Miracle-dz/UniFAR-FTUs huggingface dataset:
Rolling force prediction based on PSO optimized support vector regression

Rolling force prediction is very important in hot bar rolling process. Aiming at the problem of predicting the bar rolling force accurately, an optimal approach of support vector regression based on improved particle swarm optimization (PSO) is proposed. A mathematic model based on the support vector regression optimized by particle swarm optimization is established, and the optimal parameter of which is searched by PSO. The experiment results shows that the proposed prediction model has better prediction results than the support vector regression algorithm and BP-NN algorithm, increasing the average prediction accuracy from 78.5% to 94.1%.
Runtime security composition for sensor networks (SecureSense)

We propose a link layer security framework, named SecureSense that provides energy efficient secure communication in sensor networks. Using runtime security service composition, the proposed SecureSense enables a sensor node to optimally allocate its resources (CPU cycles, memory consumption and RF messages) to appropriate security services depending on observed external environments, internal constraints and application requirements. SecureSense service composition varies with respect to both security provision strength (how easy it can be broken) and security concerns (e.g., integrity vs. confidentiality). Compared to a stationary security model with maximum security provision, our scheme can significantly prolong the network lifetime without degrading the security requirements of the applications.
Investigating Travel Thresholds for Sports and Recreation Activities

Central to the practice of urban planning is the provision of services, which has potential public health and social welfare implications. However, service area analysis, typically, employs arbitrary travel-distance thresholds. Through this study we provide an empirical investigation into the durations and distances that respondents are willing to travel in order to engage in various sports and recreation activities. This research uses time-diary data, augmented with global positioning system information, to investigate individually based and objectively measured travel thresholds (which define travelsheds) for various sports and recreation activities in Halifax, Nova Scotia. Results accord with national time-diary data, and indicate that duration-based and distance-based travelsheds are generally in the order of 15–30 minutes and 4–20 km, respectively. Georeferenced time-diary data provide unique insights into travel thresholds, which may be used to help improve accessibility and thereby increase the frequency and duration of physical activity engagement.
Design Of Compact E-Shape Microstrip Patch Antenna For 4G Communication Systems

In this paper, the design and analysis of E-shape microstrip patch antenna for the 4G mobile communication system is presented. The shape of proposed antenna will provide the wide bandwidth which is required for the operation of 4G mobile communication systems. The operating frequency of antenna is 3GHz, The antenna design consists of a single layer of thickness 1.6 mm with dielectric constant of 4.2 and fabricated on glass epoxy material. The simulation results of proposed E-Shape antenna are done by the help of IE3D Zeland Software (Version 12.0). For the analysis of antenna we used the Cavity Model. This antenna is fed by a co-axial probe feeding. The effects of different antenna parameters like return loss, voltage standing wave ratio (VSWR) are also studied. Keywords— Microstrip patch antenna, Return loss, VSWR.
A sampling technique and its CMOS implementation with 1 Gb/s bandwidth and 25 ps resolution

This paper presents a technique and circuitry for high-resolution sampling of a digital waveform. Very fine sampling resolution is achieved by simultaneously propagating both data and clock signals through delay elements in such a way that resolution is controlled by the difference in the delay of clock and data signals. Delay units were designed using biased CMOS and differential CMOS inverters. A sampler circuit with 64 stages has been fabricated in 1.2 /spl mu/m CMOS technology, and test results show a bandwidth of up to 1 Gb/s for the input data and a sampling resolution externally adjustable between 25 and 250 ps. The fabricated circuit has shown sampling stability, monotonicity in sampling, and uniformity in sampling resolution. >
Agent-Based VPN Architecture: A Framework and the Optimal User Connectivity Problem (Static Case)

The dynamic nature of today's world, changing environment and diversity in users' needs creates a necessity to develop a self-organizing system. The system should be capable of re-structuring and reorganizing itself based on user's requirements without being guided by any outside source, so that it is managed dynamically by interaction among its components and re-configuring itself by taking feedback from previous actions. This paper proposes a self-organizing Web system (SOWS) - a Web based system that is capable of re-organizing based on the access patterns of a group of visitors. It also reduces the average time in surfing the Web site and hence the operating cost. The system is inspired by ant colony optimization (ACO), a technique of swarm intelligence (SI) whereby ant colonies interact with the environment indirectly and accomplish their task. The prototype system we have developed displays results keeping in mind the navigation pattern of user, his interests and also information that the system considers useful in general with respect to all the visitors to the Web site.
A charge-injection method for Gilbert cell biasing

A CMOS Gilbert cell mixer biasing topology is presented. The new biasing technique offers several key advantages over the traditional biasing arrangement. First, the new topology allows the designer to easily adjust the bias current present in the Gilbert cell input transistors, while maintaining bias currents in other portions of the circuit. Second, the mixer linearity can be improved using this biasing method by accurate adjustment of the input MOSFET operating point. Third, the biasing method reduces the "voltage headroom" difficulties inherent to the Gilbert cell, which uses a stacked arrangement of transistors. The importance of these adjustments with regard to the mixer conversion gain and IP3 is examined.
Statistical learning for decision making : interpretability, uncertainty, and inference

Data and predictive modeling are an increasingly important part of decision making. Here we present advances in several areas of statistical learning that are important for gaining insight from large amounts of data, and ultimately using predictive models to make better decisions. The first part of the thesis develops methods and theory for constructing interpretable models from association rules. Interpretability is important for decision makers to understand why a prediction is made. First we show how linear mixtures of rules can be used to make sequential predictions. Then we develop Bayesian Rule Lists, a method for learning small, ordered lists of rules. We apply Bayesian Rule Lists to a large database of patient medical histories and produce a simple, interpretable model that solves an important problem in healthcare, with little sacrifice to accuracy. Finally, we prove a uniform generalization bound for decision lists. In the second part of the thesis we focus on decision making from sales transaction data. We develop models and inference procedures for using transaction data to estimate quantities such as willingness-to-pay and lost sales due to stock unavailability. We develop a copula estimation procedure for making optimal bundle pricing decisions. We then develop a Bayesian hierarchical model for inferring demand and substitution behaviors from transaction data with stockouts. We show how posterior sampling can be used to directly incorporate model uncertainty into the decisions that will be made using the model. In the third part of the thesis we propose a method for aggregating relevant information from across the Internet to facilitate informed decision making. Our contributions here include an important theoretical result for Bayesian Sets, a popular method for identifying data that are similar to seed examples. We provide a generalization bound that holds for any data distribution, and moreover is independent of the dimensionality of the feature space. This result justifies the use of Bayesian Sets on high-dimensional problems, and also explains its good performance in settings where its underlying independence assumption does not hold.
Research on the Sharing E-learning Based on SOA and Semantic Web Architecture

The major problem existed in current E-Learning is the difficulty of resources sharing and searching, which is mostly caused by the weak-semantic learning resources and the poor sharing mechanism. By applying ontology and Web services technology to E-Learning, this paper proposes a sharing individualized E-Learning architecture, where the meaning of learning resources with well-defined is presented by ontology and information exchanging is supported by Web services based on SOA.
9.1.2 Overcoming Engineering Challenges of Providing an Effective User Interface to a Large Scale Distributed Synthetic Environment on the US Teragrid: A Systems Engineering Success Story

Over recent years large scale distributed synthetic environment enterprises have been evolving in a diverse range of scientific and engineering fields. These computer modelling and simulation systems are increasing in scale and dimension in order to allow scientists and engineers to explore the attributes and emergent properties of a given system design. Within the field of computational science the grid has been developed to facilitate very large scale collaborative simulation enterprises. The grid is similar to DIS/HLA in that it supports interconnectivity but differs in the sense that it supports intercommunication of large super computing resources. An important factor in the rapid adoption of the grid has been its role in enabling access to significant supercomputing resources not usually available at a single institution. However, the major challenge for the grid has been the lack of an effective and ubiquitous interface to the huge computational resource (which can comprise over 6000 CPUs distributed across the globe) at any time and from any location. This paper describes a unique user interface built on systems engineering principles and practices to solve the problem of delivering real‐time interaction (from lightweight computing devices such as PDAs to high end computing platforms) with simulations delivering high resolution 3D images. The application of our work is likely to have far reaching benefits for many sectors including: aerospace, medical informatics, engineering design, distributed simulation and modelling.
A low power symmetrically pulsed dual edge–triggered flip–flop

A dual-edge triggered flip-flop suitable for low power applications is presented. HSPICE simulations conducted in 0.11u CMOS technology using 1.2V power supply voltage show that the proposed design is comparable in energy-delay product to high-performance single-edge triggered flip-flops while maintaining lower clock power. The Energy-Delay Product improvement of 14% and 50% smaller clock load compared to previously best published dual edge-triggered storage elements are demonstrated.
Speech-transmission performance of p.c.m. systems

A method is described for calculating a quantity representative of the transmission performance of a p.c. m. system. The value obtained depends upon (a) the number of quantised output voltage states, (b) the companding law and (c) the volume of the input speech signal relative to the overload point of the system.
Clarify: Software for Interpreting and Presenting Statistical Results

Clarify is a program that uses Monte Carlo simulation to convert the raw output of statistical procedures into results that are of direct interest to researchers, without changing statistical assumptions or requiring new statistical models. The program, designed for use with the Stata statistics package, offers a convenient way to implement the techniques described in: Gary King, Michael Tomz, and Jason Wittenberg (2000). "Making the Most of Statistical Analyses: Improving Interpretation and Presentation. " American Journal of Political Science 44, no. 2 (April 2000): 347-61. We recommend that you read this article before using the software. Clarify simulates quantities of interest for the most commonly used statistical models, including linear regression, binary logit, binary probit, ordered logit, ordered probit, multinomial logit, Poisson regression, negative binomial regression, weibull regression, seemingly unrelated regression equations, and the additive logistic normal model for compositional data. Clarify Version 2.1 is forthcoming (2003) in Journal of Statistical Software.
Audio information browsing with the Sonic Browser

Collections of sound and music of increasing size and diversity are used both by general personal computer users and multimedia designers. Browsing audio collections poses several challenges to the design of effective user interfaces. We present the Sonic Browser, an application for browsing sound collections on personal computers. We describe how a novel user interface with multiple views can be applied to the browsing of audio collections. Finally, we conclude with current trends of our approach for further improvement of the system.
Design and performance of a code for the multiple access channel with feedback

In Iacobucci and DiBenedetto (1997), an extension of the deterministic feedback code proposed by Ozarow (1984) for the two-user Gaussian multiple access channel (MAC) was presented; Ozarow's code was extended to the case of U-users (U/spl ges/2) in the specific case in which all users have equal power. In the present paper, a performance analysis of the proposed code compared to BCH codes is reported. It is shown that a large gain over BCH codes with the same transmission rate and same redundancy is obtained with the new code, if redundancy exceeds a critical value which was determined by computer simulation.
The study of pre-processing method of brain vessel segmentation based on parameterized statistical model

For the 3D brain MRI parameterized statistical model segmentation method, the pre-processing method of brain image is brought forward for the model. First the DWM(directional weighted median) filter and Gaussian template are used to de-noise the brain MRI image. Then the Laplacian operator is used to sharpen the image, and the Robert operator is used to realize the edge detection, which can improve the measurement accuracy. And the MIP (maximum intensity projection) algorithm is applied to extract the largest connected component. The image of only brain vessel and part brain tissue can be obtained and the influence of background and no brain vessels like tissue to the images is eliminate. After pre-processing stage, the brain image is analyzed as the input to the parameterized statistical model. The small branches of the brain vessel can be segmented.
Short-term load forecasting for special days in anomalous load conditions using neural networks and fuzzy inference method

Conventional artificial neural network (ANN) based short-term load forecasting techniques have limitations in their use on holidays. This is due to dissimilar load behaviors of holidays compared with those of ordinary weekdays during the year and to insufficiency of training patterns. The purpose of this paper is to propose a new short-term load forecasting method for special days in anomalous load conditions. These days include public holidays, consecutive holidays, and days preceding and following holidays. The proposed method uses a hybrid approach of ANN based technique and fuzzy inference method to forecast the hourly loads of special days. In this method, special days are classified into five different day-types. Five ANN models for each day-type are used to forecast the scaled load curves of special days, and two fuzzy inference models are used to forecast the maximum and the minimum loads of special days. Finally, the results of the ANN and the fuzzy inference models are combined to forecast the 24 hourly loads of special days. The proposed method was tested with actual load data of special days for the years of 1996-1997. The test results showed very accurate forecasting with the average percentage relative error of 1.78%.
Self-tuning control of induction motor drive using neural network identifier

This study presents a new self-tuning PI speed controller with load torque observer and feedforward compensation based on neural network identification for an induction motor. A two-layer neural estimator is also used to provide a real-time adaptive estimation of the unknown motor dynamics. The widely used projection algorithm is used as the learning algorithm for this network, to minimize the difference between the motor's actual response and that predicted by the neural estimator. The proposed neural estimator uses this learning to adjust PI speed controller with a load torque observer to generate the control signal online, thereby bringing the motor output to a desired reference trajectory. The theoretical analysis, simulation and experimental results demonstrate the proposed scheme's effectiveness.
Power and sample size estimation in high dimensional biology

Genomic scientists often test thousands of hypotheses in a single experiment. One example is a microarray experiment that seeks to determine differential gene expression among experimental groups. Planning such experiments involves a determination of sample size that will allow meaningful interpretations. Traditional power analysis methods may not be well suited to this task when thousands of hypotheses are tested in a discovery oriented basic research. We introduce the concept of expected discovery rate (EDR) and an approach that combines parametric mixture modelling with parametric bootstrapping to estimate the sample size needed for a desired accuracy of results. While the examples included are derived from microarray studies, the methods, herein, are ‘extraparadigmatic’ in the approach to study design and are applicable to most high dimensional biological situations. Pilot data from three different microarray experiments are used to extrapolate EDR as well as the related false discovery rate at different sample sizes and thresholds.
Effectiveness of Testcase Prioritization using APFD Metric: Survey

Test case prioritization techniques involve scheduling over test cases in an order that improves the performance of regression testing. It is inefficient to re execute every test cases for every program function if once change occurs. Test case prioritization is to be scheduled based on higher priority than lower priority to meet some performance goal (i.e. increase in the effectiveness of testing). The performance goals are 1. rate of fault detection (how quickly faults are detected) 2. Rate of code coverage at fastest rate,3) Rate of increase of confidence in reliability during the testing process to improve the software quality. The problem of test case selection can be solved by prioritizing the test case. The main aim of my paper is to determine the effectiveness of prioritized and nonprioritized test case with the help of APFD(Average Percentage Faults Detected).
Approximate Discriminative Training of Graphical Models

Over the past decade, graphical models have emerged as a workhorse for statistical processing of data in disciplines as diverse as computer vision, natural language processing, digital communications and computational biology. Problems from all of these disciplines have in common that the objects of interest possess rich internal structure. Graphical models help us make this structure explicit and exploit it during statistical inference. The proliferation of freely available data has lead to reinforced interest in approaches that learn from existing examples how to infer the properties of previously unseen instances. Graphical models provide a sound formal framework towards this end—the discriminative learning approach seeks to estimate the parameters of a graphical model such that its predictions are consistent with the observed data. While conceptually simple, the prevalent approaches suffer from computational intractability if the underlying graphical model contains cycles. Unfortunately, this is the case in many applications of practical interest. During recent years, the understanding of approximate inference in graphical models has improved dramatically. Yet, in a learning scenario, intractability is still often dealt with in an ad-hoc or heuristic manner. This thesis aims to aid the goal of bridging this gap. The first approach we present draws heavily on tools from convex optimization. Based on a variational characterization of the inference problems in graphical models, we present a whole catalogue of equivalent formulations of discriminative learning, each exposing different merits. The idea underlying this approach is to relax the inference subproblems, that is, to optimize over a simpler constraint set. We introduce new algorithms that can be used to solve these relaxed problems more efficiently than previously possible. Alternatively, we demonstrate how the variational viewpoint allows to formulate discriminative learning as a single unconstrained convex optimization problem that can be solved using off-the-shelf solvers. Our second approach is based on a Gaussian model and allows for treatment of both discrete and continuous learning tasks. Discrete variables can be handled either via a high-dimensional encoding, or by optimizing a specific loss function. While the use of a Gaussian predictive density may seem overly restrictive at first, we demonstrate how the expressiveness of the model can be increased significantly via non-parametric conditioning. We present applications from computer vision and natural language processing that demonstrate the wide applicability of our algorithms and the importance of employing principled approximations. A highlight among our results is that we obtain the best published numbers for natural image denoising and related image restoration problems.
A Discrete Approach for Pairwise Matching of Archaeological Fragments

This article addresses the problem of automatic reconstruction of ancient artifacts from archaeological fragments. The technique described here focuses on pairwise matching of flat fragments (typically fresco fragments), and it is intended to be the core of a larger system for artifact reconstruction. Global registration techniques are challenging due to the combinatory explosion that happens in the solution space: the goal is to find the best alignment among all possible ones without an initialization. This fact defines the duality between performance and correction that we face in this work. The proposed technique defines a cost function to evaluate the quality of an alignment based on a discrete sampling of the fragments that ensures data alignment. Starting from an exhaustive search strategy, the technique progressively incorporates new features that lead to a hierarchical search strategy. Convergence and correction of the resulting technique are ensured using an optimistic cost function. Internal search calculations are optimized so the only operations performed are additions, subtractions, and comparisons over aligned data. All heavy geometric operations are carried out by the GPU on a preprocessing stage that only happens once per fragment.