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3rd International Conference on Sentiment Analysis and Deep Learning, ICSADL 2024

作者： Karpagavalli, C. Nelson, A. Raja, R. Saranya, E. Soudagar, Manzoore Elahi M. Ramaian, Christu Paul Ramco Institute of Technology Department of Artificial Intelligence and Data Science Tamil Nadu Rajapalayam626117 India Panimalar Engineering College Department of Mathematics Tamil Nadu Chennai600123 India Pandian Saraswathi Yadav Engineering College Department of Electronics and Communication Engineering Tamil Nadu Sivagangai630561 India Sri Eshwar College of Engineering Department of Computer Science and Business Systems Tamil Nadu Coimbatore641202 India Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Department of VLSI Microelectronics Tamil Nadu Chennai602105 India Hindustan Institute of Technology and Science Department of Automobile Engineering Tamil Nadu Chennai603103 India

ISBN: (纸本)9798350396157

Aircraft engines are critical, therefore any move to make them safer, more reliable, and fuel-efficient is highly encouraged. To solve the challenges of flight safety and maintenance expense during aircraft engine operation, a prognostics and health management system is deployed. This system is primarily concerned with flaw detection, health evaluation, and life prediction. Making decisions about aircraft engine operation and maintenance is strongly reliant on estimating the remaining useful life (RUL). This study provides an ensemble transfer learning model for RUL prediction using the C-MAPSS turbofan engine dataset. Pre-processing procedures such as filtering and normalization are used to improve the dataset. The proposed ensemble model for RUL prediction is then trained on the processed data. Some of the well-known transfer learning models that are compared include Xception, ResNet-50, and VGG-16. The ensemble model outperforms the other models, with the highest R2 value of 0.9901. Furthermore, the ensemble model is very effective;it can predict RUL in just 37 seconds, which suggests that it could be useful for keeping an eye on engine health and making decisions about aircraft. © 2024 IEEE.

关键词： Aircraft engines

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A Critical Investigation in Analysing the Design and Implementation of Internet of Things and Blockchain Systems for Industry 4.0 3

A Critical Investigation in Analysing the Design and Impleme...

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3rd IEEE International Conference on ICT in Business Industry and Government, ICTBIG 2023

作者： Shilpa, N. Aashima Khan, Shahzad Chauhan, Rahul Muda, Iskandar Gohatre, Umakant Bhaskar School of Engineering Sr University Department of Ece Telangana Warangal India Manipal University Jaipur India College of Clinical Pharmacy King Faisal University Department of Biomedical Sciences Saudi Arabia Graphic Era Hill University Department of Computer Science and Engineering Uttarakhand Dehradun India Universitas Sumatera Utara Medan Indonesia Smt. Indira Gandhi College of Engineering Department of Electronics and Telecommunications Engineering Maharashtra Navi Mumbai India

ISBN: (纸本)9798350343274

The Internet of Things (IoT) is gaining importance in the recent digital environment as it enables in enhancing the productivity and output of the organisation. Business enterprises are focusing to achieve competitive advantage by using the digital tools to their advantage. The IoT enables in applying different sensors which support the users in collecting the information, analyse the pattern and forecast for achieving sustainable growth and development. It has been regarded that there is a growing demand for energy consumption in the world and is expected to grow by more than 38% in the next two decades and hence companies, individuals and government are looking to implement novel methods like blockchain, edge computing and other tools in order to measure the current and future requirements, enhance interaction between energy providers and consumers so as to satisfy the needs. The usage of blockchain and edge computing foster the necessary data analysis, prepare dashboard reports and support in quick decision making. In the present conditions, it has been stated that IoT can support in the overall connectivity with the smart grid connectivity, overall automation of various functions which can support in reducing the energy bills. This study is focused in using descriptive research for analysing the key determinants of IoT and Blockchain in the organisation, the analysis states that IoT and blockchain technology supports in enhancing scalability and efficiency, achieve overall effectiveness of information flows and support the traceability and interoperability for better decision making. © 2023 IEEE.

关键词： Internet of things

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FPGA Implementation of Textural Feature Extractor and Distance Based Classifier for Multi Modal Biometric Pattern Recognition System Using Multi Resolution Approach

FPGA Implementation of Textural Feature Extractor and Distan...

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Pervasive Computing and Social Networking (ICPCSN), International Conference on

作者： S. Hariprasath M. Santhi T.N. Prabakar V. Koushick Aravind. P T. Gunasekaran Department of ECE Saranathan College of Engineering Trichy Department of ECE Sri Sairam College of Engineering Anekal Bangalore Department of ECE Veltech Institute of Science and Technology Chennai Department of Electrical and computer Engineering Mattu University Ethiopia Electrical and Electronics Engineering Section University of Technology and Applied Sciences Muscat Sultanate of Oman

The design of biometric pattern recognition with better accuracy, high robustness and good permanence is a significant design issue in pattern recognition system. A pattern recognition system in which the biological and physiological traits of the user are employed to authenticate the users is known as biometric pattern recognition system. Unimodal biometric systems do not have the required characteristics like solidity, circumvention and permanence. When noisy data acquired from the sensor is given as input, the uni modal biometrics system's accuracy may reduce. Besides the noisy input, less variation of parameters of input data belonging to same class may be a cause for decrease in accuracy of the system. Several such limitations may be removed by applying input data from multi biometric sources. In this work, the design of a textural feature extractor and the classifier for a bimodal biometric system is described. The bimodal biometric system is constructed by fusion of iris code vector and palmprint code vector at feature level. In iris pattern, using the gray level dependent matrix values obtained from wavelet packet decomposed sub-images, the features are extracted Using gabor kernel, features are extracted from the palmprint pattern. The feature vector is formed by the concatenation of both the features after normalization is employed. By binarisation, the binary feature vector is created Using Hamming Distance and its variant, the classifier produces the classification result. The RTL description of the Hamming Distance Classifier (HDC) is written using VHDL 2008 language, Verilog HDL, and the implementation is performed on Spartan 6 FPGA. With a speed grade of 4, the propagation delay of the critical path is 330.12 ns. The obtained delay is sufficient for implementing the classifier as an off-chip design for real-time implementation.

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Enhanced Performance of a Decoupling-Free High Isolation MIMO Antenna for 2.4 GHz Applications

Enhanced Performance of a Decoupling-Free High Isolation MIM...

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IEEE International Conference on Emerging & Sustainable Technologies for Power & ICT in a Developing Society (NIGERCON)

作者： E. Suganya T. Prabhu SatheeshKumar Palanisamy K. Boopathi Raja N. Sathishkumar Ayodeji Olalekan Salau Department of ECE Nitte Meenakshi Institute of Technology Bengaluru Karnataka India Department of ECE Presidency University Bengaluru Karnataka India Department of ECE BMS Institute of Technology and Management Bengaluru Karnataka India Department of ECE Kathir College of Engineering Coimbatore Tamil Nadu India Department of ECE KPR Institute of Engineering and Technology Coimbatore Tamil Nadu India Department of Electrical/Electronics and Computer Engineering Afe Babalola University Ado Ekiti Nigeria Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Chennai India

ISBN: (数字)9798331542559

ISBN: (纸本)9798331542566

This paper describes a Multiple-Input Multiple-Output (MIMO) antenna built primarily for wireless applications running at 2.4 GHz. The antenna comes in two configurations: 1x1 and 2x2. To improve the performance of a MIMO system, it is critical to reduce mutual interference between antennas. Minimizing reciprocal coupling is crucial as it significantly enhances efficiency and prevents unnecessary utilization of system space. This study addresses these concerns by offering a novel approach. The several antennas are intelligently positioned at different angles to prevent mutual interference and optimize space utilization in the system. The procedure of designing the antenna starts with a single patch antenna. This antenna has an input reflection coefficient value of -15.69 dB and dimensions of 27.59mm x 44.99mm. Subsequently, individual (1x1) and paired (2x2) components are positioned at right angles with gaps of 9.8 mm and 16.48 mm, respectively. The proposed antenna achieves and maintains an isolation of more than 39 dB for both the two and four-element MIMO configurations. The research looks at MIMO diversity parameters that provide better results, such as an Envelope Correlation Coefficient (ECC) of less than 0.5, a diversity gain (DG) of 10 dB, a total active reflection coefficient (TARC) of less than 0 dB, and a channel capacity loss (CCL) of 0.1 bits/s/Hz. The design's compatibility for WLAN applications was tested by changing the parameters in the ANSYS HFSS simulation tool.

关键词： Correlation coefficient Couplings Wireless LAN Interference Diversity methods MIMO Reflection coefficient Envelope detectors Antennas Testing

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Leveraging Machine Learning to Predict Volcanic Eruptions by Function Neural Networks 4

Leveraging Machine Learning to Predict Volcanic Eruptions by...

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4th International Conference on Inventive Research in Computing Applications, ICIRCA 2022

作者： Shyamala, K. David Solomon Raju, Y. Swarnalatha, Ch. Saidulu, D. Premala Tha, M. Department of Electronics and Communication Engineering Hyderabad India Mahatma Gandhi University UCE&1 Computer Science & Engineering Nalgonda India Dept of It Hyderabad India Guru Nanak Institute of Technology Dept of Ece Hyderabad India

ISBN: (数字)9781665497077

ISBN: (纸本)9781665497077

When it comes to volcanic eruption surveillance, techniques that rely on seismic analysis of data tend to use automatic methods that ignore the fact that volcanic formations are varied and flexible. Sophisticated procedures for data categorization and assessment, evaluation, investigation and interpretation are needed to identify small variations in seismic sequences linked with volcanic instability. Machine Learning (ML) and Deep Learning (DL) technologies are continually being devised to better machine extraction and processing of valuable data. The proposed study has employed Radial Basis Function Neural Networks (RBFNN) to predict volcanic eruptions in Indonesia. Further, the proposed RBFNN model is compared with other Neural Network (NN) models and it was observed that RBFNN has outperformed all other models in terms of accuracy. © 2022 IEEE.

关键词： Neural network models

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Semi Circle Slotted Triangular Shape Antenna Using Flexible Material

Semi Circle Slotted Triangular Shape Antenna Using Flexible ...

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2022 International Conference on Advancements in Smart, Secure and Intelligent Computing, ASSIC 2022

作者： Sharma, Deepak Singh, Vinod Kumar Vyas, Anupam Kumar, Neetendra Dwivedi, Rajesh Kumar Srgi Department of Electronics and Communication Engineering UP Jhansi India Srgi Dept. of Electrical Engineering UP Jhansi India Iet Bu Department of Ece UP Jhansi India Srgi Department of Computer Science Engineering UP Jhansi India Csjmu Department of Physics UP Kanpur India

ISBN: (纸本)9781665461092

Nowadays microwave communities are working on the design and development of the dual band, triple band & wide band antennas with partial ground. The customers prefer compact devices for WLAN, Bluetooth, PCS, and WiMAX applications, which are frequently used in tablets, medical instruments, smart phones, portable laptops and handheld electronic gadgets. The proposed antenna is consisting of a partial ground and slotted circular patch with line feed. The Triple bandwidth of proposed antenna is 45.77%, 69.20% and 12.69% suitable for triple band application. © 2022 IEEE.

关键词： Smartphones

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The IoT & MIMO Communication Technology: A New computer Communication Approach 2

The IoT & MIMO Communication Technology: A New Computer Comm...

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2nd IEEE International Conference on Communication, Security and Artificial Intelligence, ICCSAI 2023

作者： Roopa, Maria Daya Sasi, Smitha Babu, Sangita Agrawal, Archana Vinaik, Kodavanti Pradeep Patil, Sangram Chandrakant Statistics Christ University India Dayananda Sagar College of Engineering Department of Electronics and Telecommunication Engineering India Computer Science King Khalid University College of Science and Arts Rijal Alma Saudi Arabia Ece Sangam University Bhilwara India Department of Eece Andhra Pradesh Visakhapatnam India University Pune Bharati Vidyapeeth's College of Engineering Lavale Maharashtra Pune India

ISBN: (纸本)9798350369960

Although it also applies to different networks, enormous MIMO is regarded as among the key components of the next 5G networks. To achieve great spectrum effectiveness, rapid data transmission, and excessive distribution of widely dispersed consumers, enormous MIMO technology must be utilised to its full extent. Regarding the contrary the same direction, we have a wealth of study on building networks to handle rapid rates, while the advantages of using enormous MIMO networks with broadband connectivity remain well established. However, implementing large MIMO with IoT applications remains a new field since connected devices has distinct needs and limitations than internet access. The usefulness of enormous MIMO to Internet connection is examined in this research. Researchers concentrate on the enormous machine-type communications (mMTC) as well as ultra-reliable minimal latency (URLLC) general kinds of connected devices envisaged in 5G. This study closes this critical hole by outlining the advantages and disadvantages of using large MIMO enabling Wireless connection. They show many proper ways to communicate and offer perspectives on the compromises that occur whenever enormous MIMO is utilised in addition to mMTC as well as a URLLC. The topic concerning network cutting regarding wireless bandwidth including the application of large MIMO for serving multiple connected devices with extremely diverse demands are now being discussed. The major finding is that large MIMO may prove advantageous in Internet connection settings, however it necessitates close coordination between physical-layer approaches and protocols development. © 2023 IEEE.

关键词： 5G Cross-layer optimization Grant Grant-free RA Massive MIMO Mmtc NR Random access (RA) URLLC

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Enhanced Performance of a Decoupling-Free High Isolation MIMO Antenna for 2.4 GHz Applications 5

Enhanced Performance of a Decoupling-Free High Isolation MIM...

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5th IEEE International Conference on Electro-Computing Technologies for Humanity, NIGERCON 2024

作者： Suganya, E. Prabhu, T. Palanisamy, SatheeshKumar Raja, K. Boopathi Sathishkumar, N. Salau, Ayodeji Olalekan Nitte Meenakshi Institute of Technology Department of Ece Karnataka Bengaluru India Presidency University Department of Ece Karnataka Bengaluru India Bms Institute of Technology and Management Department of Ece Karnataka Bengaluru India Kathir College of Engineering Department of Ece Tamil Nadu Coimbatore India Kpr Institute of Engineering and Technology Department of Ece Tamil Nadu Coimbatore India Afe Babalola University Department of Electrical/Electronics and Computer Engineering Ado-Ekiti Nigeria Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Chennai India

ISBN: (纸本)9798331542559

关键词： 2.4 GHz DG ECC High Isolation MIMO antenna TARC WLAN

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CNN based color balancing and denoising technique for underwater images: CNN-CBDT

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Measurement: Sensors 2023年 28卷

作者： Chandra, I. Sharath Shastri, Rajveer Kushaldev Kavitha, D. Kumar, Kallakunta Ravi Manochitra, S. Babu, Penumuchi Bujji Department of ECE Matrusri Engineering College Telangana Hyderabad India Department of E & TC VPKBIET Baramati SPPU Pune India Department of Computer Science and Engineering Easwari Engineering College Ramapuram Chennai89 India Department of ECE Koneru Lakshmaih Education Foundation Guntur India Department of Computer Science and Engineering Kalasalingam Academy of Research and Education Virudhunagar626126 India Department of Electronics & Communication Engineering Aditya Engineering College Surampalem India

Underwater image deteriorates due to the scattering effect of light in the water. The challenging factor in underwater image analysis is the blurring of the image and color distortion. Various techniques were explored to offer solutions for underwater image restoration. But, still, it has some regression exists in these techniques. This proposal introduces the deep CNN-based color balancing and denoising technique (CNN-CBDT) to enhance underwater images. One of the advantages of underwater characteristics is color, mostly green and blue. Due to its low color contrast, the image exists in fuzzy nature. CNN-based CBDT restores the image with the help ReLU unit in the CNN. Lastly, the suggested method's cutting-edge performance is validated by comparing experimental findings to GLNet, Histeq, and ACE algorithms in conditions of structural similarity (SSIM), peak signal-to-noise ratio (PSNR), structural similarity (SSIM), UCIQE, and UIQM. The suggested approach eliminates the impact of underwater elements, enhancing the color of the picture. It enhances PSNR by 17% having the highest value of 19.580 and SSIM by 15% having a value of 0.952. To make it applicable to real robots, the computation speed is calculated. As a result, the proposed method achieved a computation speed of 9.868 frames per second. © 2023 The Authors

关键词： Image reconstruction

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Advanced Machine Learning Frameworks for Multimodal Image and Speech Signal Processing 3

Advanced Machine Learning Frameworks for Multimodal Image an...

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3rd International Conference for Advancement in Technology, ICONAT 2024

作者： Ramesh Naidu, P. Shandilya, Saurabh Srividya, C.N. Gujar, Shantanu Sudhir Devi, Supriya Dankan Gowda, V. Nitte Meenakshi Institute of Technology Department of Computer Science and Engineering Karnataka Bangalore560064 India Poornima College of Engineering Department of Advance Computing Rajasthan Jaipur India BGSIT Adichunchanagiri University Faculty of Engineering Management & Technology Dept. of ECE B G Nagara571448 India Rochester Institute of Technology Golisano College of Computing and Information Sciences Department of CyberSecurity RochesterNY14623 United States Alliance University Department of Mathematics Karnataka Bangalore562106 India BMS Institute of Technology and Management Department of Electronics and Communication Engineering Karnataka Bangalore India

ISBN: (纸本)9798350354171

Multimodal data processing, especially the fusion of image and speech modality, is important for future human computer interface, medical applications and security surveillance. This research proposes the new machine learning approach for the efficient handling of multimodal data in which feature extraction from images and from the speech signal is obtained by employing CNN and LSTM networks, respectively. These features are combined by a hybrid fusion approach so that the resulting algorithm would be both accurate and fast. CIFAR-10 and LibriSpeech were used to assess the framework;the highest accuracy was 92% whilst the conventional methods like MDNN and EFMS could only reach 86%. Moreover, the analysis showed that the proposed method can take a processing time of 0.15 seconds to taxonomy and 0.45 seconds for speech data, thus making it preferable for real time data. These results illustrate the framework's efficiency in predicting the multimodal input and demonstrate superiority over the prior methods. © 2024 IEEE.

关键词： Deep learning

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