Shape from defocus via diffusion

Defocus can be modeled as a diffusion process and represented mathematically using the heat equation, where image blur corresponds to the diffusion of heat. This analogy can be extended to non-planar scenes by allowing a space-varying diffusion coefficient. The inverse problem of reconstructing 3-D structure from blurred images corresponds to an "inverse diffusion" that is notoriously ill-posed. We show how to bypass this problem by using the notion of relative blur. Given two images, within each neighborhood, the amount of diffusion necessary to transform the sharper image into the blurrier one depends on the depth of the scene. This can be used to devise a global algorithm to estimate the depth profile of the scene without recovering the deblurred image, using only forward diffusion.     

Dynamic Shape and Appearance Modeling via Moving and Deforming Layers

We propose a model of the shape, motion and appearance of a scene, seen through a sequence of images, that captures occlusions, scene deformations, unconstrained viewpoint variations and changes in its radiance. This model is based on a collection of overlapping layers that can move and deform, each supporting an intensity function that can change over time. We discuss the generality and limitations of this model in relation to existing ones such as traditional optical flow or motion segmentation, layers, deformable templates and deformotion. We then illustrate how this model can be used for inference of shape, motion, deformation and appearance of the scene from a collection of images. The layering structure allows for automatic inpainting of partially occluded regions. We illustrate the model on synthetic and real sequences where existing schemes fail, and show how suitable choices of constants in the model yield existing schemes, from optical flow to motion segmentation and inpainting.     

Reducing false positives in anomaly detectors through fuzzy alert aggregation

In this paper we focus on the aggregation of IDS alerts, an important component of the alert fusion process. We exploit fuzzy measures and fuzzy sets to design simple and robust alert aggregation algorithms. Exploiting fuzzy sets, we are able to robustly state whether or not two alerts are “close in time”, dealing with noisy and delayed detections. A performance metric for the evaluation of fusion systems is also proposed. Finally, we evaluate the fusion method with alert streams from anomaly-based IDS.     

The anti-reflective algebra: structural and computational analysis with application to image deblurring and denoising

Anti-reflective (AR) boundary conditions (BC) have been introduced recently in connection with fast deblurring algorithms, both in the case of signals and images. Here we extend such BCs to d dimensions (d ≥ 1) and we study in detail the algebra induced by the AR-BCs, with strongly symmetric point spread functions (PSF), both from a structural and computational point of view. The use of the re-blurring idea and the computational features of the AR-algebra allow us to apply Tikhonov-like techniques within O(n ^d log(n)) arithmetic operations, where n ^d is the number of pixels of the reconstructed object. Extensive numerical experimentation concerning 2D images and strongly symmetric PSFs confirms the effectiveness of our proposal.      

Test Technology Newsletter

Journal of Electronic Testing -     

Joint Estimation of Location and Scatter in Complex Elliptically Symmetric Distributions

The joint estimation of the location vector and the shape matrix of a set of independent and identically Complex Elliptically Symmetric (CES) distributed observations is investigated from both the theoretical and computational viewpoints. This joint estimation problem is framed in the original context of semiparametric models allowing us to handle the (generally unknown) density generator as an infinite-dimensional nuisance parameter. In the first part of the paper, a computationally efficient and memory saving implementation of the robust and semiparmaetric efficient R-estimator for shape matrices is derived. Building upon this result, in the second part, a joint estimator, relying on the Tyler’s M-estimator of location and on the R-estimator of shape matrix, is proposed and its Mean Squared Error (MSE) performance compared with the Semiparametric Cramér-Rao Bound (SCRB).

     

Sorafenib delivery nanoplatform based on superparamagnetic iron oxide nanoparticles magnetically targets hepatocellular carcinoma

Currently,sorafenib is the only systemic therapy capable of increasing overall survival of hepatocellular carcinoma patients.Unfortunately,its side effects,particularly its overall toxicity,limit the therapeutic response that can be achieved.Superparamagnetic iron oxide nanoparticles (SPIONs) are very attractive for drug delivery because they can be targeted to specific sites in the body through application of a magnetic field,thus improving intratumoral accumulation and reducing adverse effects.Here,nanoformulations based on polyethylene glycol modified phospholipid micelles,loaded with both SPIONs and sorafenib,were successfully prepared and thoroughly investigated by complementary techniques.This nanovector system provided effective drug delivery,had an average hydrodynamic diameter of about 125 nm,had good stability in aqueous medium,and allowed controlled drug loading.Magnetic analysis allowed accurate determination of the amount of SPIONs embedded in each micelle.An in vitro system was designed to test whether the SPION micelles can be efficiently held using a magnetic field under typical flow conditions found in the human liver.Human hepatocellular carcinoma (HepG2) cells were selected as an in vitro system to evaluate tumor cell targeting efficacy of the superparamagnetic micelles loaded with sorafenib.These experiments demonstrated that this delivery platform is able to enhance sorafenib's antitumor effectiveness by magnetic targeting.The magnetic nanovectors described here represent promising candidates for targeting specific hepatic tumor sites,where selective release of sorafenib can improve its efficacy and safety profile.     

An Overview and Future Perspectives of Aluminum Batteries

A critical overview of the latest developments in the aluminum battery technologies is reported. The substitution of lithium with alternative metal anodes characterized by lower cost and higher abundance is nowadays one of the most widely explored paths to reduce the cost of electrochemical storage systems and enable long‐term sustainability. Aluminum based secondary batteries could be a viable alternative to the present Li‐ion technology because of their high volumetric capacity (8040 mAh cm^?3 for Al vs 2046 mAh cm^?3 for Li). Additionally, the low cost aluminum makes these batteries appealing for large‐scale electrical energy storage. Here, we describe the evolution of the various aluminum systems, starting from those based on aqueous electrolytes to, in more details, those based on non‐aqueous electrolytes. Particular attention has been dedicated to the latest development of electrolytic media characterized by low reactivity towards other cell components. The attention is then focused on electrode materials enabling the reversible aluminum intercalation‐deintercalation process. Finally, we touch on the topic of high‐capacity aluminum‐sulfur batteries, attempting to forecast their chances to reach the status of practical energy storage systems.