Miracle-dz/UniFAR-FTUs huggingface dataset:
The application of graph diffusion in high-level feature extraction

In this paper, a new graph diffusion method is presented to improve the high-level feature extraction performance. In this method, we construct a semantic graph by describe the concepts as nodes and the concept affinities as the weights of edges, then we use the training set and its corresponding label matrix to estimate the concept relationship, where the relationship of two concepts were measured by the inner product of its corresponding row vector. We test the method on the high-level feature extraction task of TRECVID 2009 and the experimental results show the effectiveness of the method.
Representation and Exchange of Digital Catalogues of Cutting Tools

Information management for manufacturing resources such as cutting tools is an important research topic in the context of cloud manufacturing. Vendors and customers usually use catalogues to communicate information for such manufacturing resource. Incompatibilities of information in syntax, semantics, and structure among supply chains often result in inefficient manual sharing and management of the catalogue information. It is difficult for cloud based applications to pool information from various sources. This communication failure calls for a system neutral solution for data modeling and exchange to enhance interoperability of the cutting tool catalogue information. Previous studies has present solutions for representation of the cutting tool information with STEP AP242 (ISO/DIS 10303-242) with semantic classification referring to a PLib (ISO 13584, Part Library) based dictionary. This approach can be extended for the catalogue modeling, due to functionalities for specification and configuration control of general product variants in the same standard.With a modeling approach with standardized information schemas, system architecture to guide implementation is proposed to enhance the communication in practice. Relative elements to represent vendors’ catalogues and customers’ requirements are modeled. Associations to the PLib-based dictionary complete semantics and enable information mapping between vendors and customers. Principles of the mapping are identified to facilitate implementation of related software systems. Prototypes are developed to verify the proposed system architecture. The proposed solution is promising to migrate to other types of products than cutting tools, because the data models are based on the general product models defined in AP242.Copyright © 2014 by ASME
Monte Carlo modeling of epidemiological studies

Abstract As epidemiologists search for smaller and smaller effects, the statistical uncertainty in their studies can be dwarfed by biases and systematic uncertainty. We here suggest that Monte Carlo techniques are very useful to estimate some of these biases and uncertainties, and perhaps to avoid them entirely. We illustrate this by two simple Monte Carlo simulations. First, we show how often false positive findings, and sometimes false negative findings, can result from 33 differential misclassification of the exposure status. Secondly, we show how a bias, that we call “the binning bias,” can be caused if the investigator chooses bin boundaries after he has seen the data. We show how an allowance might be made for such a bias by increasing the uncertainty bounds. This would put the presentation of the results on a par with the presentation in physical sciences where a quantitative estimate of systematic errors is routinely included with the final result. Finally, we suggest how similar Monte Carlo simul.
An educational SAR sea surface waves simulator

This paper describes a Synthetic Aperture Radar sea surface waves simulator, based on the velocity bunching theory and developed in the Matlab® programming language. The software has been designed and implemented for educational use. The present version of this software is run in classes at National Oceanographic Centre of Southampton, UK and at the Università di Napoli Parthenope, Italy.
Classification of business clusters based on information transfer events

This paper examines the utility of the attributes associated with information transfer events for classifying business clusters and understanding their workings. A field survey collected data from diverse actors in six business clusters located in Brazil, including: companies responsible for the development or final assembly of a product or service (central actors); the entity that represents the companies in each business cluster (the administrative body); and the entities seeking to transfer information to the central actors of localities (knowledge agents). We found that differences in the degree of productive complementarity on the part of central actors of business clusters were associated with variance in perceptions of information transfer events that took place in localities. To our knowledge this is the first research which analyzes and classifies business clusters based on dimensions of information transfer which are relevant to the practice of knowledge management.
Brain-computer interface for single-trial eeg classification for wrist movement imagery using spatial filtering in the gamma band

The aim of this study is to discriminate between the left and right wrist movements imagery in four different directions. To achieve this goal, the authors have applied spatial filtering on the EEG signal in the gamma frequency band to extract key features to perform classification. Specifically, the original EEG signal is transformed in to a spatial pattern and applied to the radial basis function (RBF) classifier. The authors demonstrate that spatial filtering method in multichannel EEG effectively extracts discriminant information from single-trial EEG for left and right wrist movement imagery. An average recognition rate of approximately 89% was achieved in all the four type of movements (extension, flexion, pronation and supination) between left and right wrist in five healthy subjects. The results are comparable to the highest rates reported in the literature.
The emission characteristics of liquid‐crystal lasers

Abstract— Liquid‐crystal lasers exhibit narrow linewidth, large coherence area, and low threshold laser emission. Moreover, the wavelength of the laser line can be readily tuned using a variety of different external stimuli, including electric fields. These combined features make them particularly attractive as compact tunable laser light sources. Recent experimental results with regards to the emission characteristics of chiral nematic photonic band‐edge lasers are discussed. This type of liquid‐crystal laser consists of a self‐organizing one‐dimensional photonic band structure and a gain medium in the form of a laser dye. Some of the generic features that are observed for these lasers are discussed, including the typical emission linewidth of the laser line, the change in emission energy of the laser for high excitation energies and high pump repetition rates, and the dependence of the excitation threshold and slope efficiency on the cell thickness. In addition, how the performance changes when either the molecular structure of the chiral nematic host or the gain medium is varied is considered. To conclude, results are presented on the laser emission for a wide‐temperature‐range blue phase I band‐edge laser which consists of a self‐organizing three‐dimensional photonic band structure.
Game theory and the design of self-configuring, adaptive wireless networks

Game theory provides a wealth of tools that can be applied to the design and operation of communications systems. In this article, we provide a brief introduction to game theory. We then present applications of game theory to problems in random access and power control. In the case of random access, we examine the behavior of selfish users in a simplified Aloha system; surprisingly, rational selfish users do not implement the "always transmit" strategy that one might expect. In the case of power control, we show that game theoretic techniques can yield an optimal operating point without the intervention of an external controller.
A 200 MHz steered current operational amplifier in 1.2-μm CMOS technology

In this paper, we present the design of a CMOS current operational amplifier (COA). A design technique based on current steering is proposed to enhance the frequency capability of the amplifier and achieve higher bandwidth of operation. A complete COA was designed and fabricated using a 1.2-/spl mu/m CMOS technology, the COA occupies an area of 0.13 mm/sup 2/. Results from HSPICE simulations and measurements indicate an open-loop gain of 70 dB, a gain-bandwidth product exceeding 200 MHz, and a settling time of 5.1 ns. The amplifier operated under /spl plusmn/3 V DC voltage supplies, and the power dissipation is approximately 4.5 mW.
Automatic optimum-seeking program for digital simulation

A modular computer program has been developed to assist in the task of seeking optimum solutions to problems being simulated on a digital computer. This ANS FORTRAN IV program may be interfaced with any FORTRAN simulation to monitor use of the statistical techniques of Response Surface MethodoZogy (RSM). The automated RSM program, which may be used for con strained or unconstrained optimum-seeking in conjunc tion with deterministic or Monte CarZo simulations, is now available to potential users. This paper out- Zines the theoretical basis of the program, describes its main computational aspects, and discusses its application.
Intelligent alignment of waveguide filters using a machine learning approach

The authors investigate the application of a machine learning system to the tuning of waveguide filters. This system uses techniques from pattern recognition and adaptive signal processing. The manual tuning of the waveguide filters is very time consuming and expensive and a skilled operator is required. Here, the machine learning system is adapted in such a way that it can assist an unskilled operator to perform fast and accurate tuning of these filters. The machine learning approach is based on the manipulation of some raw data to extract a set of salient features that have strong significance in the behavior of the filters. These features are derived visually, by comparing the characteristics of a tuned filter to those of a faulty filter with known levels of maladjustments. >
Supercomputing to go [Information Technology HPC]

SUPERCOMPUTING has long been regarded as a high-end technology for the big users with big applications and big processing needs. However, suppliers of supercomputers - or high-performance computing (HPC) - are now targeting a much wider audience, using a mixture of cheaper and more powerful hardware, sophisticated new system architectures, and software that allows resources to be shared or even rented online. Their overall aim is to reach users who might use one occasionally, but cannot afford a supercomputer of their own.
Low Cost Quantum Realization of Reversible Multiplier Circuit

Irreversible logic circuits dissipate heat for every bit of information that is lost. Information is lost when the input vector can not be uniquely recovered from the output vector. Theoretically reversible logic dissipates zero power since the input vector of reversible circuit can be uniquely recovered from the output vector. Reversible computation has applications in digital signal processing, low power CMOS design, DNA computing and quantum computing. This study presents an overview of the well-known reversible gates and discuss about their quantum implementation. A new PFAG gate and its quantum implementation are presented. Finally, this study proposes a novel low cost quantum realization of reversible multiplier circuit and compares its superiority with the existing counterparts
Fault Diagnosis Based on Neural Network Trained by PSO Algorithm with Information Sharing Strategy for Analog Circuit

To solve some problems in fault diagnosis for analog circuit, a neural network diagnosis method using improved PSO algorithm is proposed. For the appearance on local convergence and prematurity in the application of standard PSO algorithm, an information sharing strategy is introduced, and then improved PSO algorithm is used to train the neural network for overcoming the deficiency of BP algorithm. The simulation indicates that the proposed fault diagnosis method has the fast convergence and accuracy without local convergence and prematurity for analog circuit.
Artificial neural networks: a new approach for treating electrical engineering problems

In this paper we present an approach to introduce electric power engineering students to new topics of research. Publications show how some classes of power systems and electromagnetic problems are solved using artificial neural networks. Some students at the American University of Beirut, willing to further their careers along academic lines or in research and development, are encouraged to take special projects to investigate and laydown the foundation for more serious research. Two such developments are reported in this paper. The first is a neural network-based automatic mesh generation method capable of developing meshes that preserve major properties such as Delauney triangulation and Dirichlet tessellation. In the second application, we demonstrated the possibility of creating a single artificial neural network to estimate the critical clearing time of faults in a realistic power system with differing load levels, fault locations, and network topologies.
Modified modeling of Miller compensation for two-stage operational amplifiers

The mechanism of Miller compensation for two-stage operational amplifiers is modified. The analysis shows that both dominant and nondominant poles are with the first stage of the opamp after compensation; the second stage pole is canceled by a Miller zero. That is, the poles do not split during the frequency compensation, as previously thought. This improved understanding results in a new conceptual compensation circuit, which improves the performance of the two-stage op amps. <<ETX>>
Performance evaluation of robust controllers in earthquake structural dynamics problems with large hysteretic nonlinearities

Hybrid passive-active systems for controlling building seismic response offer the promise of combining the best features of each to achieve superior performance. Analytical investigations of building hybrid control systems designed to combine robust active control systems with passive damping provided by structural hysteresis are described. The passive control forces were introduced through hysteretic interaction between heavy architectural cladding and the supporting structure, while the active control forces were assumed to be generated by an active tendon system. Results presented for a case study structure show that the hybrid control system was more effective in reducing peak drift compared with the cases of either the passive or active systems acting alone. Moreover, it is shown that a practical robust control system of reduced order can be designed with sufficient and robust stability to tolerate as much as a 50% change in the nominal structural stiffness. At the same time, the presence of passive damping yielded reduction in peak force requirements for the active system.
Research on Prediction of Egg Freshness Based on Improved GRNN

The purpose of this paper is to add Particle Swarm Optimization algorithm to Generalized Regression Neural Network for predicting egg Haugh value and evaluating freshness degree of eggs. Firstly process the egg images with light-transmitting were obtained by the computer vision device including denoising, threshold segmentation, conversing HSI Color model and calculating the averages of hue, saturation, and intensity in the center of the image. Secondly analyze GRNN, and then particle swarm algorithm to optimize according to the predicted formula being derived. Thirdly train Improved GRNN and predicate Haugh value by HSI parameter data as the sample. The value of residual errors of Improved GRNN model are 6.38, the correct discerning rate of grading table eggs is 91.2%. It proves better than traditional BP neural network in terms of predicted accuracy and robustness.
An Anthropomorphic Solver for Raven's Progressive Matrices

This report describes a computer program for solving Raven's Progressive Matrices (RPM), a multiple choice test of abstract reasoning introduced by Dr. John C. Raven in 1936. Each RPM problem consists of a grid (or matrix) of 2x2 or 3x3 cells with graphical content, where the cell content in the bottom right corner is omitted; the solver's task is to pick the missing content from a set of eight solution candidates. We argue these problems are not only mathematical, but also psychological in nature. Due to this and other considerations such as algorithmic transparency, the program makes use of a simple cognitive model. The program solves RPM problems in a fully automatic fashion, without taking the solution candidates into account. The input is an RPM problem represented as a vector graphics file; the output is a complete or partial solution for the missing entry, represented in the same format. Internally we use multi-layered structures which enable the perception of the problems' different organizational levels. The program was tested on sections C, D and E of the Standard Progressive Matrices(SPM) and produced correct solutions for 28 of the 36 considered problems
A Image Segmentation Scheme

A BS process involves building a model of the background and extracting regions of the foreground (moving objects) with the assumptions that the camera remains stationary and there exist no movements in the background. Video object extraction is a critical task in multimedia analysis and editing. Normally, the user provides some hints of foreground and background, and then the target object is extracted from the video sequence. In this paper, we propose a object segmentation system that integrates a clustering model with Markov random field-based contour tracking and graph-cut image segmentation. The contour tracking propagates the shape of the target object, whereas the graph-cut refines the shape and improves the accuracy of video segmentation. Experimental results show that our segmentation system is efficient.
An introduction to Error Control Coding

This paper provises an introduction to Error Control Coding (ECC) widely used in digital communication and storage systems. ECC was widely used in digital communication and storage systems. ECC was discovered in 1950 after a great scientist named C.E. Shannon published his paper titled “A Mathematical Theory of Communications” in 1948 in which he theoretically showed we can make an error-free communication system given a suitable design of encoding and decoding processes. However, he did not tell us how to achieve such a great system in practice. This has made many researchers worldwide interested in the paper and trying to get to this goal since. ECC is fundamentally based on the addition of redundancy to the digital data in order to protect the data from noise and interference. The process of determining this redundancy makes a variety of encoding methodologies.
Intranet 2.0 from a Project Management Perspective

Library intranets require flexibility and efficiency and enhance the internal communication and collaborative nature of creating and organizing the institution's information. At the University of Houston Libraries, the focus was on public services, so little attention was given to the intranet—the tool every department relied on for quick access to their content. Text-heavy, static Web pages with poor organization and outdated information made the site unusable. In 2008, the University of Houston Libraries assembled a team to begin the considerable task of redesigning the intranet with Drupal, a popular open source content management system that would allow for interactive information sharing, user-centered design, and new ways of collaboration. This article outlines on the overall project management of the intranet redesign process, including methods used for collecting staff feedback, evaluating existing and potential content, creating a new information architecture focused on departments and committees, establishing new internal communication channels, creating staff enthusiasm and buy-in, and training the entire library staff.
Control of robot arm with virtual environment via the Internet

Many enterprises experience difficulty in training people to work with expensive equipment, which is needed for carrying out profitable work tasks (e.g., production line robots). Similar problems are found when work is of a complex and safety-critical nature (e.g., nuclear environments, explosive placement, surgery). A common problem faced by educational institutions concerns the limited availability of expensive robotics equipment, with which students in the didactic program can work, in order to acquire valuable "hands on" experience. This paper describes a method of education and training involving off-line usage of virtual reality environments for task planning. When tasks are developed to the satisfaction of the trainee, they are exported to remote physical hardware, via the Internet, for real-world execution. Development of the system and the training experiments is discussed, along with some of the issues raised for telerobotics and solutions to the problem of detecting collisions in the virtual world. The approach has been shown to be viable, and increases the education and training possibilities for key workers while maintaining a low cost of ownership. The downtime of mission critical equipment is minimized while the gaining of valuable experience is maximized.
Petascale visual data analysis in a production computing environment

Supporting the visualization and analysis needs of the users of the Department of Energy's premiere high-performance computing centers requires a careful engineering of software and hardware system architectures to provide maximum capability and algorithmic breadth. Data set growth follows an inverse power law that has implications for the platforms that are deployed for analysis and visualization; central storage and coupled analysis platforms are critical for petascale post-production. Software architectures like VisIt – which exploit parallel platforms, as well as provide remote capability, extensibility, and optimization – are fruitful ground for delivering new analysis capabilities for petascale applications. Finally, direct interaction with customers is key to deploying successful results.
Efficient Distributed Video Coding System without Feedback Channel

In distributed video coding (DVC) systems, the complexity of encoders is greatly reduced by removing the motion estimation operations in encoders, since the correlation between frames is utilized in decoders. The transmission of parity bits is requested through the feedback channel, until the related errors are corrected to decode the Wyner-Ziv frames. The requirement to use the feedback channel limits the application of DVC systems. In this paper, we propose an efficient method to remove the feedback channel in DVC systems. First, a simple side information generation method is proposed to calculate the amount of parity bits in the encoder, and it is shown that the proposed method yields good performance with low complexity. Then, by calibrating the theoretical entropy with three parameters, we can calculate the amount of parity bits in the encoder and remove the feedback channel. Moreover, an adaptive method to determine quantization parameters for key frames is proposed. Extensive computer simulations show that the proposed method yields better performance than conventional methods.