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Atmospheric Attenuation at mmWave below 57GHz Based on measurement in Tropical Region 16

Atmospheric Attenuation at mmWave below 57GHz Based on Measu...

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16th IEEE Malaysia international conference on Communication, MICC 2023

作者： Li, Lu Sali, Aduwati Wali, Sangin Qahtan Universiti Putra Malaysia Faculty of Engineering Department of Computer and Communication Systems Engineering Selangor Serdang43400 Malaysia

ISBN: (纸本)9798350304343

According to ITU-R P.676-12, a modified prediction model of mmWave atmospheric attenuation suitable for tropical areas is proposed in this paper, named AA-mmWave. Combined with Malaysia's air humidity, temperature, and atmospheric pressure measured data in 2020 and 2021 via sensors, the mmWave atmospheric attenuation characteristic curve below 57GHz is simulated and calculated. The results show that the attenuation rate generally increases with frequency, especially from 55GHz to 57GHz, which increases until reaching the highest point of 5.937dB/km. Additionally, the atmospheric attenuation rate of a mmWave is inversely proportional to temperature and pressure. The lowest value of attenuation rate at 32°C achieves 0.3157 dB/km. Finally, there is a proportional correlation between water vapour density and mmWave atmospheric attenuation rate. This finding has essential and indelible reference value for developing mmWave transmission technology in the tropical region. © 2023 IEEE.

关键词： Atmospheric pressure

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A Novel Method for Estimate the Structure Design Complication of Multi-Agents Based systems 2

A Novel Method for Estimate the Structure Design Complicatio...

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2nd IEEE international Maghreb Meeting of the conference on Sciences and Techniques of Automatic Control and computer Engineering, MI-STA 2022

作者： Elmarzaki, Howayda A. Abdelaziz, Tawfig M. Elakeili, Salwa Maatuk, Abdelsalam University of Benghazi Dept. Software Engineering Department Benghazi Libya Libyan International Medical University Dept. Information System Department Benghazi Libya

ISBN: (数字)9781665479189

ISBN: (纸本)9781665479189

Making a design decision in architecture design (AD) is challenging, especially when the system has many components and interactions. The complication is evident in the design phase of agents-based systems that are assigned various responsibilities, according to most works of literature. The study's purpose is to assist system designers in developing effective architectures for agents systems that are not complicated and easy to understand. Complex Task measurement is a novel estimation of entanglement between agents' tasks presented in this paper (CTM). This method is built on the use-case point perspective, however with several adjustments to meet the demands of agents. Before making a design decision, this innovative measurement can be utilized to estimate the architecture design. The findings will be put to use in a variety of situations. As a case study, the research will be applied to the book recommendations system, and the results will be presented after the method has been implemented. © 2022 IEEE.

关键词： Design

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Factor Graph Accelerator for LiDAR-Inertial Odometry (Invited Paper) 22

Factor Graph Accelerator for LiDAR-Inertial Odometry (Invite...

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IEEE/ACM 41st international conference on computer Aided-Design (ICCAD)

作者： Hao, Yuhui Yu, Bo Liu, Qiang Liu, Shaoshan Zhu, Yuhao Tianjin Univ Tianjin Peoples R China PerceptIn Santa Clara CA USA Univ Rochester Rochester MN USA

ISBN: (纸本)9781450392174

Factor graph is a graph representing the factorization of a probability distribution function, and has been utilized in many autonomous machine computing tasks, such as localization, tracking, planning and control etc. We are developing an architecture with the goal of using factor graph as a common abstraction for most, if not, all autonomous machine computing tasks. If successful, the architecture would provide a very simple interface of mapping autonomous machine functions to the underlying compute hardware. As a first step of such an attempt, this paper presents our most recent work of developing a factor graph accelerator for LiDAR-Inertial Odometry (LIO), an essential task in many autonomous machines, such as autonomous vehicles and mobile robots. By modeling LIO as a factor graph, the proposed accelerator not only supports multi-sensor fusion such as LiDAR, inertial measurement unit (IMU), GPS, etc., but solves the global optimization problem of robot navigation in batch or incremental modes. Our evaluation demonstrates that the proposed design significantly improves the real-time performance and energy efficiency of autonomous machine navigation systems. The initial success suggests the potential of generalizing the factor graph architecture as a common abstraction for autonomous machine computing, including tracking, planning, and control etc.

关键词： factor graph autonomous machine computing computer architecture robotics

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LSTM based Soft-Sensor for Estimating Nitrate Concentration in Aquaponics pond 3

LSTM based Soft-Sensor for Estimating Nitrate Concentration ...

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3rd international conference for Innovation in Technology, INOCON 2024

作者： Dharshan, A. Kumar, Purushottam Ravimaran, S. Srinivasulu Reddy, U. Saranathan College of Engineering Department of Artificial Intelligence and Data Science Trichy India National Institute of Technology Artificial Intelligence Machine Learning & Data Analytics Lab Trichy India National Institute of Technology Department of Computer Applications Machine Learning & Data Analytics Lab Trichy India

ISBN: (纸本)9798350381931

In the field of aquaponics, where fish and plants coexist in a symbiotic environment, closely monitoring nitrate levels in the water is crucial due to their profound impact on aquatic and plant well-being. Traditional nitrate measurement methods are often time-consuming and costly. Various approaches, including first principles, IoT-based sensors, and machine learning-based soft sensors, have been attempted to address this challenge. However, these efforts face challenges such as expensive sensors, infrequent data collection, multistage data processing using limited sensor types, and the need for regular maintenance like cleaning and calibration. Additionally, varied environmental conditions affect sensor suitability for different water environments, and even some machine learning-based soft sensors have proven inaccurate. In response, soft sensors, especially deep learning-based ones, have gained prominence in industrial applications for their adaptability and accuracy. These sensors provide real-time insights into complex processes without requiring expensive hardware. In this study, an innovative solution was introduced using Long Short-Term Memory (LSTM) technology, a neural network architecture in deep learning known for capturing complex temporal patterns. LSTM is well-suited for modeling and predicting nitrate concentration changes in aquaponics, trained with extensive data collected from various aquaponic ponds. Through rigorous evaluation, a remarkable MSE value of 0.00074 and an impressive R-squared score of 0.98 were achieved, holding potential for scaling up to commercial applications, benefiting aquaponics operations, supporting researchers, and enhancing sustainability and productivity in aquaponic systems. © 2024 IEEE.

关键词： Nitrates

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Threats to the Resilience of Cloud Platforms

Threats to the Resilience of Cloud Platforms

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international conference on Soft Computing and measurements (SCM)

作者： Artyom A. Balyabin Information Security Department Saint Petersburg Electrotechnical University “LETI” Saint Petersburg Russia

ISBN: (数字)9798350363708

ISBN: (纸本)9798350363715

This paper identifies current threats to the resilience of cloud platforms in accordance with the global knowledge base on adversary tactics and techniques MITRE ATT&CK. An indicator, metric, and measure of the resilience were defined. The assessment of the influence of cyberattacks on the resilience of cloud platforms was carried out on the example of the cloud platform of the Russian Federation “GosTech”.

关键词： measurement Current measurement Computational modeling Knowledge based systems computer crime Resilience

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Image Encryption using Fractional Singular Chaotic systems: An Extended Kalman Filtering Approach

Image Encryption using Fractional Singular Chaotic Systems: ...

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2022 IEEE international conference on Electrical, computer, and Energy Technologies, ICECET 2022

作者： Nosrati, K. Belikov, J. Tepljakov, A. Petlenkov, E. Tallinn University of Technology Department of Computer Systems Tallinn Estonia Tallinn University of Technology Department of Software Science Tallinn Estonia

ISBN: (数字)9781665470872

ISBN: (纸本)9781665470872

Modern and complex dynamical modeling requires effective and precise state estimation techniques to make the system modes available for control actions without expensive measurement tools. By proposing a reliable Kalman-type estimation algorithm in this study, we aim to synchronize a chaotic fractional order singular (FOS) system which introduces some important information related to memory effects and interconnection properties. Then, the proposed algorithm is used to a secure communication framework, and an image-based cryptography algorithm is provided, in which the main image is well-masked by chaotic FOS signals and is successfully decrypted from the estimated chaotic states in receiver side. Furthermore, the performance of the suggested encryption scheme are demonstrated using histograms, pixel correlation, and key space analyses. © 2022 IEEE.

关键词： Chaotic systems

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A Novel Classification Model Based on DWT and CNN-LSTM Motor EEG Imagination Signals 7

A Novel Classification Model Based on DWT and CNN-LSTM Motor...

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7th international conference on Pattern Recognition and Artificial Intelligence, PRAI 2024

作者： Yang, Yuyao Chen, Zuowen Li, Weisu Ma, Yahong Xijing University School of Electronic Information Xi'an Key Laboratory of High Pricision Industrial Intelligent Vision Measurement Technology Xi'an China

ISBN: (纸本)9798350350890

Feature extraction and pattern classification play important roles in brain-computer interface (BCI) systems, which are widely used in rehabilitation medicine, artificial intelligence and other fields. Although advanced EEG acquisition technology can generate a large amount of EEG data for different brain regions, it inevitably has disadvantages such as high cost, long time consuming and inherent high false positives. Considering the contradiction between data volume and accuracy, this paper proposes a MI EEG feature extraction and classification method based on discrete wavelet transform (DWT) and Convolutional Neural Network-Long Short Term Memory (CNN-LSTM). Based on the project BCI motor imagination EEG(MI-EGG) data set, all evaluation indexes of the proposed CNN-LSTM model are superior to other models. The prediction accuracy, precision, recall rate, F1-score and MCC of C3 and C4 channels reaches 96.43%, 96.57%, 96.57%, 96.57% and 93.02%, respectively. The reliability of the proposed model is also proved by tenfold cross-validation. The average accuracy is 99.64%, which is far higher than the other models. It can be concluded that it is a reliable method to predict MI-EEG signals by using CNN-LSTM to train the modeling ability of images. © 2024 IEEE.

关键词： Electroencephalography

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Optimizing LiDAR-Based Depth Map Accuracy Through Multiview Angle Analysis to Enhance Shape measurement Efficiency

Optimizing LiDAR-Based Depth Map Accuracy Through Multiview ...

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international joint conference on computer Science and Software Engineering (JCSSE)

作者： Possathon Teerakantapirut Rungrat Viratikul Charnchai Pluempittiwiriyawej Department of Electrical Engineering Multimedia Data Analytics and Processing Research Unit Chulalongkorn University Bangkok Thailand Department of Electrical Engineering Telecommunication Networking and Systems Chulalongkorn University Bangkok Thailand

ISBN: (数字)9798350381764

ISBN: (纸本)9798350381771

This study introduces an advanced methodology to overcome the intrinsic challenges associated with Light Detection and Ranging (LiDAR) technology integrated into Apple's devices, specifically focusing on optimizing depth map accuracy through multiview angle analysis. Despite the extensive utility of LiDAR in generating detailed three-dimensional models for various applications, from autonomous vehicle navigation to environmental conservation, the efficiency of the technology is often compromised by difficulties in accurately rendering complex geometries from multiple perspectives and by its substantial demands on computational resources. To address these limitations, depth map data are systematically collected and evaluated from four distinct viewing angles (0, 90, 180, and 270 degrees) against established ground truth benchmarks. With a threshold of Intersection over Union (IoU) set at 0.8, it is inferred that the predicted shape exhibits over 80% similarity to the actual shape. When only one view is utilized, an IoU of 0.95 is achieved, alongside a size reduction of 73.07%, a Perimeter Accuracy reduction of 1.56%, and an increase in Root Mean Square Error (RMSE) of 29.37%. This comprehensive analysis highlights the critical need for precise geometric representation and marks a significant step toward enhancing the fidelity of LiDAR measurements. The results of this research indicate a substantial improvement in the accuracy of three-dimensional representations, suggesting a potential shift in the utilization of LiDAR technology across various scientific and infrastructural domains. By proposing a novel framework that optimizes data precision and processing efficiency, this paper contributes to the ongoing discourse on improving 3D modeling and analysis techniques.

关键词： Geometry Solid modeling Laser radar Accuracy Three-dimensional displays Shape Shape measurement

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From Digital Management Platform Development to a computer-Aided Crop Rotation Engineering System in Agriculture

From Digital Management Platform Development to a Computer-A...

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international conference on Management of Large-Scale System Development (MLSD)

作者： Viktor Medennikov Information and Computing Systems Department Federal Research Center “Computer Science and Control” of the Russian Academy of Sciences Moscow Russia

ISBN: (数字)9798350375718

ISBN: (纸本)9798350375725

Thepaper deals with a formalized description of a computer-aided crop rotation engineering system based on a mathematical crop system optimization model implemented in the digital platform for industry management that would offer an ontological and functional integration of farming industry objectives from the joint science-based point of view. The paper demonstrates that when it comes to the digital economy, crop rotation should be the focal point in precision agriculture that all the farming processes are based on.

关键词： Precision agriculture Optimization models Computational modeling Crops Large-scale systems Farming

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SOCIAL NETWORK INFLUENCE ON RESIDENTIAL SOLAR PHOTOVOLTAIC ADOPTION IN AN AGENT-BASED ELECTRICITY SYSTEM MODEL

SOCIAL NETWORK INFLUENCE ON RESIDENTIAL SOLAR PHOTOVOLTAIC A...

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ASME 2024 international Design Engineering Technical conferences and computers and Information in Engineering conference, IDETC-CIE 2024

作者： Russo, Gina Dello Wu, Lei Lytle, Ashley Odonkor, Philip School of Systems and Enterprises Stevens Institute of Technology HobokenNJ07030 United States Department of Electrical and Computer Engineering Stevens Institute of Technology HobokenNJ07030 United States School of Humanities Arts and Social Sciences Stevens Institute of Technology HobokenNJ07030 United States School of Systems and Enterprises Stevens Institute of Technology HobokenNJ07030 United States

ISBN: (纸本)9780791888377

As the United States phases out traditional fossil fuels in favor of renewable energy sources, it is important to capitalize on all available avenues to increase renewable penetration. In the last decade, the costs associated with residential solar photovoltaic (PV) installations have decreased significantly, providing more homeowners with the opportunity to generate their own clean electricity. Research has found that the decision to invest in a residential solar PV system is guided by economic, social, and personal factors. Accounting for such complexities, the joint power of agent-based modeling and social network analysis is leveraged in this study to evaluate the effect of social influence on solar PV adoption. Featuring residential consumer agents with data-driven attributes, a logistic regression function to predict solar adoption, and random and small-world social network implementations, this work simulates residential solar PV adoption in New Jersey. Results indicate that including social influence in an agent-based electricity system model leads to increased installed residential solar capacity, but not necessarily higher adoption rates. These findings suggest that, with an understanding of the intricacies of consumer social networks, there are potential opportunities to bolster residential solar installations through low-cost social campaigns that motivate individuals to adopt home solar through their social ties. Copyright © 2024 by ASME.

关键词： Clean energy

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