A novel divergence-free constrained phase unwrapping method was proposed and evaluated for 4D flow MRI. The unwrapped phase field was obtained by integrating the phase variations estimated from the wrapped phase data using weighted least-squares. The divergence-free constraint for incompressible blood flow was incorporated to regulate and denoise the resulting phase field. The proposed method was tested on synthetic phase data of left ventricular flow and in vitro 4D flow measurement of Poiseuille flow. The method was additionally applied to in vivo 4D flow measurements in the thoracic aorta from 30 human subjects. The performance of the proposed method was compared to the state-of-the-art 4D single-step Laplacian algorithm. The synthetic phase data were completely unwrapped by the proposed method for all the cases with velocity encoding (venc) as low as 20% of the maximum velocity and signal-to-noise ratio as low as 5. The in vitro Poiseuille flow data were completely unwrapped with a 60% increase in the velocity-to-noise ratio. For the in-vivo aortic datasets with venc ratio less than 0.4, the proposed method significantly improved the success rate by as much as 40% and reduced the velocity error levels by a factor of 10 compared to the state-of-the-art method. The divergence-free constrained method exhibits reliability and robustness on phase unwrapping and shows improved accuracy of velocity and hemodynamic quantities by unwrapping the low-venc 4D flow MRI data.

Software development companies want new hires with strong interpersonal and problem solving skills. To ensure the development of such skills, they must be embedded throughout the curriculum. However, many students struggle to collaborate with peers and in self-regulated practice during the early stages of their course. Explicit scaffolding can, however, motivate such engagement. This tips and techniques session shows how an ice-breaker using LEGO EV3 robots at two UK institutions enhanced peer interaction and increased self-regulated practice over the first four weeks of 2016-17 and 2017-18.

I worry over topics for the syllabus, fretting over demos and presentations. And yet, I always come back to the fact that most of what my students learn and remember from my course comes from the assignments. Great assignments are hard to dream up and time-consuming to develop. With that in mind, the Nifty Assignments session is all about promoting and sharing the ideas and concrete materials of successful assignments.

Elliptic curves with small embedding degree and large prime-order subgroup are key ingredients for implementing pairing-based cryptographic systems. Such “pairing-friendly” curves are rare and thus require specific constructions. In this paper we give a single coherent framework that encompasses all of the constructions of pairing-friendly elliptic curves currently existing in the literature. We also include new constructions of pairing-friendly curves that improve on the previously known constructions for certain embedding degrees. Finally, for all embedding degrees up to 50, we provide recommendations as to which pairing-friendly curves to choose to best satisfy a variety of performance and security requirements.

Recent research results have shown that Elliptic Curve Cryptography (ECC) is feasible on resource constrained sensor nodes. In this work we demonstrate that the related but more complex primitives of Pairing Based Cryptography(PBC) are also well suited for sensor devices.We present the first in-depth study on the application and implementation of PBC to Wireless Sensor Networks (WSNs). Our implementations are all the fastest yet reported, and have been implemented across a range of WSN processors. On a system level we investigate the application of a simple non-interactive key exchange scheme that is particularly suitable for many WSN scenarios. We also present a novel variant of the key exchange protocol which can be useful in even more demanding applications, and which partially solves the problem of node compromise attacks.

Molecular imaging is a novel tool that has allowed noninvasive diagnostic imaging to transition from gross anatomical description to identification of specific tissue epitopes and observation of biological processes at the cellular level. Until recently, this technique was confined to the field of nuclear imaging; however, advances in nanotechnology have extended this research to include magnetic resonance (MR) imaging, positron emission tomography (PET), single photon emission computed tomography (SPECT), and ultrasound (US), among others. The application of nanotechnology to MR, SPECT, and US molecular imaging has generated several candidate contrast agents. We discuss the application of one multimodality platform, a targeted perfluorocarbon nanoparticle. Our results show that it is useful for noninvasive detection with all three imaging modalities and may additionally be used for local drug delivery.

This paper presents a dedicated hardware implementation of the cryptographic Tate pairing on an elliptic curve of characteristic 2 using theetaTmethod. Efficient techniques for pairing computation are discussed and optimised hardware architectures are presented. A hardware pipelining scheme is described, which provides a dramatic reduction in pairing computation time. A cryptographic processor for computation of the bilinear pairing is presented and implemented on an FPGA. It is demonstrated that an FPGA forms an ideal basis for pairing processor implementation due to ease of reconfigurability and the opportunity for rapid prototyping. Implementation results are provided for pairing calculation on an FPGA over the base field