Paraxial approximation effect on a dielectric interface analysis

We are presenting a fast new method of analysing a dielectric interface which is assumed to be cylindrically symmetrical. It is based on a multimode Gaussian beam development. Then two different cases are considered and we show the kind of results we can expect from this process. Finally, we explain in detail the relationships between the paraxial approximation and further justified simplifications which lead to an important calculation time decrease.     

Static and Dynamic Mechanical Behavior of Electron Beam‐Cured Monomer and Monomer/Liquid Crystal Systems

Summary: This paper discusses static and dynamic mechanical properties of electron beam‐cured mixtures made of the nematic liquid crystal (LC) E7 and either tripropyleneglycol diacrylate (TPGDA) or propoxylated glycerol triacrylate (GPTA) as monomers differing essentially by their functionality. Dilution of the initial mixture with LC leads to a significant weakening of the film mechanical strength. Strong effects were found on Young modulus and rubbery state modulus. As the concentration of LC increases, these mechanical parameters drop significantly together with the glass transition temperature of the polymer showing a plasticizing effect. The results for the glass transition temperatures for the polymer and the LC were confirmed by thermograms obtained by differential scanning calorimetry.

Storage tensile modulus versus temperature of EB‐cured TPGDA/E7 films for different LC concentrations.     

Registration and real-time visualization of transcranial magnetic stimulation with 3-D MR images

This paper describes a method for registering and visualizing in real-time the results of transcranial magnetic stimulations (TMS) in physical space on the corresponding anatomical locations in MR images of the brain. The method proceeds in three main steps. First, the patient scalp is digitized in physical space with a magnetic-field digitizer, following a specific digitization pattern. Second, a registration process minimizes the mean square distance between those points and a segmented scalp surface extracted from the magnetic resonance image. Following this registration, the physician can follow the change in coil position in real-time through the visualization interface and adjust the coil position to the desired anatomical location. Third, amplitude of motor evoked potentials can be projected onto the segmented brain in order to create functional brain maps. The registration has subpixel accuracy in a study with simulated data, while we obtain a point to surface root-mean-square error of 1.17+/-0.38 mm in a 24 subject study.     

Atlas-based segmentation of pathological MR brain images using a model of lesion growth

We propose a method for brain atlas deformation in the presence of large space-occupying tumors, based on an a priori model of lesion growth that assumes radial expansion of the lesion from its starting point. Our approach involves three steps. First, an affine registration brings the atlas and the patient into global correspondence. Then, the seeding of a synthetic tumor into the brain atlas provides a template for the lesion. The last step is the deformation of the seeded atlas, combining a method derived from optical flow principles and a model of lesion growth. Results show that a good registration is performed and that the method can be applied to automatic segmentation of structures and substructures in brains with gross deformation, with important medical applications in neurosurgery, radiosurgery, and radiotherapy.     

Learning eigenfunctions links spectral embedding and kernel PCA

In this letter, we show a direct relation between spectral embedding methods and kernel principal components analysis and how both are special cases of a more general learning problem: learning the principal eigenfunctions of an operator defined from a kernel and the unknown data-generating density. Whereas spectral embedding methods provided only coordinates for the training points, the analysis justifies a simple extension to out-of-sample examples (the Nystr?m formula) for multidimensional scaling (MDS), spectral clustering, Laplacian eigenmaps, locally linear embedding (LLE), and Isomap. The analysis provides, for all such spectral embedding methods, the definition of a loss function, whose empirical average is minimized by the traditional algorithms. The asymptotic expected value of that loss defines a generalization performance and clarifies what these algorithms are trying to learn. Experiments with LLE, Isomap, spectral clustering, and MDS show that this out-of-sample embedding formula generalizes well, with a level of error comparable to the effect of small perturbations of the training set on the embedding.     

Diagnosis of Intermittent Faults

The diagnosis of intermittent faults in dynamic systems modeled as discrete event systems is considered. In many systems, faulty behavior often occurs intermittently, with fault events followed by corresponding reset events for these faults, followed by new occurrences of fault events, and so forth. Since these events are usually unobservable, it is necessary to develop diagnostic methodologies for intermittent faults. Prior methodologies for detection and isolation of permanent faults are no longer adequate in the context of intermittent faults, since they do not account explicitly for the dynamic behavior of these faults. This paper addresses this issue by: (i) proposing a modeling methodology for discrete event systems with intermittent faults; (ii) introducing new notions of diagnosability associated with fault and reset events; and (iii) developing necessary and sufficient conditions, in terms of the system model and the set of observable events, for these notions of diagnosability. The definitions of diagnosability are complementary and capture desired objectives regarding the detection and identification of faults, resets, and the current system status (namely, is the fault present or absent). The associated necessary and sufficient conditions are based upon the technique of diagnosers introduced in earlier work, albeit the structure of the diagnosers needs to be enhanced to capture the dynamic nature of faults in the system model. The diagnosability conditions are verifiable in polynomial time in the number of states of the diagnosers.     

Dimensioning IP access links carrying data traffic

This paper proposes simple dimensioning rules for high speedip access links carrying data traffic. Assuming a finite source population and fair bandwidth sharing among user flows, we derive formulas relating capacity, demand and performance. These formulas allow link dimensioning for a target quality of service expressed in terms of useful per-flow throughput. They constitute a data traffic model equivalent of the Engset model for telephone access networks. Performance is shown to be largely independent of detailed traffic characteristics such as the statistical distributions of flow size and think time. Simple approximations are derived for two distinct performance regimes corresponding to transparency and saturation, respectively. Extensions to the basic model account for a heterogeneous user demand, unfair bandwidth sharing or different access rate classes. In any case, the key parameter for dimensioning is the offered traffic, defined as the average data rate a user would generate in the absence of congestion.     

Frequency-shifted optical feedback in a pumping laser diode dynamically amplified by a microchip laser

Compared with conventional optical heterodyne detection, laser optical feedback imaging (LOFI) allows for a several orders of magnitude higher intensity modulation contrast. The maximum contrast amplification is typically 10(3) for a diode laser in the gigahertz range and 10(6) for a microchip laser in the megahertz range. To take advantage of the wavelength tunability of a laser diode and of the lower resonant detection frequency of a microchip laser, we used LOFI modulation induced by the frequency-shifted optical feedback in a laser diode as a modulated pumping power for a microchip laser for resonant dynamic amplification. In this way, we were able to transfer the optical feedback sensitivity of the laser diode to the megahertz range. Application to telemetry is also reported.