One Parameter Solution of Spherically Symmetric Accretion in Various Pseudo-Schwarzschild Potentials

In this paper we have solved the hydrodynamic equations governing the spherically symmetric isothermal accretion (wind) onto (away from) compact objects using various pseudo-Schwarzschild potentials.These solutions are essentially one parameter solutions in a sense that all relevant dynamical as well as thermodynamic quantities for such a flow could be obtained (with the assumption of a one-temperature fluid) if {\it only one} flow parameter (temperature of the flow $T$) is given. Also we have investigated the transonic behaviour of such a flow and showed that for a given $T$, transitions from subsonic to the supersonic branch of accretion (wind) takes place at different locations depending on the potentials used to study the flow and we have identified these transition zones for flows in various such potentials.Comment: 9 pages, 3 black and white post-script figures. Published in the International Journal of Modern Physics D (IJMPD

A Framework for Scalable Cooperative Navigation of Autonomous Vehicles

We describe a general framework for controlling and coordinating a group of non-holonomic mobile robots equipped with range sensors, with applications ranging from scouting and reconnaissance, to search and rescue and manipulation tasks. We first describe a set of control laws that allows each robot to control its position and orientation with respect to neighboring robots or obstacles in the environment. We then develop a coordination protocol that allows the robots to automatically switch between the control laws to follow a specified trajectory. Finally, we describe two simple trajectory generators that are derived from potential field theory. The first allows each robot to plan its reference trajectory based on the information available to it. The second scheme requires sharing of information and results in a trajectory for the designated leader. Numerical simulations illustrate the application of these ideas and demonstrate the scalability of the proposed framework for a large group of robots

Contextual Localization Through Network Traffic Analysis

opportunitiesforcontentserviceproviderstooptimizethecontent delivery based on user’s location. Since sharing precise location remainsamajorprivacyconcernamongtheusers,manylocationbased services rely on contextual location (e.g. residence, cafe etc.) as opposed to acquiring user’s exact physical location. In this paper, we present PACL (Privacy-Aware Contextual Localizer), which can learn user’s contextual location just by passively monitoring user’s network traffic. PACL can discern a set of vital attributes (statistical and application-based) from user’s network traffic, and predict user’s contextual location with a very high accuracy.WedesignandevaluatePACLusingreal-worldnetwork traces of over 1700 users with over 100 gigabytes of total data. OurresultsshowthatPACL(builtusingdecisiontree)canpredict user’s contextual location with the accuracy of around 87%. I

Hybrid Control of Formations of Robots

We describe a framework for controlling a group of nonholonomic mobile robots equipped with range sensors. The vehicles are required to follow a prescribed trajectory while maintaining a desired formation. By using the leader-following approach, we formulate the formation control problem as a hybrid (mode switching) control system. We then develop a decision module that allows the robots to automatically switch between continuous-state control laws to achieve a desired formation shape. The stability properties of the closed-loop hybrid system are studied using Lyapunov theory. We do not use explicit communication between robots; instead we integrate optimal estimation techniques with nonlinear controllers. Simulation and experimental results verify the validity of our approach