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Expert Clouds and Applications (ICOECA), International Conference on

作者： Komati Divya C H Bhargavi M. Abirami Ponnaganti Arjun Pabbathi Padmaja Batini Dhanwanth Department of Computer Science and Engineering Panimalar Institute of Technology Chennai India Department of Computer Science and Engineering Vels Institute of Science Technology & Advanced Studies (VISTAS) Chennai India Department of Computer Science and Engineering St.Peter's Institute Of Higher Education And Research Chennai India

ISBN: (数字)9798350385793

ISBN: (纸本)9798350385809

Customizing radiation treatments for each patient is a formidable obstacle in the fight against cancer. Because they rely on human intervention and generalization, traditional methods often provide less-than-ideal results and unwanted side effects. Utilizing patient-specific anatomical and tumor-specific data, this research analyzes the revolutionary possibilities of machine learning for planning radiation therapy. The goal of this research work is to forecast radiation doses, identify tumor boundaries, and reduce the exposure to healthy tissue using Deep Learning (DL), Support Vector Machine (SVM) and Random Forest (RF) models. Radiation planners must prioritize data heterogeneity, model interpretability, and clinical validation if machine learning has to be widely used. Improved patient outcomes and quality of life may be achieved via the use of Machine Learning (ML) to improve the radiation efficacy and accuracy, as shown in this research study that synthesizes existing research.

关键词： Deep learning Support vector machines Accuracy Cancer treatment Vectors Planning Radiation therapy

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Low-temperature spin Seebeck effect in nonmagnetic vanadium dioxide

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Physical Review B 2024年第2期110卷 024415-024415页

作者： Renjie Luo Tanner J. Legvold Liyang Chen Henry Navarro Ali C. Basaran Deshun Hong Changjiang Liu Anand Bhattacharya Ivan K. Schuller Douglas Natelson Department of Physics and Astronomy Applied Physics Graduate Program Rice Quantum Institute Department of Physics and Center for Advanced Nanoscience Materials Science Division Department of Electrical and Computer Engineering Department of Materials Science and NanoEngineering

The spin Seebeck effect (SSE) is sensitive to thermally driven magnetic excitations in magnetic insulators. Vanadium dioxide in its insulating low-temperature phase is expected to lack magnetic degrees of freedom, as vanadium atoms are thought to form singlets upon dimerization of the vanadium chains. Instead, we find a paramagnetic SSE response in VO2 films that grows as the temperature decreases below 50 K. The field and temperature-dependent SSE voltage is qualitatively consistent with a general model of paramagnetic SSE response and inconsistent with triplet spin transport. Quantitative estimates find a spin Seebeck coefficient comparable in magnitude to that observed in strongly magnetic materials. The microscopic nature of the magnetic excitations in VO2 requires further examination.

关键词： Spin Seebeck effect Paramagnets Strongly correlated systems

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A Blockchain-Based Transactive Energy Management Scheme for Nano-Grids using Power Flow Coloring

A Blockchain-Based Transactive Energy Management Scheme for ...

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2023 International Conference on Consumer Electronics - Taiwan, ICCE-Taiwan 2023

作者： Masood, Abdullah Bin Javaid, Saher Tan, Yasuo Vassiliou, Vasos Lestas, Marios University of Cyprus Department of Computer Science Nicosia Cyprus Cyens - Centre of Excellence Nicosia Cyprus Japan Advanced Institute of Science and Technology School of Information Science Ishikawa Japan Frederick University Department of Electrical Engineering Nicosia Cyprus

ISBN: (纸本)9798350324174

The proliferation of distributed energy resources (DER) and the widespread adoption of the Internet of Things (IoT) in Prosumer Nano-Grids (P-NGs) pose severe challenges in existing energy management schemes, thus necessitating the need for the development of novel solutions which can guarantee system efficiency and security. To address such challenges, we propose a novel scheme leveraging the benefits of distributed power flow coloring, blockchain technology, and Transactive Energy Management (TEM). The proposed system model offers P-NGs a secure, transparent, trustworthy, and efficient energy management and transactions platform that can: maintain the overall voltage stability and specified power ratios against power fluctuations within the P-NG, offer an increase in energy security, provide real-time monitoring, and reduce energy waste. © 2023 IEEE.

关键词： Internet of things

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A Distillation-Based Attack Against Adversarial Training Defense for Smart Grid Federated Learning

A Distillation-Based Attack Against Adversarial Training Def...

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Consumer Communications and Networking Conference, CCNC IEEE

作者： Atef H. Bondok Mohamed Mahmoud Mahmoud M. Badr Mostafa M. Fouda Maazen Alsabaan Department of Electrical and Computer Engineering Tennessee Tech. University Cookeville TN USA Department of Electrical Engineering College of Engineering Al-Azhar University Cairo Egypt Department of Network and Computer Security College of Engineering SUNY Polytechnic Institute Utica NY USA Department of Electrical and Computer Engineering Idaho State University Pocatello ID USA Center for Advanced Energy Studies (CAES) Idaho Falls ID USA Department of Computer Engineering College of Computer and Information Sciences King Saud University Riyadh Saudi Arabia

In the advanced metering infrastructure (AMI) of the smart grid, smart meters (SMs) are deployed to collect fine-grained electricity consumption data, enabling billing, load monitoring, and efficient energy management. However, some consumers engage in fraudulent behavior by hacking their meters, leading to either traditional electricity theft or more sophisticated evasion attacks. Evasion attacks aim to illegally reduce electricity bills while deceiving theft detection mechanisms. The current methods for identifying such attacks raise privacy concerns due to the need for access to consumers' detailed consumption data to train detection mechanisms. To address privacy concerns, federated learning (FL) is proposed as a collaborative training approach across multiple consumers. Adversarial training (AT) has shown promise in countering evasion threats on machine learning models. This paper, first, investigates the susceptibility of traditional electricity theft classifiers trained by FL to evasion attacks for both independent and identically distributed (IID) and Non-IID consumption data. Then, it investigates the effectiveness of AT in securing the global electricity theft detector against evasion attacks, assuming no misbehavior from the participant consumers in the FL process. After that, we introduce a novel attack, called Distillation, which can be launched during the AT process to make the global model susceptible to evasion at inference time. Finally, extensive experiments are conducted to validate the severity of the proposed attack.

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Hybrid Signal Integrity Modeling and Analysis of Heterogeneous Integrated System With Neuromorphic Darwin Chip

IEEE Transactions on Signal and Power Integrity

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IEEE Transactions on Signal and Power Integrity 2024年 3卷 37-46页

作者： Chen, Quankun Ma, Hanzhi Li, Da Tao, Tuomin Tan, Shurun Liu, En-Xiao Schutt-Aine, Jose Li, Er-Ping Zhejiang University Key Laboratory of Advanced Micro/Nano Electronic Devices and Smart Systems and Applications College of Information Science and Electronic Engineering Hangzhou310027 China Zhejiang University University of Illinois at Urbana-Champaign Institute Haining314400 China A∗star Institute of High Performance Computing Singapore138632 Singapore University of Illinois at Urbana-Champaign Department of Electrical and Computer Engineering UrbanaIL61820 United States

This article introduces a comprehensive approach for designing and analyzing signal integrity in heterogeneous integrated systems that incorporate neuromorphic Darwin chips. The proposed integrated system architecture includes a neuromorphic Darwin chip, digital signal processing unit, microcontroller unit, field programmable gate arrays, and coding and decoding modules to encode and reconstruct analog spiking signals. The study evaluates the encoding module and the heterogeneous integration structure and conducts signal integrity analysis. To achieve optimal signal integrity performance, the article proposes a novel binocular eye diagram analysis technique. This innovative approach guides the encoding algorithm modification and improves the overall system performance. This research is the first to combine joint field-circuit simulation, heterogeneous integration modeling, and signal integrity analysis of the Darwin neuromorphic chip, and it is expected to serve as a valuable reference for future studies on similar systems. © 2022 IEEE.

关键词： Neural networks

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Introduction and Development of Cyber Physical Systems in Smart Grid with Blockchain Technology to Enhance Sustainability 2

Introduction and Development of Cyber Physical Systems in Sm...

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2nd International Conference on Augmented Intelligence and Sustainable Systems, ICAISS 2023

作者： Valarmathi, P. Manoj, G. Das, Sandip Kumar Oommen, Sujo Tiwari, Mohit B. S Abdur Rahman Crescent Institute Department of Computer Science & Engineering Tamil Nadu India Vel Tech Rangarajan Dr. Sagunthala R&d Institute of Science and Technology Department of Management Studies Tamilnadu Chennai India School of Electrical Engineering Odisha Bhubaneswar751024 India Reva University School of Electrical and Electronics Engineering Karnataka Bangalore560064 India Bharati Vidyapeeth's College of Engineering Department of Computer Science and Engineering Delhi India

ISBN: (纸本)9798350325799

The need for increased sustainability and efficiency in the energy industry has led to a major increase in interest in the development of cyber-physical systems (CPS) in the smart grid domain in recent years. In the context of the smart grid, the term "CPS"refers to a network of physical objects, sensors, and communication systems that are interconnected and allow for the smooth exchange of data and control signals to optimize the production, distribution, and use of electricity. Demand response, energy management, and grid optimization are just a few of the smart grid applications that can be successfully implemented by utilizing CPS's capabilities. On the other side, blockchain technology provides a decentralized and secure framework for logging and validating transactions among numerous organizations. Since it is distributed, there is no need for middlemen, transparency is improved, and data integrity and immutability are all guaranteed. These qualities make blockchain an excellent choice for tackling the issues that the smart grid is facing, such as data privacy, security, and stakeholder confidence. There are many advantages to integrating blockchain and CPS in the smart grid. First and foremost, a decentralized and democratized energy market is made possible by blockchain, which enables safe and effective peer-to-peer energy trade among prosumers. This increases sustainability by encouraging the use of renewable energy sources and reducing dependency on centralized power generation. In conclusion, there is enormous potential for improving sustainability through the smart grid's integration of blockchain technology with cyber-physical systems. © 2023 IEEE.

关键词： Cyber Physical System

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Visual SLAM with a Multi-Modal Semantic Framework for the Visually Impaired Navigation-Aided Device 8

Visual SLAM with a Multi-Modal Semantic Framework for the Vi...

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8th IEEE International Conference on advanced Robotics and Mechatronics, ICARM 2023

作者： Li, Shaohu Gu, Jason Feng, Ying Institute of Advanced Technology University of Science and Technology of China Hefei230031 China Dalhousie University Department of Electrical and Computer Engineering HalifaxNS Canada College of Automation Science and Engineering South China University of Technology Guangzhou510641 China

ISBN: (纸本)9798350300178

Visual Simultaneous Localization and Mapping (VSLAM) based on image feature points often contains feature points on non-stationary objects in dynamic scenes, resulting in poor localization performance. Recent research has improved VSLAM performance by removing dynamic feature points utilizing optical flow, deep learning, or multi-view geometry, however, the volume of data makes real-time operation challenging. In this study, we propose a multimodal framework for semantic VSLAM. To eliminate dynamic feature points, the system effectively blends object detection, instance segmentation, and geometry modules. To ensure high-precision pose estimation and quick back-end optimization, we exclusively use object detection in the front-end of VSLAM to mask dynamic feature points. In the keyframes of the local map, semantic segmentation is utilized to remove dynamic feature points, and an effective geometric module is employed to assess the dynamic consistency of objects that are challenging to categorize. Therefore, more feature points are retained, and the running speed is also ensured. Evaluations of the system on public datasets and in the real world show that it improves the accuracy and stability of VSLAM in dynamic environments. © 2023 IEEE.

关键词： Object detection

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IoT based Monitoring of Mushroom 2

IoT based Monitoring of Mushroom

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2nd International Conference on Sustainable Computing and Data Communication Systems, ICSCDS 2023

作者： Eben, J. Lysa Kaur, Chamandeep Thelly, Mathews T Parimita Vels Institute of Science Technology and Advanced Studies School of Computing Sciences Department of Computer Applications Chennai India Jazan University Department of Computer Science and Information Technology Jazan Saudi Arabia Kuriakose Elias College Department of Botany Mannanam India Sam Higginbottom University of Agriculture Technology & Sciences Agricultural Engineering Prayagraj India

ISBN: (纸本)9781665491990

Most of India's population relies on farming for their livelihood. Every year brings new advances in agricultural technology. The use of wireless sensors in contemporary agriculture is crucial. The employment of wireless sensors across a wide range of agricultural applications has a profoundly favourable effect on crop productivity and economic efficiency. The mushroom business is still relatively young and modest compared to other sectors of India's agricultural market. The white button mushroom is India's most commercially significant fungus and is a global favourite. It may be grown anywhere with a suitable climate and soil, but most of the world's supply comes from North India in the winter. For mycelia to flourish, the ideal temperature range is between 22 and 25 degrees Celsius. In contrast, the sweet spot for developing fruit bodies is between 14 and 18 degrees Celsius with high relative humidity. Equipment for maintaining a steady temperature and pasteurising the mushrooms, should be present in the rooms used for growing. The primary objective of this study is to automate the mushroom production plant and keep track of the crop room's environmental state to reduce the amount of human care required for the mushroom plant. The Internet of Things has also modernized agriculture industries, creating an environmental monitoring and regulating system that is used in the mushroom farm in this work. Using the Speak internet platform, users may monitor ecological conditions in a mushroom farm on their Android mobile, including the ambient temperature, relative humidity, carbon dioxide level, and light intensity. The Arduino prototype platform is employed as the sensors station's controller, ensuring that the plant parameters are within the user-specified range. Using a pair of low-power ESP8266 as Wi-Fi modems, the current parameter status is relayed to a distant monitoring station. The integrated Arduino uses the programming environment to write the controller's cod

关键词： Controllers

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Learning Disentangled Representation for Multidimensional MR Image Reconstruction 46

Learning Disentangled Representation for Multidimensional MR...

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46th Annual International Conference of the IEEE engineering in Medicine and Biology Society, EMBC 2024

作者： Zhao, Ruiyang Wang, Zepeng Lam, Fan University of Illinois Urbana-Champaign Department of Electrical and Computer Engineering UrbanaIL United States University of Illinois Urbana-Champaign Department of Bioengineering UrbanaIL United States University of Illinois Urbana-Champaign Beckman Institute for Advanced Science and Technology UrbanaIL United States

ISBN: (纸本)9798350371499

We proposed a new way to represent and reconstruct multidimensional MR images. Specifically, a representation capable of disentangling different types of features in high-dimensional images was learned via training an autoencoder with separated sets of latent spaces for image style transfer, e.g., contrast or geometry transfer. A latent diffusion model was introduced to capture the distributions of the disentangled latents for constrained reconstruction. A new formulation was developed to integrate the pre-learned representation with other complementary constraints for reconstruction from sparse data. We demonstrated the ability of our model to disentangle contrast and geometry features in multicontrast MR images, and its effectiveness in accelerated T1 and T2 mapping. © 2024 IEEE.

关键词： Magnetic resonance imaging

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Leveraging Digital Twins and AI for Enhanced Clinical Decision Support in Endometrial Cancer Treatment 5

Leveraging Digital Twins and AI for Enhanced Clinical Decisi...

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5th International Conference on IoT Based Control Networks and Intelligent Systems, ICICNIS 2024

作者： Palarimath, Suresh Maran, Pyingkodi Wilfred Blessing, N.R. Cibi, A. Kavitha, S.J. Steffi, S. College of Computing and Information Sciences University of Technology and Applied Sciences Salalah Oman Kongu Engineering College Department of Computer Application Erode India College of Computing and Information Sciences University of Technology and Applied Sciences Ibri Oman Virtusa Consulting Service Private Limited Data & Analytics Visualizaation - Delivery Chennai India Vels Institute of Science Technology and Advanced Studies Department of Computer Science and Engineering Chennai India Vel Tech Rangarajan Dr. Sagunthala R&D Institute of Science and Technology Computer Science and Engineering Chennai India

ISBN: (纸本)9798331518097

Digital twins are a relatively new concept that have lately garnered interest in the industrial sector. Digital twins are virtual models that mirror actual products. In this research, we suggest using the technology of digital twins to the field of healthcare in order to enhance clinical decision support and to provide more individualized treatment for patients. When artificial intelligence (AI) is combined with digital twins, healthcare professionals are able to more effectively analyze large volumes of varied data and increase their ability to make diagnostic and therapeutic decisions. In this research, we provide a conceptual framework for leveraging digital twins and AI to solve existing limits in cancer care, notably endometrial cancer therapy. This research study mainly focuses on cancer care, and more specifically on endometrial cancer treatment. In addition, we analyze the possible challenges and opportunities that may arise throughout the process of integrating this technology in healthcare settings. Our overarching objective is to improve the standard of treatment as well as the clinical results for people who have cancer. © 2024 IEEE.

关键词： Lung cancer

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