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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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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of water include costly and lengthy statistical and laboratory testing; hence, the concept of real-time monitoring is no longer applicable in the modern day.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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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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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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Smart Seed Classification System based on MobileNetV2 Architecture 2

Smart Seed Classification System based on MobileNetV2 Archit...

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2nd International Conference on Computing and Information Technology, ICCIT 2022

作者： Hamid, Yasir Wani, Sharyar Soomro, Arjumand Bano Alwan, Ali A. Gulzar, Yonis Abu Dhabi Polytechnic Information Security and Engineering Technology Abudhabi United Arab Emirates International Islamic University Malaysia Kulliyyah of Information and Communication Technology Department of Computer Science Gombak Malaysia College of Business Administration King Faisal University Department of Management Information Systems Al-Ahsa Saudi Arabia School of Theoretical and Applied Science Ramapo College of New Jersey MahwahNJ United States College of Business Administration King Faisal University Department of Management Information Systems Al-Ahsa Saudi Arabia

ISBN: (纸本)9781665436052

The agricultural transformation in the last decade using artificial intelligence has led to significant gains in productivity and profitability. The traditional machine learning approaches present inherent limitations in extracting features and information from image data. Deep learning techniques, particularly CNN's, help to overcome these limitations due to their multi-level architecture. Various deep learning applications in agriculture include crop disease identification, fruit classification, and germination rate monitoring. Seed image analysis is considered a significant task for the preservation of biodiversity and sustainability. This research uses MobileNetV2, a deep learning convolutional neural network (DCNNs) for seed classification. This model has been preferred due to its simple architecture and memory-efficient characteristics. A total of 14 different classes of seeds were used for the experimentation. The results indicate accuracies of 98% and 95% on training and test sets, respectively. © 2022 IEEE.

关键词： Seed

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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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Using Simulation and DT to Improve Performance of systems Operation Modeling 23

Using Simulation and DT to Improve Performance of Systems Op...

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Proceedings of the 33rd Annual International Conference on computer science and Software Engineering

作者： Grabarnik Genady Ya. Poltronieri Filippo Tortonesi Mauro Zaccarini Mattia Shwartz Larisa Department of Mathematics and Computer Science St. John’s University Queens New York USA Distributed Systems Research Group University of Ferrara Ferrara Italy AI for IT IBM T.J. Watson Research Center Yorktown Heights New York USA

The ever-increasing complexity of applications and networks man-agement demands alternative solutions for identifying of suitable deployment configurations in line with established Service Level Agreements. Some existing approaches utilize methods such as mathematical simulation and application re-enactment under a sim-ulated load implemented as digital twin. In this paper, we describe a mixed-modal framework for varying composition of the above approaches hence improving the performance of application’s ser-vice level modeling further. This framework facilitates the best simulation model performance at any given time by anticipating the model drift and by continuously monitoring performance of the mixed-modal simulation model.

关键词： System Operation Modeling Simulation Mathematical Simulation Re-Enactment Simulation Digital Twin

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The Use of Public Cloud Services Among Students for Online Learning 7

The Use of Public Cloud Services Among Students for Online L...

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7th International Conference on Information Management and Technology, ICIMTech 2022

作者： Hassan, Rosilah Murad, Dina Fitria Wahi, Wahiza Leandros, Riyan Asmak Ismail, Nurul Halimatul Awwad, Samer A. B. Selangor Bangi Malaysia BINUS Online Learning Bina Nusantara University Information Systems Department Jakarta11480 Indonesia Selangor Bangi Malaysia Applied College Princess Nourah Bint Abdulrahman University Department of Computer Science and Information System Riyadh Saudi Arabia Deanship of Information and Communication Technology Imam Abdulrahman Bin Faisal University Dammam Saudi Arabia

ISBN: (数字)9781665450904

ISBN: (纸本)9781665450904

A public cloud is a computing service offered by third-party service providers to Internet users. Some cloud service providers offer both free and paid services, where the prices are scaled to meet the needs and offered more benefits to clients than free cloud services. This paper aims to study the familiarity of public cloud services among respondents of multiple age groups and to find out the advantages and disadvantages of using the public cloud. The survey was taken randomly through google form media. Respondents are the public who use cloud services in Malaysia. A Google Form is used to gather data from the public among multiple age groups, from individuals aged 12 to 45 years and above. Based on the conducted survey, respondents show positive responses towards their knowledge and the usage of public cloud services. Findings from the survey revealed that almost 90.3% of the respondents use public cloud services, and about 75% of them think that the public cloud is reliable and safe to store data. Most respondents have indicated that Google Drive is their preferred public cloud, and the majority them use the public cloud for academic purposes. In conclusion, public cloud services are rather efficient for users with diverse usage necessities. Some public cloud services are reliable and affordable for students, as students need cloud storage for academic purposes, especially during pandemics where everything is conducted online. © 2022 IEEE.

关键词： Surveys

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Network Vulnerability Analysis in Wasserstein Spaces 16th

Network Vulnerability Analysis in Wasserstein Spaces

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16th International Conference on Learning and Intelligent Optimization, LION 16 2022

作者： Ponti, Andrea Irpino, Antonio Candelieri, Antonio Bosio, Anna Giordani, Ilaria Archetti, Francesco Department of Economics Management and Statistics University of Milano-Bicocca Milano Italy Department of Computer Science Systems and Communication University of Milano-Bicocca Milano Italy Dip. Matematica e Fisica Universitá della Campania "Luigi Vanvitelli" Caserta Caserta Italy Oaks S.R.L Milano Italy Consorzio Milano Ricerche Milano Italy

ISBN: (纸本)9783031248658

The main contribution of this paper is the proposal of a new family of vulnerability measures based on a probabilistic representation framework in which the network and its components are modelled as discrete probability distributions. The resulting histograms are embedded in a space endowed with a metric given by the Wasserstein distance. This representation enables the synthesis of a set of discrete distributions through a barycenter and the clustering of distributions. We show that analyzing the networks as discrete probability distributions in the Wasserstein space enables the definition of a new family of vulnerability measures and the assessment of the criticality of each component. Computational results on real-life networks confirm the validity of our basic assumption that distributional representation can capture the topological information embedded in a network graph and yield more meaningful metrics than vulnerability measures based on average values. The computation of the Wasserstein distance is equivalent to the solution of a min-flow problem: its computational complexity has limited its diffusion outside the imaging science community. To avoid this computational bottleneck in this paper, we focus on a statistical approach that drastically reduces the computational hurdles. This approach has been implemented in a software tool HistDAWass. The linear complexity of this approach has also enabled the analysis of large-scale networks.

关键词： Probability distributions

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