Standard PC Research Articles

Development, Validation, and Application of an Optimization Scheme for Impellers of Centrifugal Fans Using Computational Fluid Dynamics-Trained Metamodels

Abstract A quick method for the design of efficiency-optimal centrifugal fan impellers is presented. It is based on an evolutionary optimization algorithm that identifies the optimal geometrical parameters for a given aerodynamic objective function. The range of the geometrical parameters considered allows covering aerodynamic design points appropriate for the complete class of centrifugal fans. The quickness of the method stems from evaluating the objective function using metamodels. In total, four metamodels, based on local model networks (LMN) and multi-layer perceptrons (MLP), were trained and eventually aggregated to reduce the variance (stochastic) error. The training data consist of approximately 4000 characteristic curves obtained from automated numerical steady-state Reynolds-averaged Navier–Stokes (RANS) flow simulations. The computational domain as well as the number of grid nodes and their distribution in the domain were optimized in a pre-study. For verification, a grid independence study was carried out. In addition, two criteria were defined to detect aerodynamic operating points associated with non-physical performance predictions. Finally, validation was secured with experimental data from three exemplary impeller designs. The proposed optimization scheme requires a costly initial one-time computational fluid dynamics (CFD) effort, but then allows a quick design of centrifugal fan impellers for arbitrary design points. The search for an optimal centrifugal impeller requires less than 1 min on a standard personal computer, while allowing up to 105 objective function evaluations for one search. Moreover, predicted performance curves that always come along with each design were found to be very reliable in comparison with experiments.

Catch & Carry

We address the longstanding challenge of producing flexible, realistic humanoid character controllers that can perform diverse whole-body tasks involving object interactions. This challenge is central to a variety of fields, from graphics and animation to robotics and motor neuroscience. Our physics-based environment uses realistic actuation and first-person perception - including touch sensors and egocentric vision - with a view to producing active-sensing behaviors (e.g. gaze direction), transferability to real robots, and comparisons to the biology. We develop an integrated neural-network based approach consisting of a motor primitive module, human demonstrations, and an instructed reinforcement learning regime with curricula and task variations. We demonstrate the utility of our approach for several tasks, including goal-conditioned box carrying and ball catching, and we characterize its behavioral robustness. The resulting controllers can be deployed in real-time on a standard PC. 1

GPU-accelerated ray-casting for 3D fiber orientation analysis.

Orientation analysis of fibers is widely applied in the fields of medical, material and life sciences. The orientation information allows predicting properties and behavior of materials to validate and guide a fabrication process of materials with controlled fiber orientation. Meanwhile, development of detector systems for high-resolution non-invasive 3D imaging techniques led to a significant increase in the amount of generated data per a sample up to dozens of gigabytes. Though plenty of 3D orientation estimation algorithms were developed in recent years, neither of them can process large datasets in a reasonable amount of time. This fact complicates the further analysis and makes impossible fast feedback to adjust fabrication parameters. In this work, we present a new method for quantifying the 3D orientation of fibers. The GPU implementation of the proposed method surpasses another popular method for 3D orientation analysis regarding accuracy and speed. The validation of both methods was performed on a synthetic dataset with varying parameters of fibers. Moreover, the proposed method was applied to perform orientation analysis of scaffolds with different fibrous micro-architecture studied with the synchrotron μCT imaging setup. Each acquired dataset of size 600x600x450 voxels was analyzed in less 2 minutes using standard PC equipped with a single GPU.

Laguerre tessellations and polycrystalline microstructures: a fast algorithm for generating grains of given volumes

ABSTRACT We present a fast algorithm for generating Laguerre diagrams with cells of given volumes, which can be used for creating RVEs of polycrystalline materials for computational homogenisation, or for fitting Laguerre diagrams to EBSD or XRD measurements of metals. Given a list of desired cell volumes, we solve a convex optimisation problem to find a Laguerre diagram with cells of these volumes, up to any prescribed tolerance. The algorithm is built on tools from computational geometry and optimal transport theory which, as far as we are aware, have not been applied to microstructure modelling before. We illustrate the speed and accuracy of the algorithm by generating RVEs with user-defined volume distributions with up to 20,000 grains in 3D. We can achieve volume percentage errors of less than 1% in the order of minutes on a standard desktop PC. We also give examples of polydisperse microstructures with bands, clusters and size gradients, and of fitting a Laguerre diagram to 3D EBSD measurements of an IF steel.

Human Action Recognition Using Time Delay Input Radial Basis Function Networks

This paper presents a fast, vision-based method for the problem of human action representation and recognition. The first problem is addressed by constructing an action descriptor from spatiotemporal data of action silhouettes based on appearance and motion features. For action classification, a new Radial Basis Function Network (RBF), called Time Delay Input Radial Basis Function Network (TDIRBF) is proposed by introducing time delay units to the RBF in a novel approach. A TDIRBF offers a few desirable features such as an easier learning process and more flexibility. The representational power and speed of the proposed method were explored using a publicly available dataset. Based on experimental results, implemented in MATLAB and on standard PCs, the average time for constructing a feature vector for a high-resolution video was just about 20 ms/frame (or 50 fps) and the classifier speed was above 15 fps. Furthermore, the proposed approach demonstrated good performance in terms of both execution time and overall performance (a new performance measure that combines accuracy and speed into one metric).

Virtual reality framework for editing and exploring medial axis representations of nanometric scale neural structures

We present a novel virtual reality (VR) based framework for the exploratory analysis of nanoscale 3D reconstructions of cellular structures acquired from rodent brain samples through serial electron microscopy. The system is specifically targeted on medial axis representations (skeletons) of branched and tubular structures of cellular shapes, and it is designed for providing to domain scientists: i) effective and fast semi-automatic interfaces for tracing skeletons directly on surface-based representations of cells and structures, ii) fast tools for proofreading, i.e., correcting and editing of semi-automatically constructed skeleton representations, and iii) natural methods for interactive exploration, i.e., measuring, comparing, and analyzing geometric features related to cellular structures based on medial axis representations. Neuroscientists currently use the system for performing morphology studies on sparse reconstructions of glial cells and neurons extracted from a sample of the somatosensory cortex of a juvenile rat. The framework runs in a standard PC and has been tested on two different display and interaction setups: PC-tethered stereoscopic head-mounted display (HMD) with 3D controllers and tracking sensors, and a large display wall with a standard gamepad controller. We report on a user study that we carried out for analyzing user performance on different tasks using these two setups.

Design of Smart Card Chip Reader Based on STM32

<p>This paper designs a smart card chip reader based on STM32, and gives a block diagram of the overall design of the system. It introduces the design of the peripheral hardware circuit of the main control chip STM32F103CBT6 and the RF processing chip THM3070, and the format of data communication between each part and flow chart. The communication between the host computer and the reader adopts the USB CCID protocol, which supports the USB full-speed communication rate of 12Mbps, improves the interrupt response, and overcomes the shortcomings of the traditional serial port-based readers with low communication speed and poor interrupt response. At the same time, it realizes data interaction with the PC/SC standard PC application and has better versatility</p>

A hardware intelligent processing accelerator for domestic service robots

ABSTRACT We present a method for implementing hardware intelligent processing accelerator on domestic service robots. These domestic service robots support human life; therefore, they are required to recognize environments using intelligent processing. Moreover, the intelligent processing requires large computational resources. Therefore, standard personal computers (PCs) with robot middleware on the robots do not have enough resources for this intelligent processing. We propose a ‘connective object for middleware to an accelerator (COMTA),’ which is a system that integrates hardware intelligent processing accelerators and robot middleware. Herein, by constructing dedicated architecture digital circuits, field-programmable gate arrays (FPGAs) accelerate intelligent processing. In addition, the system can configure and access applications on hardware accelerators via a robot middleware space; consequently, robotic engineers do not require the knowledge of FPGAs. We conducted an experiment on the proposed system by utilizing a human-following application with image processing, which is commonly applied in the robots. Experimental results demonstrated that the proposed system can be automatically constructed from a single-configuration file on the robot middleware and can execute the application 5.2 times more efficiently than an ordinary PC.

Developing PC-Based Driving Simulator System for Driver Behavior Analysis Research

Driving simulator is one of important system for driving behavior studies. It offers a replicable virtual driving environment and it has possibility to create a various scenarios to carry out in real environment. The problem in development of this simulator is how to reduce a prices in certain point areas have made it possible to create low-cost driving simulators, but with the features that are usually found only in expensive driving simulators. This article is presented a low cost PC-based driving simulator. The aim is to use the simulator as a platform for studies of the driver’s behavior. The complete simulator is using three 27” LED screen, handle by a standard PC, typically used for computer games. As Input we using steering, pedal and shifter G29, with force feedback. For software we using city car home edition as tools to measure validity user and developing simulator to driver’s behavior study.

The Representation of Food-Related Environments in Virtual Reality

Abstract Objectives Virtual reality (VR) potentially provides an innovative tool for nutrition education/counselling. The objective of this study was to determine the a) sense of ‘presence’ (the feeling of being in a scene) experienced in two food related VR scenes b) capture information regarding the participants experiences in VR. Methods Two 3D, food-related scenes were created for this study: supermarket or fast food restaurant. The scenes were displayed using a VR head mounted display or on a standard PC monitor. The participants were able to move around each scene and could interact with various elements to obtain nutrition information about a food. Thirty-one adults were recruited for this study and reported to the laboratory on 4 occasions separated by at least 48 hours. Participants were randomized to a treatment order. On reporting to the laboratory, the participants had surface electrodes attached to determine heart rate and electrodermal activity. The participant was then required to sit quietly for 10 minutes for baseline measures to be collected. Then, in the VR treatments, a VR headset was placed on the participants head and the relevant scene displayed. For the PC treatments, the same scenes were displayed on a PC monitor. The participants were required to remain in the different scenes for at least 5 minutes. Then, the headset was removed and the participant completed questionnaires regarding their experiences in the VR and PC scenes. Results Participants ranged in age from <25 years to over 65 years. All participants used computers in their daily life but generally had no or little experience in VR. Participants reported a higher sense of presence in the VR treatments compared to the PC treatments (P < 0.05). The VR scenes also created a greater sense of the scene being the ‘dominant reality’ and elicited a greater sense that the participant were actually in the scene (P < 0.05). There was no difference in the participant's ability to complete tasks in the VR and PC scenes. Moreover, feelings of nausea were not different between the VR and PC scenes. The participant's heart rate was significantly higher in the VR treatments (P < 0.05). Conclusions This study provides data that supports the development of VR as a nutrition education/counselling tool. Further research is required to develop VR as an effective education tool. Funding Sources None.

ALGORITHM OF SKELETON-BASED STATIC HAND GESTURE RECOGNITION

We present the monocamera approach of static hand gestures recognition based on skeletonization. The problem of creating skeleton of the human’s hand, as well as body, became solvable a few years ago after inventing so called convolutional pose machines – the novel architecture of artificial neural network. Our solution uses such kind of pretrained convolutional artificial network for extracting hand joints keypoints with further skeleton reconstruction. In this work we also propose special skeleton descriptor with proving its stability and distinguishability in terms of classification. We considered a few widespread machine learning algorithms to build and verify different classifiers. The quality of the classifier’s recognition is estimated using the wellknown Accuracy metric, which identified that classical SVM (Support Vector Machines) with radial basis kernel gives the best results. The testing of the whole system was conducted using public databases containing about 3000 of test images for more than 10 types of gestures. The results of a comparative analysis of the proposed system with existing approaches are demonstrated. It is shown that our gesture recognition system provides better quality in comparison with existing solutions. The performance of the proposed system was estimated for two configurations of standard personal computer: with CPU (Central Processing Unit) only and with GPU (Graphics Processing Unit) in addition where the latest one provides realtime processing with up to 60 frames per second. Thus we demonstrate that the proposed approach can find an application in the practice.

De-homogenization of optimal multi-scale 3D topologies

This paper presents a highly efficient method to obtain high-resolution, near-optimal 3D topologies optimized for minimum compliance on a standard PC. Using an implicit geometry description we derive a single-scale interpretation of optimal multi-scale designs on a very fine mesh (de-homogenization). By performing homogenization-based topology optimization, optimal multi-scale designs are obtained on a relatively coarse mesh resulting in a low computational cost. As microstructure parameterization we use orthogonal rank-3 microstructures, which are known to be optimal for a single loading case. Furthermore, a method to get explicit control of the minimum feature size and complexity of the final shapes will be discussed. Numerical examples show excellent performance of these fine-scale designs resulting in objective values similar to the homogenization-based designs. Comparisons with well-established density-based topology optimization methods show a reduction in computational cost of 3 orders of magnitude, paving the way for giga-scale designs on a standard PC.

Assessment of Lymphovenous Anastomosis Patency: Technical Highlights.

Assessment of Lymphovenous Anastomosis Patency: Technical Highlights.

IberWQ: A GPU Accelerated Tool for 2D Water Quality Modeling in Rivers and Estuaries

Numerical models are useful tools to analyze water quality by computing the concentration of physical, chemical and biological parameters. The present work introduces a two-dimensional depth-averaged model that computes the most relevant and frequent parameters used to evaluate water quality. High performance computing (HPC) techniques based on graphic processing unit (GPU) parallelization have been applied to improve the efficiency of the package, providing speed-ups of two orders of magnitude in a standard PC. Several test cases were analyzed to show the capabilities and efficiency of the model to evaluate the environmental status of rivers and non-stratified estuaries. IberWQ will be freely available through the package Iber.

The fast parametric integral equations system in an acceleration of solving polygonal potential boundary value problems

The paper presents a new way of accelerating of polygonal potential boundary value problems solving by modified parametric integral equations system (PIES). For this purpose, known from the literature, the fast multipole method (FMM) was included into conventional PIES. Therefore the fast PIES method was obtained. FMM combined with conventional PIES also allows a significant reduction of utilization of random access memory (RAM) of the computer. It gives us a possibility of solving the large-scale engineering problems on a standard personal computer (PC). Finally, the paper presents the development of an algorithm for efficient solving of complex boundary value problems on a standard PC instead of supercomputers, which are usually used to accelerate calculations. The proposed algorithm is based on the modified PIES and allows for obtaining accurate solutions in a reasonable time on the PC, which was the main goal of our research.

Deep Learning-Based Super-resolution Ultrasound Speckle Tracking Velocimetry

Deep ultrasound localization microscopy (deep-ULM) allows sub-wavelength resolution imaging with deep learning. However, the injection of contrast agents (CAs) in deep-ULM is debatable because of their potential risk. In this study, we propose a deep learning-based super-resolution ultrasound (DL-SRU), which employs the concept of deep-ULM and a convolutional neural network. The network is trained with synthetic tracer images to localize positions of red blood cells (RBCs) and reconstruct vessel geometry at high resolution, even for CA-free ultrasound (US) images. The proposed algorithm is validated by comparing the full width at half-maximum values of the vascular profiles reconstructed by other techniques, such as the standard ULM and the US average intensity under in silico and in vitro conditions. RBC localization by DL-SRU is also compared with that by other localization approaches to validate its performance under in vivo condition, especially for veins in the human lower extremity. Furthermore, a two-frame particle tracking velocimetry (PTV) algorithm is applied to DL-SRU localization for accurate flow velocity measurement. The velocity profile obtained by applying the PTV is compared with a theoretical value under in vitro condition to verify its compatibility with the flow measurement modality. The velocity vectors of individual RBCs are obtained to determine the applicability to in vivo conditions. DL-SRU can achieve high-resolution vessel morphology and flow dynamics in vasculature, mapping 110 super-resolved images per second on a standard PC, regardless of various imaging conditions. As a result, the DL-SRU technique is much more robust in localization compared with previous deep-ULM. In addition, the performance of DL-SRU is nearly the same as that of deep-ULM in rapid computational processing and high measurement accuracy. Thus, DL-SRU might become an effective and useful instrument in clinical practice.

Deep Learning-Based Single-Shot and Real-Time Vehicle Detection and Ego-Lane Estimation


 
 
 Vision-based Forward Collision Warning System (FCWS) is a promising assist feature in a car to alleviate road accidents and make roads safer. In practice, it is exceptionally hard to accurately and efficiently develop an algorithm for FCWS application due to the complexity of steps involved in FCWS. For FCWS application, multiple steps are involved namely vehicle detection, target vehicle verification and time-to-collision (TTC). These involve an elaborated FCWS pipeline using classical computer vision methods which limits the robustness of the overall system and limits the scalability of the algorithm. Deep neural network (DNN) has shown unprecedented performance for the task of vision-based object detection which opens the possibility to be explored as an effective perceptive tool for automotive application. In this paper, a DNN based single-shot vehicle detection and ego-lane estimation architecture is presented. This architecture allows simultaneous detection of vehicles and estimation of ego-lanes in a single-shot. SSD-MobileNetv2 architecture was used as a backbone network to achieve this. Traffic ego- lanes in this paper were defined as semantic regression points. We collected and labelled 59,068 images of ego-lane datasets and trained the feature extractor architecture MobileNetv2 to estimate where the ego- lanes are in an image. Once the feature extractor is trained for ego-lane estimation the meta-architecture single-shot detector (SSD) was then trained to detect vehicles. Our experimental results show that this method achieves real-time performance with test results of 88% total precision on the CULane dataset and 91% on our dataset for ego-lane estimation. Moreover, we achieve a 63.7% mAP for vehicle detection on our dataset. The proposed architecture shows that an elaborate pipeline of multiple steps to develop an algorithm for the FCWS application is eliminated. The proposed method achieves real-time at 60 fps performance on standard PC running on Nvidia GTX1080 proving its potential to run on an embedded device for FCWS.
 
 

Heliostat field aiming strategies for solar central receivers

This thesis deals with the development of optical models for solar power tower technology. Specifically, this work is focused on modeling flux mapping and aiming strategies for central receiver systems (CRS). The resulting codes are applicable to CRS design and operation. This dissertation essentially presents four computational models. The first model, on which the rest of the models are built up, computes the flux density distribution incident on any kind of central receiver which is caused by a single heliostat. The procedure relies on the oblique projection of the receiver mesh onto the image plane, where an accurate analytic function, e.g. UNIZAR, is evaluated. Oblique projection is accomplished by transformation of coordinate systems. The 4-step projection method remarkably reproduces the distorted spot found for large incidence angles on the heliostat and the receiver. This basic model was validated against flux measurements on a flat receiver and Monte Carlo Ray Tracing simulations on a cylindrical receiver. Compared to SolTrace, the model takes 50 times less computation time and higher level of resolution. The second model was developed to determine canting errors in the facets of real heliostats. Based on a deterministic optimization algorithm, a procedure was set up to minimize the difference between computed flux maps and captured images on a lambertian target. Experimental images from THEMIS plant were employed to find out canting errors in selected CETHEL heliostats. From results of the model, one of the heliostats was successfully readjusted, significantly improving its optical quality, and validating the proposed methodology. The third model extends the basic model to superpose single heliostat flux maps in a whole field of heliostats. Shading and blocking losses are computed by parallel projection of neighbor heliostats. An aiming strategy, symmetric about the receiver equator, was developed on the basis of only one parameter: k, aiming factor. Nearly single equatorial aiming is achieved with k>3, while k=0 results in pointing to either upper or lower receiver edges. For the Gemasolar case study, an aiming factor equal to 2 yielded the most uniform flux maps, i.e. flat profile in the central region, and negligible increase in spillage losses compared to equatorial aiming. An optimal aiming strategy for molten salt receivers was implemented in the fourth model. An algorithm was developed to maximize receiver thermal output, while meeting at the same time corrosion and thermal stress limits; which were translated into allowable flux densities, AFD. Compared to unreliable single aiming, the optimized aiming strategy ensures receiver integrity and spillage losses only increase up to 4 percentage points. It was found that optimal aim points are, on average, slightly shifted towards the panel entrance. Despite the conflicting demand between adjacent panels in multi-panel receivers with serpentine flow pattern, the fit algorithm performs noticeable matching to the AFD profile. The resulting code takes around 2 minutes in a standard PC to compute the optimal aim points for a field made up of 2650 heliostats.

HRCM: An Efficient Hybrid Referential Compression Method for Genomic Big Data.

With the maturity of genome sequencing technology, huge amounts of sequence reads as well as assembled genomes are generating. With the explosive growth of genomic data, the storage and transmission of genomic data are facing enormous challenges. FASTA, as one of the main storage formats for genome sequences, is widely used in the Gene Bank because it eases sequence analysis and gene research and is easy to be read. Many compression methods for FASTA genome sequences have been proposed, but they still have room for improvement. For example, the compression ratio and speed are not so high and robust enough, and memory consumption is not ideal, etc. Therefore, it is of great significance to improve the efficiency, robustness, and practicability of genomic data compression to reduce the storage and transmission cost of genomic data further and promote the research and development of genomic technology. In this manuscript, a hybrid referential compression method (HRCM) for FASTA genome sequences is proposed. HRCM is a lossless compression method able to compress single sequence as well as large collections of sequences. It is implemented through three stages: sequence information extraction, sequence information matching, and sequence information encoding. A large number of experiments fully evaluated the performance of HRCM. Experimental verification shows that HRCM is superior to the best-known methods in genome batch compression. Moreover, HRCM memory consumption is relatively low and can be deployed on standard PCs.

Commercial Devices-Based System Designed to Improve the Treatment Adherence of Hypertensive Patients.

This paper presents an intelligent system designed to increase the treatment adherence of hypertensive patients. The architecture was developed to allow communication among patients, physicians, and families to determine each patient’s medication intake and self-monitoring of blood pressure rates. Concerning the medication schedule, the system is designed to follow a predefined prescription, adapting itself to undesired events, such as mistakenly taking medication or forgetting to take medication on time. When covering the blood pressure measurement, it incorporates best medical practices, registering the actual values in recommended frequency and form, trying to avoid the known “white-coat effect.” We assume that taking medicine precisely and measuring blood pressure correctly may lead to good adherence to the treatment. The system uses commercial consumer electronic devices and can be replicated in any home equipped with a standard personal computer and Internet access. The resulting architecture has four layers. The first is responsible for adding electronic devices that typically exist in today’s homes to the system. The second is a preprocessing layer that filters the data generated from the patient’s behavior. The third is a reasoning layer that decides how to act based on the patient’s activities observed. Finally, the fourth layer creates messages that should drive the reactions of all involved actors. The reasoning layer takes into consideration the patient’s schedule and medication-taking activity data and uses implicit algorithms based on the J48, RepTree, and RandomTree decision tree models to infer the adherence. The algorithms were first adjusted using one academic machine learning and data mining tool. The system communicates with users through smartphones (anytime and anywhere) and smart TVs (in the patient’s home) by using the 3G/4G and WiFi infrastructure. It interacts automatically through social networks with doctors and relatives when changes or mistakes in medication intake and blood pressure mean values are detected. By associating the blood pressure data with the history of medication intake, our system can indicate the treatment adherence and help patients to achieve better treatment results. Comparisons with similar research were made, highlighting our findings.