Arindam Karmakar

Bio: Arindam Karmakar is an academic researcher from Tezpur University. The author has contributed to research in topics: Time complexity & Line segment. The author has an hindex of 9, co-authored 30 publications receiving 193 citations. Previous affiliations of Arindam Karmakar include Indian Statistical Institute.

Some variations on constrained minimum enclosing circle problem

Abstract: Given a set P of n points and a straight line L, we study three important variations of minimum enclosing circle problem as follows:

k-enclosing axis-parallel square

Abstract: Let P be a set of n points in the plane. Here we present an efficient algorithm to compute the smallest square containing at least k points of P for large values of k. The worst case time complexity of the algorithm is O(n + (n - k) log2(n - k)) using O(n) space which is the best known bound for worst case time complexity.

Constrained minimum enclosing circle with center on a query line segment

Abstract: In this paper, we will study the problem of locating the center of the smallest enclosing circle of a set P of n points, where the center is constrained to lie on a query line segment The preprocessing time and space complexities of our proposed algorithm are O(nlogn) and O(n) respectively; the query time complexity is O(log^2n)

Constrained minimum enclosing circle with center on a query line segment

Abstract: In this paper, we will study the problem of locating the center of smallest enclosing circle of a set P of n points, where the center is constrained to lie on a query line segment. The preprocessing time and space complexities of our proposed algorithm are O(n log n) and 0(n) respectively; the query time complexity is O(log 2 n). We will use this method for solving the following problem proposed by Bose and Wang [3] - given r simple polygons with a total of m vertices along with the point set P, compute the smallest enclosing circle of P whose center lies in one of the r polygons. This can be solved in O(n log n+m log 2 n) time using our method in a much simpler way than [3]; the time complexity of the problem is also being improved.

Pattern Recognition and Machine Learning

Abstract: Probability Distributions.- Linear Models for Regression.- Linear Models for Classification.- Neural Networks.- Kernel Methods.- Sparse Kernel Machines.- Graphical Models.- Mixture Models and EM.- Approximate Inference.- Sampling Methods.- Continuous Latent Variables.- Sequential Data.- Combining Models.

Discrete location theory

Abstract: Ingredients of Locational Analysis (J. Krarup & P. Pruzan). The p-Median Problem and Generalizations (P. Mirchandani). The Uncapacitated Facility Location Problem (G. Cornuejols, et al.). Multiperiod Capacitated Location Models (S. Jacobsen). Decomposition Methods for Facility Location Problems (T. Magnanti & R. Wong). Covering Problems (A. Kolen & A. Tamir). p-Center Problems (G. Handler). Duality: Covering and Constraining p-Center Problems on Trees (B. Tansel, et al.). Locations with Spatial Interactions: The Quadratic Assignment Problem (R. Burkard). Locations with Spatial Interactions: Competitive Locations and Games (S. Hakimi). Equilibrium Analysis for Voting and Competitive Location Problems (P. Hansen, et al.). Location of Mobile Units in a Stochastic Environment (O. Berman, et al.). Index.

Non-line-of-sight node localization based on semi-definite programming in wireless sensor networks (アドホックネットワーク)

Abstract: An unknown-position sensor can be localized if there are three or more anchors making time-of-arrival (TOA) measurements of a signal from it. However, the location errors can be very large due to the fact that some of the measurements are from non-line-of-sight (NLOS) paths. In this paper, a semi-definite programming (SDP) based node localization algorithm in NLOS environments is proposed for ultra-wideband (UWB) wireless sensor networks. The positions of sensors can be estimated using the distance estimates from location-aware anchors as well as other sensors. However, in the absence of line-of-sight (LOS) paths, e.g., in indoor networks, the NLOS range estimates can be significantly biased. As a result, the NLOS error can remarkably decrease the location accuracy, and it is not easy to accurately distinguish LOS from NLOS measurements. According to the information known about the prior probabilities and distributions of the NLOS errors, three different cases are introduced and the respective localization problems are addressed. Simulation results demonstrate that this algorithm achieves high location accuracy even for the case in which NLOS and LOS measurements are not identifiable.

Comprehensive review of vision-based fall detection systems

Abstract: Vision-based fall detection systems have experienced fast development over the last years To determine the course of its evolution and help new researchers, the main audience of this paper, a comprehensive revision of all published articles in the main scientific databases regarding this area during the last five years has been made After a selection process, detailed in the Materials and Methods Section, eighty-one systems were thoroughly reviewed Their characterization and classification techniques were analyzed and categorized Their performance data were also studied, and comparisons were made to determine which classifying methods best work in this field The evolution of artificial vision technology, very positively influenced by the incorporation of artificial neural networks, has allowed fall characterization to become more resistant to noise resultant from illumination phenomena or occlusion The classification has also taken advantage of these networks, and the field starts using robots to make these systems mobile However, datasets used to train them lack real-world data, raising doubts about their performances facing real elderly falls In addition, there is no evidence of strong connections between the elderly and the communities of researchers

Dynamic Fuzzy-Logic Based Path Planning for Mobility-Assisted Localization in Wireless Sensor Networks.

Abstract: Mobile anchor path planning techniques have provided as an alternative option for node localization in wireless sensor networks (WSNs). In such context, path planning is a movement pattern where a mobile anchor node’s movement is designed in order to achieve a maximum localization ratio possible with a minimum error rate. Typically, the mobility path planning is designed in advance, which is applicable when the mobile anchor has sufficient sources of energy and time. However, when the mobility movement is restricted or limited, a dynamic path planning design is needed. This paper proposes a novel distributed range-free movement mechanism for mobility-assisted localization in WSNs when the mobile anchor’s movement is limited. The designed movement is formed in real-time pattern using a fuzzy-logic approach based on the information received from the network and the nodes’ deployment. Our proposed model, Fuzzy-Logic based Path Planning for mobile anchor-assisted Localization in WSNs (FLPPL), offers superior results in several metrics including both localization accuracy and localization ratio in comparison to other similar works.