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Exploring the Intersection of Art and Technology with Neural Style Transfer using Advanced Convolutional Neural Networks for Creative Image Transformations 10

Exploring the Intersection of Art and Technology with Neural...

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10th International Conference on Communication and Signal Processing, ICCSP 2024

作者： Babu, M. Rajasekar, P. Kalaiselvi, K. Shanmugam, Raju Tamilselvan, S. Jayaraman, Suganya Rajalakshmi Engineering Çollege Department of Information Technology Chennai India Sri Krishna College of Technology Department of ECE Coimbatore Tamil Nadu Kovaipudur India Hindusthan College of Engineering and Technology Department of Electronics and Communication Engineering Tamil Nadu Coimbatore India Karnavati University Unitedworld School of Computational Intelligence Gujarat India Saveetha University Saveetha School of Engineering Simats Department of Computer Science and Engineering Tamil Nadu Chennai India Adhiparasakthi Engineering College Department of Electronics and Communication Engineering Tamil Nadu Melmaruvathur India

ISBN: (纸本)9798350353068

Neural Style Transfer (NST) currently leads the artistic creation field that combines artistry with technological innovation, well master the advanced of Convolution neural network (CNN) makes image manipulation easy and with creative flair. This paper suggests the optimized approach for NST which includes a dataset selection, choosing the type model architecture, learning strategies and, all-inclusive evaluation metrics. Through meticulous experimentation, we explore four distinct dimensions of NST applications: face painting, super-slow-motion, interactive, user-customization, real-time performance along online social media. In investigating superior style transfer our product highlights the highest perceptual matching, faithful style representation, and content preservation metrics ensuring the fulfillment of artistic style without deformation of the original content. Model efficiency is achieved by introducing optimized model architectures and fast training strategies, which, in turn, provide for responsive style transfer in real time with as little latency and resource consumption as possible. This is done by adapting models within a specific artistic domain through up-tuning models which will be the source of style transfer that can be tweaked in a manner that caters for any artistic domain. Therefore, the paper research is fundamental because of the management of the user's experience by user-guided interaction, which has given interactive controls and real-time feedback that assist the users to customize stylization according to their likes and dislikes. Gratifying user scoring demonstrates the power of user-directed style transfer as a means of furthering users' engagement and artistry.t. © 2024 IEEE.

关键词： Convolutional neural networks

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Facial Emotion Analysis using Deep Learning for Seamless Human-computer Interaction 2

Facial Emotion Analysis using Deep Learning for Seamless Hum...

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2nd IEEE International Conference on Networks, Multimedia and Information Technology, NMITCON 2024

作者： Sangeetha, S. Saini, Poonam Banupriya, R. Srimathi, S. Kiruthiga, S. Aarthi, R. Srm Arts and Science College Department of computer Applications and Technology Kattankulathur Changelpattu603203 India Sir Padampat Singhania University Dept. of Computer Science & Engineering Vil. Bhatewar Teh. Vallabhnagar Rajasthan Udaipur India Pgp College Of Engineering and Technology Dept. of Electrical and Electronics Engineering India Saveetha School of Engineering Simats Department of Bio Technology Tamilnadu Chennai India Vel Tech Rangarajan Dr Sagunthala R & D Institute of Science & Technology School of Computing Department of Computer Science & Engineering Chennai India R.M.D Engineering College Department of Ece India

ISBN: (纸本)9798350372892

HCII, or human-computer intelligent interaction, is a new academic subject that tries to make computer use more intuitive. Some say machines need human-level social abilities before interacting with people. These skills include reading emotions. Due to its exponential expansion, facial emotion recognition is becoming essential to face recognition. According on photographs, machine learning can recognise expressions. This paper discusses expression recognition model training datasets. Deep learning for Facial Emotion Recognition (FER) is used in this research to teach robots to recognise and adapt to human emotions. An extensive review of deep learning models, including CNNs and CNNR, emphasises their ability to extract spatial and temporal data needed for emotion classification. FER pipeline components like data collection, preprocessing, and model architecture are examined in the paper. Transfer learning, pre-trained models, and data augmentation are studied to address low-labeled dataset concerns. Detailed model training, validation, and evaluation metrics emphasise performance assessment. This study uses the enlarged CohnKanade (CK+) dataset from a face emotion recognition experiment. The system averaged 92.81% accuracy. Eight emotions were successfully detected by the algorithm. The proposed method for emotion recognition has proven useful. © 2024 IEEE.

关键词： Contrastive Learning

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A Review on Precoding Techniques for Massive MIMO-Based Wireless Communication 2

A Review on Precoding Techniques for Massive MIMO-Based Wire...

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2nd International Conference on Microwave, Antenna and Communication, MAC 2024

作者： Kumar, Aman Kumar, Suman Singh, Indrasen Kaur, Jatinder Kaur, Sumanpreet Vellore Institute of Technology School of Electronics Engineering Tamil Nadu Vellore632014 India Chandigarh Group of Colleges Jhanjeri Division of Research & Innovation Chandigarh Engineering College Department of ECE Punjab Mohali140307 India Chandigarh Group of Colleges Jhanjeri Chandigarh Engineering College Department of Computer Science Engineering Punjab Mohali140307 India

ISBN: (纸本)9798350350104

To enhance the performance of fifth-generation (5G) or beyond 5G communication, Massive Multiple Input Multiple Output (mMIMO) technology plays a crucial role. The precoding technique is essential in optimizing the transmission process by mitigating interference and improving the overall spectral efficiency. The paper discusses various precoding strategies, including zero-forcing, minimum mean square error, maximum ratio transmission, and other advanced techniques tailored for maMIMO environments. Furthermore, the impact of channel state information (CSI) on precoding design is explored, considering both idealistic scenarios and practical channel estimation challenges. The objective is to provide researchers, engineers, and practitioners with a comprehensive understanding of the state-of-The-Art precoding techniques in mMIMO, their advantages, limitations, and potential applications in future communication systems. Through this exploration, the paper gives a thorough study of precoding techniques and mMIMO technology for next-generation communication. © 2024 IEEE.

关键词： Mean square error

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A Novel Design SDNS Framework for High end Level Communication (5G) Network 4

A Novel Design SDNS Framework for High end Level Communicati...

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4th IEEE International Conference on Advance Computing and Innovative Technologies in engineering, ICACITE 2024

作者： Deora, Mahipal Singh Saravanan, M. Sunitharekha, G. Pinkeyroshan, K.P.V. Mohammed, Safaa Hallol Abdtawfeeq, Tareq Hafdhi Bhupal Nobles University Computer Science & Engineering Udaipur India Ifet College of Engineering Department of Ece College Tamilnadu Villupuram India Hyderabad India Meenakshi College of Engineering Department of Electronics and Communication Engineering India National University of Science and Technology Collage of Pharmacy dhi qar64001 Iraq Al-farahidi University baghdad Iraq

ISBN: (纸本)9798350360165

Adding flexibility to 5G networks through circuit spinning an adaptable is a viable approach. Key components such as Network slicing, software-defined networking (SDN), and network service virtualization (NFV) are utilized in various innovative ways to enable end-to-end (E2E) isolation services. These methods facilitate the production of desired outputs, ensuring that each network slice is maintained and provisioned as necessary. The project's goal is to develop a website with planned and efficient content distribution using machine learning, incorporating dynamic slices with updated scheduling.A list of primary Key Performance Indicators (KPIs) including CPU power, bandwidth, latency, and connectivity, are used with a substrate manufacturing device that comes equipped with a protective layer to ensure that performance is optimal and safety is not compromised. In addition, the optimization approach, namely ADAM, with an MLP, is used to create network slices. Network slices for any service that is demanded are classified as massive device-type communication (mMTC), ultra-reliable low-latency communication (uRLLC), and enhanced mobile broadband (eMBB. This classification ensures that not just network slices are provided but that the resources are provided in an optimal manner. The Dijkstra algorithm is used to allocate the resources, such that the shortest path amongst the nodes of the substrate network is determined to maximize both user accessibility and the efficiency of resource utilization. These approaches, in a comprehensive manner, are sought to be introduced to improve the efficiency of the overall system in catering to the needs of modern 5 G networks. © 2024 IEEE.

关键词： 5G mobile communication systems

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Implementation and Prediction of Accurate Data Forecasting Detection with Different Approaches 4

Implementation and Prediction of Accurate Data Forecasting D...

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4th International Conference on Smart electronics and Communication, ICOSEC 2023

作者： Maheswari, B. Uma Kirubakaran, S. Saravanan, P. Jeyalaxmi, M. Ramesh, Alabazar Vidhya, R.G. St. Joseph's College of Engineering Department of Computer Science and Engineering Chennai India CMR College of Engineering and Technology Department of Computer Science Engineering Telangana Kandlakoya India Sri Sairam Institute of Technology Department of Electronics and Communication Engineering Chennai44 India Prince Shri Venkateshwara Padmavathy Engineering College Physics Chennai India B v Raju Institute of Technology Department of Computer Science and Engineering Telangana Narsapur India Hkbk College of Engineering Department of ECE Bangalore India

ISBN: (纸本)9798350300888

The ability to recognize anomalies and make accurate forecasts is essential to the practice of many disciplines, including finance, economics, meteorology, and anomaly detection. The installation and prediction of reliable forecasting detection using various methodologies are the primary objectives of this abstract's discussion. The accuracy of forecasting and detection models has been improved through the development of a variety of strategies, methodologies, and approaches. Among these are the more common statistical methods, algorithmic approaches to machine learning, and more recent deep learning methods. Each method has advantages and disadvantages, and the degree to which it is successful is highly dependent on the nature of the data as well as the specific application area. In order to put accurate forecasting detection into action, the data must first be preprocessed, then appropriate features must be identified, and finally, models must be trained utilizing the method that was selected. After then, the models are judged according to how well they can forecast the outcomes of future occurrences or identify deviations from the norm. The purpose of this abstract is to investigate and evaluate the effectiveness of several methodologies regarding accurate forecasting detection. This article addresses the benefits and drawbacks of each technique, as well as the difficulties associated with putting them into practice and their capacity to make accurate forecasts. In addition, the abstract investigates how the accuracy of predicted detection is affected by a variety of aspects including data quality, model complexity, feature selection, and training approaches. This demonstrates the significance of utilizing dependable metrics of evaluation and processes of validation in order to produce accurate and trustworthy predictions. The facts and results that are discussed in this abstract help to a greater knowledge of the many methods that can be utilized for accurate fo

关键词： Forecasting

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AI-Optimized Hardware Design for Internet of Things (IoT) Devices 5

AI-Optimized Hardware Design for Internet of Things (IoT) De...

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5th IEEE International Conference on Recent Trends in computer Science and Technology, ICRTCST 2024

作者： Vishnu Kumar, P. Kulkarni, Adokshaja Mendhe, Dinesh Keshar, Durgesh Kumar Tilak Babu, S.B.G. Rajesh, N. G Pullaiah College of Engineering and Technology Department of CSO Andhra Pradesh Kurnool India Tontadarya College of Engineering Department of Computer Science Engineering Karnataka Gadag India The State University of New Jersey Office of Research Computing Rutgers New BrunswickNJ United States Kalinga University Department of Computer Science Chhattisgarh New Raipur India Aditya Engineering College Department of ECE Surampalem India Maharaja Institute of Technology Department of Electronics and Communication Engineering Karnataka Mysore India

ISBN: (纸本)9798350351378

The Internet of Things (IoT) has emerged as a transformative paradigm, connecting billions of devices to the Internet and enabling seamless communication and data exchange. As the IoT ecosystem continues to expand, there is an increasing demand for energy-efficient, high-performance hardware tailored to the unique requirements of AI-powered applications deployed on IoT devices. This research focuses on the design and optimization of hardware architectures to meet the computational demands of AI algorithms in IoT environments. The proposed AI-optimized hardware design leverages the latest advancements in semiconductor technology and integrates specialized processing units for efficient execution of machine learning tasks. The architecture is tailored to address the constraints of IoT devices, including limited power resources, stringent size constraints, and the need for real-time processing. Key components of the design include low-power processors, hardware accelerators, and memory hierarchies optimized for AI workloads. Additionally, the system employs advanced algorithms for task offloading and distributed processing, enabling collaborative and energy-efficient execution of AI tasks across a network of interconnected IoT devices. The proposed AI-optimized hardware design is evaluated through simulations and real-world experiments, demonstrating superior performance in terms of energy efficiency, processing speed, and scalability. The results showcase the potential of the designed hardware to unlock new possibilities for AI applications in diverse IoT scenarios, ranging from smart cities and industrial automation to healthcare and environmental monitoring. The proposed research serves as a foundation for the development of next-generation IoT devices capable of seamlessly integrating AI capabilities while meeting the stringent requirements of the IoT ecosystem. © 2024 IEEE.

关键词： Internet of things

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Consecutive Irradiation and Thermal Annealing of Commercial P-Channel Power VDMOSFETs 33

Consecutive Irradiation and Thermal Annealing of Commercial ...

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33rd IEEE International Conference on microelectronics, MIEL 2023

作者： Mitrovic, N. Guirado, D. Dankovic, D. Palma, A.J. Ristic, G. Carvajal, M.A. University of Niš Faculty of Electronic Engineering Department of Microelectronics Niš18000 Serbia Hospital Universitario San Cecilio Radiophysics Unit Granada Spain University of Granada Department of Electronics and Computer Technology Granada Spain

ISBN: (纸本)9798350347760

This paper presents research on the effects caused with consecutive irradiation and thermal annealing of two commercial models of p-channel power VDMOSFETs IRF9520 and IRF9530. Threshold voltage shift, which is marked as the most important parameter for both dosimetry application, as well as reliability assesment is obtained and discussed. Experiment setup, as well as the measuring procedures are explained in more detail. Irradiation degradation causes threshold voltage shift, while the thermal annealing causes recovery. The average sensitivity of models for consecutive irradiation sessions is 9.54 ± 0.04 mV Gy-1, where averagely 91.88 % is recovered through thermal annealing. Results point out that p-channel power VDMOSFET devices could be used in some dosimetry applications. © 2023 IEEE.

关键词： Irradiation

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Simulation of the Electrical Control Unit (ECU) in Automated Electric Vehicles for Reliability and Safety Using On-Board Sensors and Internet of Things 5

Simulation of the Electrical Control Unit (ECU) in Automated...

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5th IEEE International Conference on Electrical, computer and Communication Technologies, ICECCT 2023

作者： Sunil Kumar, M. Anuradha, T. Srinivas, Yerram Santhi Sri, T. Catherine, T.J. Aurelia, Sagaya Department of Computer Science and Engineering AP Tirupathi India KCG College of Technology Department of Electrical and Electronics Engineering Chennai India Vignana Bharathi Institute of Technology Department of ECE Hyderabad India Koneru Lakshmaiah Education Foundation Department of Computer Science and Engineering Guntur India R.M.K. College of Engineering and Technology Department of EEE RSM Nagar Puduvoyal India CHRIST University Department of Computer Science Bangalore India

ISBN: (纸本)9781665493604

The adaptation of the energy storage system (ESS) with high power and energy density remains a difficulty for electric vehicles (EVs), despite the increasing demand they are experiencing around the world. A lightweight, compact ESS is necessary to deliver the responsive performance and driving range that modern vehicles need. When planning for widespread use of EVs, it's important to give careful attention to the factors of ESS selection, sizing, and administration. One of the most promising future mobility alternatives is the hybrid electric vehicle (HEV), which offers improved fuel economy and lower pollution levels. As a result, one of the most pressing needs is for automakers to develop new technologies for vehicle design that might help lessen emissions and boost economy. The environmental impact of emissions from light-duty cars is growing in tandem with the annual increase in the number of such vehicles on the road. The usage of other modes of transportation, such as ships and planes, is on the rise, but road transportation will always be the most common. Electronic Control Units, or ECUs, have been increasingly commonplace in cars during the past few decades. Vehicle network multicore CPU scheduling is notoriously difficult. This study's findings consist of a straightforward power-sharing control approach for the HESS based on battery and UC, with the goal of extending the battery's useful life in a city environment. © 2023 IEEE.

关键词： Energy storage

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Analysis of the Performance of Software Defined Networks (SDN) Versus Networks with TCP/IP Architecture 2

Analysis of the Performance of Software Defined Networks (SD...

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2nd International Conference on Intelligent Data Communication Technologies and Internet of Things, IDCIoT 2024

作者： Tanguturi, Rama Chaithanya Amutharaj, J. Prakash, N. Subramani, K. Taqui, Syed Noeman Sree Southry, S. Pace Institute of Technology and Sciences Department of Computer Science and Engineering Andhra Pradesh Ongole India Raja Rajeswari College of Engineering Department of Information Science and Engineering Bengaluru560074 India Kumaraguru College of Technology Department of Electrical and Electronics Engineering Coimbatore India Department of Physics Tamil Nadu Dindigul India Saveetha School of Engineering Department of VLSI Microelectronics Saveetha Institute of Medical and Technical Sciences Tamil Nadu Chennai602105 India Sona College of Technology Department of Electronics and Communication Engineering Tamilnadu Salem India

ISBN: (纸本)9798350327533

It is expected that the transfer speeds on average would rise from 39Mbps to 75.4Mbps. For this reason, the idea of implementing centralized networks, with easy administration and traffic management through policies, has become essential in recent years. Software-defined networks (SDN) and traditional TCPIIP architecture networks are two distinct approaches to network design and management. While TCPIIP architecture networks have been the norm for decades, SDN is a relatively new concept that promises to revolutionize network performance, flexibility, and scalability. In this work, the performance of SDN versus networks with TCPIIP architecture is analyzed and compared. In the analysis of the performance of software-defined networks and networks with TCPIIP architecture, it is predicted to have a favourable response in the evaluation of different metrics such as latency, jitter and bandwidth in both networks;but with better performance in the case of SDN networks. © 2024 IEEE.

关键词： Flexibility and Adaptability Integration with Cloud Services Management and Configuration Security Software-defined networks (SDN)

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Follow me droney! Will UWB technology do it right? 12

Follow me droney! Will UWB technology do it right?

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12th Asia Conference on Mechanical and Aerospace engineering, ACMAE 2021

作者： Hanzel, Krzysztof Department of Electronics Electrical Engineering and Microelectronics Faculty of Automatic Control Electronics and Computer Science Silesian University of Technology Akademicka 16 Gliwice44-100 Poland

Object tracking for consumer drones is currently a rapidly developing technology. Its implementation is currently carried out with the use of object-in-image detection, which often limits the latest possibilities of this technology. The disadvantage of such a solution is also the increase in energy demand and the need to provide additional computing power, or delays resulting from the transfer of this operation to the control device. The proposed solution to this problem in this article is the use of UWB (Ultra-wideband) technology, which is increasingly appearing in consumer devices. The possible yield related to the accuracy of determining the position of the object as well as the experimentally determined frequency of receiving information about the position was presented. Additional advantages, such as resistance to NLOS (non-line of sight) conditions, were also presented. © Published under licence by IOP Publishing Ltd.

关键词： Object detection

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