Standard PC Hardware Research Articles

POSC302 Discrete Event Simulation in Severe COPD: A Feasibility Study Using DICE Methodology

To assesses the feasibility of probabilistic, discrete event simulation (DES) in moderate to severe chronic obstructive pulmonary disease (COPD). To validate the model results against outcomes reported from a German COPD disease management program (DMP). Probabilistic DES was implemented using the Discretely Integrated Condition Event (DICE) framework by Caro et. al. Distributions defining model mortality and exacerbation rates were based on an epidemiological study by Rothnie et al. (99,574 patients, mean follow-up of 4.9 years) from the UK and implemented in the model through 18 patient profiles to support sub-group analyses. Exacerbations and pharmaceutical therapy with stepwise escalation/de-escalation were considered according to German treatment guidelines. Severe exacerbation costs were based on the German full inpatient statistics calculating weighted mean costs for Global Initiative for Chronic Obstructive Lung Disease (GOLD) groups 2-4 according to ICD-10 J44. Moderate exacerbation costs were based on official German reimbursement catalogues to account for outpatient visits and acute drug treatment. A total of 9,000 patients (500 per profile) were simulated to achieve stable Results: Modelled survival and exacerbation outcomes matched the Rothnie data without any major differences. Total model costs vs. reported DMP costs were: 3,145€ vs. 3,603€ (GOLD 2); 4,415€ vs. 4,579€ (GOLD 3); 5,662€ vs. 6,233€ (GOLD 4). Reported DMP cost were covered by confidence intervals of model Results: Total model runtime was 515 seconds. Generally, probabilistic DES for COPD is feasible with the DICE framework. The most recent DICE engine improved model runtime significantly and enables sensitivity analyses on standard PC hardware. Although modelled mean costs differed slightly from reported DMP costs, it is important to note that this model included COPD-related healthcare costs only while the DMP reported total healthcare costs.

An Efficient Implementation of a Census-Based Stereo Matching and Its Applications in Medical Imaging

Compared to most conventional efficient stereo matching algorithms that based on NCC (Normalized Cross-Correlation) or SAD (Sum of Absolute Difference), stereo matching based on census transform is robust to radiometric distortion. Thus, in the paper we propose a census-based efficient implementation stereo algorithm for medical imaging. Firstly, census-based stereo matching is investigated, and its specific implementation process is analyzed in detail. Secondly, in order to simplify the calculation process and improve the efficiency, the moving window and memory organization optimized techniques are used. The program runs on standard PC hardware utilizing various SSE2 instructions. Finally, stereo matching of four standard image pairs on the Middlebury image datasets and a paired cervical images obtained from clinical colposcope are implemented in an efficient way. The experimental results on simulated and real medical images prove the effectiveness of the method for the computational cost.

Robust real-time extraction of respiratory signals from PET list-mode data

Respiratory motion, which typically cannot simply be suspended during PET image acquisition, affects lesions’ detection and quantitative accuracy inside or in close vicinity to the lungs. Some motion compensation techniques address this issue via pre-sorting (‘binning’) of the acquired PET data into a set of temporal gates, where each gate is assumed to be minimally affected by respiratory motion. Tracking respiratory motion is typically realized using dedicated hardware (e.g. using respiratory belts and digital cameras). Extracting respiratory signals directly from the acquired PET data simplifies the clinical workflow as it avoids handling additional signal measurement equipment. We introduce a new data-driven method ‘combined local motion detection’ (CLMD). It uses the time-of-flight (TOF) information provided by state-of-the-art PET scanners in order to enable real-time respiratory signal extraction without additional hardware resources.CLMD applies center-of-mass detection in overlapping regions based on simple back-positioned TOF event sets acquired in short time frames. Following a signal filtering and quality-based pre-selection step, the remaining extracted individual position information over time is then combined to generate a global respiratory signal. The method is evaluated using seven measured FDG studies from single and multiple scan positions of the thorax region, and it is compared to other software-based methods regarding quantitative accuracy and statistical noise stability.Correlation coefficients around 90% between the reference and the extracted signal have been found for those PET scans where motion affected features such as tumors or hot regions were present in the PET field-of-view. For PET scans with a quarter of typically applied radiotracer doses, the CLMD method still provides similar high correlation coefficients which indicates its robustness to noise. Each CLMD processing needed less than 0.4 s in total on a standard multi-core CPU and thus provides a robust and accurate approach enabling real-time processing capabilities using standard PC hardware.

High-Precision Measurement of Sine and Pulse Reference Signals Using Software-Defined Radio

This paper addresses simultaneous, high-precision measurement and analysis of generic reference signals by using inexpensive commercial off-the-shelf Software Defined Radio hardware. Sine reference signals are digitally down-converted to baseband for the analysis of phase deviations. Hereby, we compare the precision of the fixed-point hardware Digital Signal Processing chain with a custom Single Instruction Multiple Data (SIMD) x86 floating-point implementation. Pulse reference signals are analyzed by a software trigger that precisely locates the time where the slope passes a certain threshold. The measurement system is implemented and verified using the Universal Software Radio Peripheral (USRP) N210 by Ettus Research LLC. Applying standard 10 MHz and 1 PPS reference signals for testing, a measurement precision (standard deviation) of 0.36 ps and 16.6 ps is obtained, respectively. In connection with standard PC hardware, the system allows long-term acquisition and storage of measurement data over several weeks. A comparison is given to the Dual Mixer Time Difference (DMTD) and Time Interval Counter (TIC), which are state-of-the-art measurement methods for sine and pulse signal analysis, respectively. Furthermore, we show that our proposed USRP-based approach outperforms measurements with a high-grade Digital Sampling Oscilloscope.

Applications of non-monotonic reasoning to automotive product configuration using answer set programming

In automotive industry, validation and maintenance of product configuration data is a complex task. Both orders from the customers and new product line designs from the R&D department are subject to a set of configuration rules to be satisfied. In this work, non-monotonic computational logic, answer set programming in particular, is applied to industrial-scale automotive product configuration problems. This methodology provides basic validation of the product configuration documentation and validation of single product orders, where Reiter style diagnosis provides minimal changes needed to correct an invalid order or a product configuration rule set. In addition, a method for discovering groups of product configuration variables that are strongly related can be obtained by small modification of the basic logic program, and by the usage of cautious and brave reasoning methods. As a result, options that are used in every, or respectively in no configuration, can easily be identified, as well as groups of options that are always used together or not at all. Finally it is possible to single out mandatory and obsolete options, relative to a preselected set of included or excluded options. Experimental results on an industrial dataset show applicability, example results, and computational feasibility with computation times on the order of seconds using a state-of-the-art answer set solver on standard PC hardware.

ControlIt! — A Software Framework for Whole-Body Operational Space Control

Whole Body Operational Space Control (WBOSC) enables floating-base highly redundant robots to achieve unified motion/force control of one or more operational space objectives while adhering to physical constraints. It is a pioneering algorithm in the field of human-centered Whole-Body Control (WBC). Although there are extensive studies on the algorithms and theory behind WBOSC, limited studies exist on the software architecture and APIs that enable WBOSC to perform and be integrated into a larger system. In this paper, we address this by presenting ControlIt!, a new open-source software framework for WBOSC. Unlike previous implementations, ControlIt! is multi-threaded to increase maximum servo frequencies using standard PC hardware. A new parameter binding mechanism enables tight integration between ControlIt! and external processes via an extensible set of transport protocols. To support a new robot, only two plugins and a URDF model is needed — the rest of ControlIt! remains unchanged. New WBC primitives can be added by writing Task or Constraint plugins. ControlIt!’s capabilities are demonstrated on Dreamer, a 16-DOF torque controlled humanoid upper body robot containing both series elastic and co-actuated joints, and using it to perform a product disassembly task. Using this testbed, we show that ControlIt! can achieve average servo latencies of about 0.5[Formula: see text]ms when configured with two Cartesian position tasks, two orientation tasks, and a lower priority posture task. This is 10 times faster than the 5[Formula: see text]ms that was achieved using UTA-WBC, the prototype implementation of WBOSC that is both application and platform-specific. Variations in the product’s position is handled by updating the goal of the Cartesian position task. ControlIt!’s source code is released under LGPL and we hope it will be adopted and maintained by the WBC community for the long term as a platform for WBC development and integration.

A real-time system for biomechanical analysis of human movement and muscle function

Mechanical analysis of movement plays an important role in clinical management of neurological and orthopedic conditions. There has been increasing interest in performing movement analysis in real-time, to provide immediate feedback to both therapist and patient. However, such work to date has been limited to single-joint kinematics and kinetics. Here we present a software system, named human body model (HBM), to compute joint kinematics and kinetics for a full body model with 44 degrees of freedom, in real-time, and to estimate length changes and forces in 300 muscle elements. HBM was used to analyze lower extremity function during gait in 12 able-bodied subjects. Processing speed exceeded 120 samples per second on standard PC hardware. Joint angles and moments were consistent within the group, and consistent with other studies in the literature. Estimated muscle force patterns were consistent among subjects and agreed qualitatively with electromyography, to the extent that can be expected from a biomechanical model. The real-time analysis was integrated into the D-Flow system for development of custom real-time feedback applications and into the gait real-time analysis interactive lab system for gait analysis and gait retraining.Electronic supplementary materialThe online version of this article (doi:10.1007/s11517-013-1076-z) contains supplementary material, which is available to authorized users.

Unbiased, scalable sampling of protein loop conformations from probabilistic priors.

BackgroundProtein loops are flexible structures that are intimately tied to function, but understanding loop motion and generating loop conformation ensembles remain significant computational challenges. Discrete search techniques scale poorly to large loops, optimization and molecular dynamics techniques are prone to local minima, and inverse kinematics techniques can only incorporate structural preferences in adhoc fashion. This paper presents Sub-Loop Inverse Kinematics Monte Carlo (SLIKMC), a new Markov chain Monte Carlo algorithm for generating conformations of closed loops according to experimentally available, heterogeneous structural preferences.ResultsOur simulation experiments demonstrate that the method computes high-scoring conformations of large loops (>10 residues) orders of magnitude faster than standard Monte Carlo and discrete search techniques. Two new developments contribute to the scalability of the new method. First, structural preferences are specified via a probabilistic graphical model (PGM) that links conformation variables, spatial variables (e.g., atom positions), constraints and prior information in a unified framework. The method uses a sparse PGM that exploits locality of interactions between atoms and residues. Second, a novel method for sampling sub-loops is developed to generate statistically unbiased samples of probability densities restricted by loop-closure constraints.ConclusionNumerical experiments confirm that SLIKMC generates conformation ensembles that are statistically consistent with specified structural preferences. Protein conformations with 100+ residues are sampled on standard PC hardware in seconds. Application to proteins involved in ion-binding demonstrate its potential as a tool for loop ensemble generation and missing structure completion.

Optical switch emulation in programmable software router testbed

A programmable optical router is a key enabler for dynamic service provisioning in Future Internet scenarios. It is equipped with optical switching hardware to forward information at hundreds of Gigabits/s rates and above, controlled and managed through modular and flexible procedures according to emerging standards. The possibility to test such costly optical architectures in terms of logical and physical performance, without implementing complex and expensive testbeds, is crucial to speed-up the development process of high-performance routers. To this purpose, this paper introduces the software-based emulation testbed of a programmable optical router, which is here developed and applied to test optical switching fabrics. Accurate characterization of the optical devices and physical layer aspects is implemented with the Click software router environment. Power loss and optical signal-to-noise-ratio evaluation are provided through accurate software representation of the physical characteristics of the optical devices employed. The scalability of the proposed emulation testbed is also assessed on standard PC hardware. All the obtained results prove the effectiveness of the proposed tool to emulate an optical router at different levels of granularity.

Inline inspection of textured plastics surfaces

This article focuses on the inspection of plastics web materials exhibiting irregular textures such as imitation wood or leather. They are produced in a continuous process at high speed. In this process, various defects occur sporadically. However, current inspection systems for plastics surfaces are able to inspect unstructured products or products with regular, i.e., highly periodic, textures, only. The proposed inspection algorithm uses the local binary pattern operator for texture feature extraction. For classification, semisupervised as well as supervised approaches are used. A simple concept for semisupervised classification is presented and applied for defect detection. The resulting defect-maps are presented to the operator. He assigns class labels that are used to train the supervised classifier in order to distinguish between different defect types. A concept for parallelization is presented allowing the efficient use of standard multicore processor PC hardware. Experiments with images of a typical product acquired in an industrial setting show a detection rate of 97% while achieving a false alarm rate below 1%. Real-time tests show that defects can be reliably detected even at haul-off speeds of 30 m/min. Further applications of the presented concept can be found in the inspection of other materials.

GPU and CPU cooperation parallel visualisation for large seismic data

A parallel visualisation technique for large seismic data based on a graphics processing unit (GPU) and CPU cooperation is presented. The 3D texture mapping capability commonly available in modern GPUs is employed as a core rendering engine and advantage is taken of combinations of GPU-based parallel volume visualisation and visibility tests to accelerate the whole rendering process. Experimental results show that the method achieves real-time performance for very large seismic data (10GB) on standard PC hardware.

Challenges in Wide-area Structure-from-motion

The topic of this paper is wide area structure from motion. We first describe recent progress in obtaining large-scale 3D visual models from images. Our approach consists of a multi-stage processing pipeline, which can process a recorded video stream in real-time on standard PC hardware by leveraging the computational power of the graphics processor. The output of this pipeline is a detailed textured 3D model of the recorded area. The approach is demonstrated on video data recorded in Chapel Hill containing more than a million frames. While for these results GPS and inertial sensor data was used, we further explore the possibility to extract the necessary information for consistent 3D mapping over larger areas from images only. In particular, we discuss our recent work focusing on estimating the absolute scale of motion from images as well as finding intersections where the camera path crosses itself to effectively close loops in the mapping process. For this purpose we introduce viewpoint-invariant patches (VIP) as a new 3D feature that we extract from 3D models locally computed from the video sequence. These 3D features have important advantages with respect to traditional 2D SIFT features such as much stronger viewpoint-invariance, a relative pose hypothesis from a single match and a hierarchical matching scheme naturally robust to repetitive structures. In addition, we also briefly discuss some additional work related to absolute scale estimation and multi-camera calibration.

Virtual reality: A new paranasal sinus surgery simulator

Virtual surgical training systems are of growing value. Current prototypes for endonasal sinus surgery simulation are very expensive or lack running stability. No reliable system is available to a notable number of users yet. The purpose of this work was to develop a dependable simulator running on standard PC hardware including a detailed anatomic model, realistic tools and handling, stereoscopic view, and force feedback. Descriptive. A three-dimensional voxel model was created based on a high-resolution computed tomography study of a human skull, from which the bony structures were segmented. The mucosa and organs at risk were added manually. The model may be manipulated with virtual surgical tools controlled with a low-cost haptic device, which is also used to adjust microscopic or endoscopic views. Visualization, haptic rendering, and tissue removal are represented with subvoxel resolution. The handling of the model is convincing. The haptic device provides a realistic feeling regarding the interaction between tool tip and anatomy. Three-dimensional orientation and the look and feel of virtual surgical interventions get close to reality. The newly developed system is a stable, fully operational simulator for sinus surgery based on standard PC hardware. Besides the limitations of a low-cost haptic device, the presented system is highly realistic regarding anatomy, visualization, manipulation, and the appearance of the tools. It is mainly intended for gaining surgical anatomy knowledge and for training navigation in a complex anatomical environment. Learning effects, including motor skills, have yet to be quantified.

MatTAP: A MATLAB toolbox for the control and analysis of movement synchronisation experiments

Investigating movement timing and synchronisation at the sub-second range relies on an experimental setup that has high temporal fidelity, is able to deliver output cues and can capture corresponding responses. Modern, multi-tasking operating systems make this increasingly challenging when using standard PC hardware and programming languages. This paper describes a new free suite of tools (available from http://www.snipurl.com/mattap) for use within the MATLAB programming environment, compatible with Microsoft Windows and a range of data acquisition hardware. The toolbox allows flexible generation of timing cues with high temporal accuracy, the capture and automatic storage of corresponding participant responses and an integrated analysis module for the rapid processing of results. A simple graphical user interface is used to navigate the toolbox and so can be operated easily by users not familiar with programming languages. However, it is also fully extensible and customisable, allowing adaptation for individual experiments and facilitating the addition of new modules in future releases. Here we discuss the relevance of the MatTAP (MATLAB Timing Analysis Package) toolbox to current timing experiments and compare its use to alternative methods. We validate the accuracy of the analysis module through comparison to manual observation methods and replicate a previous sensorimotor synchronisation experiment to demonstrate the versatility of the toolbox features demanded by such movement synchronisation paradigms.

Real-time visualization of large volume datasets on standard PC hardware

In medical area, interactive three-dimensional volume visualization of large volume datasets is a challenging task. One of the major challenges in graphics processing unit (GPU)-based volume rendering algorithms is the limited size of texture memory imposed by current GPU architecture. We attempt to overcome this limitation by rendering only visible parts of large CT datasets. In this paper, we present an efficient, high-quality volume rendering algorithm using GPUs for rendering large CT datasets at interactive frame rates on standard PC hardware. We subdivide the volume dataset into uniform sized blocks and take advantage of combinations of early ray termination, empty-space skipping and visibility culling to accelerate the whole rendering process and render visible parts of volume data. We have implemented our volume rendering algorithm for a large volume data of 512 × 304 × 1878 dimensions (visible female), and achieved real-time performance (i.e., 3–4 frames per second) on a Pentium 4 2.4 GHz PC equipped with NVIDIA Geforce 6600 graphics card (256 MB video memory). This method can be used as a 3D visualization tool of large CT datasets for doctors or radiologists.

Efficient Adaptive Combination of Histograms for Real-Time Tracking

We quantitatively compare two template-based tracking algorithms, Hager's method and the hyperplane tracker, and three histogram-based methods, the mean-shift tracker, two trust-region trackers, and the CONDENSATION tracker. We perform systematic experiments on large test sequences available to the public. As a second contribution, we present an extension to the promising first two histogram-based trackers: a framework which uses a weighted combination of more than one feature histogram for tracking. We also suggest three weight adaptation mechanisms, which adjust the feature weights during tracking. The resulting new algorithms are included in the quantitative evaluation. All algorithms are able to track a moving object on moving background in real time on standard PC hardware.

Driver Training Simulator for Backing Up Commercial Vehicles with Trailers

Backing up tractors with trailers is a difficult task since the kinematic behavior of articulated vehicles is complex and hard to control. Especially unskilled drivers are overstrained with the complicated steering process. To learn and practice the steering behavior of articulated vehicles, we developed a 3D driving simulator. The simulator can handle different types of articulated vehicles like semi-trailers, one- and two-axle trailers, or gigaliners. The use of a driving simulator offers many advantages over the use of real vehicles. One of the main advantages is the possibility to learn the steering behavior of all vehicle types. Drivers can be given more and better driving instructions like collision warnings or steering hints. Furthermore, the driver training costs can be reduced. Moreover, mistakes of the student do not lead to real damages and costly repairs. The hardware of the simulator consists of a low cost commercial driving stand with original truck parts, a projection of the windshield and two flat panel monitors for the left and right exterior mirrors. Standard PC hardware is used for controlling the driving stand and for generating the realtime 3D environment. Each aspect of the simulation like realistic vehicle movements or generation of different views, is handled by a specific software module. This flexible system can be easily extended which offers the opportunity for other uses than just driver training. Therefore, we use the simulator for the development and test of driver assistance systems.

MonoSLAM: Real-Time Single Camera SLAM

We present a real-time algorithm which can recover the 3D trajectory of a monocular camera, moving rapidly through a previously unknown scene. Our system, which we dub MonoSLAM, is the first successful application of the SLAM methodology from mobile robotics to the "pure vision" domain of a single uncontrolled camera, achieving real time but drift-free performance inaccessible to Structure from Motion approaches. The core of the approach is the online creation of a sparse but persistent map of natural landmarks within a probabilistic framework. Our key novel contributions include an active approach to mapping and measurement, the use of a general motion model for smooth camera movement, and solutions for monocular feature initialization and feature orientation estimation. Together, these add up to an extremely efficient and robust algorithm which runs at 30 Hz with standard PC and camera hardware. This work extends the range of robotic systems in which SLAM can be usefully applied, but also opens up new areas. We present applications of MonoSLAM to real-time 3D localization and mapping for a high-performance full-size humanoid robot and live augmented reality with a hand-held camera.

Front-End-Electronics Communication software for multiple detectors in the ALICE experiment

In the ALICE experiment at CERN, the Detector Control System (DCS) employs several interacting software components to accomplish its task of ensuring the correct operation and monitoring of the experiment. This paper describes the Front-End-Electronics Communication ( FeeCommunication) software and its role within the DCS. The FeeCommunication software's central task is passing configuration and monitoring data between the top level DCS process control and the field devices of several detectors within ALICE. The lowest level of the FeeCommunication software runs on the DCS boards, specialized embedded systems which are in direct contact with the field devices and are physically located within the detector. The middle and upper layers run on standard PC hardware located in the counting room or other external locations. This paper focuses on the design and implementation of the FeeCommunication software and the steps that were taken to fulfill the imposed reliability and performance requirements, specifically those related to safe operation in environments exposed to radiation and magnetic fields.

Real-time rendering of 3D medical data sets

Interactive exploration of three dimension (3D) medical data sets is required by many applications, but the huge amount of computational time and storage space needed for rendering do not allow the visualization of large medical data sets by now. In this paper we present a new algorithm for rendering large medical data sets at interactive frame rates on standard PC hardware. The input data is converted into a compressed hierarchical wavelet representation in a preprocessing step. During rendering, the wavelet representation is decompressed on-the-fly and rendered using hardware texture mapping. The level of detail used for rendering is adapted to the local frequency spectrum of the data and its position relative to the viewer. Using a prototype implementation of the algorithm we were able to perform an interactive walkthrough of large medical data sets on a single of-the-shelf PC.