The multi-phase Lattice Boltzmann Method (LBM) is used to explore some unprecedented aspects of laminar forced convection in a bottom heated rectangular microchannel. Important physical parameters, such as forces exerted on fluid parcels as well as on the dispersed nanoparticle phase are studied, in an attempt to elucidate the mechanism that results in establishment of a relative velocity between nanoparticles and the continuous fluid phase (slip velocity). The significance of the external forces, such as the gravitational, thermophoresis and Brownian forces is investigated. A recently established expression for the estimation of thermophoresis force in nanofluids is employed to study the true effect of thermophoresis, as other studies either neglect this effect, or are parametric or employ expressions that overestimate this effect. The results indicate that in laminar forced convection, the Brownian force has a significant effect on flow and heat transfer characteristics for low Re number flows (Re∼1-10), but thermophoresis may be safely neglected for all flow conditions. At low Re number flows, the nanofluid flow is heterogeneous, and heat transfer characteristics of nanofluid compared to the base fluid, such as Nu and convection heat transfer coefficient, significantly increase, while at higher Re numbers, such as Re = 100, flow behaves homogeneously and therefore the application of a nanofluid is not justified.

A major difficulty encountered in the application of linear parameter-varying (LPV) control is the complexity of synthesis and implementation when the number of scheduling parameters is large. Often heuristic solutions involve neglecting individual scheduling parameters, such that standard LPV controller synthesis methods become applicable. However, stability and performance guarantees are rendered void, if controller designs based on an approximate model are implemented on the original plant. In this brief, a synthesis method for LPV controllers that achieves reduced implementation complexity is proposed. The method is comprised of first synthesizing an initial controller based on a reduced parameter set. Then closed-loop stability and performance guarantees are recovered with respect to the original plant, which is considered to be accurately modeled. Iteratively solving a nonconvex bilinear matrix inequality may further improve performance. A two-degrees-of-freedom (2-DOF) and three-degrees-of-freedom robotic manipulator is considered as an illustrative example, for which experimental results indicate a good performance for controllers of reduced scheduling order. Furthermore, in the 2-DOF case, controller performance has been significantly improved.

This paperpresentsthedesignandsuccessfulexperimentalvalidationofalinearparameter-varying (LPV) controlstrategyforafour-degrees-of-freedomcontrolmomentgyroscope(CMG).TheMIMOplant is highlycoupledandnonlinear.First,alinearizedmodelwithmovingoperatingpointisusedto construct anLPVmodel.Then,agridding-basedLPVstate-feedbackcontrolisdesignedthatclearly outperforms lineartime-invariant(LTI)controllers.Moreover,awayisproposedtoselectpre-filter gains for referenceinputsthatcanbegeneralizedtoalargeclassofmechanicalsystems.Overall,thestrategy allowsasimpleimplementationinreal-timeandmaybeofinterestforapplicationssuchasattitude control ofasatellite.ThemethodisappliedtoalaboratoryscaleCMG,andexperimentalresultsillustrate that theproposedLPVcontrollerachievesindeedabetterperformanceinamuchwiderrangeof operation thanlinearcontrollersreportedintheliterature

In this letter, a novel technique is adopted for human recognition based on eye blinking waveform extracted from electro-oculogram signals. For this purpose, a database of 25 subjects is collected using Neurosky Mindwave headset. Then, the eye blinking signal is extracted and applied for identification and verification tasks. The pre-processing stage includes empirical mode decomposition to isolate electro-oculogram signal from brainwaves. Then, time delineation of the eye blinking waveform is utilized for feature extraction. Finally, linear discriminant analysis is adopted for classification. Based on the achieved results, the proposed system can identify subjects with best accuracy of 97.3% and verify them with an equal error rate of 3.7%. The obtained results in this letter confirm that eye blinking waveform carries discriminant information and is therefore appropriate as a basis for human recognition task.

Face recognition in the wild can be defined as recognizing individuals unabated by pose, illumination, expression, and uncertainties from the image acquisition. In this paper, we propose a framework recognizing human faces under such uncertainties by focusing on the pose problem while considering the other factors together. The proposed work introduces an automatic front-end stereo-based system, which starts with image acquisition and ends by face recognition. Once an individual is detected by one of the stereo cameras, its facial features are identified using a facial features extraction model. These features are used to steer the second camera to see the same subject. Then, a stereo pair is captured and 3D face is reconstructed. The proposed stereo matching approach carefully handles illumination variance, occlusion, and disparity discontinuity. The reconstructed 3D shape is used to synthesize virtual 2D views in novel poses. All these steps are done off-line in an Enrollment stage. To recognize a face from a 2D image, which is captured under unknown environmental conditions, another fast on-line stage starts by facial features detection. Then, a facial signature is extracted from patches around these facial features. Finally, this probe image is matched against the closest synthesized images. Experiments are conducted on different public databases from where we investigate the effect of each component of the proposed framework on the recognition performance. The results confirm that without training and with automatic features extraction, our proposed face recognition at a distance approach outperforms most of the state-of-the-art approaches.