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Banerjee, Supriyo
- Indian Quantum Communication Enabled Space Security
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Authors
Affiliations
1 Department of Computer Science & Engineering, Kalyani Govt. Engineering College, Kalyani, Nadia 741235, IN
1 Department of Computer Science & Engineering, Kalyani Govt. Engineering College, Kalyani, Nadia 741235, IN
Source
Indian Science Cruiser, Vol 35, No 1 (2021), Pagination: 58-62Abstract
Quantum key distribution (QKD) is the most common secure techniques for exchanging key between two or more legitimate users using some insecure network. Despite much progress, ground-based secure communication systems using QKD is not feasible due to high atmospheric loss in free space communication where the range of communication may be extended. Thus, the physicists will be able to create satellite based QKD network for secure communication. This manuscript summarizes research and development from QKD protocols between two legitimate users to Ground to satellite based QKD network. It includes the progress of secure protocols using QKD around the world in respect with infrastructure, security and the technical challenges.Keywords
Quantum Communication, Indian scenario, space security, network, satellite Communication.References
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- Estimation of Roughness of Machined Surface Using Artificial Neural Networks
Abstract Views :185 |
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of such parameters needs time consuming and costly experimentation. In this work,
artificial neural networks (ANN) is used to predict roughness parameters of machined
surface to reduce time and cost involved for the experiments. Surface roughness
parameters assessed through ANN are compared with the observed data and an
accuracy of 95.5% is reported.
Authors
Firdous Ali Khan
1,
Pritam Chatterjee
1,
Sumit Mandi
1,
Upendra Kumar Shaw
1,
Santanu Das
1,
Supriyo Banerjee
2
Affiliations
1 Department of Mechanical Engineering, Kalyani Govt. Engineering College, Kalyani- 741235, Nadia, IN
2 Department of Computer Science & Engineering, Kalyani Govt. Engineering College, Kalyani- 741235, Nadia, IN
1 Department of Mechanical Engineering, Kalyani Govt. Engineering College, Kalyani- 741235, Nadia, IN
2 Department of Computer Science & Engineering, Kalyani Govt. Engineering College, Kalyani- 741235, Nadia, IN
Source
Indian Science Cruiser, Vol 36, No 3 (2022), Pagination: 27-32Abstract
Setting appropriate machining parameters can give desired finish of a job. Selectionof such parameters needs time consuming and costly experimentation. In this work,
artificial neural networks (ANN) is used to predict roughness parameters of machined
surface to reduce time and cost involved for the experiments. Surface roughness
parameters assessed through ANN are compared with the observed data and an
accuracy of 95.5% is reported.
Keywords
Machining; shaping; surface roughness; estimation; ANN; Neural Networks.References
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