Personal Computer Environment Research Articles

DESIGN AND IMPLEMENTATION OF REAL-TIME NON-LINEAR DYNAMIC SIMULATION OF FIGHTER AIRCRAFT ON MULTICORE PROCESSOR

Simulation is a system to imitate the operation of various kinds of real-world facilities or processes for training, behavior study or entertainment purposes. Some implementation of fighter aircraft simulation is done sequentially using FORTRAN and MATLAB / Simulink. On the other hand, a multicore (parallel) computer system has been in the personal computer environment, so the multicore computing paradigm should be considered. Intel Threading Building Blocks (TBB) library need to be utilized in multicore programming. This paper discusses the design and implementation of real-time nonlinear dynamic simulation of fighter aircraft on multicore processor. The aircraft model used in this study is F16 data. Implementation of the simulation code is written in C++ with Intel TBB, OpenGL, Open Scene Graph, and Open Dynamic Engine library. The testing results of non-linear dynamics simulation of fighter aircraft show that the speedup on an Intel Core 2 Duo processor is 1.1776 x for elevator doublet input (short period), while speedup on an Intel i7 is 1.5405 x for rudder doublet input (dutch roll). Key Words: multicore computing, real-time simulation, nonlinear dynamic, fighter aircraft model

Developing a framework for heterotopias as discursive playgrounds: a comparative analysis of non-immersive and immersive technologies

The discursive space represents the reordering of knowledge gained through accumulation. In the digital age, multimedia has become the language of information, and the space for archival practices is provided by non-immersive technologies, resulting in the disappearance of several layers from discursive activities. Heterotopias are unique, multilayered epistemic contexts that connect other systems through the exchange of information. This paper describes a process to create a framework for Virtual Reality, Mixed Reality, and personal computer environments based on heterotopias to provide absent layers. This study provides virtual museum space as an informational terrain that contains a “world within worlds” and presents place production as a layer of heterotopia and the subject of discourse. Automation for the individual multimedia content is provided via various sorting and grouping algorithms, and procedural content generation algorithms such as Binary Space Partitioning, Cellular Automata, Growth Algorithm, and Procedural Room Generation. Versions of the framework were comparatively evaluated through a user study involving 30 participants, considering factors such as usability, technology acceptance, and presence. The results of the study show that the framework can serve diverse contexts to construct multilayered digital habitats and is flexible for integration into professional and daily life practices.

Use of web-based GIS applications in geography teaching-the implications from Türkiye

Geography, as a discipline which examines the earth, aims to increase analytical thinking skills by instilling spatial thinking (ST) skills in students. The youth who enrich a sense of space with these talents will effectively solve the spatial problems they encountered. In the modern world, where spatial issues are frequently being faced with, ST is extremely important for analyzing and interpreting data. Geographical information systems (GIS) appear as a method commonly used to process data and transform it into spatial information. In this context, despite the renewal of the secondary education (SE) geography curriculum in T?rkiye, with the components required for the use of GIS at the SE level, prevented the use of GIS in SE from reaching the desired level. Developments in technologies have also caused significant changes in the field of GIS with the power of cloud technologies and, the work done in the personal computer environment has been moved to the internet environment. Web GIS applications, which can be used via a web browser, are much easier to use than desktop software and can reach many users from any device with the internet. Within the scope of the study, a sample application was designed to use web GIS tools in geography teaching. With the application, it is possible to assess the students? achievements in the geography lessons regarding verbal and spatial data. Thus, it was aimed to ensure the active participation of the students in the geography lessons, develop their ST ability, and perception of space.

A Study on Improving M2M Network Security through Abnormal Traffic Control

Machine-to-machine (M2M) intelligent network devices are exposed to vulnerable networks and security threats always exist. The devices are composed of low-capacity hardware by their nature and are exposed to various security threats such as worms, viruses and distributed denial of service (DDoS) flooding attacks due to lack of security or antivirus programs installed in the personal computer environment. In this paper, we proposed a network filter that improves the security of M2M intelligent networks by configuring the network security filter in a specific form that can be adapted to M2M intelligent networks. The proposed filter increases user convenience and decreases unnecessary loss. Experimental results show that when the security filter is applied, the response speed of the device improved by more than 50% in an abnormal traffic environment with a cost of less than 10% delay, depending upon the characteristics of the device.

Developing serious games for CBRN-e training in mixed reality, virtual reality, and computer-based environments

In the last decade, chemical, biological, radioactive, nuclear, and explosive (CBRN-e) attacks have become severe risks to countries, prompting connected parties to prioritize CBRN-e training. CBRN-e training is typically performed as physical exercises, and although such training is necessary and beneficial, repeating the same training program can be time-consuming and costly. In this study, newly developed versions of two previous serious games—Hospital and Biogarden— and a new mining serious game were developed for training purposes in Virtual Reality (VR), Mixed Reality (MR), and personal computer (PC) environments. The Hospital and Biogarden games’ scenarios were based on the joint activities held in 2018 in France and Belgium as part of the EU H2020 European Network Of CBRN Training CEnters (eNOTICE) project, while the mining game was created over a replica of a training mine at a university in Turkey. Sixteen CBRN-e experts from the eNOTICE project, who took part in the physical training programs in France and Belgium, evaluated the games. For evaluation and extensive feedback, presence, system usability scale, technology acceptance model questionnaires, and open-ended questions were conducted. The findings revealed that serious games have a vast potential in CBRN-e training, and the comparisons of different environments provided invaluable testbeds giving hindsight to develop a future training program.

An Efficient Search Algorithm for Large Encrypted Data by Homomorphic Encryption

The purpose of this study is to provide an efficient search function over a large amount of encrypted data, where the bit length of each item is several tens of bits. For this purpose, we have improved the existing hybrid homomorphic encryption by enabling the longer data items to be stored while using multiple encrypted databases and by suggesting an improved search method working on top of the multiple instances of the database. Further, we found the optimal number of databases to be needed when 40-bit information, such as social security number, is stored after encryption. Through experiments, we were able to check the existence of a given (Korean) social security number of 13 decimal digits in approximately 12 s from a database that has 10 million encrypted social security numbers over a typical personal computer environment. The outcome of this research can be used to build a large-scale, practical encrypted database in order to support the search operation. In addition, it is expected to be used as a method for providing both security and practicality to the industry dealing with credit information evaluation and personal data requiring privacy.

IDD-HPO: A Proposed Model for Improving Diabetic Detection using Hyperparameter Optimization and Cloud Mapping Storage

Readmission to the hospital is an important and critical procedure for the quality of health care as it is very costly and helps in determining the quality level of the point of care provided by the hospital to the patient. This paper proposes a group model to predict readmission by choosing between Machine Learning and Deep Learning algorithms based on performance improvement. The algorithms used for Machine Learning are Logistic Regression, K-Nearest Neighbors, and Support Vector Machine, while the algorithms used for Deep Learning are a Convolutional Neural Network and Recurrent Neural Network. The reasons for the appearance of the efficiency of the model depend on the are preparation of correct parameters and the values that control the learning. This paper aims to enhance the performance of both machine learning and deep learning based readmission models using hyperparameter optimization in both Personal Computer environments and Mobile Cloud Computing systems. The proposed model is called improving detection diabetic using hyperparameter optimization , the proposed model aims to achieve the best rate of between prediction rate accuracy for hospital readmission at the same time minimizing resources such as time delay and energy consumption. Results achieved by proposed model for Logistic Regression, K-Nearest Neighbors, and Support Vector Machine are (accuracy=0.671, 0.883, 0.901, time delay=5, 7, 20, and energy consumed=25, 32, 48) respectively, for Recurrent Neural Network and Convolutional Neural Network are (accuracy=0.854, 0.963, time delay=25, 660 energy consumed=89, 895) respectively. However, this proposed model takes a lot of time and energy consumed especially in Convolutional Neural Network. So, the experiments were conducted again, but in the cloud environment, based on the existence of two types of storage to preserve the accuracy but decreasing time and energy, the proposed model in cloud environment achieve for Logistic Regression, K-Nearest Neighbors, and Support Vector Machine (accuracy=0.671, 0.883, 0.901, time delay=2, 3, 8, and energy consumed=8, 9, 11) respectively, for Recurrent Neural Network, Convolutional Neural Network (accuracy=0.854, 0.963, time delay=15, 220, and energy consumed=20, 301) respectively.

A Review of Emotion Recognition Methods Based on Data Acquired via Smartphone Sensors.

In recent years, emotion recognition algorithms have achieved high efficiency, allowing the development of various affective and affect-aware applications. This advancement has taken place mainly in the environment of personal computers offering the appropriate hardware and sufficient power to process complex data from video, audio, and other channels. However, the increase in computing and communication capabilities of smartphones, the variety of their built-in sensors, as well as the availability of cloud computing services have made them an environment in which the task of recognising emotions can be performed at least as effectively. This is possible and particularly important due to the fact that smartphones and other mobile devices have become the main computer devices used by most people. This article provides a systematic overview of publications from the last 10 years related to emotion recognition methods using smartphone sensors. The characteristics of the most important sensors in this respect are presented, and the methods applied to extract informative features on the basis of data read from these input channels. Then, various machine learning approaches implemented to recognise emotional states are described.

Embedded decision support system for ultrasound nondestructive evaluation based on extreme learning machines

Decision support systems for nondestructive evaluation of defects are often trained on a personal computer environment, which may slow the assessment task. This work investigates the feasibility of embedding extreme learning machines in microcontrollers, in order to execute in situ training for an ultrasound nondestructive evaluation. Principal component analysis and autoencoders were evaluated as dimensionality reduction methods in a data set acquired from welded SAE 1020 carbon steel plates containing four types of defects. A binary-encoded random weight approach is proposed, whose results were tested over four different probability density functions used to generate the random weights. The obtained experimental results matched the accuracy of a previous work, run in computer environment for higher dimensionality data. In this work a dimensionality reduction of 75% and elapsed time for system training of less than 0.5 ms in microcontroller were achieved, indicating the suitability of such embedded networks for the studied case.

ImmerTai: Immersive Motion Learning in VR Environments

Immersive learning in Virtual Reality (VR) environments is the developing trend for future education systems including remote physical training. This paper presents “ImmerTai”, a system that is designed for effective remote motion training, particularly for Chinese Taichi, in an immersive way. With ImmerTai, the Taichi expert’s motion is captured and delivered to remote students in CAVE, HMD and PC environments for learning. The students’ motions are also captured for motion quality assessment and a group of students can form a virtual collaborative learning scenario. We built up a Taichi motion dataset with ground truth of motion quality, and based on this, we developed and evaluated several motion quality assessment methods. Then, user tests were designed and carried out to measure and compare the learning outcomes (learning time, quality and overall efficiency) of students in Cave Automatic Virtual Environment (CAVE), Head Mounted Display (HMD) and Personal Computer (PC) environments. Meanwhile, the connections between students’ learning outcomes and their VR experience were investigated and discussed too. Our results show that ImmerTai can accelerate the learning process of students noticeably (up to 17%) compared to non-immersive learning with the conventional PC setup. However, we observed a substantial difference in the quality of the learnt motion between CAVE (26% gain) and HMD (23% drop) compared to PC (baseline). While strong VR presence can enhance the learning experience of students, their learning outcomes are not fully consistent to their experience. Overall, ImmerTai with CAVE demonstrated a significantly higher learning efficiency than other tested environments.

Calibration of gamma-ray detectors using Gaussian photopeak fitting in the multichannel spectra with a LabVIEW-based digital system

This paper reports on the development of a gamma-ray spectroscopic system for the (i) recording and (ii) processing of spectra. The utilized data read-out unit consists of a PCI digital oscilloscope, personal computer and LabVIEW™ programming environment. A pulse-height spectra of various sources were recorded with two NaI(Tl) detectors and analyzed, demonstrating the proper usage of the detectors. A multichannel analyzer implements the Gaussian photopeak fitting. The presented method provides results which are in compliance to the ones taken from commercial spectroscopy systems. Each individual hardware or software unit can be further utilized in different spectrometric user-systems. An application of the developed system for research and teaching purposes regarding the design of digital spectrometric systems has been successfully tested at the laboratories of the Department of Experimental Physics.

Service for Supporting the Mobility of Personal Computing Environment on the Cloud Storage

Service for Supporting the Mobility of Personal Computing Environment on the Cloud Storage

Bringing strong authentication and transaction security to the realm of mobile devices

Widespread usage of mobile devices in conjunction with malicious software attacks calls for the development of mobile-device-oriented mechanisms aiming to provide strong authentication and transaction security. This paper considers the eBanking application scenario and argues that the concept of using a trusted companion device can be ported to the mobile realm. Trusted companion devices involve established and proven techniques in the PC (personal computer) environment to secure transactions. Various options for the communication between mobile and companion devices are discussed and evaluated in terms of technical feasibility, usability, and cost. Accordingly, audio communication across the 3.5-mm audio jack--also known as tip-ring-ring-sleeve, or TRRS connector,--is determined to be quite appropriate. We present a proof-of-concept companion device implementing binary frequency shift keying across this interface. Results from a field study performed with the proof-of-concept device further confirm the feasibility of the proposed solution.

클라우드 기반의 N-Screen 에뮬레이터 설계 및 구현

In this paper, we propose a new design scheme of N-Screen emulator based on Cloud and then implement the emulator, in order to solve the critical point of N-Screen emulator based on Cloud. This method, without the emulator in the server, will be able to confirm the features of the emulator with a browser using Web Service. This means that the identical service is possible without regard to personal computer or mobile environment. Also, in order to emulating each different web browser engine of the various devices separately, we revise and manage the WebKit engine to be suitable to the characteristics of each device. In the previous design method, the number of emulators which can be shown in a monitor is restricted to 2 or 3. However, we show that the proposed design method can improve the performance of server to the extent that this method could operate more than 100 emulators per each server.

Parallelizing flow-accumulation calculations on graphics processing units—From iterative DEM preprocessing algorithm to recursive multiple-flow-direction algorithm

As one of the important tasks in digital terrain analysis, the calculation of flow accumulations from gridded digital elevation models (DEMs) usually involves two steps in a real application: (1) using an iterative DEM preprocessing algorithm to remove the depressions and flat areas commonly contained in real DEMs, and (2) using a recursive flow-direction algorithm to calculate the flow accumulation for every cell in the DEM. Because both algorithms are computationally intensive, quick calculation of the flow accumulations from a DEM (especially for a large area) presents a practical challenge to personal computer (PC) users. In recent years, rapid increases in hardware capacity of the graphics processing units (GPUs) provided in modern PCs have made it possible to meet this challenge in a PC environment. Parallel computing on GPUs using a compute-unified-device-architecture (CUDA) programming model has been explored to speed up the execution of the single-flow-direction algorithm (SFD). However, the parallel implementation on a GPU of the multiple-flow-direction (MFD) algorithm, which generally performs better than the SFD algorithm, has not been reported. Moreover, GPU-based parallelization of the DEM preprocessing step in the flow-accumulation calculations has not been addressed. This paper proposes a parallel approach to calculate flow accumulations (including both iterative DEM preprocessing and a recursive MFD algorithm) on a CUDA-compatible GPU. For the parallelization of an MFD algorithm (MFD-md), two different parallelization strategies using a GPU are explored. The first parallelization strategy, which has been used in the existing parallel SFD algorithm on GPU, has the problem of computing redundancy. Therefore, we designed a parallelization strategy based on graph theory. The application results show that the proposed parallel approach to calculate flow accumulations on a GPU performs much faster than either sequential algorithms or other parallel GPU-based algorithms based on existing parallelization strategies.

A 3D multiresolution lunar surface model using bicubic subdivision-surface wavelets, with interactive visualization tools

The laser altimetry (LAM) dataset obtained by Chang'E-1 (CE-1) contains about 8.6 million points, and how to use it to model and visualize the lunar surface is a problem. This paper presents a 3D, multiresolution, approximate lunar surface model based on a subdivision-surface wavelet, as well as efficient tools for rendering the three-dimensional surface at speeds approaching real-time interaction in a general Personal Computer (PC) environment. The surface model is C 2-continuous at nearly all points. The modeling and visualization method could be applied to most other global data sets.

A demand-driven virtual disk prefetch mechanism for seamless mobility of personal computing environments

In this paper, we propose a demand-driven virtual disk (VD) prefetch mechanism—DPM—to improve the performance of virtual machine (VM) at destination site for mobile personal computing environments. DPM uses an extended CoW (Copy-on-Write) virtual block device to split the traditional monolithic large-sized VD image into multiple small-sized SVD (Software Virtual Disk) images at a basic granularity of a single kind of software. Based on the fine-grained VD splitting, DPM uses an access frequency and dynamic priority-based prefetch target identifying-APTI algorithm to identify the SVDs of the software being used by the user at destination site, and prefetches those SVDs by utilizing P2P transportation mechanism at background. We have built a prototype to realize DPM on Xen virtual machine monitor (VMM). Experiments on the prototype show that DPM can effectively improve the VM performance at an unexpected destination site without any cached VD state, being helpful in achieving seamless mobility of personal computing environments in distributed virtual machine environments.

MEDECOR—A MEDICAL DECORPORATION TOOL TO ASSIST FIRST RESPONDERS, RECEIVERS, AND MEDICAL REACH-BACK PERSONNEL IN TRIAGE, TREATMENT, AND RISK ASSESSMENT AFTER INTERNALIZATION OF RADIONUCLIDES

After a radiological dispersal device (RDD) event, it is possible for radionuclides to enter the human body through inhalation, ingestion, and skin and wound absorption. From a health physics perspective, it is important to know the magnitude of the intake to perform dosimetric assessments. From a medical perspective, removal of radionuclides leading to dose aversion (hence risk reduction) is of high importance. The efficacy of medical decorporation strategies is extremely dependent upon the time of treatment delivery after intake. The "golden hour," or more realistically 3-4 h, is optimal when attempting to increase removal of radionuclides from extracellular fluids prior to cellular incorporation. To assist medical first response personnel in making timely decisions regarding appropriate treatment delivery modes, it is desirable to have a software tool that compiles existing radionuclide decorporation therapy data and allows a user to perform simple diagnosis leading to optimized decorporation treatment strategies. In its most simple application, the software is a large database of radionuclide decorporation strategies and treatments. The software can also be used in clinical interactive mode, in which the user inputs the radionuclide, estimated activity, route of intake and time since exposure. The software makes suggestions as to the urgency of treatment (i.e., triage) and the suggested therapy. Current developments include risk assessment which impacts the potential risk of delivered therapy and resource allocation of therapeutic agents. The software, developed for the Canadian Department of National Defence (DND), is titled MEDECOR (MEdical DECORporation). The MEDECOR tool was designed for use on both personal digital assistant and laptop computer environments. The tool was designed using HTML/Jscript, to allow for ease of portability amongst different computing platforms. This paper presents the features of MEDECOR, results of testing at a major NATO exercise, and future development of this tool into MEDECOR2.

Parallelized computation for computer simulation of electrocardiograms using personal computers with multi-core CPU and general-purpose GPU

Biological computations like electrocardiological modelling and simulation usually require high-performance computing environments. This paper introduces an implementation of parallel computation for computer simulation of electrocardiograms (ECGs) in a personal computer environment with an Intel CPU of Core (TM) 2 Quad Q6600 and a GPU of Geforce 8800GT, with software support by OpenMP and CUDA. It was tested in three parallelization device setups: (a) a four-core CPU without a general-purpose GPU, (b) a general-purpose GPU plus 1 core of CPU, and (c) a four-core CPU plus a general-purpose GPU. To effectively take advantage of a multi-core CPU and a general-purpose GPU, an algorithm based on load-prediction dynamic scheduling was developed and applied to setting (c). In the simulation with 1600 time steps, the speedup of the parallel computation as compared to the serial computation was 3.9 in setting (a), 16.8 in setting (b), and 20.0 in setting (c). This study demonstrates that a current PC with a multi-core CPU and a general-purpose GPU provides a good environment for parallel computations in biological modelling and simulation studies.

A 3D Painterly Rendering Framework for Photon Mapping

This paper presents a novel framework for 3D painterly rendering for photon mapping. We introduce a new non-photorealistic rendering pass and integrate it within the standard photon mapping pipeline. Our framework contains three passes. The photon tracing pass casts photons to exploit a three-dimensional scene and holds photon information in a data structure developed specifically for painterly rendering. The non-photorealistic rendering pass constructs the sparse map and informative map which preserve the attributes of strokes required for 3D painterly rendering. In this pass, we also employ an impressionistic line integral convolution algorithm producing various types of painterly rendering results. Finally, the rendering pass renders photorealistic objects generating the final image consisting of the scene with both artistic painterly styles and global illumination visual effects. We implemented our framework and developed an experimental rendering system on a personal computer environment. To the best of our knowledge, the proposed framework is the first that can take advantages of both global illumination and 3D painterly rendering environment. Experimental results and rendered images demonstrate the feasibility of our framework.