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.

We propose a novel vertebral body segmentation approach, which is based on the graph cuts technique with shape constraints. The proposed approach depends on both image appearance and shape information. Shape information is gathered from a set of training shapes. Then we estimate the shape variations using a new distance probabilistic model which approximates the marginal densities of the vertebral body and its background in the variability region using a Poisson distribution refined by positive and negative Gaussian components. To segment a vertebral body, we align its 3D shape with the training 3D shape so we can use the distance probabilistic model. Then its gray level is approximated with a Linear Combination of Gaussians (LCG) with sign-alternate components. The spatial interaction between the neighboring voxels is identified using a new analytical approach. Finally, we formulate an energy function using both appearance models and shape constraints. This function is globally minimized using s/t graph cuts to get the optimal segmentation. Experimental results show that the proposed technique gives promising results compared to other alternatives. Applications on Bone Mineral Density (BMD) measurements of vertebral body are given to illustrate the accuracy of the proposed segmentation approach.

This paper proposes an accurate, rotation invariant, and fast approach for detection of facial features from thermal images. The proposed approach combines both appearance and geometric information to detect the facial features. A texture based detector is performed using Haar features and AdaBoost algorithm. Then the relation between these facial features is modeled using a complex Gaussian distribution, which is invariant to rotation. Experiments show that our proposed approach outperforms existing algorithms for facial features detection in thermal images. The proposed approach’s performance is illustrated in a face recognition framework, which is based on extracting a local signature around facial features. Also, the paper presents a comparative study for different signature techniques with different facial image resolutions. The results of this comparative study suggest the minimum facial image resolution in thermal images, which can be used in face recognition. The study also gives a guideline for choosing a good signature, which leads to the best recognition rate.

Wireless Sensor Network (WSN) is one of the most important areas of research in the twenty- first century. WSN aims to sense a certain natural phenomenon and sends sensed data to sink using a multi - hop network. In order to increase the lifetime of the battery-based sensing nodes, it is essential to minimize the consumed energy in the sensing process. The first step to achieve this goal is to know completely the sources of energy consumption in WSNs. In this paper, sources of energy consumption at various communication layers have been studied and investigated. Furthermore, survey has been provided for existing energy models and the classification of these models into physical layer, MAC layer and cross-layer energy models. Finally, a comparison between existing available energy models has been provided.