Rapid mapping of digital integrated circuit logic gates via multi-spectral backside imaging

Modern semiconductor integrated circuits are increasingly fabricated at untrusted third party foundries. There now exist myriad security threats of malicious tampering at the hardware level and hence a clear and pressing need for new tools that enable rapid, robust and low-cost validation of circuit layouts. Optical backside imaging offers an attractive platform, but its limited resolution and throughput cannot cope with the nanoscale sizes of modern circuitry and the need to image over a large area. We propose and demonstrate a multi-spectral imaging approach to overcome these obstacles by identifying key circuit elements on the basis of their spectral response. This obviates the need to directly image the nanoscale components that define them, thereby relaxing resolution and spatial sampling requirements by 1 and 2 - 4 orders of magnitude respectively. Our results directly address critical security needs in the integrated circuit supply chain and highlight the potential of spectroscopic techniques to address fundamental resolution obstacles caused by the need to image ever shrinking feature sizes in semiconductor integrated circuits

The Long Wavelength Array Software Library

The Long Wavelength Array Software Library (LSL) is a Python module that provides a collection of utilities to analyze and export data collected at the first station of the Long Wavelength Array, LWA1. Due to the nature of the data format and large-N ($\gtrsim$100 inputs) challenges faced by the LWA, currently available software packages are not suited to process the data. Using tools provided by LSL, observers can read in the raw LWA1 data, synthesize a filter bank, and apply incoherent de-dispersion to the data. The extensible nature of LSL also makes it an ideal tool for building data analysis pipelines and applying the methods to other low frequency arrays.Comment: accepted to the Journal of Astronomical Instrumentation; 24 pages, 4 figure

ArchiveSpark: Efficient Web Archive Access, Extraction and Derivation

Web archives are a valuable resource for researchers of various disciplines. However, to use them as a scholarly source, researchers require a tool that provides efficient access to Web archive data for extraction and derivation of smaller datasets. Besides efficient access we identify five other objectives based on practical researcher needs such as ease of use, extensibility and reusability. Towards these objectives we propose ArchiveSpark, a framework for efficient, distributed Web archive processing that builds a research corpus by working on existing and standardized data formats commonly held by Web archiving institutions. Performance optimizations in ArchiveSpark, facilitated by the use of a widely available metadata index, result in significant speed-ups of data processing. Our benchmarks show that ArchiveSpark is faster than alternative approaches without depending on any additional data stores while improving usability by seamlessly integrating queries and derivations with external tools.Comment: JCDL 2016, Newark, NJ, US

Children searching information on the Internet: Performance on children's interfaces compared to Google

Children frequently make use of the Internet to search for information. However, research shows that children experience many problems with searching and browsing the web. The last decade numerous search environments have been developed, especially for children. Do these search interfaces support children in effective information-seeking? And do these interfaces add value to today’s popular search engines, such as Google? In this explorative study, we compared children’s search performance on four interfaces designed for children, with their performance on Google. We found that the children did not perform better on these interfaces than on Google. This study also uncovered several problems that children experienced with these search interfaces, which can be of use for designers of future search interfaces for children

Image database system for glaucoma diagnosis support

Tato práce popisuje přehled standardních a pokročilých metod používaných k diagnose glaukomu v ranném stádiu. Na základě teoretických poznatků je implementován internetově orientovaný informační systém pro oční lékaře, který má tři hlavní cíle. Prvním cílem je možnost sdílení osobních dat konkrétního pacienta bez nutnosti posílat tato data internetem. Druhým cílem je vytvořit účet pacienta založený na kompletním očním vyšetření. Posledním cílem je aplikovat algoritmus pro registraci intenzitního a barevného fundus obrazu a na jeho základě vytvořit internetově orientovanou tři-dimenzionální vizualizaci optického disku. Tato práce je součásti DAAD spolupráce mezi Ústavem Biomedicínského Inženýrství, Vysokého Učení Technického v Brně, Oční klinikou v Erlangenu a Ústavem Informačních Technologií, Friedrich-Alexander University, Erlangen-Nurnberg.This master thesis describes a conception of standard and advanced eye examination methods used for glaucoma diagnosis in its early stage. According to the theoretical knowledge, a web based information system for ophthalmologists with three main aims is implemented. The first aim is the possibility to share medical data of a concrete patient without sending his personal data through the Internet. The second aim is to create a patient account based on a complete eye examination procedure. The last aim is to improve the HRT diagnostic method with an image registration algorithm for the fundus and intensity images and create an optic nerve head web based 3D visualization. This master thesis is a part of project based on DAAD co-operation between Department of Biomedical Engineering, Brno University of Technology, Eye Clinic in Erlangen and Department of Computer Science, Friedrich-Alexander University, Erlangen-Nurnberg.

DRIVER Technology Watch Report

This report is part of the Discovery Workpackage (WP4) and is the third report out of four deliverables. The objective of this report is to give an overview of the latest technical developments in the world of digital repositories, digital libraries and beyond, in order to serve as theoretical and practical input for the technical DRIVER developments, especially those focused on enhanced publications. This report consists of two main parts, one part focuses on interoperability standards for enhanced publications, the other part consists of three subchapters, which give a landscape picture of current and surfacing technologies and communities crucial to DRIVER. These three subchapters contain the GRID, CRIS and LTP communities and technologies. Every chapter contains a theoretical explanation, followed by case studies and the outcomes and opportunities for DRIVER in this field

Nmag micromagnetic simulation tool - software engineering lessons learned

We review design and development decisions and their impact for the open source code Nmag from a software engineering in computational science point of view. We summarise lessons learned and recommendations for future computational science projects. Key lessons include that encapsulating the simulation functionality in a library of a general purpose language, here Python, provides great flexibility in using the software. The choice of Python for the top-level user interface was very well received by users from the science and engineering community. The from-source installation in which required external libraries and dependencies are compiled from a tarball was remarkably robust. In places, the code is a lot more ambitious than necessary, which introduces unnecessary complexity and reduces main- tainability. Tests distributed with the package are useful, although more unit tests and continuous integration would have been desirable. The detailed documentation, together with a tutorial for the usage of the system, was perceived as one of its main strengths by the community.Comment: 7 pages, 5 figures, Software Engineering for Science, ICSE201