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Automated laser machining and electro chemical honing of stellite alloy

Interactions 2025年第1期246卷 1-11页

作者： Rani, S. Sheeba Kumar, P. Vinoth Mohan, E. Praveena, R. Kamineni, Jagath Narayana Mahesh, Gadham setty Guru Ganesh, N. Balaji Kondaiah, E. Venkata Ram, S. Department of Electronics and Communication Engineering Sri Eshwar College of Engineering Tamilnadu Coimbatore India Department of Electrical and Electronics Engineering Sri Krishna College of Engineering and Technology Coimbatore India Department of Mechanical Engineering Shanmuganathan Engineering College Arasampatti Tamilnadu Pudukottai India Department of Chemistry Bannari Amman Institute of Technology Tamilnadu Sathyamangalam India Department of Mechanical Engineering Sri Venkateswara College of Engineering Andhra Pradesh Tirupati517507 India Department of Mechanical Engineering Gokaraju Rangaraju Institute of Engineering and Technology Hyderabad India

The surface roughness of the material is playing an essential role in metal forming process. In recent days, research about surface roughness of the advanced alloys has been increased. The current research work is to evaluate the surface roughness of stellite 4 alloy using automated laser and electro chemical honing processes. The experimental results are compared and conduct the further analysis for the better surface roughness. Taguchi and grey relational optimize technique is used to optimize the various input constrains. The dependent and independent variables are correlated with Pareto chart and area plot. The machining process is enhanced by automated system. It’s concluded that machinability of stellite 4 alloy has been studied by laser machining and electro chemical honing processes. © The Author(s), under exclusive licence to Springer Nature Switzerland AG 2025.

关键词： Honing

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Proposal, Design, and Performance Investigation of ZnO-Based ISFET pH Sensor 3rd

Proposal, Design, and Performance Investigation of ZnO-Based...

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3rd International Conference on Micro and Nanoelectronics Devices, Circuits and Systems, MNDCS 2023

作者： Porwal, Ankita Sahu, Chitrakant Shafi, Nawaz Department of Electronics and Communication Engineering Malaviya National Institute of Technology Jaipur Rajasthan Jaipur302017 India School of Electronics Engineering Vellore Institute of Technology Vellore India

ISBN: (纸本)9789819944941

This study investigates pH sensing using an ion sensitive field effect transistor (ISFET) built from the nanomaterial zinc oxide (ZnO) and using a thin film transistor (TFT) design. The device shows an excellent Ion/Ioff ratioâ€‰>â€‰106 for low-power applications with high transconductance (gmâ€‰=â€‰0.342 mS/µm), representing the available surface area for detecting hydrogen ions (H+). The device has a variety of figures of merit (FoMs), including average sensitivity (current & voltage) and signal-to-noise ratio (SNR). One type of sensing metric is the variation in threshold voltage. The impact of pH on different parameters (potential, drain current, transconductance) is analyzed. The simulated device’s sensitivity response is near the Nernestian limit of 59 mV/pH. The planned device shows good stability and sensitivity for sensing pH values during simulation. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Zinc oxide

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Ultra Low Power and High Speed Electronic Circuits Using Double Gate Tunnel Field Effect Transistor

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Journal of The Institution of Engineers (India): Series B 2025年第2期106卷 713-733页

作者： Kumar, Ch. Pavan Sivani, K. Department of Electronics and Communication Engineering Kakatiya Institute of Technology & Science Telangana Warangal506015 India Department of Electronics and Instrumentation Engineering Kakatiya Institute of Technology & Science Telangana Warangal506015 India Kakatiya Institute of Technology & Science Telangana Warangal506015 India

In the context of the modern electronic landscape, Tunnel Field-Effect Transistors (TFETs) have emerged as promising alternatives to traditional MOSFETs for digital circuit design, despite their lower ON current and slower performance. This study investigates the simulation of various digital logic gates and circuits, including inverters, AND, OR, NAND, NOR, XOR, and XNOR gates, as well as Half Adders, Full Adders, and Ring Oscillators, using the software Cadence Virtuoso in 20 nm technology. Both homogeneous and heterogeneous TFETs are considered. The evaluation focuses primarily on two crucial parameters: power consumption and delay. Additionally, the performance of the Ring Oscillator is assessed in terms of its oscillation frequency. Comparative analysis with existing circuitry demonstrates significant enhancements in both power efficiency and speed, making these suitable in developing ultra-low-power & high-speed applications which operate on battery. © The Institution of Engineers (India) 2024.

关键词： Electric power utilization

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Modelling of CO2 Gas Sensing using Spectral Envelope of SoI Integrated Racetrack Resonator

Modelling of CO2 Gas Sensing using Spectral Envelope of SoI ...

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2024 JSAP-Optica Joint Symposia, JSAP 2024

作者： Vardhan, Shalini Gupta, Naveen Kumar Kushwaha, Aditya Verma, Priyanka Singh, Ritu Raj Department of Electronics and Communication Engineering Netaji Subhas University of Technology Delhi India Department of Electronics and Communication Engineering Indian Institute of Information Technology Ranchi India

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Farming Futures: Machine Learning Models for Crop Yield Prediction 3

Farming Futures: Machine Learning Models for Crop Yield Pred...

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3rd IEEE International Conference on Distributed Computing and Electrical Circuits and electronics, ICDCECE 2024

作者： Kushalatha, M.R. Sangamesh Shashank, B.C. Kerudi, Siddarth M. Mamatha, A.S. Nitte Meenakshi Institute Of Technology Department Of Electronics And Communication Engineering Bengaluru India Nmam Institute Of Technology Department Of Electronics And Communication Engineering Bengaluru Nitte India

ISBN: (纸本)9798350318609

India's economy depends heavily on agriculture, and choosing the right crops is essential to reducing losses. Crop yield is influenced by factors such as soil conditions and weather patterns. A rational assessment of many aspects is included in data analysis, which is essential for researching agricultural output. Pre-season crop mapping, which is crucial for early warningson agricultural supply networks and output, is one way that machine learning algorithms support agricultural surveillance and the food industry. gathered and examined datasets that combined meteorological and soil data. Pre-season crop-type maps were analyzed and recommended using machine learning techniques, such as XG Boost, Decision Tree, Linear Regression, Bagging Regressor, and Random Forest, based on historical data. Crop forecast was based on the same parameters that were taken into account during computation. Among the evaluated machine learning algorithms, Random Forest achieved the highest accuracy of 0.986572, closely followed by Bagging Regressor with an accuracy of 0.986569. Machine learning-based pre-season crop mapping reduces trade tensions and dangers by providing early warnings on agricultural supply networks. This approach improves agricultural surveillance, assisting farmers and other stakeholders in the food industry in making well-informed decisions. © 2024 IEEE.

关键词： Decision trees

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Cepstral and acoustic ternary pattern based hybrid feature extraction approach for end-to-end bangla speech recognition

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Journal of Ambient Intelligence and Humanized Computing 2023年第12期14卷 16903-16919页

作者： Dua, Mohit Akanksha Dua, Shelza Department of Computer Engineering National Institute of Technology Kurukshetra India Department of Electronics Communication and Engineering Punjab Engineering College Chandigarh Chandigarh India

In the last three decades, a lot of work has been done for building Automatic Speech Recognition (ASR) systems for well-established languages such as English, Chinese, etc. However, for implementing a Large Vocabulary Continuous Speech Recognition (LVCSR) system for low resource languages, the research work is also growing rapidly in corpus-focused areas. Hence, there is a requirement of benchmarking large corpus in case of low-resource language like Bangla such that prejudice results can be avoided and limitations can be handled. In the proposed work, an openly available large-scale Bangla speech corpus provided by Google has been used. The work in this paper proposes a combination of image inspired features with well explored cepstral features to build front end feature extraction phase. It uses integration of Convolutional Neural Network (CNN) and bi-directional Long-short Term Memory (bi-LSTM) with Connectionist Temporal Classification (CTC) loss function to implement backend acoustic model. The experiments employ static and dynamic features of Mel-frequency Cepstral Coefficients (MFCC), Constant Q Cepstral Coefficients (CQCC), and Gammatone Cepstral Coefficients (GTCC) techniques, one by one, with Acoustic Ternary Patterns (ATP) features. The proposed work investigates the effect of these various hybrid front-end approaches with CNN, bi-LSTM and integration of these two models. The novelty of this paper lies in the fact that fusion of ATP with cepstral features improves performance of the proposed low resource language ASR system, where the proposed combination of ATP-dynamic CQCC features with integrated backend acoustic model shows a relative improvement of 10–15% in Word Error Rate (WER) over all other experimented combinations. Further to exploit the noise robust nature of GTCC features, the ATP-dynamic GTCC features with integrated CNN-bi-LSTM back-end model are evaluated in noisy scenario, also. © 2023, The Author(s), under exclusive licence to Springer-V

关键词： Long short-term memory

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Three-dimensional reconstruction of precession warhead based on multi-view micro-Doppler analysis

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Journal of Systems engineering and electronics 2024年第3期35卷 541-548页

作者： ZHANG Rongzheng WANG Yong MAO Jian School of Electronics and Information Engineering Harbin Institute of TechnologyHarbin 150001China Laboratory of Pinghu Pinghu 314200China

The warhead of a ballistic missile may precess due to lateral moments during release. The resulting micro-Doppler effect is determined by parameters such as the target's motion state and size. A three-dimensional reconstruction method for the precession warhead via the micro-Doppler analysis and inverse Radon transform(IRT) is proposed in this paper. The precession parameters are extracted by the micro-Doppler analysis from three radars, and the IRT is used to estimate the size of targe. The scatterers of the target can be reconstructed based on the above parameters. Simulation experimental results illustrate the effectiveness of the proposed method in this paper.

关键词： time-frequency analysis inverse Radon transform(IRT) precession warhead

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RFM Analysis Using K-means Algorithm for Customer Segmentation 5th

RFM Analysis Using K-means Algorithm for Customer Segmentati...

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5th International Conference on Computing and Network communications, CoCoNet 2023

作者： Lalitha, S. Reddy, M. Uday Hussain, G. Nasir Reddy, N. Vishnu Lokhesh Department of Electronics and Communication Engineering Amrita School of Engineering Amrita Vishwa Vidyapeetham Bengaluru India

ISBN: (纸本)9789819747108

Customer Segmentation is one of the most crucial areas in the field of targeted marketing. This proposed work aims to develop an environment between customers and retailers to set up a beneficial scheme that can help achieve maximum benefit for both customer groups and retailers, for the purpose of creating a win–win situation between customers and retailers. This work focuses on applying RFM Analysis in the field of e-commerce with an overview to segment the customers into different groups. Here, a simple clustering technique using K-means in combination with the Elbow method is applied for the proposed task. Initially, to calculate the ideal number of clusters, the Elbow approach is used, followed by K-means to cluster the group of customers. The customers are divided into groups viz Champions, Can’t Lose, Loyal, promising, require activation, and followed by customers divided into optimal clusters for categorizing the customers into different groups. The experimental results depicted the formation of 3 optimal clusters, in which cluster 1 is more reliable and efficient in terms of loyal customers which contains high monetary amounts and frequency, but low recency values when compared to the other two clusters. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Sales

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Efficientnet-Based Deep Learning Approach for Video Forgery Detection and Authentication 10

Efficientnet-Based Deep Learning Approach for Video Forgery ...

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

作者： Gowri Priyaa, V. Jaya Harrish, M. Udhayakumar, M. Jothieswaran, N. Dinesh, K. Coimbatore Institute of Technology Electronics and Communication Engineering Coimbatore India

ISBN: (纸本)9798350353068

The technological advancements in artificial intelligence have made it a lot easier to create forged videos that are difficult to distinguish from reality. Fake videos also called deep fakes are created with greater accuracy and precision. Detecting and removing fake data on the internet can prevent misinformation and rumors from spreading. To achieve this, detection methods must be robust, generalized, fast, and accurate enough to detect fake data. In this paper, deepfakes are created using Generative Adversarial Network (GAN) and used for dataset training. The deepfakes are found to be different from real ones by various parameters like facial expressions, irregularities in the image, etc. This project focuses on detecting the manipulated face of the person in a frame using the EfficientNet B4 algorithm. The EfficientNet B4 model is more accurate than EfficientNet B0 and less complex than EfficientNet B7. The modified EfficientNet B4 outperforms the existing EfficientNet B0 in terms of accuracy. The probabilities of deep fakes in each frame are calculated and on average the video is detected as real or fake. This model demonstrates a very successful detection rate of more than 92%. Finally, modified EfficientNet B4 is compared with other models' performance. © 2024 IEEE.

关键词： Discriminators

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Ovarian Cancer Detection using Microarray gene data and Deep Learning Techniques 8

Ovarian Cancer Detection using Microarray gene data and Deep...

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8th International Conference on electronics, communication and Aerospace technology, ICECA 2024

作者： Santhosh Kumar, M. Kalaiyarasi, M. Harikumar, R. Saravanan, R. Bannari Amman Institute of Technology Biomedical Engineering Erode India Bannari Amman Institute of Technology Electronics and Instrumentation Engineering Erode India Bannari Amman Institute of Technology Electronics and Communication Engineering Erode India Muthayammal Engineering College Electronics and Communication Engineering Namakkal India

ISBN: (纸本)9798350367904

Ovarian cancer, is one of the most common gynecologic problem, that poses a severe threat to women's health. Since there are tens of thousands of genes in the microarray data for ovarian cancer, it is extremely Challenge to detect the disease. Reducing computational complexity by selecting the most relevant genes is crucial due to the limited sample size and high dimensionality of these datasets. In this research, the ANOVA method is applied for initial gene selection during the preprocessing stage. Next, performing statistical parameter analysis to gain insights into the dataset. Feature selection and dimensionality reduction are conducted using a Genetic Algorithm to optimize gene selection. For prediction, a Graph Convolutional Network (GCN) is employed to classify normal and abnormal samples. Finally, the performance of the model is evaluated through comprehensive metrics analysis. This workflow is designed to improve the accuracy and efficiency of ovarian cancer detection by focusing on the most informative genes and utilizing advanced Deep learning techniques. This proposed methodology is achieved the accuracy with 85.93% demonstrating that the integration of GA for feature optimization with GCN for classification significantly improves detection performance compared to traditional methods. © 2024 IEEE.

关键词： Microarrays

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