Miracle-dz/UniFAR-FTUs huggingface dataset:
A novel high performance Generalized Discontinuous PWM algorithm for reduced current ripple and switching losses using imaginary switching times

This paper presents a novel high performance generalized discontinuous pulse width modulation (GDPWM) algorithm for reduced current ripple without angle and sector estimation. The conventional space vector modulator with equal division of zero state vectors time is modified to generate different discontinuous modulating waves. A constant-variable mu ranging from 0 to 1 is used to generate infinite number of modulating waves. Also, the classical triangle comparison approach is replaced by a new space vector-based method. Based on the stator flux ripple, the value of mu that gives minimum stator flux ripple will be selected. With mu equal to 0, 1 and 0.5, DPWMMAX, DPWMMIN, and conventional SVPWM (CSVPWM) sequences can be generated respectively. For simplicity only three values of mu are considered. The proposed method results in reduced current ripple and switching losses over CSVPWM at all modulation indices. To validate the proposed method, simulation is carried out on V/f controlled induction motor and the no-load current waveforms at different modulation indices are presented and %THDs is compared.
Development and validation of an instrument to evaluate the content effectiveness of video games: a pilot study

Storytelling is the key design element in game design. Content preparation, which examines ‘what information users want’, yielded valuable data concerning the necessary attributes of storytelling in a web-based interface. After identifying 17 basic components of video games, the authors of this paper were able to categorise the components based on guidelines reported from the literature review into four major factors: game play; game setup; game control; and game help. In order to evaluate these factors, an experiment was conducted that required participants to evaluate three PC games selected for their distinctiveness and popularity: Need for Speed VII: Most Wanted; StarCraft; and Diablo II. Twelve highly skilled video game players were selected as participants. After playing each game, participants were asked to complete a 56-question survey and the results of a multiple regression analysis revealed that all items of development and content preparation are not of equal importance. Fantasy, pressure, reward/bonus, next step, pressure, status, menu, goal, character, hints and instruction are the components that have the most significant impact on user satisfaction.
Spearcons Improve Navigation Performance and Perceived Speediness in Korean Auditory Menus

For decades, auditory menus using both speech (usually text-to-speech, TTS) and non-speech sounds have been extensively studied. Researchers have developed situation-optimized auditory menus involving such cues as auditory icons, earcons, spearcons, and spindex. Spearcons have generally outperformed other cues in terms of providing both contextual information and item-specific information. However, little research has been devoted to exploration of spearcons in languages other than English, or the use of spearcon-only auditory menus. In this study, we evaluated the use of spearcons in Korean menus, as well as the use of spearcons alone. Twenty-five native Korean speakers navigated through a two-dimensional auditory menu presented via TTS, with or without spearcon enhancements. Korean spearcons were successful. Participants also rated the spearcon-enhanced menu as seeming speedier and more fun than the TTS-only menu. After a short learning period, mean time-to-target in the auditory menu was even faster with spearcons alone, compared to traditional TTS-only menus.
The contribution of mathematical modelling to the practice of project management

This paper looks at the contribution that mathematical modelling has made to project management over the past 50 years, and the contribution it is currently making and can make in the future. Project Management started with well-defined foundations posing precise, well-defined problems. In its growing phase, modellers played an essential role in taking the problems defined by the project-management world and offering solutions, from the original PERT, through resource allocation and levelling procedures, Monte Carlo simulation models, criticality analyses and so on. Since then, however, while the project management field itself has tried to establish its procedures, keeping to its philosophical stance, much of the mathematical-modelling world has continued along its trajectory, producing ever more complex solutions to ever more complex models, motivated by mathematical impressiveness rather than the need to solve real-world problems. This paper outlines much of this work, some of which does find its way into project-network software but much of which languishes in journals. However, over the last decade or so, Operational Researchers have begun to build models of projects that are systemic and dynamic and explain many of the behaviours of projects that conventional decomposition models do not; and at the same time, some of the Project Management world has started to realize the limitations of its philosophical stance and started looking to build new theory for modern, complex, dynamic projects. As these two trends come together, it is essential that modellers are at the forefront of building this new theory.
Error Compensation for Single Pair-Pole Encoder Based on Ellipse Hypothesis

An error compensation method for the single pair-pole encoder has been discussed in this paper. This article analyzed offset, sensitivity error, quadrature error and ferromagnetic interference error of single-pole magnetic encoder to obtain the expression of each error. In order to facilitate error compensation, the common expression for describing the error has been summed up. The process for formatting the error can be assumed as the process of changing from circle to ellipse. Therefore the inverse of this process is the same as the process of error compensation. The experimental results show that the accuracy of magnetic encoder which used this method could reach ±1, thus the error compensation effect is obvious. The magnetic encoder which applied this method has the advantages of low-cost, high-precision and convenient to use.
Statistical methods for bioimpedance analysis

Abstract This paper gives a basic overview of relevant statistical methods for the analysis of bioimpedance measurements, with an aim to answer questions such as: How do I begin with planning an experiment? How many measurements do I need to take? How do I deal with large amounts of frequency sweep data? Which statistical test should I use, and how do I validate my results? Beginning with the hypothesis and the research design, the methodological framework for making inferences based on measurements and statistical analysis is explained. This is followed by a brief discussion on correlated measurements and data reduction before an overview is given of statistical methods for comparison of groups, factor analysis, association, regression and prediction, explained in the context of bioimpedance research. The last chapter is dedicated to the validation of a new method by different measures of performance. A flowchart is presented for selection of statistical method, and a table is given for an overview of the most important terms of performance when evaluating new measurement technology.
Automatic registration of 3D images using surface curvature

This paper presents a new technique to perform automatically the 3-D registration of two 3-D scanner images. The aim is to compute the rigid geometric transform existing between two views of the same object, taken into two different positions. The basis of our method is to extract characteristic 3-D lines from the two images, called crest lines, and to compute the geometric transform that map one of the set of lines onto the other one. Our method is fully automatic. It is also very fast and robust, because the crest lines set is a very compact and stable representation of the principal geometric information of a 3-D image. We present in this paper the example of an automatic registration performed with two 3-D images of a skull.
Vector quantization of image pyramids with the ECPNN algorithm

A recent algorithm for single rate vector quantization is used for coding image pyramids. The algorithm, called entropy-constrained pairwise-nearest-neighbor (ECPNN), designs codebooks by merging the pair of Voronoi regions which gives the most decrease in entropy for a given increase in distortion. In terms of performance in the mean-squared-error sense the algorithm produces codebooks with the same performance as the ECVQ design algorithm. The main advantage over ECVQ is that ECPNN algorithm enables much faster codebook design. A single pass through the ECPNN design algorithm, which progresses from large to successively smaller rates, allows the storage of any desired number of optimal intermediate-rate codebooks. In the context of pyramid coding, this feature is especially desirable, since the ECPNN design algorithm must be run for each sub-band and storage of codebooks of different rates are required for each subband. Good results at 0.5 bpp, judged both visually and using peak-to-peak SNR criterion, have been obtained by coding image pyramids using ECPNN codebooks.
International Conference : Spatial Thinking and Geographic Information Sciences 2011 An Approach to Modeling Spatial Perception for Geovisualization

The amount and intensity of geographic information make it complicated to perform geographic data management and spatial analysis. In this paper we propose a novel approach exploring technical and perceptual issues in reducing information intensity; especially in the representation of geographic information. Foveation is a perceptually lossless degradation of the intensity of visual information. We apply established foveation algorithms on georeferenced raster and vector data sets and obtain visualizations in the form of foveated stereoscopic gaze contingent displays. Our concept is called as Geofoveation, and it is informed by vision research, computer graphics and geovisualization. In this paper, we discuss geofoveation as a means to demonstrate how spatial perception can be modeled and integrated into geographic visualizations. We hope to contribute to the discourse on intelligent geographic data management as well as provide initial steps to knowledge integration from certain aspects of spatial perception to geovisualization. © 2011 Published by Elsevier Ltd. Selection and/or peer-review under responsibility of Yasushi Asami
Analysis and optimization of parameters used in training a cascade classifier

Training a cascade classifier for object detection using Local Binary Pattern (LBP) and Histogram of Gradients (HOG) features is computationally exorbitant. If the parameters of training are not chosen appropriately, the training may take weeks to complete with the output of an inefficient classifier. The state-of-the-art face recognition applications demand accurate and reliable cascade classifiers. Open Computer Vision (OpenCV) organization provides libraries which accomplish the training task once all parameters are given as inputs. In this paper we analyze the parameters experimentally and concluded with an optimal range of values for each of these parameters. Testing of the generated classifiers with optimal parameters values is performed on a dataset of 4000 test images. The training of these classifiers with optimal parameters takes an average training time of 25000 sec and provides average true positive detection of 88%.
Comparision of AODV and Directed Diffusion Routing Protocol under Flooding and Sybil Attack

AODV and Directed Diffusion are reactive and data centric routing protocols respectively. They are basically used in Wireless Sensor Network. AODV was developed in Nokia Research Centre in university of California and was first used in ZigBee. They are most efficient routing protocols in terms of establishing shortest path for the transmission of data so as to maintain the battery power for a longer period. But these protocols still have many security flaws as they are prone to various attacks. In this paper performance of AODV and DD routing protocol is tested under Flooding and Sybil attack which are very treacherous attacks which affects the network performance to a large extent. The protocols are compared with respect to packet delivery and loss ratio, end to end delay and number of control messages flooded in the network. The results show that DD protocol is a more reliable protocol than AODV with greater packet delivery ratio and lesser end to end delay than AODV.
Group projects and the computer science curriculum

Group projects in computer science are normally delivered with reference to good software engineering practice. The discipline of software engineering is rapidly evolving, and the application of the latest ‘agile techniques’ to group projects causes a potential conflict with constraints imposed by regulating bodies on the computer science curriculum. This paper explores the issues, and argues that there are strong educational reasons for modifying the computer science curriculum to accommodate these new software engineering methodologies.
Comparison of Computational Tools for Protein-Protein Interaction (PPI) Mapping and Analysis

Several visualization tools for the mapping of protein-protein interactions have been developed in recent years. However, a systematic comparison of the virtues and limitations of different PPI visualization tools has not been carried out so far. In this study, we compare seven commonly used visualization tools, based on input and output file format, layout algorithm, database integration, Gene Ontology annotation and accessibility of each tool. The assessment was carried out based on brain disease datasets. Our suggested tools, NAViGaTOR, Cytoscape and Gephi perform competitively as PPI network visualization tools, can be a reference for future researches on PPI mapping and analysis.
Singularity-robustness and task-prioritization in configuration control of redundant robots

The authors present a singularity-robust task-prioritized reformulation of the configuration control for redundant robot manipulators. This reformation suppresses large joint velocities to induce minimal errors in the task performance by modifying the task trajectories. Furthermore, the same framework provides a means for assignment of priorities between the basic task of end-effector motion and the user-defined additional task for utilizing redundancy. This allows automatic relaxation of the additional task constraints in favor of the desired end-effector motion when both cannot be achieved exactly. <<ETX>>
Verification Probability Control Method in Sensor Networks

Wireless sensor networks (WSN) collect and report event information to users in many applications (e.g. monitoring, tracking). Due to the lack of infrastructure and management in the sensor field, WSN are vulnerable to many security attacks. In false report injections attacks, attackers inject false reports into the network through compromised nodes. The false reports cause false alarms at the base station (BS) and waste energy at forwarding nodes. In the statistical en-route filtering scheme (SEF), a countermeasure to the attacks, sensor nodes detect and drop false reports by endorsing and verifying event reports using shared keys. In SEF, each node verifies received reports based on fixed probability and do not consider sending nodes. For this reason, unnecessary energy consumption occurs. In this paper, we propose the verification probability control method in sensor networks. In the method, each node controls its verification probabilities for its neighbor nodes. The number of recent valid reports, hop counts from the BS and the last verification probability are used to determine the verification probability for a node. We confirm that our method decreases energy consumption of filtering schemes in sensor networks.
Recall of patterns using binary and gray-scale autoassociative morphological memories

Morphological associative memories (MAM's) belong to a class of artificial neural networks that perform the operations erosion or dilation of mathematical morphology at each node. Therefore we speak of morphological neural networks. Alternatively, the total input effect on a morphological neuron can be expressed in terms of lattice induced matrix operations in the mathematical theory of minimax algebra. Neural models of associative memories are usually concerned with the storage and the retrieval of binary or bipolar patterns. Thus far, the emphasis in research on morphological associative memory systems has been on binary models, although a number of notable features of autoassociative morphological memories (AMM's) such as optimal absolute storage capacity and one-step convergence have been shown to hold in the general, gray-scale setting. In previous papers, we gained valuable insight into the storage and recall phases of AMM's by analyzing their fixed points and basins of attraction. We have shown in particular that the fixed points of binary AMM's correspond to the lattice polynomials in the original patterns. This paper extends these results in the following ways. In the first place, we provide an exact characterization of the fixed points of gray-scale AMM's in terms of combinations of the original patterns. Secondly, we present an exact expression for the fixed point attractor that represents the output of either a binary or a gray-scale AMM upon presentation of a certain input. The results of this paper are confirmed in several experiments using binary patterns and gray-scale images.
Transmitting time-dependent multimedia data in FDDI networks

Timely delivery of multimedia data is one of the key requirements in a distributed multimedia system. In a token ring network, this can be achieved by a proper control of channel access. FDDI uses a timed token MAC protocol to control the token rotation time. During network initialization, a protocol parameter called target token rotation time (TTRT) is determined. Its value indicates the expected token rotation time. Each node is assigned a fraction of the TTRT, known as synchronous bandwidth, for transmitting its synchronous messages which may be deadline constrained. In this paper, we study methods used for setting the protocol parameters such as TTRT and synchronous bandwidth. The goal is to satisfy the message deadlines. In particular, we discuss the use of the normalized proportional scheme for allocating the synchronous bandwidth. We derive a condition on the utilization factor for ensuring timely delivery of all synchronous messages, and show that 33% utilization is the tight lower bound, regardless of the number of nodes, message lengths, periods, etc. Based on this scheme, we address proper selection of TTRT and trade-off between buffer size and the capability of meeting message deadlines.
Implementation of Fast MPC with a Quadrotor for Obstacle Avoidance

This paper proposes a realtime guidance technique for an autonomous vehicle moving through obstacles. The method combines a geometric convexification technique with Model Predictive Control (MPC). Several recent advances in MPC are exploited in order to achieve efficient computation. Experimental results are given using a quadrotor in an instrumented flying arena to show the feasibility of real-time control.
Face and eye tracking for sub-hologram-based digital holographic display system

Sub-hologram based holographic display method is one of the most practical approaches for realizing big size holographic display. However, this method needs highly accurate face and eye tracking function in real-time to enable precise steering of backlight and generation of corresponding sub-hologram for each video frame. We theoretically estimated several parameters, such as accuracy, speed and distance from an observer, required for the eye tracking function and developed an eye tracking system whose objective is accurate and fast 3D positioning of left and right pupils of an observer. Experimental results show that the system obtains accurate 3D pupil positions with an error less than 3 mm at 30 frames per second under disturbing conditions such as more than 2m distance and an observer wearing glasses. Therefore, our implementation could be sufficiently applied to the sub-hologram based display system.
A Degree-Based Clustering Technique for VLSI Placement

In this paper, clustering for the circuit placement problem is examined from the perspective of wire length contribution from groups of nets. First, the final wire length data of groups of nets with different degrees are extracted and studied. It is illustrated that nets with high-degree contribute a high percentage to the total wire length. To remedy this problem, a clustering algorithm for placement is proposed that focuses on clustering nets with high-degree. This new clustering algorithm is implemented as a preprocessing step in the placement stage. ICCAD04 benchmark circuits abstracted from IBM are used to validate the placement quality by using four academic placers with and without the proposed preprocessing step. Experiments show that the overall placement results can be improved by up to 5%.
Fault Injection for SCADE Models

A fault injection method based on SCADE was designed in this paper, which can simulate hardware failure in the form of software fault. Injecting faults into SCADE models brings forward fault tolerance testing to development phase avoiding the cost of changing fault-tolerant design by modifying the code. Development and fault injection test are in the same environment, which improves the efficiency of the project management. Speed and location measuring subsystem of ATP was selected as the target model in this paper and faults were injected into it in SCADE. The effect on the system of the injected fault can be observed in the simulation process. The weakness of the fault-tolerant design of the system can be found with this method.
A Bayesian Framework for Statistical Inference from Gene Expression Data

SYNOPTIC ABSTRACT DNA microarray technology allows researchers to monitor simultaneously the expression levels of thousands of genes across different time points, experimental conditions or different tissue samples which create an enormous wealth of information. It also poses a challenge for data analysis because the number of variables, usually thousands of genes, is much higher than the number of experiments. Although many analysis questions may be of interest, the primary goal is to determine the genes, which best discriminate among the different types of tissues. Recently several statistical methods have been developed to achieve this goal. In this paper we applied a hierarchical Bayesian model for microarray to identify a set of candidate genes and the selected genes were subjected to Fuzzy C means (FCM) clustering procedure to assess the prediction performance. We applied the methods to Leukemia dataset of Golub, Slonim, Tamayo, Huard, Gaasenbeek, Mesirov, Coller, Loh, Downing, Caligiuri, Bloomfield and Lander (1999), hereinafter refered as GST(1999). The procedure of gene selection was tested on simulated datasets.