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International Conference on ICT in Business Industry & Government (ICTBIG)

作者： Shuchi Bhadula Myasar Mundher Adnan Rakesh Kumar Gopal Kaliyaperumal Bolleddu Devananda Rao Nandini Shirish Boob Department of Computer Science & Eng Graphic Era Deemed to be University Dehradun Department of Computers Techniques Eng College of Technical Engineering The Islamic University Najaf Iraq Department of Computer Eng. & Applications GLA University Mathura Department of Mechanical Engineering New Horizon College of Eng Bangalore India Department of Computer Science and Eng MLR Institute of Technology Hyderabad Telangana Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Tamilnadu

ISBN: (数字)9798331518981

ISBN: (纸本)9798331518998

The Internet of Things' (IoT) explosive growth has created new opportunities for creative solutions in a variety of sectors. However, since real-world surroundings are complicated and variable, creating and assessing IoT prototypes may be difficult. The usage of test beds as a vital instrument for evaluating performance and fostering creativity in the creation of IoT prototypes is the main topic of this research study. Test-beds allow developers and researchers to analyse critical performance parameters like latency, scalability, energy efficiency, and interoperability by offering repeatable, controlled settings that mimic real-world situations. This paper examines many IoT test-bed frameworks and their uses in a range of industries, such as industrial automation, smart cities, and healthcare. It also looks at methods for assessing the dependability and effectiveness of IoT devices and the difficulties brought about by the ever-changing nature of IoT ecosystems. Before deploying a prototype, developers may improve its scalability and robustness by iterating iteratively on test-beds. With a focus on their potential to support large-scale IoT deployments and address the emerging challenges of security, privacy, and data management, the paper concludes by discussing future trends in IoT prototype evaluation and the evolving role of test-beds in fostering innovation.

关键词： Performance evaluation Technological innovation Smart cities Scalability Prototypes Throughput Real-time systems Internet of Things Security Edge computing

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Data Mining for Predictive Analytics and Optimization of Treatment Plans in Cardiovascular Disease Management using Neural Networks

Data Mining for Predictive Analytics and Optimization of Tre...

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Advancements in Smart, Secure and Intelligent Computing (ASSIC), International Conference on

作者： Seeniappan Kaliappan V. Balaji G. Bhavani Bharathi Srinivas Aluvala Department of Mechatronics Engineering KCG College of Technology Karapakkam Chennai Tamil Nadu India Department of Mechanical Engineering Loyola Institute of Technology Chennai Tamil Nadu India School of Computer Science & Artificial Intelligence SR University Warangal Telangana

This section presents an integrated method that merges data mining techniques and neural networks to augment predictive analytics and optimize treatment strategies for managing cardiovascular diseases. Algorithm 1 focuses on data preprocessing and feature selection, essential for refining the patient data. It standardizes the input data by normalization and selects critical features using information gain, enhancing the subsequent neural network's efficiency in predicting disease progression. Algorithm elucidates the training of a neural network, utilizing the pre-processed data and disease progression labels. The neural network undergoes iterative training to minimize the loss function, resulting in a trained model capable of accurate predictions. Algorithm emphasizes the utilization of the trained neural network to optimize personalized treatment plans for cardiovascular disease management. By predicting disease progression and evaluating treatment options, it tailors' treatment plans to individual patient needs, thereby enhancing the effectiveness of healthcare interventions. The proposed method is demonstrated to surpass traditional techniques such as Linear Regression, Decision Trees, Support Vector Machines, K-Nearest Neighbors, Random Forest, and Logistic Regression in terms of critical performance metrics. Leveraging the power of neural networks, the proposed method showcases superior accuracy, precision, recall, Fl score, and AUC-ROC. Moreover, a comprehensive evaluation involving sensitivity, specificity, Matthews Correlation Coefficient (MCC), Negative Predictive Value (NPV), Youden's Index, and Cohen's Kappa consistently emphasizes its advanced performance, validating its potential to revolutionize cardiovascular disease management.

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Modeling and Simulation to Improve Real Electric Vehicles Charging Processes by Integration of Renewable Energies and Buffer Storage 22

Modeling and Simulation to Improve Real Electric Vehicles Ch...

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Proceedings of the Winter Simulation Conference

作者： Konstantin Sing Pierre Mertiny Marco Pruckner Department of Mechanical Engineering University of Erlangen-Nürnberg Erlangen GERMANY Department of Mechanical Engineering University of Alberta Edmonton Alberta CANADA Modeling and Simulation Institute of Computer Science University of Würzburg Am Hubland Würzburg GERMANY

The present study explores a simulation model combining system dynamics and discrete-event simulation for an electric vehicle charging system. For the representation of the charging demand the model employs data from an actual facility for vehicle charging. While being connected to the electrical grid, the system is augmented by a solar photovoltaic installation and stationary battery energy storage. Multiple simulation runs were performed to analyze the considered energy system over a 1-year period and compare relevant output parameters for different system configurations and system locations. Results show that a solar photovoltaic installation can be effectively integrated. For the degree of self-sufficiency, high values of 87% can be achieved with combined solar photovoltaic and battery energy storage systems.

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TANDEM3D: Active Tactile Exploration for 3D Object Recognition

TANDEM3D: Active Tactile Exploration for 3D Object Recogniti...

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IEEE International Conference on Robotics and Automation (ICRA)

作者： Jingxi Xu Han Lin Shuran Song Matei Ciocarlie Department of Computer Science Columbia University New York NY USA Department of Mechanical Engineering Columbia University New York NY USA

Tactile recognition of 3D objects remains a challenging task. Compared to 2D shapes, the complex geometry of 3D surfaces requires richer tactile signals, more dexterous actions, and more advanced encoding techniques. In this work, we propose TANDEM3D, a method that applies a co-training framework for exploration and decision making to 3D object recognition with tactile signals. Starting with our previous work, which introduced a co-training paradigm for 2D recognition problems, we introduce a number of advances that enable us to scale up to 3D. TANDEM3D is based on a novel encoder that builds 3D object representation from contact positions and normals using PointNet++. Furthermore, by enabling 6DOF movement, TANDEM3D explores and collects discriminative touch information with high efficiency. Our method is trained entirely in simulation and validated with real-world experiments. Compared to state-of-the-art baselines, TANDEM3D achieves higher accuracy and a lower number of actions in recognizing 3D objects and is also shown to be more robust to different types and amounts of sensor noise.

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Intel Software Guard Extensions in Internet of Things Scenarios: A Systematic Mapping Study 8

Intel Software Guard Extensions in Internet of Things Scenar...

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8th International Conference on Future Internet of Things and Cloud, FiCloud 2021

作者： Will, Newton Carlos Gomes Valadares, Dalton Cezane De Souza Santos, Danilo Freire Perkusich, Angelo Federal University of Technology-Paraná Computer Science Department Dois Vizinhos Brazil Federal Institute of Pernambuco Mechanical Engineering Department Caruaru Brazil Federal University of Campina Grande Campina Informatics and Electrical Engineering Center Computer Science Grande Brazil

ISBN: (纸本)9781665425742

Due to the Internet of Things (IoT) devices' processing and memory constraints, the processing and analysis of data acquired by such devices are generally performed in a fog or cloud environment, which offers more processing power. When delegating data processing to third parties, it is necessary to ensure their confidentiality and their owners' privacy, which can be achieved using a Trusted Execution Environment, such as Intel SGX. In this paper, we present a systematic mapping study to review recent works related to the use of Intel SGX architecture in IoT scenarios. We conduct the study by selecting 35 papers published between 2017 and 2020 and providing a comprehensive overview of the application scenarios and solutions when combining Intel SGX and the IoT paradigm. © 2021 IEEE.

关键词： Internet of things

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Fully implantable and battery-free wireless optoelectronic system for modulable cancer therapy and real-time monitoring

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npj Flexible Electronics 2023年第1期7卷 119-130页

作者： Kiho Kim In Sik Min Tae Hee Kim Do Hyeon Kim Seungwon Hwang Kyowon Kang Kyubeen Kim Sangun Park Jongmin Lee Young Uk Cho Jung Woo Lee Woon-Hong Yeo Young Min Song Youngmee Jung Ki Jun Yu Functional Bio-integrated Electronics and Energy Management Lab School of Electrical and Electronic EngineeringYonsei University50 Yonsei-roSeodaemun-guSeoul 03722Republic of Korea Center for Biomaterials Biomedical Research InstituteKorea Institute of Science and Technology(KIST)Seoul 02792Republic of Korea Department of Fusion Research and Collaboration Biomedical Research InstituteSeoul National University HospitalSeoul 03080Republic of Korea School of Electrical Engineering and Computer Science(EECS) Gwangju Institute of Science and Technology(GIST)Gwangju 61005Republic of Korea PA1 Foundry BusinessSamsung Electronics1 Samsungjeonja-roHwaseong-siGyeonggi-doRepublic of Korea R&D Center DNA Lab PharmaresearchSeongnam 13486Republic of Korea KU-KIST Graduate School of Converging Science and Technology Korea UniversitySeoul 136-705Republic of Korea School of Materials Science and Engineering Energy Materials for Soft Electronics LaboratoryPusan National UniversityBusan 46241Republic of Korea IEN Center for Human-Centric Interfaces and Engineering and George W.Woodruff School of Mechanical Engineering Georgia Institute of TechnologyAtlantaGA 30332USA Wallace H.Coulter Department of Biomedical Engineering Georgia Institute of Technology and Emory University School of MedicineAtlantaGA 30332USA Institute for Robotics and Intelligent Machines Parker H.Petit Institute for Bioengineering and BiosciencesInstitute for MaterialsNeural Engineering CenterGeorgia Institute of TechnologyAtlantaGA 30332USA School of Electrical and Electronic Engineering YU-Korea Institute of Science and Technology(KIST)InstituteYonsei University50Yonsei-roSeodaemun-guSeoul 03722Republic of Korea

Photodynamic therapy(PDT)is attracting attention as a next-generation cancer treatment that can selectively destroy malignant tissues,exhibit fewer side effects,and lack pain during *** PDT systems have recently been developed to resolve the issues of bulky and expensive conventional PDT systems and to implement continuous and repetitive *** implantable PDT systems,however,are not able to perform multiple functions simultaneously,such as modulating light intensity,measuring,and transmitting tumor-related data,resulting in the complexity of cancer ***,we introduce a flexible and fully implantable wireless optoelectronic system capable of continuous and effective cancer treatment by fusing PDT and hyperthermia and enabling tumor size monitoring in *** system exploits micro inorganic light-emitting diodes(μ-LED)that emit light with a wavelength of 624 nm,designed not to affect surrounding normal tissues by utilizing a fully programmable light intensity ofμ-LED and precisely monitoring the tumor size by Si phototransistor during a long-term implantation(2–3 weeks).The superiority of simultaneous cancer treatment and tumor size monitoring capabilities of our system operated by wireless power and data transmissions with a cell phone was confirmed through in vitro experiments,ray-tracing simulation results,and a tumor xenograft mouse model in *** all-in-one single system for cancer treatment offers opportunities to not only enable effective treatment of tumors located deep in the tissue but also enable precise and continuous monitoring of tumor size in real-time.

关键词： optoelectronic enable battery

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Simultaneous Synchronization and Calibration for Wide-baseline Stereo Event Cameras

arXiv

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arXiv 2023年

作者： Xing, Wanli Lin, Shijie Zheng, Guangze Du, Yanjun Pan, Jia The Department of Computer Science The University of Hong Kong Hong Kong The Department of Mechanical and Automation Engineering The Chinese University of Hong Kong Hong Kong

Event-based cameras are increasingly utilized in various applications, owing to their high temporal resolution and low power consumption. However, a fundamental challenge arises when deploying multiple such cameras: they operate on independent time systems, leading to temporal misalignment. This misalignment can significantly degrade performance in downstream applications. Traditional solutions, which often rely on hardware-based synchronization, face limitations in compatibility and are impractical for long-distance setups. To address these challenges, we propose a novel algorithm that exploits the motion of objects in the shared field of view to achieve millisecond-level synchronization among multiple event-based cameras. Our method also concurrently estimates extrinsic parameters. We validate our approach in both simulated and real-world indoor/outdoor scenarios, demonstrating successful synchronization and accurate extrinsic parameters estimation. Code: https://***/FORREST1901/ssac Copyright © 2023, The Authors. All rights reserved.

关键词： Cameras

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Machine-Learning for Discovery of Descriptors for Gas-Sensing: A Case Study of Doped Metal Oxides

SSRN

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SSRN 2024年

作者： Su, Meng Guo, Yongchang Hong, Xiaobo Zheng, Yangong Faculty of Electrical Engineering and Computer Science Ningbo University Ningbo315211 China Faculty of Mechanical Engineering and Mechanics Ningbo University Ningbo315211 China Institute of Advanced Energy Storage Technology and Equipment Ningbo University Ningbo315211 China

Conventionally, gas sensors are studied based on functional materials, case by case, using the inefficient "trial-and-error" method. In this study, 872 datasets with 32 features of doped oxides, extracted from the literature, were used to analyze the key features of gas-sensing reactions and understand gas-sensing mechanisms from a global perspective using a genetic algorithm-optimized artificial neural network. Shapley additive explanations were employed to determine the importance and relationships of the features. Based on the physical meaning of the important features, the characteristics of doped oxides and gas molecules are described in terms of sensitivity, measurement range, and selectivity. The reactivity of gas molecules has a strong impact on the sensitivity of doped oxides;sensors with low sensitivity usually have a wide measuring range. The selectivity of doped oxides depends on a suitable doping ratio, oxygen configurations, and the electrophilicity of gas molecules. The best characteristics of the doped oxides in gas sensing were predicted using a generative adversarial net. This study contributes toward improving gas-sensing materials. © 2024, The Authors. All rights reserved.

关键词： Chemical sensors

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Distributed Delay-Tolerant Strategies for Equality-Constraint Sum-Preserving Resource Allocation

arXiv

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arXiv 2023年

作者： Doostmohammadian, Mohammadreza Aghasi, Alireza Vrakopoulou, Maria Rabiee, Hamid R. Khan, Usman A. Charalambous, Themistoklis Faculty of Mechanical Engineering Semnan University Semnan Iran Department of Electrical Engineering and Computer Science Oregon State University United States University of Melbourne Melbourne Australia Department of Computer Engineering Sharif University of Technology Tehran Iran Electrical and Computer Engineering Department Tufts University MA United States School of Electrical Engineering Aalto University Espoo Finland School of Electrical Engineering University of Cyprus Nicosia Cyprus

This paper proposes two nonlinear dynamics to solve constrained distributed optimization problem for resource allocation over a multi-agent network. In this setup, coupling constraint refers to resource-demand balance which is preserved at all-times. The proposed solutions can address various model nonlinearities, for example, due to quantization and/or saturation. Further, it allows to reach faster convergence or to robustify the solution against impulsive noise or uncertainties. We prove convergence over weakly connected networks using convex analysis and Lyapunov theory. Our findings show that convergence can be reached for general sign-preserving odd nonlinearity. We further propose delay-tolerant mechanisms to handle general bounded heterogeneous time-varying delays over the communication network of agents while preserving all-time feasibility. This work finds application in CPU scheduling and coverage control among others. This paper advances the state-of-the-art by addressing (i) possible nonlinearity on the agents/links, meanwhile handling (ii) resource-demand feasibility at all times, (iii) uniform-connectivity instead of all-time connectivity, and (iv) possible heterogeneous and time-varying delays. To our best knowledge, no existing work addresses contributions (i)-(iv) altogether. Simulations and comparative analysis are provided to corroborate our contributions. © 2023, CC BY.

关键词： Resource allocation

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Modeling Human Strategy for Flattening Wrinkled Cloth Using Neural Networks

Modeling Human Strategy for Flattening Wrinkled Cloth Using ...

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IEEE International Conference on Systems, Man and Cybernetics

作者： Nilay Kant Ashrut Aryal Rajiv Ranganathan Ranjan Mukherjee Charles Owen Department of Mechanical Engineering Michigan State University East Lansing Michigan USA Department of Kinesiology Michigan State University East Lansing Michigan USA Department of Computer Science and Engineering Michigan State University East Lansing Michigan USA

ISBN: (数字)9781665410205

ISBN: (纸本)9781665410212

This paper explores a novel approach to model strategies for flattening wrinkled cloth learning from humans. A human participant study was conducted where the participants were presented with various wrinkle types and tasked with flattening the cloth using the fewest actions possible. A camera and Aruco marker were used to capture images of the cloth and finger movements, respectively. The human strategies for flattening the cloth were modeled using a supervised regression neural network, where the cloth images served as input and the human actions as output. Before training the neural network, a series of image processing techniques were applied, followed by Principal Component Analysis (PCA) to extract relevant features from each image and reduce the input dimensionality. This reduction decreased the model's complexity and computational cost. The actions predicted by the neural network closely matched the actual human actions on an independent data set, demonstrating the effectiveness of neural networks in modeling human actions for flattening wrinkled cloth.

关键词： Training Computational modeling Neural networks Fingers Predictive models Feature extraction Data models Computational efficiency Cybernetics Principal component analysis

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