Digital Complex Correlator for a C-band Polarimetry survey

The international Galactic Emission Mapping project aims to map and characterize the polarization field of the Milky Way. In Portugal it will cartograph the C-band sky polarized emission of the Northern Hemisphere and provide templates for map calibration and foreground control of microwave space probes like ESA Planck Surveyor mission. The receiver system is equipped with a novel receiver with a full digital back-end using an Altera Field Programmable Gate Array, having a very favorable cost/performance relation. This new digital backend comprises a base-band complex cross-correlator outputting the four Stokes parameters of the incoming polarized radiation. In this document we describe the design and implementation of the complex correlator using COTS components and a processing FPGA, detailing the method applied in the several algorithm stages and suitable for large sky area surveys.Comment: 15 pages, 10 figures; submitted to Experimental Astronomy, Springe

Investigating distributed simulation with COTS simulation packages: Experiences with Simul8 and the HLA

Commercial-off-the-shelf simulation packages (CSPs) are used widely in industry. Several research groups are currently working towards the creation of distributed simulation with these CSPs. The motivations to do this are various and are largely unproven as there are very few good examples of this kind of distributed simulation in practice. Our goal is therefore to create a distributed simulation environment using CSPs that will allow end users to make their own decisions as to whether this technology will be useful. This paper presents continuing research in creating such an environment using the CSP Simul8 and the High Level Architecture, the IEEE 1516 standard for distributed simulation. The scope of this paper is limited to the CSPI-PDG Type I Interoperability Reference Model

Parallel Discrete Event Simulation with Erlang

Discrete Event Simulation (DES) is a widely used technique in which the state of the simulator is updated by events happening at discrete points in time (hence the name). DES is used to model and analyze many kinds of systems, including computer architectures, communication networks, street traffic, and others. Parallel and Distributed Simulation (PADS) aims at improving the efficiency of DES by partitioning the simulation model across multiple processing elements, in order to enabling larger and/or more detailed studies to be carried out. The interest on PADS is increasing since the widespread availability of multicore processors and affordable high performance computing clusters. However, designing parallel simulation models requires considerable expertise, the result being that PADS techniques are not as widespread as they could be. In this paper we describe ErlangTW, a parallel simulation middleware based on the Time Warp synchronization protocol. ErlangTW is entirely written in Erlang, a concurrent, functional programming language specifically targeted at building distributed systems. We argue that writing parallel simulation models in Erlang is considerably easier than using conventional programming languages. Moreover, ErlangTW allows simulation models to be executed either on single-core, multicore and distributed computing architectures. We describe the design and prototype implementation of ErlangTW, and report some preliminary performance results on multicore and distributed architectures using the well known PHOLD benchmark.Comment: Proceedings of ACM SIGPLAN Workshop on Functional High-Performance Computing (FHPC 2012) in conjunction with ICFP 2012. ISBN: 978-1-4503-1577-

DEEPMIR: A DEEP neural network for differential detection of cerebral Microbleeds and IRon deposits in MRI

Lobar cerebral microbleeds (CMBs) and localized non-hemorrhage iron deposits in the basal ganglia have been associated with brain aging, vascular disease and neurodegenerative disorders. Particularly, CMBs are small lesions and require multiple neuroimaging modalities for accurate detection. Quantitative susceptibility mapping (QSM) derived from in vivo magnetic resonance imaging (MRI) is necessary to differentiate between iron content and mineralization. We set out to develop a deep learning-based segmentation method suitable for segmenting both CMBs and iron deposits. We included a convenience sample of 24 participants from the MESA cohort and used T2-weighted images, susceptibility weighted imaging (SWI), and QSM to segment the two types of lesions. We developed a protocol for simultaneous manual annotation of CMBs and non-hemorrhage iron deposits in the basal ganglia. This manual annotation was then used to train a deep convolution neural network (CNN). Specifically, we adapted the U-Net model with a higher number of resolution layers to be able to detect small lesions such as CMBs from standard resolution MRI. We tested different combinations of the three modalities to determine the most informative data sources for the detection tasks. In the detection of CMBs using single class and multiclass models, we achieved an average sensitivity and precision of between 0.84-0.88 and 0.40-0.59, respectively. The same framework detected non-hemorrhage iron deposits with an average sensitivity and precision of about 0.75-0.81 and 0.62-0.75, respectively. Our results showed that deep learning could automate the detection of small vessel disease lesions and including multimodal MR data (particularly QSM) can improve the detection of CMB and non-hemorrhage iron deposits with sensitivity and precision that is compatible with use in large-scale research studies