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The rapid deployment of phasor measurements units (PMUs) supporting Wide Area Measurement System (WAMS) in smart grid has opened opportunities to enhance the grid operations through the introduction of WAMS applications. However, the increased deployment of synchrophasor technologies increases the effective attack surface available to attackers and exposesWAMS applications. Such applications have strict and stringent delay requirements, e.g., end to end delay as well as delay variation between measurements from different PMUs.

To ensure flight safety of aircraft structures, it is necessary to have regular maintenance using visual and nondestructive inspection (NDI) methods. In this paper, we propose an automatic image-based aircraft defect detection using Deep Neural Networks (DNNs). To the best of our knowledge, this is the first work for aircraft defect detection using DNNs. We perform a comprehensive evaluation of state-of-the-art feature descriptors and show that the best performance is achieved by vgg-f DNN as feature extractor with a linear SVM classifier.

We propose a benchmark curve that measures the inherent complexity of a detection problem. The benchmark curve is built using a sequence of simple detection methods based upon random projection. It is parameterized by the area above the receiver-operating characteristic curve of the detection method and its computational cost. It divides the plane into regions that can be used to characterize the computational and structural advantages of a given detection method. Numerical illustrations are provided.

Medical research suggests that the area of the IVC and its temporal variation imaged by bedside ultrasound is useful in guiding resuscitation of the critically-ill. Unfortunately, gaps in the vessel wall and intraliminal artifact represents a challenge for both manual and existing algorithm-based segmentation techniques.

Analyzing the asymmetry of anatomical shapes is one of the cornerstones of efficient computerized diagnosis. In the application of scoliotic trunk analysis, one major challenge is the high variability and complexity of deformations due to the pathology itself, and to changes of body poses, for instance, torsos acquired in lateral bending poses for surgical planning. In this paper, we present a novel and fully automatic approach to analyzing the asymmetry of deformable trunk shapes.

Quantitative scratch assay is significant in cell motility study for tissue repair, evolution of disease, drug treatment, and cancer metastasis. To overcome challenges in traditional manual operations in scratch assay, computational scratch assay is introduced, where image processing algorithms are exploited for cell motility quantification. In this new research realm, dedicated analysis tools are under-developed, which provides many opportunities for researchers expert on signal processing.

The alternating direction method of multipliers has become in recent years the most widely used proximal method for signal processing. In this paper, we lay the groundwork for a new notion of proximity and use it to illustrate that the method (ADMM) is actually somewhat of a maladroit rearrangement of a new, more practical procedure that generalizes the Douglas-Rachford algorithm. Compared to ADMM, the algorithm that we propose enjoys not only a more sensible form, but also a more general convergence result.