An accelerator design for speedup of Java execution in consumer mobile devices

In today’s consumer electronics market, Java has become one of the most important programming languages for the rapid development of mobile applications – spanning from home appliances/controllers, mobile and communication devices, to network-centric applets. However, the demand for high-performance low-power Java-based consumer mobile applications puts forward new challenges to the system design and implementation. This paper analyzes the energy consumption, execution efficiency, and speed issues of Java applications in a typical consumer mobile device environment. By adopting a hardware-assisted approach, we introduce a Java accelerator with a companion Java virtual machine. The accelerator is designed in an asynchronous style, and can be integrated with most existing processors and operating systems. The core architecture, design philosophy, and implementation considerations are presented in detail in this paper.     

Going into the wild in child–robot interaction studies: issues in social robotic development

As robots move into more human centric environments we require methods to develop robots that can naturally interact with humans. Doing so requires testing in the real-world and addressing multidisciplinary challenges. Our research is focused on child–robot interaction which includes very young children, for example toddlers, and children diagnosed with autism. More traditional forms of human–robot communication, such as speech or gesture recognition, may not be appropriate with these users, where as touch may help to provide a more natural and appropriate means of communication for such instances. In this paper, we present our findings on these topics obtained from a project involving a spherical robot that acquires information regarding natural touch from analysing sensory patterns over-time to characterize the information. More specifically, from this project we have derived important factors for future consideration, we describe our iterative experimental methodology of testing in and out of the ‘wild’ (lab based and real world), and outline discoveries that were made by doing so.     

Enhancement of multimodal biometric segregation using unconstrained cohort normalisation

This paper presents an investigation into the effects, on the accuracy of multimodal biometrics, of introducing unconstrained cohort normalisation (UCN) into the score-level fusion process. Whilst score normalisation has been widely used in voice biometrics, its effectiveness in other biometrics has not been previously investigated. This study aims to explore the potential usefulness of the said score normalisation technique in face biometrics and to investigate its effectiveness for enhancing the accuracy of multimodal biometrics. The experimental investigations involve the two recognition modes of verification and open-set identification, in clean mixed-quality and degraded data conditions. Based on the experimental results, it is demonstrated that the capabilities provided by UCN can significantly improve the accuracy of fused biometrics. The paper presents the motivation for, and the potential advantages of, the proposed approach and details the experimental study.     

Co-Incubation with PPARβ/δ Agonists and Antagonists Modeled Using Computational Chemistry: Effect on LPS Induced Inflammatory Markers in Pulmonary Artery

Peroxisome proliferator activated receptor beta/delta (PPARβ/δ) is a nuclear receptor ubiquitously expressed in cells, whose signaling controls inflammation. There are large discrepancies in understanding the complex role of PPARβ/δ in disease, having both anti- and pro-effects on inflammation. After ligand activation, PPARβ/δ regulates genes by two different mechanisms; induction and transrepression, the effects of which are difficult to differentiate directly. We studied the PPARβ/δ-regulation of lipopolysaccharide (LPS) induced inflammation (indicated by release of nitrite and IL-6) of rat pulmonary artery, using different combinations of agonists (GW0742 or L−165402) and antagonists (GSK3787 or GSK0660). LPS induced release of NO and IL-6 is not significantly reduced by incubation with PPARβ/δ ligands (either agonist or antagonist), however, co-incubation with an agonist and antagonist significantly reduces LPS-induced nitrite production and Nos2 mRNA expression. In contrast, incubation with LPS and PPARβ/δ agonists leads to a significant increase in Pdk−4 and Angptl−4 mRNA expression, which is significantly decreased in the presence of PPARβ/δ antagonists. Docking using computational chemistry methods indicates that PPARβ/δ agonists form polar bonds with His287, His413 and Tyr437, while antagonists are more promiscuous about which amino acids they bind to, although they are very prone to bind Thr252 and Asn307. Dual binding in the PPARβ/δ binding pocket indicates the ligands retain similar binding energies, which suggests that co-incubation with both agonist and antagonist does not prevent the specific binding of each other to the large PPARβ/δ binding pocket. To our knowledge, this is the first time that the possibility of binding two ligands simultaneously into the PPARβ/δ binding pocket has been explored. Agonist binding followed by antagonist simultaneously switches the PPARβ/δ mode of action from induction to transrepression, which is linked with an increase in Nos2 mRNA expression and nitrite production.     

The analysis of the addition of stochasticity to a neural tree classifier

This paper describes various mechanisms for adding stochasticity to a dynamic hierarchical neural clusterer. Such a network grows a tree-structured neural classifier dynamically in response to the unlabelled data with which it is presented. Experiments are undertaken to evaluate the effects of this addition of stochasticity. These tests were carried out using two sets of internal parameters, that define the characteristics of the neural clusterer.A Genetic Algorithm (GA) using appropriate cluster criterion measures in its fitness function was used to search the parameter space for these instantiations. It was found that the addition of non-determinism produced more reliable clustering performances especially on unseen real-world data.Finally, deliberately changing the tree shape by varying key parameters was investigated, illustrated and systematically analysed.     

A survey of robot learning from demonstration

We present a comprehensive survey of robot Learning from Demonstration (LfD), a technique that develops policies from example state to action mappings. We introduce the LfD design choices in terms of demonstrator, problem space, policy derivation and performance, and contribute the foundations for a structure in which to categorize LfD research. Specifically, we analyze and categorize the multiple ways in which examples are gathered, ranging from teleoperation to imitation, as well as the various techniques for policy derivation, including matching functions, dynamics models and plans. To conclude we discuss LfD limitations and related promising areas for future research.     

Combustion synthesis and subsequent sintering of titanium-matrix composites

Titanium-matrix composites containing up to 50 wt% TiC particles were prepared by combustion synthesis using elemental powders. The products were subsequently sintered at 1160°C for various periods of time. SEM and X-ray diffraction were used to assess the changes that took place during the two stages of processing. Products of combustion synthesis containing in excess of 25%TiC contained large cracks as well as agglomerates of carbide particles that were undesirable from the point of view of reinforcing the metal. The carbide obtained by combustion synthesis had a higher carbon content than that expected according to the Ti-C equilibrium phase diagram, due to the non-equilibrium nature of the reaction. During the sintering stage, the carbide of non-equilibrium composition reacted with titanium to yield the carbide of equilibrium composition. The composition changes were investigated and their significance on the sintering process is discussed.     

Large-eddy simulation of supercritical fluid injection

Mixing is a key point for thrust and efficiency of combustion systems. It becomes crucial in the case Liquid Rocket Engines as large investments are involved. Besides, the pressure in liquid rocket combustion chamber often exceeding the critical point of loaded propellants, mixing becomes an important scientific issue as fluid properties differ from classical ideal gas assumption. In this study, two configurations are studied to evaluate the impact of subgrid models on mixing. Firstly, Mayer's experiments of trans- and super-critical nitrogen jet injection into a warm nitrogen atmosphere have been numerically investigated with a structured numerical code called SiTCom-B. SiTCom-B solves Direct Numerical Simulations and Large Eddy Simulations equations for perfect or real gas equation of states. In this study, Soave–Redlich–Kwong (SRK) and Peng–Robinson equation of state are used with appropriated thermodynamics relations and validated against NIST data. Three-dimensional LES are conducted for two cases (cases 3 and 4 in Mayer et al.'s reference [1]) with real-gas NSCBC treatment. Several sub-grid scale models are tested and the results are compared to experimental data for density on jet axis: a very good agreement is obtained on a light mesh (11.6 million of points) with the SRK equation of state and standard Smagorinsky model. Flow structures are evidenced with Schlieren snapshots. Secondly, the Mascotte test-bench from ONERA is simulated with SiTCom-B based on Soave–Redlich–Kwong equation of state and Smagorinsky models. The simulated non-reacting case is characterized by a very short liquid oxygen dense core because of the strong density and velocity gradients boosting mixing efficiency.     

Modelling and optimizing sequential imperfect preventive maintenance

This paper deals with the problem of scheduling imperfect preventive maintenance (PM) of some equipment. It uses a model due to Kijima in which each application of PM reduces the equipment's effective age (but without making it as good as new). The approach presented here involves minimizing a performance function which allows for the costs of minimal repair and eventual system replacement as well as for the costs of PM during the equipment's operating lifetime. The paper describes a numerical investigation into the sensitivity of optimum schedules to different aspects of an age-reduction model (including the situation when parts of a system are non-maintainable—i.e., unaffected by PM).