Simulation of integrated components for ultrashort pulse generation

This paper reviews simulations of integrated components for ultra-short pulse generation and shaping. Optimised component designs are reported, minimising the major impact that chirp and saturation effects have, even where ultra-fast nonlinearities are used. © 2005 OSA/IPRA.

Increased carbon nanotube area density after catalyst generation from cobalt disilicide using a cyclic reactive ion etching approach

We used a cyclic reactive ion etching (RIE) process to increase the Co catalyst density on a cobalt disilicide (CoSi2) substrate for carbon nanotube (CNT) growth. Each cycle of catalyst formation consists of a room temperature RIE step and an annealing step at 450 °C. The RIE step transfers the top-surface of CoSi2 into cobalt fluoride; while the annealing reduces the fluoride into metallic Co nanoparticles. We have optimized this cyclic RIE process and determined that the catalyst density can be doubled in three cycles, resulting in a final CNT shell density of 6.6 × 10 11 walls·cm-2. This work demonstrates a very effective approach to increase the CNT density grown directly on silicides. © 2014 AIP Publishing LLC.

Soft robotics: The next generation of intelligent machines

There has been an increasing interest in applying biological principles to the design and control of robots. Unlike industrial robots that are programmed to execute a rather limited number of tasks, the new generation of bio-inspired robots is expected to display a wide range of behaviours in unpredictable environments, as well as to interact safely and smoothly with human co-workers. In this article, we put forward some of the properties that will characterize these new robots: soft materials, flexible and stretchable sensors, modular and efficient actuators, self-organization and distributed control. We introduce a number of design principles; in particular, we try to comprehend the novel design space that now includes soft materials and requires a completely different way of thinking about control. We also introduce a recent case study of developing a complex humanoid robot, discuss the lessons learned and speculate about future challenges and perspectives.

Predictive entropy search for efficient global optimization of black-box functions

We propose a novel information-theoretic approach for Bayesian optimization called Predictive Entropy Search (PES). At each iteration, PES selects the next evaluation point that maximizes the expected information gained with respect to the global maximum. PES codifies this intractable acquisition function in terms of the expected reduction in the differential entropy of the predictive distribution. This reformulation allows PES to obtain approximations that are both more accurate and efficient than other alternatives such as Entropy Search (ES). Furthermore, PES can easily perform a fully Bayesian treatment of the model hyperparameters while ES cannot. We evaluate PES in both synthetic and real-world applications, including optimization problems in machine learning, finance, biotechnology, and robotics. We show that the increased accuracy of PES leads to significant gains in optimization performance.