Miracle-dz/UniFAR-FTUs huggingface dataset:
Vernier Motor and Its Desin

A vernier motor is an unexcited inductor synchronous motor in which a small displacement of the rotor produces a large displacement of the axes of permeance. It runs at a slow speed as if it were geared down from the speed of the rotating field set up by the stator. To design a vernier motor is equivalent to designing a polyphase reluctance motor with an odd-shaped rotor so that the airgap permeance distribution is a displaced triangular wave.
On the naturalness of software

Natural languages like English are rich, complex, and powerful. The highly creative and graceful use of languages like English and Tamil, by masters like Shakespeare and Avvaiyar, can certainly delight and inspire. But in practice, given cognitive constraints and the exigencies of daily life, most human utterances are far simpler and much more repetitive and predictable. In fact, these utterances can be very usefully modeled using modern statistical methods. This fact has led to the phenomenal success of statistical approaches to speech recognition, natural language translation, question-answering, and text mining and comprehension. We begin with the conjecture that most software is also natural, in the sense that it is created by humans at work, with all the attendant constraints and limitations---and thus, like natural language, it is also likely to be repetitive and predictable. We then proceed to ask whether (a) code can be usefully modeled by statistical language models and (b) such models can be leveraged to support software engineers. Using the widely adopted n-gram model, we provide empirical evidence supportive of a positive answer to both these questions. We show that code is also very regular, and, in fact, even more so than natural languages. As an example use of the model, we have developed a simple code completion engine for Java that, despite its simplicity, already improves Eclipse's completion capability. We conclude the paper by laying out a vision for future research in this area.
Force oscillations in contact motion of industrial robots: an experimental investigation

Describes the results of research on the oscillatory behavior that has been systematically experienced in the force measured during contact motion of an industrial robot. Two phenomena have been found to be mainly responsible for it, namely, the arm structural elasticity due to the torsional flexibility of the joints and the torque ripple of the brushless motors. The structural elasticity is characterized by a resonance frequency which varies with the arm configuration, while the torque ripple has harmonics of frequency proportional to the motor velocity. Most severe oscillations, and even loss of the contact, arise when the resonance and ripple frequency values come closer or match, so that elasticity and ripple excite each other.
Performance prediction and field weakening simulation of a brushless DC motor

The performance of a brushless DC motor (BDCM) for low and high speed operation in traction applications is investigated. A simulation model combines the structures of both the machine and the inverter and is used to predict the drive characteristics at base speed and above in the field weakening range. Simulation results show that the motor drive can successfully achieve constant power operation at up to three times the base speed, with high efficiency. Optimum conditions are found for a wide speed range.
Buffer overflow vulnerability diagnosis for commodity software

Buffer overflow attacks have been a computer security threat in software-based systems and applications for decades. The existence of buffer overflow vulnerabilities makes the system susceptible to Internet worms and denial of service (DDoS) attacks which can cause huge social and financial impacts. Due to its importance, buffer overflow problem has been intensively studied. Researchers have proposed different techniques to defend against unknown buffer overflow attacks. They have also investigated various solutions, including automatic signature generation, automatic patch generation, etc., to automatically protect computer systems with known vulnerabilities. The effectiveness and efficiency of the automatic signature generation approaches and the automatic patch generation approaches are all based on the accurate understanding of the vulnerabilities, the buffer overflow vulnerability diagnosis (BOVD). Currently, the results of automatic signature generation and automatic patch generation are far from satisfaction due to the insufficient research results from the automatic BOVD. This thesis defines the automatic buffer overflow vulnerability diagnosis (BOVD) problem and provides solutions towards automatic BOVD for commodity software. It targets on commodity software when source code and symbol table are not available. The solutions combine both of the dynamic analysis techniques and static analysis techniques to achieve the goal. Based on the observation that buffer overflow attack happens when the size of the destination buffer is smaller than the total number of writes after the data copy process if the buffer overflow attack happens through a data copy procedure, the diagnosis results return the information of the size of destination buffer, the total number of writes of a data copy procedure and how the user inputs are related with them. They are achieved through bound analysis, loop analysis and input analysis respectively. We demonstrate the effectiveness of this thesis approach using real world vulnerable applications including the buffer overflow vulnerabilities attacked by the record-setting Slammer and Blaster worms. This thesis also does the complete case study for buffer overflow vulnerabilities which may have independent interests to researchers. Our buffer overflow case study results can help other researchers to design more effective defense systems and debugging tools against buffer overflow attacks. Keywords. software security, vulnerability diagnosis, vulnerability defense, buffer overflow, loop analysis, bound checking.
Fast Cellular Automata Implementation on Graphic Processor Unit (GPU) for Salt and Pepper Noise Removal

Noise removal operation is commonly applied as pre-processing step before subsequent image processing tasks due to the occurrence of noise during acquisition or transmission process. A common problem in imaging systems by using CMOS or CCD sensors is appearance of the salt and pepper noise. This paper presents Cellular Automata (CA) framework for noise removal of distorted image by the salt and pepper noise. In order to enhance the performance of the designed CA for noise removal, a parallel programming approach has been adopted and implemented on GPU. The results obtained show that the proposed CA models implemented on general purpose processor and GPU are able to suppress noise in high noise intensity up to 90 percents. The proposed CA implemented on GPU has successfully outperformed the method implemented on CPU by factor of 2 for gray scale image and factor of 10 for color images.
Semantics and aesthetics inference for image search: statistical learning approaches

The automatic inference of image semantics is an important but highly challenging research problem whose solutions can greatly benefit content-based image search and automatic image annotation. In this thesis, I present algorithms and statistical models for inferring image semantics and aesthetics from visual content, specifically aimed at improving real-world image search. First, a novel approach to automatic image tagging is presented which furthers the state-of-the-art in both speed and accuracy. The direct use of automatically generated tags in real-world image search is then explored, and its efficacy demonstrated experimentally. An assumption which makes most annotation models misrepresent reality is that the state of the world is static, whereas it is fundamentally dynamic. I explore learning algorithms for adapting automatic tagging to different scenario changes. Specifically, a meta-learning model is proposed which can augment a black-box annotation model to help provide adaptability for personalization, time evolution, and contextual changes. Instead of retraining expensive annotation models, adaptability is achieved through efficient incremental learning of only the meta-learning component. Large scale experiments convincingly support this approach. In image search, when semantics alone yields many matches, one way to rank images further is to look beyond semantics and consider visual quality. I explore the topic of data-driven inference of aesthetic quality of images. Owing to minimal prior art, the topic is first explored in detail. Then, methods for extracting a number of high-level visual features, presumed to have correlation with aesthetics, are presented. Through feature selection and machine learning, an aesthetics inference model is trained and found to perform moderately on real-world data. The aesthetics-correlated visual features are then used in the problem of selecting and eliminating images at the high and low extremes of the aesthetics scale respectively, using a novel statistical model. Experimentally, this approach is found to work well in visual quality based filtering. Finally, I explore the use of image search techniques for designing a novel image-based CAPTCHA, a Web security test aimed at distinguishing humans from machines. Assuming image search metrics to be potential attack tools, they are used in the loop to design attack-resistant CAPTCHAs.
Telephone service survivability

The public switched telephone network (PSTN) was designed for high reliability and availability and has achieved an impressive record of service over its history. People today expect continuous telephone service, even in emergencies such as the loss of commercial power or construction cable cuts. The PSTN, however, was not designed for survivability in the face of malicious or catastrophic faults that cause total failure of one of its subscriber-line switching offices. Such a failure could potentially disrupt phone service to thousands of customers for days or weeks, depending on the extent of the damage. Fortunately, modern switching equipment is designed for distribution over a wide geographic area and several subscriber-line architectures are available to address survivability issues. This paper defines the issue of subscriber-line survivability and identifies architectures that improve survivability.
Robust Wireless Localization in Harsh Mixed Line-of-Sight/Non-Line-of-Sight Environments

This PhD thesis considers the problem of locating some target nodes in different wireless infrastructures such as wireless cellular radio networks and wireless sensor networks. To be as realistic as possible, mixed line-of-sight and non-line-of-sight (LOS/NLOS) localization environment is introduced. Both the conventional non-cooperative localization and the new emerging cooperative localization have been studied thoroughly. Owing to the random nature of the measurements, probabilistic methods are more advanced as compared to the old-fashioned geometric methods. The gist behind the probabilistic methods is to infer the unknown positions of the target nodes in an estimation process, given a set of noisy position related measurements, a probabilistic measurement model, and a few known reference positions. In contrast to the majority of the existing methods, harsh but practical constraints are taken into account: neither offline calibration nor non-line-of-sight state identification is equipped in the desired localization system. This leads to incomplete knowledge about the measurement error statistics making the inference task extremely challenging. Two new classes of localization algorithms have been proposed to jointly estimate the positions and measurement error statistics. All unknown parameters are assumed to be deterministic, and maximum likelihood estimator is sought after throughout this thesis. The first class of algorithms assumes no knowledge about the measurement error distribution and adopts a nonparametric modeling. The idea is to alternate between a pdf estimation step, which approximates the exact measurement error pdf via adaptive kernel density estimation, and a parameter estimation step, which resolves a position estimate numerically from an approximated log-likelihood function. The computational complexity of this class of algorithms scales quadratically in the number of measurements. Hence, the first class of algorithms is applicable primarily for non-cooperative localization in wireless cellular radio networks. In order to reduce the computational complexity, a second class of algorithms resorts to approximate the measurement error distribution parametrically as a linear combination of Gaussian distributions. Iterative algorithms that alternate between updating the position(s) and other parameters have been developed with the aid of expectation-maximization (EM), expectation conditional maximization (ECM) and joint maximum a posterior -maximum likelihood (JMAP-ML) criteria. As a consequence, the computational complexity turns out to scale linearly in the number of measurements. Hence, the second class of algorithms is also applicable for cooperative localization in wireless sensor networks.
A Model for America Reads

A study implemented and evaluated the Book Buddies model using National Service volunteers as tutors for high-risk first-grade students in a high-poverty urban setting. Using a design with a randomly assigned control group, results show that tutoring sessions produced clear effects. Children who received 40 Book Buddies lessons significantly surpassed students in the control group in letter identification, word reading in isolation, and accurate reading in context. Results contribute to understanding of the constraints and conditions of implementing effective volunteer tutoring in challenging circumstances; as well as underscoring the importance of key components for ensuring quality tutoring for the America Reads Challenge. Contains 18 references, and a table and a figure of data. (EF) Reproductions supplied by EDRS are the best that can be made from the original document.
Biocomputing '97 - Proceedings of the Pacific Symposium

The Pacific Symposium on Biocomputing (PSB) 2014 is an international, multidisciplinary conference for the presentation and discussion of current research in the theory and application of computational methods in problems of biological significance. Presentations are rigorously peer reviewed and are published in an archival proceedings volume. PSB 2014 will be held from January 3 7, 2014 in Kohala Coast, Hawaii. Tutorials and workshops will be offered prior to the start of the conference. PSB 2014 will bring together top researchers from the US, the Asian Pacific nations, and around the world to exchange research results and address open issues in all aspects of computational biology. It is a forum for the presentation of work in databases, algorithms, interfaces, visualization, modeling, and other computational methods, as applied to biological problems, with emphasis on applications in data-rich areas of molecular biology. The PSB has been designed to be responsive to the need for critical mass in sub-disciplines within biocomputing. For that reason, it is the only meeting whose sessions are defined dynamically each year in response to specific proposals. PSB sessions are organized by leaders of research in biocomputing's hot topics. In this way, the meeting provides an early forum for serious examination of emerging methods and approaches in this rapidly changing field.
Power spectral density of binary digital pulse streams in the presence of independent uniform timing jitter

The spectral occupancy and composition of a chosen digital signaling technique when the data pulse stream is nonideal, due for instance to implementation imperfections, are important considerations in the design of a practical communication system. One source of imperfection is timing jitter where the rising and falling transitions do not occur at the nominal data transition time instants, nevertheless, the time instants are offset by random amounts relative to the nominal one. The amount of timing shift per transmission interval is random and is typically characterized by a discrete stationary random process (independent of the data sequence). The purpose of this paper is to characterize the power spectral density (PSD) of baseband binary data streams in the presence of uniform timing jitter. Examples of uncorrelated data pulse stream, and i.i. d. data stream are given. Interesting results emerge when the generating sequence {a/sub n/} is uncorrelated. For generating sequences {a/sub n/} that are non-zero mean, timing jitter has the effect of widening the main lobe of the spectrum and increasing the sidelobes. When the generating sequence is zero mean and uncorrelated, a rather surprising result is that the timing jitter does not effect the PSD. Simulation results are also presented to verify the analysis.
Metal Insulator Gate Geometric HEMT: Novel Attributes and Design Consideration for High Speed Analog Applications

Improvement in breakdown voltage (BVds) and speed of the device are the key issues among the researchers for enhancing the performance of HEMT. Increased speed of the device aspires for shortened gate length (L g ), but due to lithographic limitation, shortening L g below sub-micrometer requires the inclusion of various metal-insulator geometries like T-gate on to the conventional architecture. It has been observed that the speed of the device can be enhanced by minimizing the effect of upper gate electrode on device characteristics, whereas increase in the BVds of the device can be achieved by considering the finite effect of the upper gate electrode. Further, improvement in BV ds can be obtained by applying field plates, especially at the drain side. The important parameters affecting BVds and cut-off frequency (f T ) of the device are the length, thickness, position and shape of metal-insulator geometry. In this context, intensive simulation work with analytical analysis has been carried out to study the effect of variation in length, thickness and position of the insulator under the gate for various metal-insulator gate geometries like T-gate, Γ-gate, Step-gate etc., to anticipate superior device performance in conventional HEMT structure.
THE USE OF PICTURE SERIES TO INCREASE STUDENTSâ€™ SPEAKING ABILITY

Penelitian ini bertujuan untuk mengetahui penggunaan picture series untuk meningkatkan kemampuan berbicara siswa dalam procedure text di kelas tiga SMPN 1 Gadingrejo dan aspek yang paling meningkat. Subjek penelitian ini adalah kelas IX.5 dengan jumlah siswa 30 orang. Penelitian ini adalah penelitian kuantitatif dengan menggunakan desain one group pretest-posttest. Kemampuan berbicara siswa akan diberi nilai berdasarkan aspek micro skill yaitu pronunciation, vocabulary , dan grammar kemudian dianalisa dan dihitung dengan menggunakan SPSS. Hasil penelitian menunjukkan bahwa nilai rata-rata dari pretest dan posttest meningkat dengan signifikan dari 47,47 ke 62,93 dan level signifikansi 0,00 (<0,05). Hasil penelitian juga menunjukkan bahwa vocabulary adalah aspek yang paling meningkat dengan peningkatan sebesar 7,10. Dapat disimpulkan bahwa picture series dapat digunakan sebagai media pembelajaran berbicara, khususnya procedure text. Untuk penelitian mendatang disarankan untuk fokus ke aspek macro skill . Â This research was aimed at investigating the use of picture series to increase studentsâ€™ speaking ability in procedure text at the third grade of SMPN 1 Gadingrejo and what the most increasing aspect is. The subject of this research was class IX.5 with 30 students. This was quantitative research with one group pretest-posttest design. Studentsâ€™ speaking were given score based on micro skill aspects they are pronunciation, vocabulary, and grammar then analyzed and computed by using SPSS. The result showed that the mean score from pretest and posttest increased significantly from 47.47 to 62.93 and the significant 2-tailed was 0.00 (<0.05). The result also showed that vocabulary is the most increasing aspect with the increase about 7/10. It could be concluded that picture series is able to be used as a learning media for speaking, especially for procedure text. For the further research is suggested to focus on speaking macro skill aspects. Keywords: picture series, procedure text, speaking ability.
Creating R Packages: A Tutorial

This tutorial gives a practical introduction to creating R packages. We discuss how object oriented programming and S formulas can be used to give R code the usual look and feel, how to start a package from a collection of R functions, and how to test the code once the package has been created. As running example we use functions for standard linear regression analysis which are developed from scratch.
Multiupdate mode quantum evolutionary algorithm and its applications to combination and permutation problems

Based on the concept and principles of quantum computing, this paper proposes a new evolutionary algorithm called multiupdate mode quantum evolutionary algorithm (MMQEA). MMQEA, like the other classic evolutionary algorithms, is also characterized by the representation of the individual evaluation function and the population dynamics; however, instead of binary, numeric, or symbolic representation, MMQEA uses two interactional q‐bit strings as an individual. Update modes are introduced as a variational operation that evolves the individuals to make a better solution. The proposed individual structure and update modes are inspired by quantum entanglement. Update modes perform as reproducing the states of a pair of q‐bit strings of individual simultaneously. For guiding the individual evolution to maintain the population diversity and avoid prematurity, each q‐bit string of individual provides its evolutionary history information to another. To demonstrate its effectiveness and applicability, the proposed algorithm was tested on two famous combinatorial optimization problems, namely, the knapsack problem and flow shop problem. The results show that MMQEA performs very well compared to quantum evolutionary algorithm (QEA) and the conventional genetic algorithm. © 2012 Institute of Electrical Engineers of Japan. Published by John Wiley & Sons, Inc.
Provider Oriented Hierarchical Corporative in Content Delivery Network Proposal

The performance of Content Delivery Network (CDN) system is affected by the collaboration among CDN servers. This paper proposes a collaborative caching among CDN servers known as provider oriented hierarchical cooperative content outsourcing (POHC). In order to highlight the advantages of our proposed framework, OMNeT++ software is used for simulation in order to compare with the non-cooperative pull-based content outsourcing (Non-CPB). The result from simulation shows that our proposal is more efficient in term of packet loss and reliability versus the Non-CPB mode. After collecting relevant status of all CDN servers in online network, this paper uses DBSPT-Circular-PO algorithm to build POHC with root, which is the content provider.
H∞ Tracking Control for a Class of Uncertain Time-delay Systems

This paper considers the  H tracking control for a class of uncertain systems with time-delay and disturbance. The solvable sufficient conditions for the tracking control with state time delay are presented. The tracking controller is designed by using convex combination technique and Lyapunov functional method. The controller enables the system to meet the model reference  H tracking performance index. The delay-independent  H tracking control criteria is derived and formulated in the form of linear matrix inequality (LMI). A numerical simulation example is presented to verify the validity of the proposed method.