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15th International Conference on Information and Communication Technology Convergence, ICTC 2024

作者： Kim, Jaeseong Song, Minje Jung, Youngjin Choi, Hyunjong Song, Minhyup Electronics and Telecommunications Research Institute Photonic/Wireless Devices Research Division Daejeon Korea Republic of School of electrical engineering Korea university Seoul Korea Republic of Korea university Department of Computer Science and Engineering Seoul Korea Republic of

ISBN: (纸本)9798350364637

We have demonstrated that a 1.5 Gbps signal can be transmitted through a microwave photonic filter (MWPF) in the 24 GHz band, which belongs to the FR3 (Frequency Range 3) upper mid band (7-24 GHz), a range gaining attention for 6G communication. The MWPF was implemented with a free spectral range (FSR) of 12.3 GHz, a center frequency of 24.67 GHz, and a 3dB bandwidth of 2.24 GHz. In the signal quality test, The PRBS (Pseudo-Random Binary Sequence) signals with data rates of 500 Mbps, 1 Gbps, and 1.5 Gbps that passed through the MWPF were received by the BERT (Bit Error Ratio Tester), and the eye diagrams were successfully checked for each. This results successfully demonstrated the potential application of MWPFs in future 6G communications. Taking advantage of the programmable nature of MWPF, we plan to conduct additional signal quality tests in other 6G frequency bands. In future work, by utilizing the coherence properties of the optical frequency comb (OFC), we aim to achieve significant data rates, further proving that MWPFs are suitable devices for 6G communication. © 2024 IEEE.

关键词： Microwave photonics

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A Comparison of Cloud Security Mechanisms 2

A Comparison of Cloud Security Mechanisms

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2nd International Conference on Automation, computing and Renewable Systems, ICACRS 2023

作者： Saravanan, D. Maragatharajan, M. Nirmala, E. Naveen, P. Francis Saviour Devaraj, A. School of Computing Science and Engineering VIT Bhopal University Madhya Pradesh Kothrikalan India Department of Electrical and Electronics Engnieering KPR Institute of Engineering and Technology Tamil Nadu Coimbatore India Department of IT College of Computing & Information Sciences University of Technology and Applied Sciences Ibri Oman

ISBN: (纸本)9798350340235

Cloud computing is a broad term that can be understood differently depending on the context. Its primary goal is to reduce capital expenses on infrastructure, operating systems, deployment, and storage. The National Institute of Standards and Technology (NIST) defines cloud computing as on-demand network access to a shared pool of computing resources that can be rapidly provisioned and released with minimal management effort or service provider interaction. Cloud computing operates using virtualization and parallel computing principles and provides services like IaaS, PaaS, SaaS, and NaaS. The cloud is classified into public, private, hybrid, and community clouds, each with its own unique features. The major issue of cloud computing is to transfer the data with security breaches. Various security systems are available to protect the data security in the cloud. This research study has discussed some security models like Advanced encryption standard algorithm, Biometric model, Elliptic-curve cryptography with Diffie-Hellman algorithm and a new technique vector-matrix based encryption algorithm. The execution time for these algorithms were compared and results were discussed. © 2023 IEEE.

关键词： Advanced encryption standard algorithm Biometric model Cloud security Decryption Diffie-Hellman algorithm Elliptic-curve cryptography Hybrid cloud vector-matrix based encryption algorithm

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Shared-task Self-supervised Learning for Estimating Free Movement Unified Parkinson's Disease Rating Scale III 46

Shared-task Self-supervised Learning for Estimating Free Mov...

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

作者： Shuqair, Mustafa Jimenez-Shahed, Joohi Ghoraani, Behnaz Florida Atlantic University Department of Electrical Engineering & Computer Science Boca RatonFL United States Icahn School of Medicine at Mount Sinai New YorkNY United States

ISBN: (纸本)9798350371499

The Unified Parkinson's Disease Rating Scale (UP-DRS) is used to recognize patients with Parkinson's disease (PD) and rate its severity in clinical settings. Machine learning and wearables can reduce the need for clinical examinations and provide a reliable estimation of the severity of PD at home. This work introduces a multi-channel convolutional neural network to estimate UPDRS part III from motion data recorded by two wearable sensors, considering the gyroscope signals and their spectrogram representations. A novel shared-task self-supervised learning is then employed to leverage the knowledge extracted from the signal to improve the estimation during patients' free-body movements. We utilize 24 PD subjects' data performing daily activities. The estimated UPDRS-III showed an improved correlation with the clinical examinations from 0.67 to 0.81, reducing the mean absolute error from 7.75 to 6.96. Our investigation demonstrates the potential of our approach in providing a reliable estimation of PD severity scores during subjects' daily routines. It can also provide comprehensive information to help physicians manage the disease and adjust the dose and interval of PD medications. © 2024 IEEE.

关键词： Self-supervised learning

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A Data-Driven Analytical Approach on Digital Adoption and Digital Policy for Pharmaceutical Industry in India 7th

A Data-Driven Analytical Approach on Digital Adoption and Di...

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7th International Conference on Microelectronics and Telecommunication engineering, ICMETE 2023

作者： Rana, Anup Sarkar, Bikramjit Parida, Raj Kumar Adhikari, Saurabh Anandha Lakshmi, R. Akila, D. Pal, Souvik Liverpool Business School Liverpool John Moores University Liverpool United Kingdom Department of Computer Science and Engineering JIS College of Engineering Kalyani India School of Computing Science and Engineering Galgotias University Greater Noida India School of Engineering Swami Vivekananda University Kolkata India Department of Computer Application Anna Adarsh College for Women Chennai India Department of Computer Applications Saveetha College of Liberal Arts and Sciences SIMATS Deemed University Chennai India Department of Management Information Systems Saveetha College of Liberal Arts and Sciences Saveetha Institute of Medical and Technical Sciences Chennai India Kolkata India

ISBN: (纸本)9789819995615

The Indian pharmaceutical industry has witnessed exponential growth over the past few decades to become the third largest globally in volume terms and a leading supplier of affordable generics. This paper provides a comprehensive assessment of the industry’s evolution, structure, performance metrics, priorities, challenges and future outlook. Secondary data from government reports, industry associations, company documents and academic literature is synthesized to develop a holistic perspective. The analysis indicates that innovation, research productivity, clinical trials, ethical practices and sustainability shape the industry’s development. Strategic priorities include strengthening R&D capabilities, expanding exports, leveraging digital technologies across operations, ensuring robust supply chains and adopting global quality and ESG standards. Investments in innovation have risen but remain below international benchmarks. While generics account for majority revenues, emerging segments like biosimilars and complex drugs offer growth opportunities. Digital adoption is increasing with data analytics and AI gaining prominence. However, issues related to regulations, pricing control, IP protection, skill shortages and environmental impact need resolution through appropriate reforms and public–private collaboration. India’s vision to become a leading pharmaceutical innovation and manufacturing hub would require reinforcing policy frameworks around IP protection, pricing, regulatory efficiency, skill building and technology adoption. Overall, the industry holds immense potential for beneficial growth but realizing it would necessitate concentrated efforts from stakeholders to build organizational capabilities, foster collaboration and imbibe global best practices. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Supply chains

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A Model for the Analytical Performance of Data Lake in Stock Market Analysis with Databricks Delta Lake

A Model for the Analytical Performance of Data Lake in Stock...

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2023 International Conference on Self Sustainable Artificial Intelligence Systems, ICSSAS 2023

作者： Kamalakkannan, S. Yasmin, A. Arunkumar, R. Kavitha, P. School of Computing Sciences Vels Institute of Science Technology & Advanced Studies Department of Information Technology Chennai Pallavaram India Jbas College for Women Department of Computer Science Chennai India University of South Wales Faculty of Computing Engineering and Science Treforest/Newport Campus United Kingdom School of Computing Sciences Vels Institute of Science Technology & Advanced Studies Department of Computer Applications Chennai Pallavaram India

ISBN: (纸本)9798350300857

Stock market investments are highly rewarding but also high in risk. Modern investors use variety of tools to take informed investment decisions. In the current era of digital world, financial service industry has generated huge volume and immense verities of data with extreme speed. Due to the rapid growth in data collection and the heterogeneous nature and complexity of the data, there is a need for Big Data analytical solution that would be able to deal with the stock market data. Large volumes of unstructured, heterogeneous raw data can be stored in a massively scalable manner using data lakes, which are the ideal solution to the big data storage conundrum. The ability of a data lake to preserve data in its original format while processing it at runtime using a schema on-read technique is its key feature. The challenge faced in the data lake is performing analytics which is a significant tool to calculate and analyze the stock market. The proposed architecture of Azure Databricks DeltaLake (ADDL) with Azure DataLake Storage Generation 2 (ADLSG2) is used for analytical processes like Fibonacci retracement for better stock analysis, which aid in forecasting the market price for better investment. As a result, the research focus is to produce a storage having read as well as write capabilities by taking into consideration the Extract-Load-Transform (ELT) operation on the datasource. In this experimental databricks implementation, runtime is performed using open source of Apache Spark API and a highly improved execution engine, which results in a significant performance improvement when comparing to the standard source of Apache Spark available on the ADLS platform. Additionally, the Fibonacci retracement level calculation is achieved with the analytics and forecasting of test close price with various ML and DL techniques such as KNN, LSTM are compared with original price of the test data for better prediction of forecast close price. © 2023 IEEE.

关键词： Big data

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Multi-Modal Transformer and Reinforcement Learning-Based Beam Management

IEEE Networking Letters

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IEEE Networking Letters 2024年第4期6卷 222-226页

作者： Ghassemi, Mohammad Zhang, Han Afana, Ali Sediq, Akram Bin Erol-Kantarci, Melike University of Ottawa School of Electrical Engineering and Computer Science OttawaONK1N 6N5 Canada Ericsson Inc Research and Development Department OttawaONK2K 2V6 Canada

Beam management is an important technique to improve signal strength and reduce interference in wireless communication systems. Recently, there has been increasing interest in using diverse sensing modalities for beam management. However, it remains a big challenge to process multi-modal data efficiently and extract useful information. On the other hand, the recently emerging multi-modal transformer (MMT) is a promising technique that can process multi-modal data by capturing long-range dependencies. While MMT is highly effective in handling multi-modal data and providing robust beam management, integrating reinforcement learning (RL) further enhances their adaptability in dynamic environments. In this letter, we propose a two-step beam management method by combining MMT with RL for dynamic beam index prediction. In the first step, we divide available beam indices into several groups and leverage MMT to process diverse data modalities to predict the optimal beam group. In the second step, we employ RL for fast beam decision-making within each group, which in return maximizes throughput. Our proposed framework is tested on a 6G dataset. In this testing scenario, it achieves higher beam prediction accuracy and system throughput compared to both the MMT-only based method and the RL-only based method. © 2019 IEEE.

关键词： Decision making

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Enhancing Android Security: Static Analysis for Robust Protection and Resilient Defences using Deep Learning 7

Enhancing Android Security: Static Analysis for Robust Prote...

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7th International Conference on I-SMAC (IoT in Social, Mobile, Analytics and Cloud), I-SMAC 2023

作者： Abhishek, S. Anjali, T. Prakash, Prathiba Nair, Arun Karunakaran Amrita Vishwa Vidyapeetham Amrita School of Computing Department of Computer Science and Engineering Amritapuri India University of Greenwich Old Navel College Department of Computer Science LondonSE10 9LS United Kingdom

ISBN: (纸本)9798350341485

Android has been a popular target for criminal actions due to its widespread use as a prominent mobile operating system, requiring the creation of sufficient security measures. Android has grown exposed to illegal activities due to its open-source design and ability to install third-party apps without centralized oversight. The suggested study thoroughly examines the rights that programs ask for to see whether there are any noticeable differences between the permissions used by malware samples and their benign equivalents. Based on complex authorization patterns, various machine learning methods are applied to distinguish between the two groups effectively. The discoveries spur the creation of solid security frameworks, eventually reducing the dangers of potential malware attacks. © 2023 IEEE.

关键词： Mobile security

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Blockchain-based Secure UAV-assisted Battlefield Operation underlying 5G 14

Blockchain-based Secure UAV-assisted Battlefield Operation u...

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14th International Conference on Information and Knowledge Technology, IKT 2023

作者： Pancholi, Dhruvi Jadav, Nilesh Kumar Tanwar, Sudeep Garg, Deepak Zanjani, S. Mohammadali Institute of Technology Nirma University Department of Computer Science and Engineering Ahmedabad 382481 India SR University Distinguished Professor School of Computer Science and Artificial Intelligence Warangal 506371 India Islamic Azad University Najafabad Branch Department of Electrical Engineering Najafabad Iran

ISBN: (纸本)9798350349412

The use of unmanned aerial vehicles (UAVs) has significantly increased during the past few years. UAVs have a wide range of applications, but their employment in the defense sector has grown significantly in recent years as a result of their exceptional effectiveness in reducing casualties and enabling the accomplishment of high-profile as well as time-sensitive operations. UAVs are helpful for numerous defense-related operations, such as border patrol, surveillance, emergency management, damage assessment, delivery of medical supplies and armaments, and many others. However, because wireless communication is open, UAVs can be used to undermine the effectiveness of military operations. In this paper, we developed a blockchain-based framework to mitigate security threats from UAVs operating in a combat scenario. The MATLAB virtual environment is used to simulate a battlefield scenario, including UAVs, sensors, enemies, and friendly nodes. The UAV scenario also includes an enemy that continuously manipulates the UAVs to endanger battlefield operations. The suggested framework employs machine learning algorithms to find malicious UAVs. Further, the proposed framework uses blockchain technology to increase security and lessen data tampering attempts. A smart contract (for data validation) is deployed on an IPFS-based public blockchain. In addition, 5G technology is used, which makes it possible for UAVs and the control station to communicate effectively with lower packet error rates and latency. The proposed framework is evaluated by considering evaluation metrics, such as 5G packet error rate, latency, and transaction and execution costs for the blockchain. © 2023 IEEE.

关键词： Blockchain

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Air Purification Robotics using Cloud and Deep Q Networks for Autonomous Systems 1

Air Purification Robotics using Cloud and Deep Q Networks fo...

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1st International Conference on Innovative Sustainable Technologies for Energy, Mechatronics and Smart Systems, ISTEMS 2024

作者： Reddy, Elangovan Guruva Damodhar, T. S. Balaji Yuvaraj, S. Vathani, B. Santha Sivakumar, S. Malathi, N. Koneru Lakshmaiah Education Foundation Department of Artificial Intelligence and Data Science Andhra Pradesh Vaddeswaram India Madanapalle Institute of Technology and Science Department of Electrical and Electronics Engineering Andhra Pradesh Madanapalle India Srm Institute of Science and Technology Faculty of Engineering and Technology Department of Electronics and Communication Engineering Tamil Nadu Kattankulathur India Mahalashmi Women's College of Arts and Science Department of Management Studies Tamil Nadu Chennai India Department of Computer Science Tamil Nadu Perambalur India Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Saveetha University Department of Computer Science and Engineering Tamil Nadu Chennai India

ISBN: (纸本)9798350384246

Rising urban air pollution poses serious health dangers. Through robots, cloud computing, and deep reinforcement learning, this research proposes a new air purification method. The suggested autonomous system analyzes real-time air quality parameters and optimizes air purification tactics using cloud-based data processing. Advanced sensors and purification processes allow robotic agents to intelligently adapt to shifting pollution patterns in dynamic settings. Robots use Deep Q Networks (DQN) to learn and change their purifying tactics based on past data and environmental input. Cloud computing and deep learning improve air purification efficiency and enable real-time pollution response decision-making. The autonomous system is scalable and adaptable to varied urban environments since it requires little human involvement. The cloud-based design lets autonomous agents communicate and coordinate to improve air quality. Experimental findings show that the suggested strategy improves air purification over previous approaches. This system advances environmental robotics and lays the groundwork for intelligent, autonomous systems that handle urban air quality and pollution issues. © 2024 IEEE.

关键词： Robotics

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A Machine Learning Model Predicts Crop-Energy Yields in Agrivoltaics

A Machine Learning Model Predicts Crop-Energy Yields in Agri...

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IEEE Conference Photovoltaic Specialists (PVSC)

作者： Farrukh ibne Mahmood Muhammad Ashraful Alam Nauman Zafar Butt Department of Electrical Engineering School of Science and Engineering LUMS Lahore Pakistan School of Electrical and Computer Engineering Purdue University West Lafayette IN USA

ISBN: (数字)9781665464260

ISBN: (纸本)9781665475822

Agrivoltaic (AV) systems can simultaneously produce food and energy, providing a much-needed solution to balance the food-energy nexus for the growing global population. In this context, it is crucial to identify optimal design solutions applicable to worldwide locations with reliable food and energy output. Typically, physics-based or mechanistic models are used to predict the food-energy yield for AV. However, these models require significant processing power and time, particularly when applied to multiple locations. Therefore, in this paper, we employ machine learning (ML) to predict crop and photovoltaic (PV) yields for an AV system. Our data is derived from an in-house physics-based model validated with field measurements. We demonstrate how the ML-based model can rapidly (within minutes) and accurately (R 2 ~ 0.99) predict yields for thousands of locations, a task that would take days using the physics-based model.

关键词： Photovoltaic systems Estimation Crops Machine learning Predictive models Reliability engineering Data models

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