Author
Anindita Sengupta
Other affiliations: Techno India
Bio: Anindita Sengupta is an academic researcher from Indian Institute of Engineering Science and Technology, Shibpur. The author has contributed to research in topics: Control theory & PID controller. The author has an hindex of 9, co-authored 54 publications receiving 268 citations. Previous affiliations of Anindita Sengupta include Techno India.
Papers
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15 May 2011
40 citations
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TL;DR: In this paper, the authors employed wavelet based noise removal technique to remove measurement noise from differential pressure transmitter (DPT) output indicating the level of a process tank, where the liquid level system was approximated as a first order plant with time delay.
33 citations
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TL;DR: The developed system to measure different yarn parameters i.e. diameter, diameter variation, number of thick/thin places and neps, hairiness indices, and number of hairs in a single run with the help of image processing is developed.
23 citations
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01 May 2011
18 citations
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TL;DR: In this article, a low cost mechanized and computerised system was developed to study complete behaviour of bending specially for technical textiles, which can measure dynamic bending behaviour by graphical bending load-deflection, cyclic bending, bending stress relaxation.
16 citations
Cited by
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01 Jan 1999
TL;DR: 1. Control Methodology 2. Dynamical Systems 3. Applications to Social and Environmental Problems 4.
Abstract: 1. Control Methodology 2. Dynamical Systems 3. Applications to Social and Environmental Problems
325 citations
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TL;DR: This study aimed to develop a novel AD detection system with better performance than existing systems, and observed that the pathological brain detection system is superior to latest 6 other approaches.
Abstract: Detection of Alzheimer's disease (AD) from magnetic resonance images can help neuroradiologists to make decision rapidly and avoid missing slight lesions in the brain. Currently, scholars have proposed several approaches to automatically detect AD. In this study, we aimed to develop a novel AD detection system with better performance than existing systems. 28 ADs and 98 HCs were selected from OASIS dataset. We used inter-class variance criterion to select single slice from the 3D volumetric data. Our classification system is based on three successful components: wavelet entropy, multilayer perceptron, and biogeography-base optimization. The statistical results of our method obtained an accuracy of 92.40 ± 0.83%, a sensitivity of 92.14 ± 4.39%, a specificity of 92.47 ± 1.23%. After comparison, we observed that our pathological brain detection system is superior to latest 6 other approaches.
116 citations
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TL;DR: In this paper, a review of prior art on liquid level sensing is initially presented, and the operational characteristics and performance of a novel capacitive-type water level measurement system are investigated through simulations and experimental tests conducted in two water storage tanks of a city-scale water distribution network.
84 citations
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TL;DR: A numerical method based on an m-set of general, orthogonal triangular functions (TF) is proposed to approximate the solution of nonlinear Volterra-Fredholm integral equations.
79 citations
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TL;DR: The proposed method had a good denoising performance and was better than the wavelet transform method in the signal-to-noise ratio (SNR), root mean square error (RMSE) and partial correlation index and was ideally suited for the online denoisation of the hydropower unit vibration signal.
Abstract: A denoising method for a hydropower unit vibration signal based on variational mode decomposition (VMD) and approximate entropy is proposed. The signal was decomposed by VMD into discrete numbers of modes, then the approximate entropy of each mode was computed. These modes were reconstructed according to a preset threshold of the approximate entropy. Finally, the denoising of the hydropower unit vibration signal can be achieved. A simulation signal and real signals of hydropower unit vibration were used to verify the proposed method. The results showed that the proposed method had a good denoising performance and was better than the wavelet transform method in the signal-to-noise ratio (SNR), root mean square error (RMSE) and partial correlation index. It was ideally suited for the online denoising of the hydropower unit vibration signal.
71 citations