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.

Distributed location estimation system using WLAN received signal strength fingerprints

Abstract: Location Estimation has become important for many applications of indoor wireless networks. Received Signal Strength (RSS) fingerprinting methods have been widely used for location estimation. The accuracy and response time of estimation are critical issue in location estimation system. Most of the location estimation system suffers with the problem of scalability and unavailability of all the access points at all the location for large site. In this paper, we have proposed a distributed location estimation method, which divide the location estimation system into subsystems. Our method partition the input signal space and output location space into clusters on the basis of visibility of access points at various locations of the site area. Each cluster of input signal space together with output location subspace is used to learn the association between RSS fingerprint and their respective location in a subsystem. We have compared our results with benchmark RADAR method. Experimental results show that our method provide better results in terms of accuracy and response time in comparison to centralized systems, in which a single system is used for large site.

A novel approach to classificatory problem using neuro-fuzzy architecture

Abstract: In this paper, we propose a new method for solving these problems inspired from the neuro-fuzzy logic approach for classificatory problems. We first cluster the training data based on class identification of inputs. A sort of fuzzy approach serves as a means to classify the unknown inputs. Rules are in the form of representative of every cluster and their matching class. The centre and power of the representative are the parameters that are optimised using a training algorithm and further by Genetic Algorithms. We tested the algorithm on the famous classificatory problem of picture learning.

Breast Cancer Diagnosis Using Optimized Attribute Division in Modular Neural Networks

Abstract: The complexity of problems has led to a shift toward the use of modular neural networks in place of traditional neural networks. The number of inputs to neural networks must be kept within manageable limits to escape from the curse of dimensionality. Attribute division is a novel concept to reduce the problem dimensionality without losing information. In this paper, the authors use Genetic Algorithms to determine the optimal distribution of the parameters to the various modules of the modular neural network. The attribute set is divided into the various modules. Each module computes the output using its own list of attributes. The individual results are then integrated by an integrator. This framework is used for the diagnosis of breast cancer. Experimental results show that optimal distribution strategy exceeds the well-known methods for the diagnosis of the disease.

Multimodal Biometric Systems

Abstract: The uni-modal biometric systems making use of a single biometric modality have a limited performance that restricts their applicability in real life scenarios. The multimodal biometric systems make use of two or more modalities that together achieve much higher performances. In this chapter we discuss the means to fuse three modalities to make a more robust system. We first discuss the fusion of speech, lip, and face. This system uses Hidden Markov Models for the classification and an integration technique called as late integration for decision making from the three modalities. We then discuss the fusion of face, speech and fingerprint. Here each of the individual biometric modalities would make use of modular neural network which would then be combined using a fuzzy integration technique. The last model we discuss would carry the fusion of fingerprint, face and hand geometry. This system uses a variety of fusion techniques including a sum rule, linear discriminant function and decision trees.

CBDF Based Cooperative Multi Robot Target Searching and Tracking Using BA

Abstract: Path planning is a problem where the objective to reach up to target from source without collide with obstacle This problem would be complex when it is considered with multi robot and unknown environment. Reaching up to the target is considered as an optimization problem where the objective to minimize the distance, time and energy. This paper use the Bat algorithm (BA) for the movement of robot form one location to next location with optimizes the time, distance and energy. Here the direction of the movement is given by clustering based distribution factors (CBDF) that guide the robot to move in different direction. Different parameters are calculated during the moving of robots that help to analyze the process of target searching and tracking. Simulation is done with both simple and complex environment and results shows that the method works in both cases in searching and tracking.

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.