Fish tagging experiments: a prelude to an extensive tag-recovery programme on Lake Victoria

The present study is an early stage of this programme and examines several species of fishes under controlled conditions to delineate responses to tagging as a function of type of tag, species, size, and sex of fish, and position of tag placement. It is intimately related to another phase of research currently being conducted by the author on age and growth of several species important to the fishery of Lake Victoria (e.g. Tilapia spp., and several catfishes, Clarias mossambicus Peters and Bagrus docmac (Forskal). Data reported are both a reflection of growth studies and an attempt to achieve insight into tag loss, growth, and mortality that might be expected to occur in these species under lake conditions with reference to the above parameters.

Fast L0-based sparse signal recovery

This paper develops an algorithm for finding sparse signals from limited observations of a linear system. We assume an adaptive Gaussian model for sparse signals. This model results in a least square problem with an iteratively reweighted L2 penalty that approximates the L0-norm. We propose a fast algorithm to solve the problem within a continuation framework. In our examples, we show that the correct sparsity map and sparsity level are gradually learnt during the iterations even when the number of observations is reduced, or when observation noise is present. In addition, with the help of sophisticated interscale signal models, the algorithm is able to recover signals to a better accuracy and with reduced number of observations than typical L1-norm and reweighted L1 norm methods. ©2010 IEEE.

Strategies for product recovery decisions

Product recovery is beset by uncertainty regarding the quality of end-of-life (EOL) products, and in order to ascertain the reusability of these products, they have to undergo expensive tests. This undermines the profitability of the recovery process. The key to improve the effectiveness of product recovery is to improve the quality of information available before testing. Emerging data capture technologies can significantly improve the availability of information. However, in order to maximise the potential of these technologies, appropriate decision-making algorithms that exploit such information must be developed. We model the recovery process using a decision-theoretic approach, and derive strategies to ascertain the reusability of EOL products, and also to decide when tests are beneficial. We show that improving the quality of information leads to increase in effectiveness of the recovery process by reducing the need for tests. Copyright © 2009 Inderscience Enterprises Ltd.

Energy recovery from high-frequency clocks using DC-DC converters

Large digital chips use a significant amount of energy to distribute a multi-GHz clock. By discharging the clock network to ground every cycle, the energy stored in this large capacitor is wasted. Instead, the energy can be recovered using an on-chip DC-DC converter. This paper investigates the integration of two DC-DC converter topologies, boost and buck-boost, with a high-speed clock driver. The high operating frequency significantly shrinks the required size of the L and C components so they can be placed on-chip; typical converters place them off-chip. The clock driver and DC-DC converter are able to share the entire tapered buffer chain, including the widest drive transistors in the final stage. To achieve voltage regulation, the clock duty cycle must be modulated; implying only single-edge-triggered flops should be used. However, this minor drawback is eclipsed by the benefits: by recovering energy from the clock, the output power can actually exceed the additional power needed to operate the converter circuitry, resulting in an effective efficiency greater than 100%. Furthermore, the converter output can be used to operate additional power-saving features like low-voltage islands or body bias voltages. ©2008 IEEE.

Optimal control with stochastic PDE constraints and uncertain controls

The optimal control of problems that are constrained by partial differential equations with uncertainties and with uncertain controls is addressed. The Lagrangian that defines the problem is postulated in terms of stochastic functions, with the control function possibly decomposed into an unknown deterministic component and a known zero-mean stochastic component. The extra freedom provided by the stochastic dimension in defining cost functionals is explored, demonstrating the scope for controlling statistical aspects of the system response. One-shot stochastic finite element methods are used to find approximate solutions to control problems. It is shown that applying the stochastic collocation finite element method to the formulated problem leads to a coupling between stochastic collocation points when a deterministic optimal control is considered or when moments are included in the cost functional, thereby forgoing the primary advantage of the collocation method over the stochastic Galerkin method for the considered problem. The application of the presented methods is demonstrated through a number of numerical examples. The presented framework is sufficiently general to also consider a class of inverse problems, and numerical examples of this type are also presented. © 2011 Elsevier B.V.

Optimal design of Functionally Graded Materials using a procedural model and Particle Swarm Optimization

A new method for the optimal design of Functionally Graded Materials (FGM) is proposed in this paper. Instead of using the widely used explicit functional models, a feature tree based procedural model is proposed to represent generic material heterogeneities. A procedural model of this sort allows more than one explicit function to be incorporated to describe versatile material gradations and the material composition at a given location is no longer computed by simple evaluation of an analytic function, but obtained by execution of customizable procedures. This enables generic and diverse types of material variations to be represented, and most importantly, by a reasonably small number of design variables. The descriptive flexibility in the material heterogeneity formulation as well as the low dimensionality of the design vectors help facilitate the optimal design of functionally graded materials. Using the nature-inspired Particle Swarm Optimization (PSO) method, functionally graded materials with generic distributions can be efficiently optimized. We demonstrate, for the first time, that a PSO based optimizer outperforms classical mathematical programming based methods, such as active set and trust region algorithms, in the optimal design of functionally graded materials. The underlying reason for this performance boost is also elucidated with the help of benchmarked examples. © 2011 Elsevier Ltd. All rights reserved.