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Global Conference for Advancement in Technology (GCAT)

作者： M.S.Kavitha Nitish Vashisht Girish Kalele M.Vigenesh S.Kannimuthu Shilpa M. Katikar Electronics and Communication Engineering R.M.K.Engineering College Chennai India Chitkara Centre for Research and Development Chitkara University Himachal Pradesh India Centre of Research Impact and Outcome Chitkara University Rajpura Punjab India Department of Computer Science Engineering Karpagam Academy of Higher Education Coimbatore Department of Information Technology Karpagam College of Engineering Coimbatore Computer Vishwakarma Institute of Technology Pune

ISBN: (数字)9798350376685

ISBN: (纸本)9798350376692

There is significant possibility to use bandwidth (in the 28 - 38 GHz bands) and indeed the 60 GHz band for mitochondrial and classmates’ wireless technologies as the price of enormously WiFi processors at micrometer wave (nanometers) rates continues to fall. Using thruster cell towers and a wireless service rolling based on the measured path particular input at bandwidth of 38 GHz and 60 GHz, this research provides dimensions of urban cytoskeleton and participant multi - band tv stations. The transmit delay distributed and insertion loss as a role of divorce maximum range and base station referring directions for a variety of real-life settings. According to the statistics provided here, carriers have significant dynamic routing delay distributions and can take advantage of a wide range of aiming angles to just provide dispersal connections at 38 GHz, where unimpeded lines of follow free space antenna radiation loss. At 60 GHz, there is noticeably more route loss, less variation in lag patterns, and fewer special station orientations for establishing links. We segments known correlations between RMS interruption spread and aerial aiming angles in both the 38 GHz and 60 GHz bands. We also note that extra route loss (above empty space) has a negative correlation with transponder spacing.

关键词： Wireless communication TV Program processors Poles and towers Millimeter wave measurements Bandwidth Receivers Propagation losses Delays Wireless fidelity

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A new fringe analysis method based on shared bottom network for single frame 3D reconstruction 15

A new fringe analysis method based on shared bottom network ...

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15th International Conference on Information Optics and Photonics, CIOP 2024

作者： Lu, Jin Zhang, Min Jie Li, Mu H. Li, Mu J. Wang, Jin Wu, Pei Y. Sun, Wei Li, Lin School of Communication and Information Engineering XI'AN University of Posts&Telecommunications 618 Chang'an West St. Shaanxi Xi'an710121 China School of Computer Science and Technology Xi’an University of Posts and Telecommunications Xi’an China China Academy of Industrial Internet Electronic city technology research and development center Chaoyang Beijing100020 China College of Engineering Ohio State University ColumbusOH43210 United States Wuhan University of Technology 1178 Heping Avenue Wuchang Hube Wuhan430061 China

ISBN: (数字)9781510686175

ISBN: (纸本)9781510686168

Recently, deep learning-based methods have been employed in optical measurement. The fringe to phase method based on deep learning can achieve high-precision 3D topography measurement and is applied to various optical metrology tasks, including phase extraction, phase unwrapping, fringe order determination, depth estimation, and other crucial steps. However, it appears simplistic to obtain images of each metrological task from a single fringe pattern. This paper proposes a novel network that effectively extracts the semantic features of fringe patterns by incorporating the design architecture of Transformer while retaining the advantages of convolutional networks. The architecture primarily consists of a backbone, decoder, and feature extraction block which enhance the features at different frequencies within a single fringe pattern. The backbone and decoder are specifically designed for wrapped phase prediction tasks. Experimental results demonstrate that the network accurately predicts the wrapped phase from a single fringe pattern. In comparison with previous methods, this paper's approach offers several contributions: an efficient utilization of a new type of encoder for extracting high-level semantic features from fringe patterns;moreover, only a single grayscale image is required as input for the network without relying on color composite images or additional prior information. © 2024 SPIE.

关键词： Fringe analysis Feature extraction Convolution Semantics Network architectures Phase unwrapping 3D modeling Deep learning Phase shifts Projection systems

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Transforming HR in Finance by Leveraging IoT and AI for Strategic Talent Management

Transforming HR in Finance by Leveraging IoT and AI for Stra...

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2024 International Conference on Innovative Computing, Intelligent communication and Smart Electrical Systems, ICSES 2024

作者： Indoria, Devadutta Dakshinamoorthy, Barani Karthik, M. Sharma, Manish Kaliappan, S. Manikandan, G. Vikram Dev University Department of Commerce Odisha Jeypore India Sharda School of Business Studies Sharda university Department of Human Resource Management &ob Uttar Pradesh Greater Noida India Shri Vishnu Engineering College for Women Department of Management Studies Andhra Pradesh Bhimavaram India Graphic Era Deemed to be University Department of Computer Science and Engineering Uttarakhand Dehradun India Lovely Professional University Phagwara Division of Research and Development Punjab India Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Saveetha University Department of Electronics and Communication Engineering Chennai India

ISBN: (纸本)9798331543617

Financial institutions have recognized the value of AI and the IoT in enhancing strategic human resource management. The paper presents a comprehensive integration framework that utilizes AI and IoT technologies. The focus is on key aspects of human resources operations, such as employee recruitment, retention, employee engagement, and performance evaluations. Productivity and efficiency can be maximized by 72% and 92%, respectively, through the application of AI algorithms and predictive analytics in IoT devices used in the collection of real-time data for HR operations. The proposed framework will be diverse in performance indicators, have appropriate strategies for change management, robust security measures, and pipelined data meant for long-term use. It consequently contributes to the design of a dynamic and personalized human resource management strategy based on insights derived from data. © 2024 IEEE.

关键词： Decentralized finance

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A Binary Sparse Uniform Modem System for Resource Allocation in 5G and Beyond Networks

A Binary Sparse Uniform Modem System for Resource Allocation...

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Mobile Networks and Wireless communications (ICMNWC), IEEE International Conference on

作者： Saravanan G Ramy AlFatlawy Kirtanjot Kaur K. Kavitha Devi Anita Sofia Liz. D. R Department of Electronics and Communication Engineering Sri Sairam Institute of Technology Chennai India Department of Computers Techniques Engineering College of Technical Engineering The Islamic University Najaf Iraq University Centre for Research & Development Chandigarh University Mohali India Department of Electronics and Communication Engineering SRM Institute of Science and Technology Chennai India Department of Computer Science and Engineering New Prince Shri Bhavani College of Engineering and Technology Chennai India

ISBN: (数字)9798331528348

ISBN: (纸本)9798331528355

In this era, resource allocation in 5th Generation (5G) and beyond networks establishes an effective, dynamic and intelligent management for resources of networks. However, the traditional Machine Learning (ML) and Deep Learning (DL) approaches exhibit challenges like latency optimization, user mobility and spectrum efficiency for improved network performance. In order to solve these issues, the proposed model utilizes Binary Sparse Uniform Modem (BSUM) for resource allocation. Primarily, the Deep Slice and Secure 5G & Long Term Evolution (LTE) Wireless dataset is used for allocating the resources. Subsequently, the preprocessing technique accompanies Normalization, Scaling and Transformation which helps for aligning feature scales, avoiding from dominant features that leads to equitable and optimized allocation of resources like spectrum and power. Next step involves extraction of features using Deep Convolutional Neural Networks (DCNN) that helps in identification of patterns and relationships and enhancing the scalability. Further, Classification model is incorporated with BSUM for adapting seamless dynamic environment of networks and preliminaries required for resource allocation. The results of the proposed model are superior to the existing models by achieving 35.92 Megabits Per Second (MBPS) and 89.23% Interactivity score.

关键词： Wireless communication Adaptation models 5G mobile communication Feature extraction Dynamic scheduling Modems Resource management Convolutional neural networks communication system security Long Term Evolution

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AI-Powered Multi View Face Video Super-Resolution Techniques for Real-Time Video Processing

AI-Powered Multi View Face Video Super-Resolution Techniques...

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Integrated Intelligence and communication Systems (ICIICS), International Conference on

作者： D.V. Manjunatha Meenakshi Maindola Reny Jose S. Kaliappan Mitul Patel Ramya Maranan Department of Electronics & Communication Engineering Alvas Institute of Engineering & Technology Moodabidri India Department of Computer Science & Engineering Graphic Era Deemed to be University Dehradun India Department of Computer Applications Marian College Kuttikkanam (Autonomous) Idukki India Division of Research and Development Lovely Professional University Phagwara India Faculty of Engineering and Technology Parul institute of Engineering and Technology Parul University Vadodara India Department of Research and Innovation Saveetha School of Engineering SIMATS Chennai India

ISBN: (数字)9798331504960

ISBN: (纸本)9798331504977

In order to improve the visual quality of low-resolution video frames, this study introduces a new super-resolution method for real-time video processing that is powered by artificial intelligence. With little computational overhead, the suggested approach uses a hybrid deep learning model to reconstruct high-resolution frames. This model mixes Convolutional Neural Networks (CNNs) with Generative Adversarial Networks (GANs). The suggested method outperforms state-of-the-art methods by 12.3% in PSNR and 8.7% in SSIM, according to experimental results on benchmark video datasets. The PSNR is 34.56 dB and the SSIM is 0.94. Also, running on a GTX 1080Ti GPU, the system shows an average processing speed of 30 fps, which is great for real-time apps. The suggested strategy is effective in eliminating artifacts, improving visual clarity, and retaining fine details in various real-time video processing contexts, according to both quantitative and qualitative results.

关键词： Visualization Interpolation Computational modeling Superresolution Streaming media Mathematical models Real-time systems Kernel Spatial resolution Faces

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Video SAR Image Fusion Using the Effective Reflection Coefficient

Video SAR Image Fusion Using the Effective Reflection Coeffi...

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2021 CIE International Conference on Radar, Radar 2021

作者： Song, Dan Tharmarasa, Ratnasingham Han, Kun Li, Guopeng Kirubarajana, Thiagalingam McDonald, Mike College of Information and Communication National University of Defense Technology Xi'an China McMaster University Department of Electrical and Computer Engineering Hamilton Canada Defence Research and Development Canada Ottawa Canada

ISBN: (纸本)9781665498142

The video synthetic aperture radar (ViSAR), as a mode for sensing in an ever-wider synthetic aperture by sequentially forming SAR images on a series of contiguous or overlapping sub-apertures, has the promising capability to capture wide-angle scattering behavior of objects. In this paper, a novel method for ViSAR image fusion is proposed. First, the ViSAR imaging mode is analyzed and modeled. Based on this model, the reflection coefficient (RC) is then estimated and the significant RC is detected in each sub-aperture image. These detection results are then fused across sub-aperture images to detect scatterers in the area of interest. The performance of the proposed method is evaluated using a simulated scenario and compared with that of the conventional ViSAR image fusion method based on the generalized likelihood ratio test. Numerical results demonstrate the advantages of the proposed method in capturing the aspect-dependent scattering characteristics as well as the spatial structure of objects. © 2021 IEEE.

关键词： Aspect-dependent scattering image fusion video synthetic aperture radar (ViSAR)

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Taking LM as the Brain: A Novel Approach Integrating Language Model and Generative Agent for Intelligent Decision Systems

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International Journal of Network Security 2024年第3期26卷 386-393页

作者： Yang, Jingkang Cai, Xiaodong Liu, Yining Chen, Mingyao Chang, Chin-Chen School of Information and Communication Guilin University of Electronic Technology Guilin541004 China School of Computer Science and Information Security Guilin University of Electronic Technology Guilin541004 China Research and Development Department Guilin Topintelligent Communication Technology Company Limited Guilin541004 China Department of Information Engineering and Computer Science Feng Chia University Taichung 407102 Taiwan

The Large Language Model (LLM) has demonstrated significant capabilities in intelligent robotics and Autonomous Driving(AD). Compared to traditional end-to-end models, decision reasoning in the form of language exhibits enhanced generalization and interpretability. To harness the inferential decision-making capabilities of large models more effectively, we propose a generative intelligent agent capable of translating various sensor signals into language descriptions, thereby utilizing Language Models (LMs) for decision-making. Furthermore, to address the practical deployment challenges of large models, we fine-tuned a GPT-2 model on an intelligent traffic corpus. The fine-tuned model performs competitively with LLMs in the domain of traffic decision-making. This approach can potentially deploy LMs directly on computationally constrained in-vehicle platforms to reduce communication latency. We evaluated the proposed method on a simulated dataset, and the results indicate that the pure language decision model surpasses the vector-based and fusion models. Moreover, our method exhibits significantly higher efficiency than LLMs. © (2024), (International Journal of Network Security). All rights reserved.

关键词： Decision making

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Efficient Region-Aware Neural Radiance Fields for High-Fidelity Talking Portrait Synthesis

Efficient Region-Aware Neural Radiance Fields for High-Fidel...

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International Conference on computer Vision (ICCV)

作者： Jiahe Li Jiawei Zhang Xiao Bai Jun Zhou Lin Gu School of Computer Science and Engineering State Key Laboratory of Software Development Environment Jiangxi Research Institute Beihang University School of Information and Communication Technology Griffith University RIKEN AIP The University of Tokyo

This paper presents ER-NeRF, a novel conditional Neural Radiance Fields (NeRF) based architecture for talking portrait synthesis that can concurrently achieve fast convergence, real-time rendering, and state-of-the-art performance with small model size. Our idea is to explicitly exploit the unequal contribution of spatial regions to guide talking portrait modeling. Specifically, to improve the accuracy of dynamic head reconstruction, a compact and expressive NeRF-based Tri-Plane Hash Representation is introduced by pruning empty spatial regions with three planar hash encoders. For speech audio, we propose a Region Attention Module to generate region-aware condition feature via an attention mechanism. Different from existing methods that utilize an MLP-based encoder to learn the cross-modal relation implicitly, the attention mechanism builds an explicit connection between audio features and spatial regions to capture the priors of local motions. Moreover, a direct and fast Adaptive Pose Encoding is introduced to optimize the head-torso separation problem by mapping the complex transformation of the head pose into spatial coordinates. Extensive experiments demonstrate that our method renders better high-fidelity and audio-lips synchronized talking portrait videos, with realistic details and high efficiency compared to previous methods. Code is available at https://***/Fictionarry/ER-NeRF.

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Optimized Nonlinear Controller for MPPT of PV Module under Variation Irradiance Condition

Optimized Nonlinear Controller for MPPT of PV Module under V...

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Global Conference for Advancement in Technology (GCAT)

作者： Simranjeet Nanda Shivangi Gupta Abhishek Singla K. Yuvaraj G. Premananthan S.A. Tiwaskar Centre of Research Impact and Outcome Chitkara University Rajpura Punjab India Quantum University Research Center Quantum University Chitkara Centre for Research and Development Chitkara University Himachal Pradesh India Department of Computer Science Karpagam Academy of Higher Education Coimbatore India Department of Electronics and Communication Engineering Karpagam College of Engineering Coimbatore India Computer Engg. Vishwakarma Institute of Technology Pune India

ISBN: (数字)9798350376685

ISBN: (纸本)9798350376692

To combat nonlinearity brought about by solar panel arrays until consequently maximize power harvesting, maximal power position (MPP) surveillance methods have historically been towards the cutting edge of solar power technologies. Various MPP tracking devices are available, including ones with fixed as well as variable move sizes. A constant walk-size MPP monitor is used, and this causes steady-state energy swings close target the MPP. That standard variable walk-size MPP monitor, contrary to popular belief, uses the same multiplication ratio to speed up convergence, but its reaction time turns out to have been slow under unclear PV settings. In order to precisely identify the genuine MPP, an inverse fractional neural synergetic controllers (FNSC) based MPP monitor is presented throughout this study. The suggested improved FNSC assures no continuous power fluctuations throughout a wide range of dynamics. Mpg even with uneven illuminations. Under stable state while changing scenarios, the method built around fractional math makes use of macroeconomic variables to enhance the efficiency that the put forward FNSC. Utilizing OPAL-RT (OP5700), the efficacy associated with the suggested tailored FNSC was confirmed. The findings for this research support the viability in developing an FNSC-driven Mcp tracker that would provide faster MPP monitoring. Under a steady-state while changing scenarios, the maximum possible time of tracking obtained using the suggested FNSC has below 21, 80 while 20, 80 ms, accordingly Additionally, it surpasses the performance of the current MPP monitors by guaranteeing 99.7% monitoring efficiency.

关键词： Target tracking Surveillance Lighting Real-time systems Steady-state Macroeconomics Solar panels Monitoring Standards Optimization

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Power Budget Calculation and Sensitivity Enhancement for N×25 Gbps TDM-PON Configuration

Power Budget Calculation and Sensitivity Enhancement for N×...

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Sciences and Techniques of Automatic Control and computer engineering MI-STA, IEEE International Maghreb Meeting of the Conference on

作者： Ahmed B Feituri M.F.L. Abdullah Abdulhameed Almabrok Swedan Hager A Suliman Faculty Of Electrical and Electronic Engineering Universiti Tun Hussen Onn Malaysia Batu Pahat Malaysia Applied Research and Development Authority The Electronic and Communication Research Center Tripoli Libya Department of Computer Science University of Misurata Misurata Libya

Passive Optical Network (PON) standards relied on Time Division Multiplexing (TDM) and Wavelength Division Multiplexing (WDM) methods to maximize data rate. The latest 25GS-PON specifications were declared by Multi-Source Agreement (MSA) group in 2022. The current work stacks two and four 25-Gb/s TDM-PON downstream (DS) channels. The simulation, evaluation, and outcomes are completed using various versions of *** software. O-band wavelength plan as well as Continuous-Wave (CW) laser with Non-Return-to-Zero/On-Off-Keying (NRZ-OOK) format support the system to mitigate Chromatic Dispersion (CD) and upgrade the system capacity. The power budget of 31.1 dB is suitable for Optical Distribution Network (ODN) standards, and the standard value of receiver sensitivity (-24.1 dBm) is achieved. The transmitted power of 1, 4, and 7 dBm are needed for 32, 64, and 128 users/λ, respectively. The transmission is successful for 512 subscribers and 20 km optical reach without the use of amplifiers, Forward Error Correction (FEC), and compensation techniques. The effect of four-wave mixing (FWM) is solved by the wavelength space of 0.8 nm as well as the slight increase in transmitted power from 7 to 7.8 dBm. The open and clear eye diagrams prove the quality of received signals. The simplicity of structure is high because of utilizing basic components and Intensity Modulation/Direct Detection (IM/DD) strategy without amplification and equalization. FEC and compensation techniques are recommended for future work.

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