Modeling and design of energy efficient variable stiffness actuators

In this paper, we provide a port-based mathematical framework for analyzing and modeling variable stiffness actuators. The framework provides important insights in the energy requirements and, therefore, it is an important tool for the design of energy efficient variable stiffness actuators. Based on new insights gained from this approach, a novel conceptual actuator is presented. Simulations show that the apparent output stiffness of this actuator can be dynamically changed in an energy efficient way

Improving the efficiency of individualized designs for the mixed logit choice model by including covariates.

Recent research shows that the inclusion of choice related demo- and sociographics in discrete choice models aids in modeling the choice behavior of consumers substantially. However, the increase in efficiency gained by accounting for covariates in the design of a choice experiment has thus far only been demonstrated for the conditional logit model. Previous findings are extended by using covariates in the construction of individualized Bayesian D-efficient designs for the mixed logit choice model. A simulation study illustrates how incorporating covariates affecting choice behavior yields more efficient designs and more accurate estimates and predictions at the individual level. Moreover, it is shown that the possible loss in design efficiency and therefore in estimation and prediction accuracy from including choice unrelated respondent characteristics is negligible.Covariate; Discrete choice experiment; Mixed logit choice model; Individual efficient design; Hierarchical Bayes estimation;

A high-speed distortionless predictive image-compression scheme

A high-speed distortionless predictive image-compression scheme that is based on differential pulse code modulation output modeling combined with efficient source-code design is introduced. Experimental results show that this scheme achieves compression that is very close to the difference entropy of the source

Library Design in Combinatorial Chemistry by Monte Carlo Methods

Strategies for searching the space of variables in combinatorial chemistry experiments are presented, and a random energy model of combinatorial chemistry experiments is introduced. The search strategies, derived by analogy with the computer modeling technique of Monte Carlo, effectively search the variable space even in combinatorial chemistry experiments of modest size. Efficient implementations of the library design and redesign strategies are feasible with current experimental capabilities.Comment: 5 pages, 3 figure

Interpolation-based parameterized model order reduction of delayed systems

Three-dimensional electromagnetic methods are fundamental tools for the analysis and design of high-speed systems. These methods often generate large systems of equations, and model order reduction (MOR) methods are used to reduce such a high complexity. When the geometric dimensions become electrically large or signal waveform rise times decrease, time delays must be included in the modeling. Design space optimization and exploration are usually performed during a typical design process that consequently requires repeated simulations for different design parameter values. Efficient performing of these design activities calls for parameterized model order reduction (PMOR) methods, which are able to reduce large systems of equations with respect to frequency and other design parameters of the circuit, such as layout or substrate features. We propose a novel PMOR method for neutral delayed differential systems, which is based on an efficient and reliable combination of univariate model order reduction methods, a procedure to find scaling and frequency shifting coefficients and positive interpolation schemes. The proposed scaling and frequency shifting coefficients enhance and improve the modeling capability of standard positive interpolation schemes and allow accurate modeling of highly dynamic systems with a limited amount of initial univariate models in the design space. The proposed method is able to provide parameterized reduced order models passive by construction over the design space of interest. Pertinent numerical examples validate the proposed PMOR approach

Parameterized model order reduction of delayed systems using an interpolation approach with amplitude and frequency scaling coefficients

When the geometric dimensions become electrically large or signal waveform rise times decrease, time delays must be included in the modeling. We present an innovative PMOR technique for neutral delayed differential systems, which is based on an efficient and reliable combination of univariate model order reduction methods, amplitude and frequency scaling coefficients and positive interpolation schemes. It is able to provide parameterized reduced order models passive by construction over the design space of interest. Pertinent numerical examples validate the proposed PMOR approach

Visible and infrared photocurrent enhancement in a graphene-silicon Schottky photodetector through surface-states and electric field engineering

The design of efficient graphene-silicon (GSi) Schottky junction photodetectors requires detailed understanding of the spatial origin of the photoresponse. Scanning-photocurrent-microscopy (SPM) studies have been carried out in the visible wavelengths regions only, in which the response due to silicon is dominant. Here we present comparative SPM studies in the visible ($\lambda$ = 633nm) and infrared ($\lambda$ = 1550nm) wavelength regions for a number of GSi Schottky junction photodetector architectures, revealing the photoresponse mechanisms for silicon and graphene dominated responses, respectively, and demonstrating the influence of electrostatics on the device performance. Local electric field enhancement at the graphene edges leads to a more than ten-fold increased photoresponse compared to the bulk of the graphene-silicon junction. Intentional design and patterning of such graphene edges is demonstrated as an efficient strategy to increase the overall photoresponse of the devices. Complementary simulations and modeling illuminate observed effects and highlight the importance of considering graphene's shape and pattern and device geometry in the device design

Computational structure‐based drug design: Predicting target flexibility

The role of molecular modeling in drug design has experienced a significant revamp in the last decade. The increase in computational resources and molecular models, along with software developments, is finally introducing a competitive advantage in early phases of drug discovery. Medium and small companies with strong focus on computational chemistry are being created, some of them having introduced important leads in drug design pipelines. An important source for this success is the extraordinary development of faster and more efficient techniques for describing flexibility in three‐dimensional structural molecular modeling. At different levels, from docking techniques to atomistic molecular dynamics, conformational sampling between receptor and drug results in improved predictions, such as screening enrichment, discovery of transient cavities, etc. In this review article we perform an extensive analysis of these modeling techniques, dividing them into high and low throughput, and emphasizing in their application to drug design studies. We finalize the review with a section describing our Monte Carlo method, PELE, recently highlighted as an outstanding advance in an international blind competition and industrial benchmarks.We acknowledge the BSC-CRG-IRB Joint Research Program in Computational Biology. This work was supported by a grant from the Spanish Government CTQ2016-79138-R.J.I. acknowledges support from SVP-2014-068797, awarded by the Spanish Government.Peer ReviewedPostprint (author's final draft

Energy efficiency considerations in integrated IT and optical network resilient infrastructures

The European Integrated Project GEYSERS - Generalised Architecture for Dynamic Infrastructure Services - is concentrating on infrastructures incorporating integrated optical network and IT resources in support of the Future Internet with special emphasis on cloud computing. More specifically GEYSERS proposes the concept of Virtual Infrastructures over one or more interconnected Physical Infrastructures comprising both network and IT resources. Taking into consideration the energy consumption levels associated with the ICT today and the expansion of the Internet in size and complexity, that incurring increased energy consumption of both IT and network resources, energy efficient infrastructure design becomes critical. To address this need, in the framework of GEYSERS, we propose energy efficient design of infrastructures incorporating integrated optical network and IT resources, supporting resilient end-to-end services. Our modeling results quantify significant energy savings of the proposed solution by jointly optimizing the allocation of both network and IT resources

An architecture-based dependability modeling framework using AADL

For efficiency reasons, the software system designers' will is to use an integrated set of methods and tools to describe specifications and designs, and also to perform analyses such as dependability, schedulability and performance. AADL (Architecture Analysis and Design Language) has proved to be efficient for software architecture modeling. In addition, AADL was designed to accommodate several types of analyses. This paper presents an iterative dependency-driven approach for dependability modeling using AADL. It is illustrated on a small example. This approach is part of a complete framework that allows the generation of dependability analysis and evaluation models from AADL models to support the analysis of software and system architectures, in critical application domains