation, and industrial operations. This research proposes a novel approach to evaluating underwater image quality by integrating conventional machine learning methods with advanced deep learning architectures. The fusion process, employing Support Vector Regression (SVR), known as FTDUIQA (Fusion of Traditional and Deep Learning Models for Underwater Image Quality Assessment), combines well-established deep learning-based models - CNNIQA (Convolutional Neural Network for No-Reference Image Quality Assessment), DBCNN (Deep Bilinear Convolutional Neural Network), and HyperIQA (HyperNet for Image Quality Assessment) - with the classical IQA model, MSAEQA (Multi-scale and Antagonistic Energy Distribution). Additionally, we incorporate a modified deep learning model, MAUIQA (Multi-scale AttentionNet for Underwater Image Quality Assessment), which has been adapted from its image super-resolution application. This hybrid approach leverages the strengths of both deep learning and traditional techniques to provide a robust and comprehensive assessment of underwater image quality.

关键词： Image quality Deep learning Support vector machines Adaptation models Image color analysis Superresolution Distortion Underwater navigation Convolutional neural networks Colored noise

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Rural Retail Revolution AIML Driven Voice-Based Billing System in Kannada

Rural Retail Revolution AIML Driven Voice-Based Billing Syst...

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International Conference on Computation System and Information Technology for Sustainable Solutions (CSITSS)

作者： Sharvani. G. S Channaveeragouda. K. Patil Krishna. S. Chavan Nandeesh. J. Kalakatti Seema. R. Kulkarni Soumya Patil Department of Computer Science and Engineering Visvesvaraya Technological University Tontadarya College of Engineering Gadag India Department of Computer Science and Design Visvesvaraya Technological University Tontadarya College of Engineering Gadag India

ISBN: (数字)9798331505462

ISBN: (纸本)9798331505479

The aim of the voice-based billing system in Kannada is traditional billing by leveraging speech recognition. This project focuses on creating an efficient system for generating bills and transactions using spoken Kannada. Gaps in current systems include language barriers, limited accessibility, and timeconsuming processes. The methodology involves data collection for training the voice recognition model, developing robust speech recognition using machine learning, implementing NLP for command understanding, designing a user-friendly interface, recommendations of items based on previous orders of the customer and integrating with the billing system. Expected outcomes include enhanced accessibility for Kannada speakers, improved efficiency, user satisfaction, cultural inclusivity, and streamlined transactions. By addressing these gaps and leveraging technology, this proposed system aims to transform the billing domain with inclusivity and innovation.

关键词： Training Technological innovation Accuracy Customer satisfaction Speech recognition Manuals Machine learning Transforms Standards Business

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Exploiting Deep Reinforcement Learning for Multi-AUV Assisted VoI-Maximum Data Collection in UWSNs

Exploiting Deep Reinforcement Learning for Multi-AUV Assiste...

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2024 IEEE Global Communications Conference, GLOBECOM 2024

作者： Gu, Xuan Tang, Jiarun Huang, Xiao He, Jianhua Xu, Jing Huazhong University of Science and Technology School of Electronic Information and Communications Wuhan China China Ship Development and Design Center Wuhan China University of Essex School of Computer Science and Electronic Engineering ColchesterCO4 3SQ United Kingdom

ISBN: (纸本)9798350351255

Reliable and timely data collection is an important and challenging problem for underwater wireless sensor networks (UWSNs), partly due to the very slow underwater communication and the difficulty in recharging the sensors. In this paper, we exploit the use of autonomous underwater vehicles (AUVs) for UWSN data collection task. Specifically, we investigate how to maximize the value of information (VoI) for data collection through joint optimization of cluster head (CH) selection and multi-AUV path planning. We formulate the joint optimization problem for the task, taking into account the energy constraints of sensor nodes. To solve the problem, we propose a deep reinforcement learning algorithm based on an encoder-decoder architecture. The entire UWSN system is fed into the encoder network, followed by a composite decoder consisting of an AUV selection decoder and a cluster access sequence decoder to obtain the cluster access sequence for each AUV. Based on the determined sequences, we further utilize the dynamic programming algorithm to achieve optimal CH selection. Finally, we obtain the sequence of AUVs accessing the selected CHs. Simulation results demonstrate that the proposed learning-based approach converges and achieves a higher VoI than the existing benchmark algorithms. © 2024 IEEE.

关键词： Decoding

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Classification of DDoS in Software-Defined IoT Networks using Hybrid Feature Selection Technique with DL 3

Classification of DDoS in Software-Defined IoT Networks usin...

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3rd International Conference on Smart Technologies in Computing, Electrical and Electronics, ICSTCEE 2022

作者： Muthulakshmi, K. Kalaivani, K. Uma, J. Primya, T. Sivakumaran, A.R. Selvapandian, D. ECE Sri Krishna College of Technology Coimbatore India SNS College of Engineering Department of Computer Science and Design Coimbatore India Amrita School of Engineering Amrita Vishwa Vidyapeetham Department of Computer Science and Engineering Coimbatore India KPR Institute of Engineering and Technology Department of Computer Science and Engineering Coimbatore India Malla Reddy Engineering College for Women Department of Information Technology Secunderabad India Karpagam Academy of Higher Education Department of Computer Science and Engineering Coimbatore India

ISBN: (纸本)9781665456647

A new network technology called Software Defined Networking (SDN) enables centralised network management. The SDN's potential features improve network security and make it easier to create threat detection systems through software programmes that make use of open APIs. However, the new technology generates dangers and security issues that are not present in the traditional networks that are now in use. One of the most frequent assaults that can stop the operation of the network and render the majority of network services inaccessible to customers is a distributed denial of service attack (DDoS). Because so many network variables are considered and machine learning-based anomaly detection methods must be used, it is still difficult to identify DDos assaults on SDN setups effectively. In order to analyse the most thorough significant elements of DDoS attacks in SDN networks, this research planned to apply feature selection methods Information Gain (IG), Random Forest (RF), and Embedded feature model. The accuracy of the attack detection system will be increased, and the incidence of false alarms will be decreased, by using the most pertinent features. To address the issue of DDoS attacks in SDNs, a Deep Learning (DL) technique based on Convolutional Neural Network (CNN) is also put out. The study analyses and evaluates InSDN, measures the effectiveness of the suggested DL model on the SDN controller, and tests the performance of the network in terms of network throughput and end-To-end latency. The outcomes demonstrate that the DL technique effectively detects DDoS attacks in SDN settings without materially affecting controller performance. © 2022 IEEE.

关键词： Denial-of-service attack

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