Electro-optical characterization of IC compatible microcantilevers

The aim of this work is to simulate and optically characterize the piezoelectric performance of complementary metal oxide semiconductor (CMOS) compatible microcantilevers based on aluminium nitride (AlN) and manufactured at room temperature. This study should facilitate the integration of piezoelectric micro-electro-mechanical systems (MEMS) such as microcantilevers, in CMOS technology. Besides compatibility with standard integrated circuit manufacturing procedures, low temperature processing also translates into higher throughput and, as a consequence, lower manufacturing costs. Thus, the use of the piezoelectric properties of AlN manufactured by reactive sputtering at room temperature is an important step towards the integration of this type of devices within future CMOS technology standards. To assess the reliability of our fabrication process, we have manufactured arrays of free-standing microcantilever beams of variable dimension and studied their piezoelectric performance. The characterization of the first out-of-plane modes of AlN-actuated piezoelectric microcantilevers has been carried out using two optical techniques: laser Doppler vibrometry (LDV) and white light interferometry (WLI). In order to actuate the cantilevers, a periodic chirp signal in certain frequency ranges was applied between the device electrodes. The nature of the different vibration modes detected has been studied and compared with that obtained by a finite element model based simulation (COMSOL Multiphysics), showing flexural as well as torsional modes. The correspondence between theoretical and experimental data is reasonably good, probing the viability of this high throughput and CMOS compatible fabrication process. To complete the study, X-ray diffraction as well as d33 piezoelectric coefficient measurements were also carried out.

Simulation of a mechanical thrombectomy device based in the use of self-expandable stents for the blood clots extraction

Recently, we have presented some studies concerning the analysis, design and optimization of one experimental device developed in the UK - GPTAD - which has been designed to remove blood clots without the need to make contact with the clot itself, thereby potentially reducing the risk of problems such as downstream embolisation. Based on the idea of a modification of the previous device, in this work, we present a model based in the use of stents like the SolitaireTM FR, which is in contact with the clot itself. In the case of such devices, the stent is self-expandable and the extraction of the blood clot is faciliatated by the stent, which must be inside the clot. Such stents are generally inserted in position by using the guidewire inserted into the catheter. This type of modeling could potentially be useful in showing how the blood clot is moved by the various different forces involved. The modelling has been undertaken by analyzing the resistances, compliances and inertances effects. We model an artery and blood clot for range of forces for the guidewire. In each case we determine the interaction between blood clot, stent and artery.

FPGA Acceleration of Monte Carlo-based Financial Simulation: Design Challenges and Lessons Learnt

The simulation of interest rate derivatives is a powerful tool to face the current market fluctuations. However, the complexity of the financial models and the way they are processed require exorbitant computation times, what is in clear conflict with the need of a processing time as short as possible to operate in the financial market. To shorten the computation time of financial derivatives the use of hardware accelerators becomes a must.

A Novel Method for Radio Propagation Simulation Based on Automatic 3D Environment Reconstruction

In this paper, a novel method to simulate radio propagation is presented. The method consists of two steps: automatic 3D scenario reconstruction and propagation modeling. For 3D reconstruction, a machine learning algorithm is adopted and improved to automatically recognize objects in pictures taken from target regions, and 3D models are generated based on the recognized objects. The propagation model employs a ray tracing algorithm to compute signal strength for each point on the constructed 3D map. Our proposition reduces, or even eliminates, infrastructure cost and human efforts during the construction of realistic 3D scenes used in radio propagation modeling. In addition, the results obtained from our propagation model proves to be both accurate and efficient

A novel method for radio propagation simulation based on automatic 3D environment reconstruction

In this paper, a novel method to simulate radio propagation is presented. The method consists of two steps: automatic 3D scenario reconstruction and propagation modeling. For 3D reconstruction, a machine learning algorithm is adopted and improved to automatically recognize objects in pictures taken from target region, and 3D models are generated based on the recognized objects. The propagation model employs a ray tracing algorithm to compute signal strength for each point on the constructed 3D map. By comparing with other methods, the work presented in this paper makes contributions on reducing human efforts and cost in constructing 3D scene; moreover, the developed propagation model proves its potential in both accuracy and efficiency.

A stochastic model updating method for parameter variability quantification based on response surface models and Monte Carlo simulation

Stochastic model updating must be considered for quantifying uncertainties inherently existing in real-world engineering structures. By this means the statistical properties,instead of deterministic values, of structural parameters can be sought indicating the parameter variability. However, the implementation of stochastic model updating is much more complicated than that of deterministic methods particularly in the aspects of theoretical complexity and low computational efficiency. This study attempts to propose a simple and cost-efficient method by decomposing a stochastic updating process into a series of deterministic ones with the aid of response surface models and Monte Carlo simulation. The response surface models are used as surrogates for original FE models in the interest of programming simplification, fast response computation and easy inverse optimization. Monte Carlo simulation is adopted for generating samples from the assumed or measured probability distributions of responses. Each sample corresponds to an individual deterministic inverse process predicting the deterministic values of parameters. Then the parameter means and variances can be statistically estimated based on all the parameter predictions by running all the samples. Meanwhile, the analysis of variance approach is employed for the evaluation of parameter variability significance. The proposed method has been demonstrated firstly on a numerical beam and then a set of nominally identical steel plates tested in the laboratory. It is found that compared with the existing stochastic model updating methods, the proposed method presents similar accuracy while its primary merits consist in its simple implementation and cost efficiency in response computation and inverse optimization.

B2DI a bayesian BDI agent model with causal belief updating based on MSBN

In this paper, we introduce B2DI model that extends BDI model to perform Bayesian inference under uncertainty. For scalability and flexibility purposes, Multiply Sectioned Bayesian Network (MSBN) technology has been selected and adapted to BDI agent reasoning. A belief update mechanism has been defined for agents, whose belief models are connected by public shared beliefs, and the certainty of these beliefs is updated based on MSBN. The classical BDI agent architecture has been extended in order to manage uncertainty using Bayesian reasoning. The resulting extended model, so-called B2DI, proposes a new control loop. The proposed B2DI model has been evaluated in a network fault diagnosis scenario. The evaluation has compared this model with two previously developed agent models. The evaluation has been carried out with a real testbed diagnosis scenario using JADEX. As a result, the proposed model exhibits significant improvements in the cost and time required to carry out a reliable diagnosis.

Simulation-based evaluation of susceptibility distortion correction methods in diffusion MRI for connectivity analysis

Connectivity analysis on diffusion MRI data of the whole-brain suffers from distortions caused by the standard echo-planar imaging acquisition strategies. These images show characteristic geometrical deformations and signal destruction that are an important drawback limiting the success of tractography algorithms. Several retrospective correction techniques are readily available. In this work, we use a digital phantom designed for the evaluation of connectivity pipelines. We subject the phantom to a “theoretically correct” and plausible deformation that resembles the artifact under investigation. We correct data back, with three standard methodologies (namely fieldmap-based, reversed encoding-based, and registration- based). Finally, we rank the methods based on their geometrical accuracy, the dropout compensation, and their impact on the resulting connectivity matrices.

Evaluation of three solar and daylighting control systems based on Calumen II, Ecotect and Radiance simulation programs to obtain an energy efficient and healthy interior in the experimental building prototype SDE10

Evaluation of three solar and daylighting control systems based on Calumen II, Ecotect and Radiance simulation programs to obtain an energy efficient and healthy interior in the experimental building prototype SDE10

Cooperative Learning Model based on Multi-Agent Architecture for Embedded Intelligent Systems

Cooperative systems are suitable for many types of applications and nowadays these system are vastly used to improve a previously defined system or to coordinate multiple devices working together. This paper provides an alternative to improve the reliability of a previous intelligent identification system. The proposed approach implements a cooperative model based on multi-agent architecture. This new system is composed of several radar-based systems which identify a detected object and transmit its own partial result by implementing several agents and by using a wireless network to transfer data. The proposed topology is a centralized architecture where the coordinator device is in charge of providing the final identification result depending on the group behavior. In order to find the final outcome, three different mechanisms are introduced. The simplest one is based on majority voting whereas the others use two different weighting voting procedures, both providing the system with learning capabilities. Using an appropriate network configuration, the success rate can be improved from the initial 80% up to more than 90%.