Santanu Santra

National Yang Ming Chiao Tung University

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Jun-Wei Hsieh

National Yang Ming Chiao Tung University

Utpal Roy

Ping-Yang Chen

National Yang Ming Chiao Tung University

Cheng-Han Chou

National Yang Ming Chiao Tung University

Sanghamitra Bandyopadhyay

Indian Statistical Institute

Ming-Ching Chang

University at Albany, State University of New York

Pinaki Pratim Acharjya

Haldia Institute of Technology

Chih-Sheng Huang

National Yang Ming Chiao Tung University

Chi‐Fang Lin

Yuan Ze University

Chi‐Hung Lin

National Yang Ming Chiao Tung University

Cooperative Institutions

Yuan Ze University

National Yang Ming Chiao Tung University

National Taiwan Ocean University

National Taiwan University

National Cheng Kung University

National Central University

National Sun Yat-sen University

Indian Statistical Institute

Jadavpur University

National Institute of Technology Agartala

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An Improved Scheme on Morphological Image Segmentation Using the Gradients

International Journal of Advanced Computer Science and Applications (2013)

Pinaki Pratim Santanu Santra Dibyendu Ghoshal

An improved scheme for contour detection with better performance measure has been proposed. It is based on the response of human visual system during visualization of any type of an image. The scheme consisted of two parts namely to find the edge of the image by using the modified mask of Laplacian of Gaussian edge operator and subsequent modulation of the edge by using watershed algorithm. The method has been applied to a digital image and better performance measure of contour detection has been achieved.

Blob detection

Image gradient

Morphological gradient

10.14569/ijacsa.2013.040215

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Citations (5)

Design and Implementation of Quantum Cellular Automata Based Novel Adder Circuits

Santanu Santra Utpal Roy

Quantum cellular automaton

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Design and Optimization of Parity Generator and Parity Checker Based On Quantum-dot Cellular Automata

Zenodo (CERN European Organization for Nuclear Research) (2014)

Santanu Santra Utpal Roy

Parity (physics)

10.5281/zenodo.1091768

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Design and Simulation of Quantum Cellular Automata Based XOR Gate With Optimize Complexity and Cell Count

Journal of Emerging Technologies and Innovative Research (2015)

Arpita Samanta Santra Santanu Santra

Quantum dot Cellular Automata (QCA) is a recent and potentially attractive technology to implement computing architectures at the nano scale. The basic primitive in QCA is the majority gate. The QCA cells have features on the very low nanometer scale, much smaller than the present state of art size of the smallest transistor. In this paper we present a novel implementations for XOR gate using QCA cells with minimum complexity, minimization of clock require for getting output and cell count in comparison with the already proposed designs. The proposed circuit was varied using simulation from QCA-designer tool. The proposed QCA implementation is useful for building more complex circuits.

XOR gate

Quantum cellular automaton

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Design and Implementation of Quantum Cellular Automata Based Novel Adder Circuits

World Academy of Science, Engineering and Technology, International Journal of Computer, Electrical, Automation, Control and Information Engineering (2014)

Santanu Santra Utpal Roy

10.5281/zenodo.1091118

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Citations (23)

IN SILICO DESIGN OF LIGANDS USING PROPERTIES OF TARGET ACTIVE SITES

Science, engineering, and biology informatics (2007)

Sanghamitra Bandyopadhyay Santanu Santra Ujjwal Maulik Heinz Muehlenbein

10.1142/9789812708892_0008

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Multi-fusion feature pyramid for real-time hand detection

Multimedia Tools and Applications (2022)

Chuan-Wang Chang Santanu Santra Jun‐Wei Hsieh Pirdiansyah Hendri Chi‐Fang Lin

Pyramid (geometry)

Feature (linguistics)

Interface (matter)

10.1007/s11042-021-11897-7

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Gradient Descent Effects on Differential Neural Architecture Search: A Survey

IEEE Access (2021)

Santanu Santra Jun-Wei Hsieh Chi‐Fang Lin

Gradient Descent, an effective way to search for the local minimum of a function, can minimize training and validation loss of neural architectures and also be incited in an appropriate order to decrease the searching cost of neural architecture search. In recent trends, the neural architecture search (NAS) is enormously used to construct an automatic architecture for a specific task. Mostly well-performed neural architecture search methods have adopted reinforcement learning, evolutionary algorithms, or gradient descent algorithms to find the best-performing candidate architecture. Among these methods, gradient descent-based architecture search approaches outperform all other methods in terms of efficiency, simplicity, computational cost, and validation error. In view of this, an in-depth survey is necessary to cover the usefulness of gradient descent method and how this can benefit neural architecture search. We begin our survey with basic concepts of neural architecture search, gradient descent, and their unique properties. Our survey then delves into the impact of gradient descent method on NAS and explores the effect of gradient descent in the search process to generate the candidate architecture. At the same time, our survey reviews mostly used gradient-based search approaches in NAS. Finally, we provide the current research challenges and open problems in the NAS-based approaches, which need to be addressed in future research.

Stochastic Gradient Descent

10.1109/access.2021.3090918

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A Study And Analysis on Computer Network Topology For Data Communication

Santanu Santra Pinaki Pratim Acharjya

In recent days for computing, distributed computer systems have become very important and popular issue. It delivers high end performance at a low cost. Autonomous computers are connected by means of a communication network in a distributed computing environment which is arranged in a geometrical shape called network topology. In the present paper a detailed study and analysis on network topologies is presented. Definitions of Physical and Logical Topologies are also provided.

Logical topology

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MS-DARTS: Mean-Shift Based Differentiable Architecture Search

arXiv (Cornell University) (2021)

Jun-Wei Hsieh Ming-Ching Chang Ping-Yang Chen Santanu Santra Cheng-Han Chou

Differentiable Architecture Search (DARTS) is an effective continuous relaxation-based network architecture search (NAS) method with low search cost. It has attracted significant attentions in Auto-ML research and becomes one of the most useful paradigms in NAS. Although DARTS can produce superior efficiency over traditional NAS approaches with better control of complex parameters, oftentimes it suffers from stabilization issues in producing deteriorating architectures when discretizing the continuous architecture. We observed considerable loss of validity causing dramatic decline in performance at this final discretization step of DARTS. To address this issue, we propose a Mean-Shift based DARTS (MS-DARTS) to improve stability based on sampling and perturbation. Our approach can improve bot the stability and accuracy of DARTS, by smoothing the loss landscape and sampling architecture parameters within a suitable bandwidth. We investigate the convergence of our mean-shift approach, together with the effects of bandwidth selection that affects stability and accuracy. Evaluations performed on CIFAR-10, CIFAR-100, and ImageNet show that MS-DARTS archives higher performance over other state-of-the-art NAS methods with reduced search cost.

Smoothing

10.48550/arxiv.2108.09996

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Citations (1)