Scalable Interactive Volume Rendering Using Off-the-shelf Components

This paper describes an application of a second generation implementation of the Sepia architecture (Sepia-2) to interactive volu-metric visualization of large rectilinear scalar fields. By employingpipelined associative blending operators in a sort-last configuration a demonstration system with 8 rendering computers sustains 24 to 28 frames per second while interactively rendering large data volumes (1024x256x256 voxels, and 512x512x512 voxels). We believe interactive performance at these frame rates and data sizes is unprecedented. We also believe these results can be extended to other types of structured and unstructured grids and a variety of GL rendering techniques including surface rendering and shadow map-ping. We show how to extend our single-stage crossbar demonstration system to multi-stage networks in order to support much larger data sizes and higher image resolutions. This requires solving a dynamic mapping problem for a class of blending operators that includes Porter-Duff compositing operators

Towards Hardware-Based Application Fingerprinting with Microarchitectural Signals for Zero Trust Environments

The interactions between software and hardware are increasingly important to computer system security. This research collects sequences of microprocessor control signals to develop machine learning models that identify software tasks. The proposed approach considers software task identification in hardware as a general problem with attacks treated as a subset of software tasks. Two lines of effort are presented. First, a data collection approach is described to extract sequences of control signals labeled by task identity during real (i.e., non-simulated) system operation. Second, experimental design is used to select hardware and software configuration to train and evaluate machine learning models. The machine learning models significantly outperform a Naive classifier based on Euclidean distances from class means. Various configurations produce balanced accuracy scores between 26.08% and 96.89%

A Real Time Image Processing Subsystem: GEZGIN

In this study, a real-time image processing subsystem, GEZGIN, which is currently being developed for BILSAT-1, a 100kg class micro-satellite, is presented. BILSAT-1 is being constructed in accordance with a technology transfer agreement between TÜBITAK-BILTEN (Turkey) and SSTL (UK) and planned to be placed into a 650 km sunsynchronous orbit in Summer 2003. GEZGIN is one of the two Turkish R&D payloads to be hosted on BILSAT-1. One of the missions of BILSAT-1 is constructing a Digital Elevation Model of Turkey using both multi-spectral and panchromatic imagers. Due to limited down-link bandwidth and on-board storage capacity, employment of a realtime image compression scheme is highly advantageous for the mission. GEZGIN has evolved as an implementation to achieve image compression tasks that would lead to an efficient utilization of both the down-link and on-board storage. The image processing on GEZGIN includes capturing of 4-band multi-spectral images of size 2048x2048 8- bit pixels, compressing them simultaneously with the new industry standard JPEG2000 algorithm and forwarding the compressed multi-spectral image to Solid State Data Recorders (SSDR) of BILSAT-1 for storage and down-link transmission. The mission definition together with orbital parameters impose a 6.5 seconds constraint on real-time image compression. GEZGIN meets this constraint by exploiting the parallelism among image processing units and assigning compute intensive tasks to dedicated hardware. The proposed hardware also allows for full reconfigurability of all processing units

Towards Hardware-Based Application Fingerprinting with Microarchitectural Signals for Zero Trust Environments

The interactions between software and hardware are increasingly important to computer system security. This research collects sequences of microprocessor control signals to develop machine learning models that identify software tasks. The proposed approach considers software task identification in hardware as a general problem with attacks treated as a subset of software tasks. Two lines of effort are presented. First, a data collection approach is described to extract sequences of control signals labeled by task identity during real (i.e., non-simulated) system operation. Second, experimental design is used to select hardware and software configuration to train and evaluate machine learning models. The machine learning models significantly outperform a Naive classifier based on Euclidean distances from class means. Various configurations produce balanced accuracy scores between 26.08% and 96.89%

Field Deployment of an Ambient Vibration-Based Scour Monitoring System at Baildon Bridge, UK

Scour, the loss of material around bridge foundations due to hydraulic action, is the main cause of bridge failures in the United Kingdom and in many other parts of the world. Various techniques have been used to monitor bridge scour, ranging from scuba divers using crude depth measuring instrumentation to high-tech sonar and radar-based systems. In contrast to most other techniques, vibration-based scour monitoring uses accelerometers to provide real-time monitoring whilst also being robust and relatively simple to install. This is an indirect technique that aims to measure changes in the dynamic response of the structure due to the effects of scour, rather than attempting to measure scour directly. To date, research on vibration-based scour monitoring has been limited to laboratory-based experiments and numerical simulations, both of which have indicated that the natural frequencies of bridges should indeed be sensitive to scour. Due to pre-existing scouring, and planned repair work, Baildon Bridge in Shipley, Yorkshire provided a rare opportunity to validate vibration-based scour monitoring in both a scoured and a repaired state. A sensor system was deployed with 10 Epson low-noise, high-sensitivity accelerometers to measure the ambient vibration of the bridge before, during, and after the repair. This paper describes the installation of the accelerometer-based system, the numerical modelling of the bridge and the model updating carried out with the initial findings. Initial operational modal analysis has found two consistent vibration modes of the bridge that were scour sensitive according to the updated numerical model. But the variability of the measured frequencies, compared to the expected scour induced change in frequency, indicates a potential challenge for monitoring scour of small span bridges with vibration-based methods

OSWALD: OpenCL Smith–Waterman on Altera’s FPGA for Large Protein Databases

The well-known Smith–Waterman algorithm is a high-sensitivity method for local sequence alignment. Unfortunately, the Smith–Waterman algorithm has quadratic time complexity, which makes it computationally demanding for large protein databases. In this paper, we present OSWALD, a portable, fully functional and general implementation to accelerate Smith–Waterman database searches in heterogeneous platforms based on Altera’s FPGA. OSWALD exploits OpenMP multithreading and SIMD computing through SSE and AVX2 extensions on the host while taking advantage of pipeline and vectorial parallelism by way of OpenCL on the FPGAs. Performance evaluations on two different heterogeneous architectures with real amino acid datasets show that OSWALD is competitive in comparison with other top-performing Smith–Waterman implementations, attaining up to 442 GCUPS peak with the best GCUPS/watts ratio.First published June 30, 2016. Article available in: Vol. 32, Issue 3, 2018.Facultad de Informátic