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.

Path Planning for Multiple Mobile Robots by Priority Assignment

Abstract: Priority assignment in the multi-robot path planning becomes a key challenge in the present situation. The motivation for the priority assignment is to utilize the search space for all the robots and minimize the overall path length. When the number of robots increases, the complexity also increases for the same search space. This paper gives the idea about how to assign the priority and choose the best minimum path length. The simulation results show the output performed and success rate of the robots.

Wave Front Method Based Path Planning Algorithm for Mobile Robots

Abstract: Path planning problem revolves around finding a path from start node to goal node without any collisions. This paper presents an improved version of Focused Wave Front Algorithm for mobile robot path planning in static 2D environment. Existing wave expansion algorithms either provide speed or optimality. We try to counter this problem by preventing the full expansion of the wave and expanding specific nodes such that optimality is retained. Our proposed algorithm ‘Optimally Focused Wave Front algorithm’ provides a very attractive package of speed and optimality. It allocates weight and cost to each node but it defines cost in a different fashion and employs diagonal distance instead of Euclidean distance. Finally, we compared our proposed algorithm with existing Wave Front Algorithms. We found that our proposed approach gave optimal results when compared with Focused Wave Front Algorithm and faster results when compared with Modified Wave Front Algorithm.

Multi-robot exploration using wave front algorithm

Abstract: In this paper, an approach to multi-robot exploration where key issue is to decrease the exploration time is presented. A popular concept for the exploration problem is based on the notion of frontiers from where target points are allocated to multiple robots. Exploring an environment is then about entering into the unexplored area by moving towards the targets. To do so, they must have an optimal path planning algorithm that finds the shortest route with minimum time. Our main contributions are three fold: (1) a wave front-based path planning algorithm for robot navigations; (2) an assignment method to better distribute the robots over the environment and (3) a concept of frontiers pruning for reducing the computation burden. The proposed approach has been tested through computer simulation.

Real-time BigData and Predictive Analytical Architecture for healthcare application

Abstract: The amount of data produced within health informatics has grown to be quite vast. The large volume of data generated by various vital sign monitoring devices needs to be analysed in real time to alert the care providers about changes in a patients condition. Data processing in real time has complex challenges for the large volume of data. The real-time system should be able to collect millions of events per seconds and handle parallel processing to extract meaningful information efficiently. In our study, we have proposed a real-time BigData and Predictive Analytical Architecture for healthcare application. The proposed architecture comprises three phases: (1) collection of data, (2) offline data management and prediction model building and (3) real-time processing and actual prediction. We have used Apache Kafka, Apache Sqoop, Hadoop, MapReduce, Storm and logistic regression to predict an emergency condition. The proposed architecture can perform early detection of emergency in real time, and can analyse structured and unstructured data like Electronic Health Record (EHR) to perform offline analysis to predict patient’s risk for disease or readmission. We have evaluated prediction performance on different benchmark datasets to detect an emergency condition of any patient in real time and possibility of readmission.

Enhanced Mode Selection Algorithm for H.264 encoder for Application in Low Computational power devices

Abstract: The intent of the H.264 AVC project was to create a standard capable of providing good video quality at substantially lower bit rates than previous standards without increasing the complexity of design so much that it would be impractical or excessively expensive to implement. An additional goal was to provide enough flexibility to allow the standard to be applied to a wide variety of applications. To achieve better coding efficiency, H.264 AVC uses several techniques such as inter mode and intra mode prediction with variable size motion compensation, which adopts Rate Distortion Optimization (RDO). This increases the computational complexity of the encoder especially for devices with lower processing capabilities such as mobile and other handheld devices. In this paper, we propose an algorithm to reduce the number of mode and sub mode evaluations in inter mode prediction. Experimental results show that this fast intra mode selection algorithm can lessen about 75 percent encoding time with little loss of bit rate and visual quality.

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.