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Your search keyword '"Kalaichelvi, V."' showing total 180 results
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180 results on '"Kalaichelvi, V."'

3. Ego Vehicle Lane Detection and Key Point Determination Using Deep Convolutional Neural Networks and Inverse Projection Mapping

Author

Bolimera Anudeepsekhar, Muthalagu Raja, Kalaichelvi V., and Singh Abhilasha

Subjects
ego lane detection, deep convolution neural network, ego lane analysis system, driving scanrios, occlusions, autonomous vehicle, Transportation and communication, K4011-4343
Abstract

Ego lane detection is one of the key techniques in Ego Lane Analysis System (ELAS) implemented in smart autonomous driving cars for lane detection in roads. This technique has been extensively studied in recent years because of its accurate and robust detection of shape and location of lanes. The conventional methods are less robust and computationally expensive since they have several challenges in localization of lanes due to presence of occlusions on roads. So to avoid these issues, this paper uses a novel 2-stage lane detection method using deep convolutional neural network to detect the lanes and its key-points by optimally fit a curve to the lane to compensate on above mentioned issues. The proposed methodology for lane detection predicts the key-points accurately and it robust under various weather conditions and highway driving scenarios. In terms of performance, this technique is fast and robust with low computational cost and has high performance when deployed on autonomous vehicle-based systems.

Published
2023
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4. Prediction of Bottleneck Points for Manipulation Planning in Cluttered Environment using a 3D Convolutional Neural Network

Author

Patil, Indraneel, Rout, B. K., and Kalaichelvi, V.

Subjects
Computer Science - Robotics, Computer Science - Artificial Intelligence, Computer Science - Data Structures and Algorithms, Computer Science - Machine Learning, Electrical Engineering and Systems Science - Systems and Control
Abstract

Latest research in industrial robotics is aimed at making human robot collaboration possible seamlessly. For this purpose, industrial robots are expected to work on the fly in unstructured and cluttered environments and hence the subject of perception driven motion planning plays a vital role. Sampling based motion planners are proven to be the most effective for such high dimensional planning problems with real time constraints. Unluckily random stochastic samplers suffer from the phenomenon of 'narrow passages' or bottleneck regions which need targeted sampling to improve their convergence rate. Also identifying these bottleneck regions in a diverse set of planning problems is a challenge. In this paper an attempt has been made to address these two problems by designing an intelligent 'bottleneck guided' heuristic for a Rapidly Exploring Random Tree Star (RRT*) planner which is based on relevant context extracted from the planning scenario using a 3D Convolutional Neural Network and it is also proven that the proposed technique generalises to unseen problem instances. This paper benchmarks the technique (bottleneck guided RRT*) against a 10% Goal biased RRT star planner, shows significant improvement in planning time and memory requirement and uses ABB 1410 industrial manipulator as a platform for implantation and validation of the results., Comment: 7 pages, 12 figures

Published
2019

5. Real-Time Fog Removal Using Google Maps Aided Computer Vision Techniques

Author

DSouza, Ashlyn Selena, Mohamed, Rifah, Mishika, Kalaichelvi, V., Angrisani, Leopoldo, Series Editor, Arteaga, Marco, Series Editor, Panigrahi, Bijaya Ketan, Series Editor, Chakraborty, Samarjit, Series Editor, Chen, Jiming, Series Editor, Chen, Shanben, Series Editor, Chen, Tan Kay, Series Editor, Dillmann, Rüdiger, Series Editor, Duan, Haibin, Series Editor, Ferrari, Gianluigi, Series Editor, Ferre, Manuel, Series Editor, Hirche, Sandra, Series Editor, Jabbari, Faryar, Series Editor, Jia, Limin, Series Editor, Kacprzyk, Janusz, Series Editor, Khamis, Alaa, Series Editor, Kroeger, Torsten, Series Editor, Liang, Qilian, Series Editor, Martín, Ferran, Series Editor, Ming, Tan Cher, Series Editor, Minker, Wolfgang, Series Editor, Misra, Pradeep, Series Editor, Möller, Sebastian, Series Editor, Mukhopadhyay, Subhas, Series Editor, Ning, Cun-Zheng, Series Editor, Nishida, Toyoaki, Series Editor, Pascucci, Federica, Series Editor, Qin, Yong, Series Editor, Seng, Gan Woon, Series Editor, Speidel, Joachim, Series Editor, Veiga, Germano, Series Editor, Wu, Haitao, Series Editor, Zhang, Junjie James, Series Editor, Goel, Nilesh, editor, Hasan, Shazia, editor, and Kalaichelvi, V., editor

Published
2020
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6. Time Series Prediction of Weld Seam Coordinates for 5 DOF Robotic Manipulator Using NARX Neural Network

Author

Singh, Abhilasha, Kalaichelvi, V., Karthikeyan, R., Angrisani, Leopoldo, Series Editor, Arteaga, Marco, Series Editor, Panigrahi, Bijaya Ketan, Series Editor, Chakraborty, Samarjit, Series Editor, Chen, Jiming, Series Editor, Chen, Shanben, Series Editor, Chen, Tan Kay, Series Editor, Dillmann, Rüdiger, Series Editor, Duan, Haibin, Series Editor, Ferrari, Gianluigi, Series Editor, Ferre, Manuel, Series Editor, Hirche, Sandra, Series Editor, Jabbari, Faryar, Series Editor, Jia, Limin, Series Editor, Kacprzyk, Janusz, Series Editor, Khamis, Alaa, Series Editor, Kroeger, Torsten, Series Editor, Liang, Qilian, Series Editor, Martín, Ferran, Series Editor, Ming, Tan Cher, Series Editor, Minker, Wolfgang, Series Editor, Misra, Pradeep, Series Editor, Möller, Sebastian, Series Editor, Mukhopadhyay, Subhas, Series Editor, Ning, Cun-Zheng, Series Editor, Nishida, Toyoaki, Series Editor, Pascucci, Federica, Series Editor, Qin, Yong, Series Editor, Seng, Gan Woon, Series Editor, Speidel, Joachim, Series Editor, Veiga, Germano, Series Editor, Wu, Haitao, Series Editor, Zhang, Junjie James, Series Editor, Goel, Nilesh, editor, Hasan, Shazia, editor, and Kalaichelvi, V., editor

Published
2020
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14. Vision-Based Forward Kinematics Using ANN for Weld Line Detection with a 5-DOF Robot Manipulator

Author

Martin, Don Joe, Saraiya, Aaditya, Kalaichelvi, V., Karthikeyan, R., Kacprzyk, Janusz, Series Editor, Pal, Nikhil R., Advisory Editor, Bello Perez, Rafael, Advisory Editor, Corchado, Emilio S., Advisory Editor, Hagras, Hani, Advisory Editor, Kóczy, László T., Advisory Editor, Kreinovich, Vladik, Advisory Editor, Lin, Chin-Teng, Advisory Editor, Lu, Jie, Advisory Editor, Melin, Patricia, Advisory Editor, Nedjah, Nadia, Advisory Editor, Nguyen, Ngoc Thanh, Advisory Editor, Wang, Jun, Advisory Editor, Bhattacharyya, Siddhartha, editor, Chaki, Nabendu, editor, Konar, Debanjan, editor, Chakraborty, Udit Kr., editor, and Singh, Chingtham Tejbanta, editor

Published
2018
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18. Secured Data Transmission in IoT using Homomorphic Encryption

Author

S, Suganya., K, Ramasamy., Srinivasan, A., M, Suba., Kalaichelvi, V., and S, Swaminathan.

Subjects
CGAN technology, IOT systems
Abstract

The IoT systems transfer highly sensitive data in a network and majority of the people needs IoT technology in real time applications like smart home, smart health care etc., A number of security algorithms exists to protect the IoT systems but with some time complexity. Deep learning is considered to be an efficient technique to analyze threats and respond to attacks and security incidents instantly and accurately. Conditional Generative Adversarial Network (CGAN) is one of the deep learning technique that protects data based on conditions created by generator and discriminator models. CGANs are useful for getting features of choice in generated data. This work use the CGAN feasibility to controllable the data encryption and decryption part in GAN network. This work solve the time complexity issues using Algebraic Matrix in Conditional GAN (AMCGAN) and Fully Homomorphic Encryption (FHE) algorithm. The advantage of using algebraic matrix is to reduce the time complexity and input complexity in cryptography process. It performs both addition and multiplication at the same time, and can compute any operation instantly. There are many encryption techniques used to encrypt the data but the same time they have more time to decrypt the result. Because the mathematical evaluation are complex to derived in encryption part and otherwise. So, this work considered to address the time complexity problem by solving the easiest mathematical derivations in encryption and decryption part. Also we noticed that the fully homomorphic encryption algorithm have less encryption time compared to Chaotic Algorithm and it has minimal time complexity than existing algorithms such as RSA algorithm.

Published
2022

26. Path planning of mobile manipulator for navigation and object clean-up

Author

Saraiya, Aaditya Asit, Das, Sangram Keshari, Rout, B.K., and Kalaichelvi, V.

Abstract

Industry and warehouses have been paying lots of attention to mobile manipulator-based path planner problems. This paper focuses on multi-target object clean-up operations using vision sensor which has ample industrial applications. In this work a vision-based path planning approach has been implemented using A* algorithm in order to avoid the obstacles and reach the goal location using the shortest path. The algorithm was developed to classify objects in the workspace as handleable/non-handleable from real-time measurements. In case of multi-object clean-up operations, a priority is set depending on the scenario and a weighted cost function approach is proposed. A series of simulation experiments are conducted to test the effectiveness of the proposed algorithm. The entire workflow of the mobile manipulation-based path planner is demonstrated using various scenarios. This problem has lot of relevance in real world.

Published
2023
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30. A stable image steganography: a novel approach based on modified RSA algorithm and 2–4 least significant bit (LSB) technique.

Author

Kalaichelvi, V., Meenakshi, P., Vimala Devi, P., Manikandan, H., Venkateswari, P., and Swaminathan, S.

Abstract

Nowadays, due to the rapid technological growth of information technology billions of data transferred through the network around the world in a fraction of seconds. To protect the transferred information from unauthorized access, security mechanism is required. At this point, significant role is performed by a technology called information security. So, to provide security, various techniques have been evolved such as cryptography, steganography and digital watermarking. Cryptography converts the message into scrambled format whereas in steganography the secret message is buried in the digital media. In this paper, both cryptography and steganography is combined to safeguard the information. First, the message is encrypted with the help of revised RSA algorithm to convert the message into secret message. Second, the cover image is segmented into edge and non-edge pixels using Canny edge detection technique and then N1(two) and N2 (four) bits of the secret messages are buried into the edge and non-edge pixel areas using LSB technique. Finally, the essential parameters like N1, N2 and the length values are implanted into the last four pixels of the cover-image to obtain the stego-image. At the recipient side, the hidden data are extracted from the stego-image after extracting N1, N2 and length information. Then, the extracted secret data is decrypted using revised RSA algorithm. Modified RSA algorithm improves the security by using two pairs of keys. At last, the various performance metrics parameters such as entropy, MSE, peak signal to noise ratio and histogram are measured. It is observed that the proposed system attains improved efficiency, security and imperceptibility properties of information hiding from the results. [ABSTRACT FROM AUTHOR]

Published
2021
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