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SEr Analysis of GFdM System οver α – μ Fading Channel for 5G Wireless Applications

Journal of Engineering science and technology review 2024年第4期17卷 205-209页

作者： Jada, Lingaiah Shiyamala, S. School of Electrical and Communication Engineering ECE Vel Tech Rangarajan Sagunthala R&D Institute of Science and Technology Avadi Tamilnadu Chennai600 062 India

A flexible radio waveform, generalised frequency division multiplexing (GFdM) allows for significant degrees of freedom in adjusting the number of time slots, subcarriers, and pulse shaping filters. The GFdM is one of the multi-carrier techniques that has been proposed to achieve 5G requirements. Initially, this study discusses about the derivation of novel analytical expression of symbol error rate under α − μ fading channel using the GFdM system. The proposed derivation includes rayleigh, Nakagami-m, and Nakagami-q fading channels as special cases for different α and μ values. Further, a simulation test-bed has been developed in MATLAB to access the simulation results and they are in good agreement with the derived theoretical results for various simulation parameters such as different roll-off factors, modulation orders and various fading parameters. © 2024 School of science, dUTH. All rights reserved.

关键词： MATLAB

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Semantic Segmentation of Aerial Images Using deep Learning

Semantic Segmentation of Aerial Images Using Deep Learning

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2024 International Conference on Signal Processing, Computation, Electronics, Power and Telecommunication, IConSCEPT 2024

作者： Mariammal, G. B.k, Amit. Sridevi, S. Lotus, r. Lal, Aasshish Ansari, Sabir Ali Vel Tech Rangarajan Dr. Sagunthala R&D Institute of Science and Technology Chennai India

ISBN: (纸本)9798331540685

Semantic segmentation is a computer vision function that incorporates classifying each pixel in an image into a precise category or class. Unlike classification functions where the goal is to assign an exclusive label to the whole image, semantic segmentation furnishes a more detailed concern of the image by segmenting it into regions corresponding to distinct objects or structures. Similarly, this paper represents the model which scheme for utilizing the datasets of spatial resolutions also site for teaching a completely convolutional neural specification named the U-Net to finish the segmentation of aerial images. The dataset comprises 72 images captured by MBrSC (Mohammed Bin rashid Space Centre) satellites, organized into six larger tiles. Each image is remarked with pixel-sharp semantic segmentation across six distinct classes: Vegetation, Water, Land (unpaved area), road, Building and Unlabeled. Given the variability in image sizes, a crucial preprocessing step involves resizing the images to dimensions divisible by 256 and then extracting manageable patches. This ensures uniformity, enabling seamless processing and analysis. The accompanying mask images, initially provided in rGB format with HEX color codes, undergo conversion to standard rGB values. Subsequently, these rGB values are translated into integer labels, which are further transformed into a one-hot encoded format. This transformation is essential for facilitating the learning procedure of the deep learning model. The core of the project employs a U-Net architecture, a type of convolutional neural network (CNN) prominent for its effectiveness in image segmentation tasks. U-Net's structure, characterized by its symmetric contracting and expansive paths coupled with skip connections, allows for precise localization and classification of each pixel in the image. The network processes the preprocessed patches, generating segmented outputs that categorize each pixel according to the predefined classes.

关键词： Semantic Segmentation

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design and implementation of novel variants of multi-level inverters for traction and heavy vehicle applications

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International Journal of Powertrains 2023年第1期12卷 54-80页

作者： Kumar, S. Sri Krishna dhal, P.K. Department of Electrical and Electronics Engineering Vel Tech Rangarajan Dr. Sagunthala R&D Institute of Science and Technology Chennai India

In the advent of increased transportation systems, the importance and growth of heavy vehicle movements and their applications play a significant role. The transportation sector involving heavy vehicles needs appropriate components for systematic movements. With the prominence of components utilised in heavy vehicle systems and power train mechanism, this paper develops novel variants of multi-level inverters for effective functioning of the vehicular system. Along with the classic multi-level inverter (MLI), new variants – modified multi-level inverter and reduced switch multi-level inverter are designed with complete operation based on their switching states and are implemented for a nine level output voltage module. The proposed inverter models for the heavy vehicle system is tested and validated based on the total harmonic distortion (THd). For each level and their switching states, THd is evaluated and the inverter performance is proved for its superiority for heavy vehicle system and their applicability in power train applications. Copyright © 2023 Inderscience Enterprises Ltd.

关键词： Power quality

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IrS-assisted UAV Communications for smart applications 1

IRS-assisted UAV Communications for smart applications

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1st International Conference on Innovations in Communications, Electrical and Computer Engineering, ICICEC 2024

作者： Kumar, J. Arun raaj, r. Sathesh Harshini, V. Lalitha Sri ramya, vellaturi rajendra, degala durga Vel Tech Rangarajan Dr. Sagunthala R&d Institute of Science and Technology Chennai India

ISBN: (纸本)9798350376517

Intelligent reflecting surface (IrS) can adjust wave-front phase, frequency, amplitude, and polarization without radio frequency (rF) chains through passive reflections. IrS alters wireless communication channels with low energy consumption and boosts spectrum efficiency. The deployment of large number of inexpensive passive reflecting elements configures wireless channels to maximize system performance. Unmanned aerial vehicle (UAV) communication has garnered interest due to its high mobility, ease of integration with several technologies, and flexible deployment. However, UAV communication is sensitive to security concerns and obstacles in real-time applications. IrS combined with UAV will likely collaborate to attain unprecedented capabilities in difficult scenarios. Both IrS and UAV improve performance by active wireless propagation modification in 3d space using maneuver control and smart signal reflections. The manuscript also proposes different research ideas for IrS-assisted UAV communications towards the design of low-energy and low-cost smart applications. © 2024 IEEE.

关键词： Unmanned aerial vehicles (UAV)

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Improving the Autonomous Vehicle Vision with Synthetic data using Gen AI 3

Improving the Autonomous Vehicle Vision with Synthetic Data ...

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3rd International Conference on Intelligent data Communication technologies and Internet of Things, IdCIoT 2025

作者： Kadiyapu, durga Chandra Mouli Mangali, Venkata Sai Thummuru, Jaideep reddy Sattula, Sriya Annapureddy, Sasivardhan reddy Arunkumar, M.S. Computer Science and Engineering Vel Tech Rangarajan Dr. Sagunthala R&D Institute of Science and Technology Chennai India

ISBN: (纸本)9798331527549

This research focuses on addressing the challenges associated with training object detection and segmentation models for autonomous vehicles using real-world data, which is often difficult to collect, limited in diversity, and expensive to annotate. To overcome these limitations, we propose a novel framework for generating synthetic datasets using deep learning techniques and generative AI. Specifically, we utilize the Stable diffusion models to generate synthetic images, and annotate them using a combination of the Segment Anything Model (SAM) and the CVAT tool. The annotations are further refined to produce label files, and the dataset is split into training and validation sets. For evaluation, the YOLOv8 is applied for object detection and segmentation tasks using the synthetic dataset. Additionally, the Salesforce/Blip image-captioning model is integrated to provide descriptive captions for the detected objects. Experimental results demonstrate that synthetic datasets generated by this approach not only provide coverage of complex and rare scenarios but also achieve comparable or superior performance compared to real-world datasets in object detection and segmentation tasks. These findings underscore the potential of synthetic data to serve as a cost-effective and scalable alternative to real-world data for autonomous vehicle training. © 2025 IEEE.

关键词： Adversarial machine learning

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Segmentation of the Corpus Callosum from Brain Magnetic resonance Images Using dual deep Learning Classifiers and Optimized U-Shaped Neural Networks

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SN Computer science 2024年第1期5卷 1页

作者： Sarma, A. Padmanabha Saranya, G. Department of Biomedical Engineering Vel Tech Rangarajan Dr. Sagunthala R&D Institute of Science and Technology Chennai India

Segmentation of the corpus callosum (CC) from Mr images is an important step in neuroimaging analysis for various applications, such as brain morphometry, tractography, and connectivity analysis. This research proposes a deep learning-based model for segmenting CC in T1-weighted midsagittal brain MrIs. The model consists of five major phases, namely, image augmentation, pre-processing, segmentation, feature extraction, and feature selection. The collected raw MrI images are augmented using synthetic minority oversampling technique (SMOTE) and pre-processed using improved Gabor filtering and contrast stretch for contrast enhancement. The region of Interest (rOI) has been identified through optimized U-NETS using a new hybrid optimization model. Shape, texture, and colour features are extracted from the segmented image, from which optimal features are selected through proposed hybrid optimization model. The CC segmentation phase will employ a dual deep-learning classifiers approach, with bidirectional LSTM and convolutional block attention module (CBAM) trained using optimally selected features, and a modified convolutional neural network (M-CNN) to provide final detected outcomes. The proposed model aims to improve the accuracy of CC segmentation in brain MrIs. © The Author(s),.

关键词： Corpus callosum Improved Gabor filtering Modified convolutional neural network (M-CNN) Optimized U-NETS

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Green data Center Power Flow Management with renewable Energy Sources and Interlinking Converter 1

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2nd International Conference on Computational Intelligence in Machine Learning, ICCIML 2022

作者： razzaq, Syed Abdul Jayasankar, Vairavasamy Vel Tech Rangarajan Dr. Sagunthala R&D Institute of Science and Technology Chennai India

ISBN: (数字)9789819979547

ISBN: (纸本)9789819979530

The trend of upgrading data centers (dC) with integration of renewable energy sources (rES), smart micro grids, high level of security management tools like data center infrastructure management (dCIM) contributing toward green clean energy and reducing greenhouse gases and CO2 emission. While evaluating energy and sustainability for green data center, the power sources with their efficiency are to be properly selected with their future impacts. Incorporating the cold and hot aisle containment has benefited efficiently in cooling, energy saving, airflow and water management. dC are modernizing with the highest level of certification from United States green building council (USGBC), uptime institute and certified energy efficient for data center awards (CEEdA). In this paper, an interlinking converter (ILC) is designed which is connected with distributed generation (dG) and energy storage system (ESS) forming energy routers with a potential to transfer power bidirectional to AC/dC-loads. ILC performs on an outer loop droop control providing less power and switching losses also cost effective with reduce circulating currents. Integration of ILC can be at higher level of power system and also be effective at UPS section. With the proposed scheme, dC achieves excellent power management with high power density in case of overloading, outages and during emergency shutdowns. The other benefit is surplus power can be sold when electricity prices are high. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Power management

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Preparation of Zinc Oxide nanoparticle (ZnONPs)-Chitosan nanoparticle (CSNPs)-Alginate Scaffold for biological applications

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Nanotechnology for Environmental Engineering 2025年第1期10卷 1-8页

作者： Nivetha, dhanasekar Chandramohan, Subburaman Department of Biotechnology Vel Tech Rangarajan Dr. Sagunthala R & D Institute of Science and Technology Avadi Chennai6000062 India

In the present study, an aqueous extract of the plant Azadirachta indica was used to synthesize zinc oxide nanoparticles (ZnONPs) and chitosan nanoparticles (CSNPs). Both nanoparticles were subjected to Fourier infrared (FTIr) spectroscopy, X-ray diffraction (Xrd), Scanning electron microscopy (SEM) with Energy-dispersive X-ray spectrometer (EdX), dynamic light scattering (dLS) and thermal gravimetric analysis (TGA) for characterization. FTIr analysis reveals distinct peaks corresponding to different functional groups in each material. The Xrd spectra confirms the semi-crystalline structure of CSNPs and the amorphous character of ZnONPs, while SEM analysis shows rod shaped morphology for both nanoparticles. dLS analysis provides insights into the size distributions of synthesized ZnONPs and CSNPs. Evaluation of toxicity demonstrates concentration-dependent effects on zebrafish embryos, highlighting the importance of nanoparticle concentrations in biological applications. Antioxidant activity assays reveal potent scavenging properties of ZnONPs and CSNPs, with the scaffold showing slightly lower activity. Mechanical testing demonstrates the robustness of the Alginate-ZnONPs-CSNPs scaffold, and TGA analysis elucidates its thermal behavior. The prepared Scaffold should be studied in animal models in the near future for various biomedical applications. © The Author(s), under exclusive licence to Springer Nature Switzerland AG 2025.

关键词： Fourier transform infrared spectroscopy

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Simulation and implementation of a reconfigurable dual-function pixel

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Optoelectronics Letters 2024年第8期20卷 454-459页

作者： Shaher dwik Gurusamy Sasikala Department of Electronics&Communication Engineering School of Electrical&CommunicationVel Tech Rangarajan Dr Sagunthala R&D Institute of Science and TechnologyChennai 600062India

This paper presents the simulation and implementation of a reconfigurable pixel that serves both data acquisition and energy harvesting *** main topic focuses on switching between the two operating modes of the photodiode:photoconductive and photovoltaic *** proposed model can be used to design novel optical sensors with energy harvesting capability,such as position sensitive device(PSd)and complementary metal oxide semiconductor(CMOS)image sensors,which can extend the battery lifetime of the whole optical ***,we can overcome power supply problems like wiring and changing batteries frequently,especially in hard-to-reach places like space(cube satellites)or even underwater wireless optical communication(UWOC).The proposed pixel architecture offers the advantage of a minimalistic design with only four ***,it does come with a drawback in the form of higher noise *** simulation was achieved using MATLAB,and the implementation was performed using the programmable system-on-chip(PSoC)*** results showed that the functionality of the dual-function pixel is correct,and the scheduling of both energy harvesting and signal sensing functions was successfully achieved.

关键词： implementation harvesting function

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Influence of nano AL2O3 on compression ignition engine characteristics fuelled with Mahua biodiesel

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Materials Today: Proceedings 2023年 72卷 2238-2244页

作者： Backiyaraj, A. Parthasarathy, M. Murugu Nachippan, N. Senthilkumar, P.B. Kumaran, T. Department of Mechatronics Engineering Agni College of Technology Thalambur Chennai 600130 India Department of Automobile Engineering Vel Tech Rangarajan Dr Sagunthala R&D Institute of Science and Technology Chennai 600062 India Department of Aeronautical Engineering Vel Tech Rangarajan Dr Sagunthala R&D Institute of Science and Technology Chennai 600062 India

Growing energy demand and rising emissions have prompted testing on alternative energy sources for IC engine applications. Alternative energies are commonly extracted from biomass waste, algae and plant seeds. Many environmental difficulties develop as a result of the use of petroleum products in IC engines. As a result, adapting an alternative fuel to an existing engine may necessitate some design changes in order to achieve greater or nearly equal performance of conventional fuels. The primary objective of this experiment is to increase the efficiency of the engine and reduce the concentration of HC, CO2 and NOx. The experimental investigation was assessed in a single cylinder, compression ignition engine fuelled with mahua biodiesel and its diesel blends with AL2O3 nano additive. The engine was analysed with different test fuels such as diesel, 10 % mahua biodiesel + 90 % diesel (B10), 20 % mahua biodiesel + 80 % diesel (B20), 70 % diesel (B30) at different load conditions. From the tested fuel, 20 % mahua biodiesel + 80 % diesel (B20) showed reasonable performance and tail pipe emission compared to other biodiesel blends. The preparation of nano blended biodiesel was based on 80 % of diesel, 20 % of mahua biodiesel and 10, 25, 50 ppm of nano AL2O3. The 50 ppm of nano AL2O3 blended biodiesel shows better thermal efficiency and reduced emissions due to presence of nanoparticle in the blends which enhance the fuel atomization characteristics. BTE of 50 ppm of nano AL2O3 blended biodiesel increased 6.05 %, when compared with the diesel. The emission of HC, CO, NOx, and Smoke by 50 ppm of nano AL2O3 blended biodiesel was reduced by 19.52 %, 29.81 %, 14.48 %, and 23.35 %, respectively when correlated to base fuel. On whole, it was concluded that nano AL2O3 blended mahua biodiesel found to be a better alternative energy for unmodified existing CI engine. © 2022

关键词： Aluminum oxide

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