Millisecond Pulsars, their Evolution and Applications

Millisecond pulsars (MSPs) are short-period pulsars that are distinguished from "normal" pulsars, not only by their short period, but also by their very small spin-down rates and high probability of being in a binary system. These properties are consistent with MSPs having a different evolutionary history to normal pulsars, viz., neutron-star formation in an evolving binary system and spin-up due to accretion from the binary companion. Their very stable periods make MSPs nearly ideal probes of a wide variety of astrophysical phenomena. For example, they have been used to detect planets around pulsars, to test the accuracy of gravitational theories, to set limits on the low-frequency gravitational-wave background in the Universe, and to establish pulsar-based timescales that rival the best atomic-clock timescales in long-term stability. MSPs also provide a window into stellar and binary evolution, often suggesting exotic pathways to the observed systems. The X-ray accretion-powered MSPs, and especially those that transition between an accreting X-ray MSP and a non-accreting radio MSP, give important insight into the physics of accretion on to highly magnetised neutron stars.Comment: Has appeared in Journal of Astrophysics and Astronomy special issue on 'Physics of Neutron Stars and Related Objects', celebrating the 75th birth-year of G. Srinivasa

Real-Time Planning with Primitives for Dynamic Walking over Uneven Terrain

We present an algorithm for receding-horizon motion planning using a finite family of motion primitives for underactuated dynamic walking over uneven terrain. The motion primitives are defined as virtual holonomic constraints, and the special structure of underactuated mechanical systems operating subject to virtual constraints is used to construct closed-form solutions and a special binary search tree that dramatically speed up motion planning. We propose a greedy depth-first search and discuss improvement using energy-based heuristics. The resulting algorithm can plan several footsteps ahead in a fraction of a second for both the compass-gait walker and a planar 7-Degree-of-freedom/five-link walker.Comment: Conference submissio

Input Design for System Identification via Convex Relaxation

This paper proposes a new framework for the optimization of excitation inputs for system identification. The optimization problem considered is to maximize a reduced Fisher information matrix in any of the classical D-, E-, or A-optimal senses. In contrast to the majority of published work on this topic, we consider the problem in the time domain and subject to constraints on the amplitude of the input signal. This optimization problem is nonconvex. The main result of the paper is a convex relaxation that gives an upper bound accurate to within $2/\pi$ of the true maximum. A randomized algorithm is presented for finding a feasible solution which, in a certain sense is expected to be at least $2/\pi$ as informative as the globally optimal input signal. In the case of a single constraint on input power, the proposed approach recovers the true global optimum exactly. Extensions to situations with both power and amplitude constraints on both inputs and outputs are given. A simple simulation example illustrates the technique.Comment: Preprint submitted for journal publication, extended version of a paper at 2010 IEEE Conference on Decision and Contro