Sparse time-frequency data analysis : a multi-scale approach

In this work, we further extend the recently developed adaptive data analysis method, the Sparse Time-Frequency Representation (STFR) method. This method is based on the assumption that many physical signals inherently contain AM-FM representations. We propose a sparse optimization method to extract the AM-FM representations of such signals. We prove the convergence of the method for periodic signals under certain assumptions and provide practical algorithms specifically for the non-periodic STFR, which extends the method to tackle problems that former STFR methods could not handle, including stability to noise and non-periodic data analysis. This is a significant improvement since many adaptive and non-adaptive signal processing methods are not fully capable of handling non-periodic signals. Moreover, we propose a new STFR algorithm to study intrawave signals with strong frequency modulation and analyze the convergence of this new algorithm for periodic signals. Such signals have previously remained a bottleneck for all signal processing methods. Furthermore, we propose a modified version of STFR that facilitates the extraction of intrawaves that have overlaping frequency content. We show that the STFR methods can be applied to the realm of dynamical systems and cardiovascular signals. In particular, we present a simplified and modified version of the STFR algorithm that is potentially useful for the diagnosis of some cardiovascular diseases. We further explain some preliminary work on the nature of Intrinsic Mode Functions (IMFs) and how they can have different representations in different phase coordinates. This analysis shows that the uncertainty principle is fundamental to all oscillating signals.

Development of semicrystalline morphology of poly(L-lactic acid) during processing of a vascular scaffold

New and promising treatments for coronary heart disease are enabled by vascular scaffolds made of poly(L-lactic acid) (PLLA), as demonstrated by Abbott Vascular’s bioresorbable vascular scaffold. PLLA is a semicrystalline polymer whose degree of crystallinity and crystalline microstructure depend on the thermal and deformation history during processing. In turn, the semicrystalline morphology determines scaffold strength and biodegradation time. However, spatially-resolved information about the resulting material structure (crystallinity and crystal orientation) is needed to interpret in vivo observations.

The first manufacturing step of the scaffold is tube expansion in a process similar to injection blow molding. Spatial uniformity of the tube microstructure is essential for the consistent production and performance of the final scaffold. For implantation into the artery, solid-state deformation below the glass transition temperature is imposed on a laser-cut subassembly to crimp it into a small diameter. Regions of localized strain during crimping are implicated in deployment behavior.

To examine the semicrystalline microstructure development of the scaffold, we employed complementary techniques of scanning electron and polarized light microscopy, wide-angle X-ray scattering, and X-ray microdiffraction. These techniques enabled us to assess the microstructure at the micro and nano length scale. The results show that the expanded tube is very uniform in the azimuthal and axial directions and that radial variations are more pronounced. The crimping step dramatically changes the microstructure of the subassembly by imposing extreme elongation and compression. Spatial information on the degree and direction of chain orientation from X-ray microdiffraction data gives insight into the mechanism by which the PLLA dissipates the stresses during crimping, without fracture. Finally, analysis of the microstructure after deployment shows that it is inherited from the crimping step and contributes to the scaffold’s successful implantation in vivo.