Anupam Shukla

Bio: Anupam Shukla is an academic researcher from Indian Institute of Information Technology and Management, Gwalior. The author has contributed to research in topics: Artificial neural network & Motion planning. The author has an hindex of 22, co-authored 215 publications receiving 1896 citations. Previous affiliations of Anupam Shukla include Indian Institutes of Information Technology.

Clock gated low power sequential circuit design

Abstract: In this work, our focus is on study and analysis of various clock gating technique and design and analysis of clock gating based low power sequential circuit at RTL level. Virtex-6 is 40-nm FPGA, on which we implement our circuit to re-assure power reduction in sequential circuit. Clock gating is implemented on smaller circuit called D flip-flop and on larger circuit called 16-bit register. The percentage of reduction in dynamic power especially clock power is verified for different device operating frequency. Here, we achieved 87.09%, 88.02%, 88.02%, and 88.01% clock power reduction in this work when clock period is 1ns, 0.1ns, 0.01ns and 0.001ns respectively. Design and implementation result shows that there is reduction in dynamic power especialy significant reduction in clock power We also achieved 15%, 14.22%, 14.58%, 14.57% and 14.57% dynamic power reduction when clock period is 10ns, 1ns, 0.1ns, 0.01ns, and 01ps respectively.

Dynamic environment robot path planning using hierarchical evolutionary algorithms

Abstract: The problem of path planning deals with the computation of an optimal path of the robot, from source to destination, such that it does not collide with any obstacle on its path. In this article we solve the problem of path planning separately in two hierarchies. The coarser hierarchy finds the path in a static environment consisting of the entire robotic map. The resolution of the map is reduced for computational speedup. The finer hierarchy takes a section of the map and computes the path for both static and dynamic environments. Both the hierarchies make use of an evolutionary algorithm for planning. Both these hierarchies optimize as the robot travels in the map. The static environment path is increasingly optimized along with generations. Hence, an extra setup cost is not required like other evolutionary approaches. The finer hierarchy makes the robot easily escape from the moving obstacle, almost following the path shown by the coarser hierarchy. This hierarchy extrapolates the movements of the various objects by assuming them to be moving with same speed and direction. Experimentation was done in a variety of scenarios with static and mobile obstacles. In all cases the robot could optimally reach the goal. Further, the robot was able to escape from the sudden occurrence of obstacles.

Multi-robot multi-target dynamic path planning using artificial bee colony and evolutionary programming in unknown environment

Abstract: Navigation or path planning is the basic need for movement of robots. Navigation consists of two foremost concerns, target tracking and hindrance avoidance. Hindrance avoidance is the way to accomplish the task without clashing with intermediate hindrances. In this paper, an evolutionary scheme to solve the multi-agent, multi-target navigation problem in an unknown dynamic environment is proposed. The strategy is a combination of modified artificial bee colony for neighborhood search planner and evolutionary programming to smoothen the resulting intermediate feasible path. The proposed strategy has been tested against navigation performances on a collection of benchmark maps for A* algorithm, particle swarm optimization with clustering-based distribution factor, genetic algorithm and rapidly-exploring random trees for path planning. Navigation effectiveness has been measured by smoothness of feasible paths, path length, number of nodes traversed and algorithm execution time. Results show that the proposed method gives good results in comparison to others.

Quality assessment of modeled protein structure using physicochemical properties.

Abstract: Physicochemical properties of proteins always guide to determine the quality of the protein structure, therefore it has been rigorously used to distinguish native or native-like structure from other predicted structures. In this work, we explore nine machine learning methods with six physicochemical properties to predict the Root Mean Square Deviation (RMSD), Template Modeling (TM-score), and Global Distance Test (GDT_TS-score) of modeled protein structure in the absence of its true native state. Physicochemical properties namely total surface area, euclidean distance (ED), total empirical energy, secondary structure penalty (SS), sequence length (SL), and pair number (PN) are used. There are a total of 95,091 modeled structures of 4896 native targets. A real coded Self-adaptive Differential Evolution algorithm (SaDE) is used to determine the feature importance. The K-fold cross validation is used to measure the robustness of the best predictive method. Through the intensive experiments, it is found that Random Forest method outperforms over other machine learning methods. This work makes the prediction faster and inexpensive. The performance result shows the prediction of RMSD, TM-score, and GDT_TS-score on Root Mean Square Error (RMSE) as 1.20, 0.06, and 0.06 respectively; correlation scores are 0.96, 0.92, and 0.91 respectively; R(2) are 0.92, 0.85, and 0.84 respectively; and accuracy are 78.82% (with ± 0.1 err), 86.56% (with ± 0.1 err), and 87.37% (with ± 0.1 err) respectively on the testing data set. The data set used in the study is available as supplement at http://bit.ly/RF-PCP-DataSets.

Computer vision : a modern approach = 计算机视觉 : 一种现代的方法

Abstract: From the Publisher: The accessible presentation of this book gives both a general view of the entire computer vision enterprise and also offers sufficient detail to be able to build useful applications. Users learn techniques that have proven to be useful by first-hand experience and a wide range of mathematical methods. A CD-ROM with every copy of the text contains source code for programming practice, color images, and illustrative movies. Comprehensive and up-to-date, this book includes essential topics that either reflect practical significance or are of theoretical importance. Topics are discussed in substantial and increasing depth. Application surveys describe numerous important application areas such as image based rendering and digital libraries. Many important algorithms broken down and illustrated in pseudo code. Appropriate for use by engineers as a comprehensive reference to the computer vision enterprise.

Coverage control for mobile sensing networks

Abstract: This paper presents control and coordination algorithms for groups of vehicles. The focus is on autonomous vehicle networks performing distributed sensing tasks where each vehicle plays the role of a mobile tunable sensor. The paper proposes gradient descent algorithms for a class of utility functions which encode optimal coverage and sensing policies. The resulting closed-loop behavior is adaptive, distributed, asynchronous, and verifiably correct.

Artificial neural networks

Abstract: Artificial neural networks (ANNs) constitute a class of flexible nonlinear models designed to mimic biological neural systems. In this entry, we introduce ANN using familiar econometric terminology and provide an overview of ANN modeling approach and its implementation methods. † Correspondence: Chung-Ming Kuan, Institute of Economics, Academia Sinica, 128 Academia Road, Sec. 2, Taipei 115, Taiwan; ckuan@econ.sinica.edu.tw. †† I would like to express my sincere gratitude to the editor, Professor Steven Durlauf, for his patience and constructive comments on early drafts of this entry. I also thank Shih-Hsun Hsu and Yu-Lieh Huang for very helpful suggestions. The remaining errors are all mine.