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A Resilient Hierarchical Checkpointing Algorithm for distributed systems Running on Cluster Federation 1

7th International Symposium on Security in Computing and communications, SSCC 2019

作者： Mansouri, Houssem Pathan, Al-Sakib Khan Laboratory of Networks and Distributed Systems Computer Science Department Faculty of Sciences Ferhat Abbas Setif University 1 Sétif Algeria Department of Computer Science and Engineering Southeast University Dhaka Bangladesh

ISBN: (数字)9789811548253

ISBN: (纸本)9789811548246

In distributed systems, checkpointing method can be used to ensure fault-tolerance, which in turn could help the system’s security and stability. In simple terms, checkpointing means saving status information of a system. In this work, a novel hierarchical checkpointing algorithm for distributed systems is proposed, which runs on cluster federation. This algorithm is based on two well-known techniques in the literature and it ensures that a locally consistent state is always maintained in each cluster with a global optimistic logging technique between clusters. The proposed algorithm synchronizes the intra- and inter-cluster checkpointing process in such way that each checkpoint taken locally in any cluster is a segment of the consistent global state. Compared to other works, our scheme has low message cost as it makes sure that only few processes in each cluster record checkpoints for any execution operation. © Springer Nature Singapore Pte Ltd. 2020.

关键词： Fault tolerance

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RETRACTED ARTICLE: Obstacles Uncovering System for Slender Pathways Using Unmanned Aerial Vehicles with Automatic Image Localization Technique

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International Journal of Computational Intelligence systems 2023年第1期16卷 1-14页

作者： Selvarajan, Shitharth Manoharan, Hariprasath Khadidos, Alaa O. Shankar, Achyut Khadidos, Adil O. Onyema, Edeh Michael Department of Computer Science Kebri Dehar University Kebri Dehar Ethiopia School of Built Environment Engineering and Computing Leeds Beckett University Leeds UK Department of Electronics and Communication Engineering Panimalar Engineering College Chennai India Department of Information Systems Faculty of Computing and Information Technology King Abdulaziz University Jeddah Saudi Arabia Center of Research Excellence in Artificial Intelligence and Data Science King Abdulaziz University Jeddah Saudi Arabia Secure Cyber Systems Research Group (SCSRG) WMG University of Warwick Coventry UK School of Computer Science Engineering Lovely Professional University Phagwara India Department of Information Technology Faculty of Computing and Information Technology King Abdulaziz University Jeddah Saudi Arabia Mathematics and Computer Science Coal City University Enugu Nigeria

In this study, unidentified flying machines are built with real-time monitoring in mid-course settings for obstacle avoidance in mind. The majority of the currently available methods are implemented as comprehensive monitoring systems, with significant success in monitored applications like bridges, railways, etc. So, the predicted model is developed exclusively for specific monitoring settings, as opposed to the broad conditions that are used by the current approaches. Also, in the design model, the first steps are taken by limiting the procedure to specific heights, and the input thrust that is provided for take up operation is kept to a minimum. Due to the improved altitudes, the velocity and acceleration units have been cranked up on purpose, making it possible to sidestep intact objects. In addition, Advanced Image Mapping Localization (AIML) is used to carry out the implementation process, which identifies stable sites at the correct rotation angle. Besides, Cyphal protocol integration improves the security of the data-gathering process by transmitting information gathered from sensing devices. The suggested system is put to the test across five different case studies, where the designed Unmanned aerial vehicle can able to detect 25 obstacles in the narrow paths in considered routs but existing approach can able to identify only 14 obstacle in the same routes.

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Self-excitation of Terahertz Plasmons in Graphene FETs Enabled by Transit-time Negative Dynamic Conductance

Self-excitation of Terahertz Plasmons in Graphene FETs Enabl...

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Advancement in Technology (ICONAT), International Conference for

作者： Maxim Ryzhii Victor Ryzhii Taiichi Otsuji Vladimir Mitin Michael S. Shur Department of Computer Science and Engineering University of Aizu Aizu-Wakamatsu Japan Research Institute of Electrical Communication Tohoku University Sendai Japan Department of Electrical Engineering University at Buffalo SUNY Buffalo New York USA Department of Electrical Computer and Systems Engineering Rensselaer Polytechnic Institute Troy New York USA

The interband Zener-Klein interband tunneling in the field-effect transistors (FETs) with the graphene channels consisting of the reverse-biased p-i-n junctions enables the negative dynamic conductance associated with the electrons and hole propagation delays. This can lead to the self-excitation of plasmons in the gated p- and n-regions. For the submicrometer gate lengths, the frequency of these plasmons is in the terahertz (THz) range. The plasmon self-excitation in this regime can be used for the generation of THz radiation.

关键词： Ring oscillators Terahertz wave imaging PIN photodiodes Graphene Field effect transistors Logic gates Tunneling

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Terahertz plasmonic resonances in coplanar graphene nanoribbon structures

arXiv

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arXiv 2024年

作者： Ryzhii, V. Tang, C. Otsuji, T. Ryzhii, M. Shur, M.S. Research Institute of Electrical Communication Tohoku University Sendai980-8577 Japan Frontier Research Institute for Interdisciplinary Studies Tohoku University Sendai980-8577 Japan Department of Computer Science and Engineering University of Aizu Aizu-Wakamatsu965-8580 Japan Department of Electrical Computer and Systems Engineering Rensselaer Polytechnic Institute TroyNY12180 United States

We analyze plasmonic oscillations in the coplanar graphene nanoribbon (GNR) structures induced by the applied terahertz (THz) signals and calculate the GNR impedance. The plasmonic oscillations in the GNR structures are associated with the electron and hole inductances and the lateral inter-GNR capacitance. A relatively low inter-GNR capacitance enables the resonant excitation of the THz plasmonic oscillations in the GNR structures with long GNRs. The GNR structures under consideration can be used in different THz devices as the resonant structures incorporated in THz detectors, THz sources using resonant-tunneling diodes, photomixers, and surface acoustic wave sensors. © 2024, CC BY.

关键词： Graphene

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AI-Driven Fault Detection in Smart Grids

AI-Driven Fault Detection in Smart Grids

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Electrical Power Engineering, communication and Computing Technology(ODICON), Odisha International Conference on

作者： S. Pradeep C. R. Edwin Selva Rex D. Kalaiyarasi Ramya Dhandapani M. Sakthivel K. Vijaipriya Department of Electronics and Communication Engineering S. A. Engineering College Tamil Nadu India Department of Electrical and Electronic Engineering Vignana Bharathi Institute of Technology Hyderabad Telangana India Department of Electronics and Communication Engineering Panimalar Engineering College Chennai Tamil Nadu India Department of Computer Science and Business Systems Sri Eshwar College of Engineering Coimbatore Tamil Nadu India Department of Electronics and Communication Engineering Velammal College of Engineering and Technology Madurai Tamil Nadu India Department of Electronics and Communication Engineering Sri Sai Ranganathan Engineering College Coimbatore Tamil Nadu India

ISBN: (数字)9798350354379

ISBN: (纸本)9798350354386

The use of artificial intelligence (AI) for fault finding in smart grids, with the end goal of improving the reliability and efficiency of ultramodern power systems. Smart grids, which are distinguished by their intricate architectural design and massive data collection, are equipped with cutting-edge approaches for the quick and precise identification of breakdowns. Through the utilization of artificial intelligence techniques, such as Deep Reinforcement Learning (DRL) method can analyze big datasets, identify anomalies, and accurately predict implicit errors with a high degree of precision. In this research, artificial intelligence algorithms that are colorful are investigated, along with their effectiveness in real-time monitoring and defect finding within smart grids. The advantages of AI-driven defect discovery over traditional methods are demonstrated through case studies and simulations. These advantages include important breakthroughs in response time, delicacy, and functional adaptability. The incorporation of artificial intelligence into the management of smart grids holds the potential to bring about substantial breakthroughs in the areas of grid stability maintenance, time-out reduction, and energy distribution optimization. The purpose of this research is to provide a complete analysis of the role that artificial intelligence plays in defect finding. It also offers insight into future advances and practical applications in smart grid systems.

关键词： Power engineering Power system stability Prediction algorithms Stability analysis Real-time systems Smart grids Power system reliability Time factors Artificial intelligence Optimization

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An Intelligent Face Detection by Corner Detection using Special Morphological Masking System and Fast Algorithm 2

An Intelligent Face Detection by Corner Detection using Spec...

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2nd International Conference on Smart Electronics and communication, ICOSEC 2021

作者： Arulkumar, V. Jaya Prakash, S. Subramanian, E.K. Thangadurai, N. Sri Krishna College of Engineering and Technology Computer Science and Business Systems Coimbatore India Sir CR Reddy College of Engineering Department of Computer Science and Engineering Andhra Pradesh Eluru India Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Department of Computer Science and Engineering Chennai India Electronic and Communication Engineering Faculty of Engineering and Technology !!!Centre For Emerging Technologies Karnataka India

ISBN: (纸本)9781665433686

This research study has applied facial recognition techniques using the angle detection algorithm. Also, a fast angle detection algorithm has been used here, but modified it by applying a shielding technique to create a technique related to loud noise. This article describes twelve facial signs that include the corner of the left eye, the corner of the right eye, the left eyebrow, the right eyebrow, the corner of the lip, and the nostril. It consists of two parts;first, a private browsing technique has been performed to filter the image from noise. The proposed method is based on the assumption that an image is available from the front (fully front). Skin areas were first detected using a color-based learning algorithm and six sigma techniques on RGB, HSV, and NTSC scales. Other analyzes involve morphological processing using the detection of the borderline and the detection of the reflection from the light source of the eye commonly referred to as the eye point. In the second step, a fast angle detection algorithm has been used to detect the placeholders on the face. The Fast Angle Finder works on the Angular Response Function (CRF) which is calculated as the minimum change in intensity in all possible directions. Finally, a comparison has been made with other filtering techniques based on the proposed protection techniques. This article has performed different experiments by using the IRIS Face Database, BioID, and the Cohn Canada Database. The recognition rate obtained by the proposed method is appreciable. © 2021 IEEE.

关键词： Face recognition

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The Mechanism of Motion Direction Detection Based on Hassenstein-Reichardt Model 6

The Mechanism of Motion Direction Detection Based on Hassens...

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6th International Conference on Computational Intelligence and Applications, ICCIA 2021

作者： Yan, Chenyang Todo, Yuki Tang, Zheng Kanazawa University Division of Electrical Engineering and Computer Science Kanazawa Japan Kanazawa University Faculty of Electrical Information and Communication Engineering Kanazawa Japan University of Toyama Department of Intelligence Information Systems Toyama Japan

ISBN: (纸本)9781665439336

Motion direction detection is an important part of the visual system. In the past few decades, research on the directional selectivity of motion detection is of great progress. However, they are all concentrated on the cellular level and lack a full understanding in systemic level. To understand motion direction detection, we present a novel motion detection mechanism using the Hassenstein-Reichardt (HR) model. We mimic a subunit of the HR model, propose a local motion direction detective neuron which can detect a specific direction of motion by performing a multiplication operation on one photoreceptor input with a fixed temporal delay and its adjacent photoreceptor input without delay. The proposed neuron can be extended to a two-dimensional vision, for ease of understanding, 8 neurons are implemented in detecting 8 directions of motion. Furthermore, we assume that each photoreceptor in the receptive field has its own local motion direction detective neurons and therefore we can obtain the global motion direction by accumulating the number of activations of all local motion direction detective neurons. The computer simulations have validated that the mechanism we proposed is reliable. We believe it may provide a strong support in understanding motion direction detection and other more complex processing in the visual system. © 2021 IEEE.

关键词： Motion analysis

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Self-Healing for Software Defined Networking

Self-Healing for Software Defined Networking

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Applied Artificial Intelligence and Computing (ICAAIC), International Conference on

作者： Arun Biradar Madhavarapu Chandan Y M Raghavendra K. Chidambarathanu I. Thamarai Anuj Raturi School of Computer Science and Engineering Reva University Bengaluru Karnataka India Department of Computer science and Engineering Koneru Lakshmaiah Education Foundation Vaddeswaram Andhra Pradesh India Department of Electronics and Communication Engineering GSSS Institute of Engineering and Technology for Women Mysuru Kamataka India Department of Computer Science and Business Systems R.M.K. Engineering College Kavaraipettai Tamil Nadu Department of Computer science and Engineering Panimalar Engineering College Chennai City Campus Chennai Tamil Nadu Department of Mechanical Engineering Graphic Era Deemed to be University Dehradun Uttarakhand India

The advent of software-defined networking and virtualization of network functions has brought numerous advantages; however, to achieve the flexibility and programmability envisaged in these technologies, new components in the control and management planes were introduced. Such components require fast recovery because without management the entire data plane is inoperable. To deal with flaws in these plans, the self-healing technique is used, explored in the work that is summarized in this document. The results prove the self-healing efficiency in network slices with strict quality requirements and also demonstrate that the introduced framework is capable of self-healing, that is, healing the degraded environment as well as healing itself.

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Active RIS-Aided Massive MIMO With Imperfect CSI and Phase Noise

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IEEE Transactions on Wireless communications 2025年

作者： Peng, Zhangjie Zhu, Jianchen Pan, Cunhua Zhang, Zaichen Da Costa, Daniel Benevides Elkashlan, Maged Karagiannidis, George K. Shanghai Normal University College of Information Mechanical and Electrical Engineering Shanghai200234 China Southeast University National Mobile Communications Research Laboratory Nanjing210096 China Interdisciplinary Research Center for Communication Systems and Sensing Department of Electrical Engineering Dhahran31261 Saudi Arabia Queen Mary University of London School of Electronic Engineering and Computer Science LondonE1 4NS United Kingdom Aristotle University of Thessaloniki Department of Electrical and Computer Engineering Greece Artificial Intelligence and Cyber Systems Research Center Lebanon

As a recently proposed reconfigurable intelligent surface (RIS) architecture, active RIS has drawn considerable interest. The important feature of the active RIS is its ability to strengthen the impinging signals to mitigate the multiplicative fading effect inherent in passive RIS-aided systems. Herein, we explore the performance of an active RIS-aided uplink multi-user massive multiple-input multiple-output (MIMO) system, considering the phase noise at the RIS. Furthermore, a two-timescale scheme is utilized, where the base station (BS) beamforming is designed based on the instantaneous aggregated channel state information (CSI), while the statistical CSI is used for designing the phase shifts of the active RIS. In addition, the linear minimum mean square error (LMMSE) estimator is adopted to estimate the aggregated channel, which combines both the cascaded and direct channels. According to the estimated channel, a closedform expression for the lower bound of achievable rate is derived. Based on the theoretical expressions, the power scaling laws for the considered system are also investigated. Specifically, when each user’s transmit power is proportionally reduced by the quantity of BS antennas M or RIS elements N, we find that the amplified thermal noise causes the lower bound of the achievable rate to approach zero as M or N tends to infinity. Moreover, an optimization approach based on a genetic algorithm (GA) is introduced to obtain the optimal phase shifts for maximizing the achievable rate. Numerical results reveal that the active RIS can greatly enhance the performance of the massive MIMO system compared to its passive counterpart. © 2002-2012 IEEE.

关键词： Genetic algorithms

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Efficient Drinking Water Quality Analysis using Machine Learning Model with Hyper-Parameter Tuning

Efficient Drinking Water Quality Analysis using Machine Lear...

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International Conference on ent Computing and Control systems (ICICCS)

作者： M Uma Maheswari R. Sudharsanan M Arthy Anne Jenefer L Oormila V Samuthira Pandi Department of Electrical and Electronics Engineering RMD Engineering College Tamil Nadu India Department of Information Technology SRM Institute of Science and Technology Ramapuram Tamil Nadu India Department of Data Science and Business Systems SRM Institute of Science and Technology Kattankulathur Tamil Nadu India Department of Electronics and Communication Engineering Panimalar Engineering College Tamil Nadu India Department of Computer Science Engineering Agni College of Technology Tamil Nadu India Department of Electronics ad Communication Engineering SRM Institute of Science and Technology Ramapuram Tamilnadu India

The conventional approaches to determining the quality of water include costly and lengthy statistical and laboratory testing; hence, the concept of real-time monitoring is no longer applicable in the modern day. The disastrous implications of poor water quality demand that there be a remedy that is both more expedient and more realistic. This research study examines a variety of supervised machine learning algorithms in order to identify the quality of the water. A variety of variables, such as the pH value, the hardness, the solids, the chloramines, the sulfates, the conductivity, the organic carbon, the trihalomethanes, the turbidity, and the potability of the water, are essential for determining the quality of the water. Random Forest (RF) and Decision Tree (DT) are used in order to ascertain the caliber of the water that is suitable for human consumption. The standard laboratory approach for testing the quality of water is one that takes a lot of time and may be somewhat expensive at times. In a very short length of time, the algorithms that are proposed in this study are able to provide an estimation of the quality of drinking water. DT has a height accuracy F1 score of 99%, whereas RF has a score of 87.86% and a precision of 82.36%. The difference in these two scores is due to the fact that DT’s precision is lower. The suggested method demonstrates its potential for usage in real-time water quality monitoring systems by achieving appropriate accuracy with a minimal set of parameters. To prove the usefulness of this program, this is essential.

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