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So-called spatial web queries retrieve web content representing points of interest, such that the points of interest have descriptions that are relevant to query keywords and are located close to a query location. Two broad categories of such queries exist. The first encompasses queries that retrieve single spatial web objects that each satisfy the query arguments. Most proposals belong to this category. The second category, to which this paper's proposal belongs, encompasses queries that support exploratory user behavior and retrieve sets of objects that represent regions of space that may be of interest to the user. Specifically, the paper proposes a new type of query, the top- $k$ k spatial textual cluster retrieval ($k$ k -STC) query that returns the top- $k$ k clusters that (i) are located close to a query location, (ii) contain objects that are relevant with regard to given query keywords, and (iii) have an object density that exceeds a given threshold. To compute this query, we propose a DBSCAN-based approach and an OPTICS-based approach that rely on on-line density-based clustering and that exploit early stop conditions. Empirical studies on real data sets offer evidence that the paper's proposals can find good quality clusters and are capable of excellent performance. [ABSTRACT FROM AUTHOR]

We consider the fundamental problem of matching a template to a signal. We do so by M-estimation, which encompasses procedures that are robust to gross errors (i.e., outliers). Using standard results from empirical process theory, we derive the convergence rate and the asymptotic distribution of the M-estimator under relatively mild assumptions. We also discuss the optimality of the estimator, both in finite samples in the minimax sense and in the large-sample limit in terms of local minimaxity and relative efficiency. Although most of the paper is dedicated to the study of the basic shift model in the context of a random design, we consider many extensions towards the end of the paper, including more flexible templates, fixed designs, the agnostic setting, and more. [ABSTRACT FROM AUTHOR]

A very important function of an issue tracking system is to assign labels to issue reports, such as bug, feature, enhancement, etc., in order to categorize issues to facilitate various development activities. In practice, it is very common that an issue has multiple labels. However, current works are mainly based on single-label prediction, which are not suitable for just-in-time multi-labeling services, due to the low efficiency. Therefore, in this paper, we propose MULA, a just-in-time MUlti-LAbeling system, which learns and automatically assigns multiple labels to issue reports. We have built a dataset with 81,601 entries and 11 labels, as the first benchmark for this task, and implemented a GitHub app. To the best of our knowledge, this is the first work and tool for online multi-labeling GitHub issues based on their categories. We conduct a comprehensive empirical study, including comparisons with five commonly adopted labeling models that show the superiority of MULA, as well as an evaluation that shows high consistency between MULA’s suggestions and developers’ opinions. [ABSTRACT FROM AUTHOR]

Asynchronous event-driven server architecture has been considered as a superior alternative to the thread-based counterpart due to reduced multithreading overhead. In this paper, we conduct empirical research on the efficiency of asynchronous Internet servers, showing that an asynchronous server may perform significantly worse than a thread-based one due to two design deficiencies. The first one is the widely adopted one-event-one-handler event processing model in current asynchronous Internet servers, which could generate frequent unnecessary context switches between event handlers, leading to significant CPU overhead of the server. The second one is a write-spin problem (i.e., repeatedly making unnecessary I/O system calls) in asynchronous servers due to some specific runtime workload and network conditions (e.g., large response size and non-trivial network latency). To address these two design deficiencies, we present a hybrid solution by exploiting the merits of different asynchronous architectures so that the server is able to adapt to dynamic runtime workload and network conditions in the cloud. Concretely, our hybrid solution applies a lightweight runtime request checking and seeks for the most efficient path to process each request from clients. Our results show that the hybrid solution can achieve from 10 to 90 percent higher throughput than all the other types of servers under the various realistic workload and network conditions in the cloud. [ABSTRACT FROM AUTHOR]

Remote laboratories give students the opportunity of experimenting in STEM by using the Internet to control and measure an experimental setting. Remote laboratories are increasingly used in the classroom to complement, or substitute for, hands-on laboratories, so it is important to know its learning value. While many authors approach this question through qualitative analyses, this paper reports a replicated quantitative study that evaluates the teaching performance of one of these resources, the virtual instrument systems in reality (VISIR) remote laboratory. VISIR, described here, is the most popular remote laboratory for basic analog electronics. This paper hypothesizes that use of a remote laboratory has a positive effect on students’ learning process. This report analyzes the effect of the use of VISIR in five different groups of students from two different academic years (2013–2014 and 2014–2015), with three teachers and at two educational levels. The empirical experience focuses on Ohm’s Law. The results obtained are reported using a pretest and post-test design. The tests were carefully designed and analyzed, and their reliability and validity were assessed. The analysis of knowledge test question results shows that the post-test scores are higher that the pretest. The difference is significant according to Wilcoxon test ( p <0.001 ), and produces a Cohen effect size of 1.0. The VISIR remote laboratory’s positive effect on students’ learning processes indicates that remote laboratories can produce a positive effect in students’ learning if an appropriate activity is used. [ABSTRACT FROM PUBLISHER]

Nitrogen (N) has been linked to different ecosystem processes, and retrieving this important foliar biochemical constituent from remote sensing observations is of widespread interest. Since N is not explicitly represented in physically based radiative transfer models, empirical methods have been used as an alternative. The spectral bands selected during the calibration of empirical methods have been interpreted in the context of light-N interactions and, consequently, ecosystem function. The low amount of leaves on shrubs and their sparse distribution in drylands create an environment, in which the canopy structure and the bright background soil play an important role in the canopy total radiation budget. In this paper, we examine the assumption that removing the impact of canopy structure and soil will result in improved N retrieval using the empirical methods. We report the inconsistencies in the selection of spectral bands among the empirical approaches. Moreover, these methods are sensitive to the scale of the study and band transformations. Using the generalized theory of canopy spectral invariants, we found that at the canopy scale, a combination of canopy structure and soil dominates the total canopy radiation. At the plot scale, soil contributes up to 95% of the total reflectance. Correction for these two confounding factors leads to no correlation between N and vegetation reflectance at both scales. We conclude that while cross-validated predictive models may be statistically achievable, caution should be taken when interpreting their results in the context of N-light interactions and ecosystem function. Our approach can be extended to all terrestrial ecosystems with multiple layers of canopy and understory. [ABSTRACT FROM AUTHOR]

Data consolidation is a challenging issue in data integration. The usefulness of data increases when it is linked and fused with other data from numerous (Web) sources. The promise of Big Data hinges upon addressing several big data integration challenges, such as record linkage at scale, real-time data fusion, and integrating Deep Web. Although much work has been conducted on these problems, there is limited work on creating a uniform, standard record from a group of records corresponding to the same real-world entity. We refer to this task as record normalization. Such a record representation, coined normalized record, is important for both front-end and back-end applications. In this paper, we formalize the record normalization problem, present in-depth analysis of normalization granularity levels (e.g., record, field, and value-component) and of normalization forms (e.g., typical versus complete). We propose a comprehensive framework for computing the normalized record. The proposed framework includes a suit of record normalization methods, from naive ones, which use only the information gathered from records themselves, to complex strategies, which globally mine a group of duplicate records before selecting a value for an attribute of a normalized record. We conducted extensive empirical studies with all the proposed methods. We indicate the weaknesses and strengths of each of them and recommend the ones to be used in practice. [ABSTRACT FROM AUTHOR]

Current regulations leave a few television (TV) white spaces in populated urban areas where spectrum shortage is mostly experienced. As TV set feedback becomes essential in the next generation terrestrial TV standard, an opportunistic TV spectrum sharing based on TV receiver activity information and transmit power control is proposed to exploit the underutilized active TV channels. Based on investigation of the spatial–spectral–temporal characteristics of TV receiver activities, analytical models are developed to capture the spatio-temporal distributions of available spectrum and corresponding capacity. The influence of multiple factors, such as feedback delay, spectrum handover overhead, ranking order, and distribution of TV channel popularity are discussed and modeled. The proposed power control mechanism is verified through experiments at representative campus and residential environments. Empirical data-based simulations and geographic analyses are conducted to evaluate the developed models and further profile the spectrum opportunities within a cell, across New York city (NYC) and other 273 cities in the United States. In NYC, the proposed solution provides a $3.8$ – $11.7$ -fold increase of average spectrum availability, and $2.5$ – $6.6$ -fold increase of capacity from current regulations. By investigating the feasibility and prospects of this approach, this paper intends to motivate further discussions in policy, business, and privacy aspects to reach its significant potential. [ABSTRACT FROM AUTHOR]

Representation-based classification (RC) methods such as sparse RC (SRC) have attracted great interest in pattern recognition recently. Despite their empirical success, few theoretical results are reported to justify their effectiveness. In this paper, we establish the theoretical guarantees for a general unified framework termed as atomic representation-based classification (ARC), which includes most RC methods as special cases. We introduce a new condition called atomic classification condition (ACC), which reveals important geometric insights for the theory of ARC. We show that under such condition ARC is provably effective in correctly recognizing any new test sample, even corrupted with noise. Our theoretical analysis significantly broadens the range of conditions under which RC methods succeed for classification in the following two aspects: (1) prior theoretical advances of RC are mainly concerned with the single SRC method while our theory can apply to the general unified ARC framework, including SRC and many other RC methods; and (2) previous works are confined to the analysis of noiseless test data while we provide theoretical guarantees for ARC using both noiseless and noisy test data. Numerical results are provided to validate and complement our theoretical analysis of ARC and its important special cases for both noiseless and noisy test data. [ABSTRACT FROM AUTHOR]

This paper proposes a wide-range control scheme of correlated color temperature (CCT) and illuminance of a light-emitting diode (LED) lamp, using two-component color blending. A simple control algorithm is implemented by using Grassmann's law of color mixing and McCamy's formula of CCT. Light from a white LED source is blended with that from a blue LED or a red LED to get, respectively, a CCT value higher or lower than that of the white CCT. The prototype LED lighting system can independently produce a variable CCT ranging from 2500 to 12500 K and a variable illuminance ranging from 5 to 120 lx. The algorithm is experimentally validated by hardware implementation. An empirical model to estimate the luminous parameters of the individual LED is also implemented. Experimental results show that although the system is an open-loop one, it is quite accurate with respect to set point CCT and illuminance. Despite acceptable performance in respect of these two quantities, a large deviation from the color of an ideal black body radiator is noticeable in a part of the CCT range, and a remedy of the problem has been suggested. [ABSTRACT FROM AUTHOR]

This paper considers the problem of sequential empirical coordination, where the objective is to achieve a given value of the expected uniform deviation between the state-action empirical averages and the statistical expectations under a given strategic probability measure with respect to a given universal Glivenko–Cantelli class of test functions. A communication constraint is imposed on the Shannon entropy of the resulting action sequence. It is shown that the fundamental limit on the output entropy is given by the minimum of the mutual information between the state and the action processes under all strategic measures that have the same marginal state process as the target measure and approximate the target measure to desired accuracy with respect to the underlying Glivenko–Cantelli seminorm. The fundamental limit is shown to be asymptotically achievable by the tree-structured codes. [ABSTRACT FROM AUTHOR]

Production Lead Time \mbi (LT) is the average time a part spends in the system, being processed or waiting for processing. In systems with unlimited buffers, \mbi LT may be orders of magnitude larger than the total processing time, leading to serious economic and quality problems. At present, no systematic analytical methods for evaluation, analysis, and control of \mbi LT in systems with machines having up- and downtime characterized by continuous random variables are available. This paper is intended to develop such methods. Specifically, we address synchronous serial lines with exponential machines and derive formulas for \mbi LT as a function of machine parameters and raw material release rate. Using these formulas, we develop methods for open- and closed-loop raw material release, which result in the desired \mbi LT. For asynchronous exponential lines, we provide an upper bound on LT. For non-exponential lines (e.g., Weibull, gamma, and log-normal), we offer an empirical formula for \mbi LT as an affine function of the coefficient of variation. The results reported in this paper enable a new paradigm for production systems management, namely: manage a production system so that the desired \mbi LT is ensured, while the throughput is maximized. [ABSTRACT FROM AUTHOR]

The autonomous vehicle, as an emerging and rapidly growing field, has received extensive attention for its futuristic driving experiences. Although the fast developing depth sensors and machine learning methods have given a huge boost to self-driving research, existing autonomous driving vehicles do meet with several avoidable accidents during their road testings. The major cause is the misunderstanding between self-driving systems and human drivers. To solve this problem, we propose a humanlike driving system in this paper to give autonomous vehicles the ability to make decisions like a human. In our method, a convolutional neural network model is used to detect, recognize, and abstract the information in the input road scene, which is captured by the on-board sensors. And then a decision-making system calculates the specific commands to control the vehicles based on the abstractions. The biggest advantage of our work is that we implement a decision-making system which can well adapt to real-life road conditions, in which a massive number of human drivers exist. In addition, we build our perception system with only the depth information, rather than the unstable RGB data. The experimental results give a good demonstration of the efficiency and robustness of the proposed method. [ABSTRACT FROM AUTHOR]

We developed a new empirical model for corotating plasma in the Jovian magnetosphere. The model, named the corotation plasma environment model version 2 (CPEMv2), considers the charge density, velocity vector, and ion temperature based on Galileo/plasma system (PLS) ion data. In addition, we develop hot electron temperature and density models based on Galileo/PLS electron data. All of the models provide respective quantities in the magnetic equator plane of 9–30 $\mathrm {R}_{J}$ , while the charge density model can be extended to 3-D space. A characteristic feature of the CPEM is its support of the percentile as a user input. This feature enables us to model extreme conditions in addition to normal states. In this paper, we review the foundations of the new empirical model, present a general derivation algorithm, and offer a detailed formulation of each parameter of the CPEMv2. As all CPEM parameters are of the analytical form, their implementation is straightforward, and execution involves the use of a small number of computational resources. The CPEM is flexible; for example, it can be extended, as new data (from observations or simulation results) become available. The CPEM can be used for the mission operation of the European Space Agency’s mission to Jupiter, JUpiter ICy moons Explorer (JUICE), and for future data analyses. [ABSTRACT FROM AUTHOR]

In this paper, we propose a method for simulating the response of SRAM cells to particles through basic electrical models of the composing transistors without empirical parameters to be set. This simplified approach is motivated by the fact that the SPICE parameters are often not known by the end-user. In this approach, the transient currents induced by primary or secondary ions are calculated in the framework of the diffusion/collection model. Then, they are used to calculate the voltage variation at each node of the cell in which the current of each of the four latch transistors is accounted for and is given by a standard first-order model in which three major regions of operation are modeled (subthreshold, triode and saturation). Presented results are in good agreement with ion/neutron/proton experiment for 150 nm, 90 nm and 65 nm SRAMs. Even if the proposed approach would need further validation, this paper is a first step to give guideline on how calculating SEU cross section based on not detailed technological parameters instead of empirical ones. The strong point of our proposal is that it dynamically accounts for the behavior of the four transistors of refreshing loop in the cell. [ABSTRACT FROM AUTHOR]

Context: Several text books and papers published between 2000 and 2002 have attempted to introduce experimental design and statistical methods to software engineers undertaking empirical studies. Objective: This paper investigates whether there has been an increase in the quality of human-centric experimental and quasi-experimental journal papers over the time period 1993 to 2010. Method: Seventy experimental and quasi-experimental papers published in four general software engineering journals in the years 1992-2002 and 2006-2010 were each assessed for quality by three empirical software engineering researchers using two quality assessment methods (a questionnaire-based method and a subjective overall assessment). Regression analysis was used to assess the relationship between paper quality and the year of publication, publication date group (before 2003 and after 2005), source journal, average coauthor experience, citation of statistical text books and papers, and paper length. The results were validated both by removing papers for which the quality score appeared unreliable and using an alternative quality measure. Results: Paper quality was significantly associated with year, citing general statistical texts, and paper length (p < 0.05). Paper length did not reach significance when quality was measured using an overall subjective assessment. Conclusions: The quality of experimental and quasi-experimental software engineering papers appears to have improved gradually since 1993. [ABSTRACT FROM PUBLISHER]

Planar electromagnetic interference (EMI) filter has good application prospect in power electronics system with the advantages of small volume and good high-frequency performance. However, a complete theoretical system for the design of planar EMI filter has not been proposed yet. In this paper, an integrated structure formed by an annular “inductor–capacitor” (LC unit) is proposed first, which can be used as the basic cell of the planar EMI filter. A general arc transmission theory is also proposed, based on which the distributed-parameter model of the LC unit is established. Then, this LC unit can be equivalent to the two-port network by employing the model transformation method. Besides, the turn-to-turn electromagnetic parameter is extracted using finite element method, which provides the foundation for the research of LC unit and electromagnetic performance of the filter. The design method and the implementation of the whole planar EMI filter are elaborated in details, and the empirical initial values of the related structural sizes are also presented in this paper. In order to verify the proposed design theory, several EMI filters formed by LC units are designed according to the specifications. Common-mode insertion losses of the EMI filter are calculated and measured, respectively. The results verify the feasibility of the design theory. [ABSTRACT FROM AUTHOR]

This paper describes an empirical study that assesses the economic value of various forms of organizational learning that are common in semiconductor manufacturing. This study considers three business environments: very large-scale integrated circuits, commodity components, and specialty parts with high value added. This study finds that the economic value of organizational learning varies over time, the relative economic value of different learning practices varies over time, the external economic environment strongly influences the economic value of different learning practices, the economic value of learning can be positive or negative, and the synergy between different kinds of learning can be positive or negative. The primary contribution of this paper is an empirically grounded model of organizational learning in the semiconductor manufacturing industry, which potentially enhances the profitability of semiconductor manufacturing ventures. The model helps practicing managers make investment decisions through scenario planning and identifies learning strategies that are tailored to their particular economic environment. This paper also provides insight into the structure of technological knowledge in semiconductor manufacturing, which has historically been treated as a black box. [ABSTRACT FROM PUBLISHER]

This paper considers the problem of recognizing activities as they occur in surveillance video. Activities are high-level nonatomic semantic concepts which may have complex temporal structure. Activities are not easily identifiable using image features, but rather by the recognition of their composing events. Unfortunately, these composing events may only be observed up to a particular certainty. This paper describes particle filter Petri Net (PFPN), an activity recognition process that combines uncertain event observations to determine the likelihood that a particular activity is taking place in a video sequence. Our paper is based on previous study in which activities are specified as Petri Nets. The stochastic PFPN framework proposed in this paper improves over existing deterministic approaches to activity recognition by enabling the certainty reasoning required for coping with inherent ambiguity in both low-level video processing and activity definition. Furthermore, the PFPN approach reduces the dependence on a duration model and enables the creation of holistic activity models. Often when activity recognition frameworks are proposed they are strongly paired with a particular methodology for low-level video processing and event recognition. Each proposed approach is then applied to a nonstandard dataset. In our experiments, we provide an empirical comparison of our approach with leading activity recognition approaches across several datasets, using a constant event recognition as input. Our results illustrate the tradeoff between deterministic and stochastic activity recognition approaches. Furthermore, our experiments suggest that the holistic PFPN approach is more robust for activity recognition in the surveillance video domain than competing approaches. [ABSTRACT FROM AUTHOR]

Though very important in software engineering, linking artifacts of the same type (clone detection) or different types (traceability recovery) is extremely tedious, error-prone, and effort-intensive. Past research focused on supporting analysts with techniques based on Natural Language Processing (NLP) to identify candidate links. Because many NLP techniques exist and their performance varies according to context, it is crucial to define and use reliable evaluation procedures. The aim of this paper is to propose a set of seven principles for evaluating the performance of NLP techniques in identifying equivalent requirements. In this paper, we conjecture, and verify, that NLP techniques perform on a given dataset according to both ability and the odds of identifying equivalent requirements correctly. For instance, when the odds of identifying equivalent requirements are very high, then it is reasonable to expect that NLP techniques will result in good performance. Our key idea is to measure this random factor of the specific dataset(s) in use and then adjust the observed performance accordingly. To support the application of the principles we report their practical application to a case study that evaluates the performance of a large number of NLP techniques for identifying equivalent requirements in the context of an Italian company in the defense and aerospace domain. The current application context is the evaluation of NLP techniques to identify equivalent requirements. However, most of the proposed principles seem applicable to evaluating any estimation technique aimed at supporting a binary decision (e.g., equivalent/nonequivalent), with the estimate in the range [0,1] (e.g., the similarity provided by the NLP), when the dataset(s) is used as a benchmark (i.e., testbed), independently of the type of estimator (i.e., requirements text) and of the estimation method (e.g., NLP). [ABSTRACT FROM AUTHOR]

For the semiconductor packaging industry, the issue of wire sag has not been touched upon because only one single layer of wire bond is constructed along the perimeter of the chip system. Over-sag deflections may not cause any problems. However, driven by the demands for smaller and faster advanced microelectronic devices, modern 3-D and multichip modules have become the solution of choice in delivering higher integration and more multifunctional products to meet consumer needs. For 3-D and multichip packaging, excessive wire sag can lead to wires touching against the lower layer, causing a short-circuit and failure of chip functionality. Therefore, gaining knowledge of wire bond sag theory becomes extremely important for advanced packaging. We propose an empirical model to predict the sag stiffness of a wire bond. The sag stiffness of a wire bond is defined to represent its resistance to the sag drag force occurring during the encapsulation process by the molding flow. A higher sag stiffness of a wire bond gives the premise of less sag deflection, thereby avoiding short-circuiting and/or double wire crosstalk due to the closeness of the top-layer to the bottom-layer wire bonds. The theory of sag regarding semiconductor wire bonding has not been documented, to our knowledge, in the literature. Therefore, to deal with this issue, this paper will primarily focus on the theoretical and numerical analysis of sag deflection of a wire bond. A set of sag experiments is conducted to verify the validity of the sag model hypothesis outlined in the earlier paper. Subsequently, an empirical sag model could be put forward to predict the sag stiffness of a wire bond if the corresponding bond spans, bond heights, and material properties are known. The engineered design of a wire bond can be improved through the use of this sag equation. By making use of the proposed sag model, one can attain not only a first-order estimation of the sag deflection but also omit the tedious procedures of numerical analysis. [ABSTRACT FROM PUBLISHER]

In a decentralized and self-configuring network, the communication devices are considered as autonomous decision makers that sense their environment and that implement optimal transmission schemes. It is essential that these autonomous devices cooperate and coordinate their actions, to ensure the reliability of the transmissions and the stability of the network. We study a point-to-point scenario in which the encoder and the decoder implement decentralized policies that are coordinated. The coordination is measured in terms of empirical frequency of symbols of source and channel. The encoder and the decoder perform a coding scheme, such that the empirical distribution of the symbols is close to a target joint probability distribution. We characterize the set of achievable target probability distributions for a point-to-point source-channel model, in which the encoder is non-causal and the decoder is strictly causal, i.e., it returns an action based on the observation of the past channel outputs. The objectives of the encoder and of the decoder are captured by some utility function, evaluated with respect to the set of achievable target probability distributions. In this paper, we investigate the maximization problem of a utility function that is common to both encoder and decoder. We show that the compression and the transmission of information are particular cases of the empirical coordination. [ABSTRACT FROM AUTHOR]

Coherence time plays an important role in ocean surface observation by means of synthetic aperture radar (SAR). For X-band, the autocorrelation function (ACF) of the backscatter from the sea surface has been predicted using models. Up to now there exist only few experimental data and an empirical analysis of the sea surface coherence time based on a larger amount of data is missing. In this paper, we show how space-based bistatic SAR along-track interferometry is used for the first time to measure the ACF. We achieve this by statistical analysis of the interferometric scene coherence as a function of the along-track interferometric time lag, instead of indirect estimation from the Doppler bandwidth. We use many data acquisitions of opportunity for that purpose, thereby exploiting the diversity of along-track baselines between the two SAR sensors of the TanDEM-X satellite formation. The ACF is related to the temporal decorrelation, which is not directly observable. We extract this component by quantifying and compensating for all other sources of decorrelation that contribute to the observed coherence. [ABSTRACT FROM PUBLISHER]

Surface soil moisture (SSM) is an important parameter at the land–atmosphere interface. In past decades, passive microwave remote sensing offers a good opportunity for obtaining SSM on a global scale, and many downscaling methods have been proposed using the triangle-based empirical soil moisture relationship models to overcome the limitation of coarse spatial resolution of its SSM products for regional applications. This paper aimed to examine and compare the effectiveness of five typical triangle-based empirical soil moisture relationship models for estimating SSM with Landsat-5 data and in situ measurements from the Maqu network on the northeastern part of the Tibetan Plateau for nine cloud-free days. The results showed that the model that treats the SSM as a second-order polynomial with land surface temperature, vegetation indices (VIs), and surface albedo as inputs exhibited the best performance compared with the results of other models. The VI comparison indicated that the use of the normalized difference VI or the fractional vegetation cover in this model outperformed other VIs, with the root-mean-square deviation of approximately 0.055 m3/m3 and the coefficient of determination ( \textR^2 ) above 0.78 at the nine-day average level. In addition, a significant spatial scale effect of the model was also found through analyzing the model fitting results at different window sizes. The study provides important insight into the best empirical relationship models for capturing soil moisture dynamics. These models can support the passive microwave soil moisture data spatial downscaling and validation applications in future studies. [ABSTRACT FROM PUBLISHER]

This paper presents a novel approach for the power transformer differential protection based on empirical Fourier transform (EFT). The EFT is a novel transform technique, which is derived from discrete Fourier transform (DFT) with certain modifications based on the nature of current waveforms during internal fault, inrush, and current-transformer (CT) saturation. The fundamental component estimated by EFT is equal to DFT estimation for internal fault currents and zero or a very low value for inrush currents and CT saturation currents. The fundamental component estimated by EFT is used in the biased restraint characteristic with a deviation factor (DF) to make tripping decisions for relays. The DF describes the waveform deviation rate of differential current waveform from a pure sinusoidal waveform. The proposed EFT-based differential protection algorithm (EFT-DPA) is validated and compared with the conventional DFT-based differential protection algorithm (DFT-DPA) through modeling for an existing real-time power transformer in Tamil Nadu Transmission Corp. Ltd. (TANTRANSCO), Tamilnadu, India. Also, the performance of the EFT-DPA is investigated with the fault recorder data taken from the field differential relay of the same power transformer. The power transformer modeling is carried using PSCAD and the EFT-DPA is implemented in MATLAB. [ABSTRACT FROM AUTHOR]

This paper focuses on the problem of structure and motion recovery from uncalibrated image sequences. It has been empirically proven that image measurement uncertainties can be modeled spatially and temporally by virtue of reprojection residuals. Consequently, a spatial-and-temporal-weighted factorization (STWF) algorithm is proposed to handle significant noise contained in the tracking data. This paper presents three novelties and contributions. First, the image reprojection residual of a feature point is demonstrated to be generally proportional to the error magnitude associated with the image point. Second, the error distributions are estimated from a different perspective, that of the reprojection residuals. The image errors are modeled both spatially and temporally to cope with different kinds of uncertainties. Previous studies have considered only the spatial information. Third, based on the estimated error distributions, an STWF algorithm is proposed to improve the overall accuracy and robustness of traditional approaches. Unlike existing approaches, the proposed technique does not require prior information of image measurement and is easy to implement. Extensive experiments on synthetic data and real images validate the proposed method. [ABSTRACT FROM AUTHOR]

Large-scale multiple-input–multiple-output (MIMO) networks, together with relay technology, have received considerable attention due to their remarkable capacity potential. Nevertheless, the majority of recent research studies on the asymptotic capacity of large-scale MIMO amplify-and-forward (AF) relay networks are limited to a fundamental assumption of small-scale fading, where the effect of large-scale fading, including shadow fading and path loss, is ignored. Motivated by this observation, in this paper, we present an extended model for the asymptotic capacity analysis in a composite fading environment, where Rayleigh fading, shadow fading, and path loss effect are incorporated altogether. Moreover, a realistic relay distribution is considered for real-world application of our capacity analysis. The proposed model consists of one source and one destination that are both equipped with $M$ antennas, which communicate through $K$ relays, each having $N$ antennas. We first employ random matrix theory to derive the empirical cumulative distribution of the channel covariance matrix and analyze the asymptotic capacity per source–destination antenna pair as both $M$ and $NK$ tend to infinity, but their ratio remains as a constant. We then consider the asymptotic capacity under two asymptotic cases for a finite and an infinite number of relays, respectively. It is shown through both theoretical analysis and Monte Carlo simulations that increasing the shadowing standard deviation can improve asymptotic capacity, whereas increasing the path loss exponent would lead to a capacity reduction. Our simulation results also reveal the effects of the relay distribution, as well as the source and relay transmission powers, on the asymptotic capacity. [ABSTRACT FROM PUBLISHER]

This paper surveys, reviews, and critiques analytic models of MOSFET short-channel effects (SCEs) in subthreshold published over the past four decades. In the first half of this paper, the published models on SCEs are categorized into the following four main groups based on their approach: 1) charging sharing models; 2) empirical expressions; 3) polynomial potential models; and 4) analytic solutions to 2-D Poisson's equation. The strength and weakness of each approach are elaborated in terms of its physical soundness and predictive ability. A key development was the exponential dependence of SCE on channel length (L), SCE \sim\exp(-L/l0), leading to the introduction of scale length (l0). In the second half of this paper, the predictions of each analytic SCE model are examined by generic 2-D numerical simulations. In particular, the merit of each model is judged by its prediction on the scale length (l0) as a function of the thickness and dielectric constant (\kappa) of the gate insulator. Only one model, i.e., the generalized scale length model that treated the silicon and insulator regions as two distinct dielectric regions with shared boundary conditions, correctly predicted the MOSFET scale length under all dielectric constant and thickness conditions. A variation of the generalized scale length model applies to recent multiple-gate MOSFETs near the limit of scaling. [ABSTRACT FROM PUBLISHER]

Many applications of machine learning involve analysis of sparse high-dimensional data, in which the number of input features is larger than the number of data samples. Standard inductive learning methods may not be sufficient for such data, and this provides motivation for nonstandard learning settings. This paper investigates a new learning methodology called learning through contradictions or Universum support vector machine (U-SVM). U-SVM incorporates a priori knowledge about application data, in the form of additional Universum samples, into the learning process. This paper investigates possible advantages of U-SVM versus standard SVM, and describes the practical conditions necessary for the effectiveness of the U-SVM. These conditions are based on the analysis of the univariate histograms of projections of training samples onto the normal direction vector of (standard) SVM decision boundary. Several empirical comparisons are presented to illustrate the practical utility of the proposed approach. [ABSTRACT FROM AUTHOR]

The advent of routine collection of high-quality digital photography provides for traditional uses, as well as “remote sensing” uses such as the monitoring of environmental indicators. A well-devised monitoring system, based on consistent data and methods, provides the opportunity to track and communicate changes in features of interest in a way that has not previously been possible. Data that are geometrically and radiometrically consistent are fundamental to establishing systems for monitoring. In this paper, we focus on models for the radiometric calibration of mosaics consisting of thousands of images. We apply the models to the data acquired by the Australian Commonwealth Scientific and Industrial Research Organisation and its partners as part of regular systematic acquisitions over the city of Perth for a project known as Urban Monitor. One goal of the project, and hence the model development, is to produce annually updated mosaics calibrated to reflectance at 0.2-m ground sample distance for an area of approximately 9600 \km^2. This equates to terabytes of data and, for frame-based instruments, tens of thousands of images. For the experiments considered in this paper, this requires mosaicking estimates derived from 3000 digital photographic frames, and the methods will shortly be expanded to 30 000+ frames. A key part of the processing is the removal of spectral variation due to the viewing geometry, typically attributed to the bidirectional reflectance distribution function (BRDF) of the land surface. A variety of techniques based on semiempirical BRDF kernels have been proposed in the literature for correcting the BRDF effect in single frames, but mosaics with many frames provide unique challenges. This paper presents and illuminates a complete empirical radiometric calibration method for digital aerial frame mosaics, based on a combined model that uses kernel-based techniques for BRDF correction and incorporates additive and multiplicative terms for correcting other effects, such as variations due to the sensor and atmosphere. Using ground truth, which consists of laboratory-measured white, gray, and black targets that were placed in the field at the time of acquisition, we calculate the fundamental limitations of each model, leading to an optimal result for each model type. We demonstrate estimates of ground reflectance that are accurate to approximately 10%, 5%, and 3% absolute reflectances for ground targets having reflectances of 90%, 40%, and 4%, respectively. [ABSTRACT FROM AUTHOR]

In this paper, we present the experimental results of a new spray paint algorithm presented in previous publications. Both theory and simulations indicate that the proposed method allows a robotic manipulator to paint a given surface using substantially lower joint torques than with conventional approaches. In this paper, we confirm this by implementing the algorithm on an ABB robot and we find that the joint torques needed to follow the trajectory are substantially lower than for the conventional approach. [ABSTRACT FROM AUTHOR]

Very little data exists regarding the long-term aging performance of space-rated oscillators in the non-operating mode. This paper provides empirical evidence that may be used to estimate the performance of unpowered oscillators for long-term space missions, as well as an aid in validating the worst-case analyses that are routinely performed on these oscillators. The proper operation of any space vehicle is dependent on the performance of the onboard master oscillator. For maximum reliability, the onboard clock is often provided as a dualor triple-redundant ensemble, with one active oscillator. The backup oscillator(s) are usually powered off and may be off for a significant percentage of the mission. A significant parameter of importance for oscillator performance is the aging rate. Papers have been presented on the aging performance of active oscillators in space, but very little data exists regarding the long-term aging performance of oscillators in the nonoperating mode. The aging rate data are extremely important for predicting the expected performance of these backup oscillators after 10 or 15 years in of non-operation in space. This paper presents performance data derived from temperature controlled crystal oscillators and oven controlled crystal oscillators that have been dormant for decades. [ABSTRACT FROM AUTHOR]

The objective of this paper is to examine the link between the attenuation coefficients and the interferometric phase center heights, for several frequencies from P- to L-band, and to study the extent to which it depends on the canopy architecture and description. This paper relies on the use of a coherent and full polarimetric scattering model, which simulates the fields backscattered by a forested area. In the first part, we study the behavior with a frequency of the interferometric phase center heights, and in the second part, we focus on the attenuation coefficients. Then, we compare the behaviors of these two quantities, and we propose to empirically derive a relation between these two quantities and the mean forest height. Finally, we investigate if a change in the initial forest or radar configuration has an impact on the determination of this relation. [ABSTRACT FROM AUTHOR]

In the area of cost-sensitive learning, inductive learning algorithms have been extended to handle different types of costs to better represent misclassification errors. Most of the previous works have only focused on how to deal with misclassification costs. In this paper, we address the equally important issue of how to handle the test costs associated with querying the missing values in a test case. When an attribute contains a missing value in a test case, it may or may not be worthwhile to take the extra effort in order to obtain a value for that attribute, or attributes, depending on how much benefit the new value will bring about in increasing the accuracy. In this paper, we consider how to integrate test-cost-sensitive learning with the handling of missing values in a unified framework that includes model building and a testing strategy. The testing strategies determine which attributes to perform the test on in order to minimize the sum of the classification costs and test costs. We show how to instantiate this framework in two popular machine learning algorithms: decision trees and naive Bayesian method. We empirically evaluate the test-cost-sensitive methods for handling missing values on several data sets. [ABSTRACT FROM AUTHOR]

This paper presents a technique to predict the die temperature of a MOSFET based on an empirical model derived following an offline thermal characterization. First, a method for the near-simultaneous measurement of die temperature during controlled power dissipation is presented. The method uses a linear arbitrary waveform power controller which is momentarily disconnected at regular intervals to allow the forward voltage drop of the MOSFET's antiparallel diode to be measured. Careful timing ensures the power dissipation is not significantly affected by the repeated disconnection of the power controller. Second, a pseudorandom binary sequence-based system identification approach is used to determine the thermal transfer impedance, or cross coupling between the dice of two devices on shared cooling using the near-simultaneous measurement and control method. A set of infinite impulse response digital filters are fitted to the cross-coupling characteristics and used to form a temperature predictor. Experimental verification shows excellent agreement between measured and predicted temperature responses to power dissipation. Results confirm the usefulness of the technique for predicting die temperatures in real time without the need for on-die sensors. [ABSTRACT FROM AUTHOR]

Software fault prediction is a valuable exercise in software quality assurance to best allocate limited testing resources. Classification is one of the effective methods for software fault prediction. The classification models are trained based on the datasets obtained by mining software historical repositories. However, the performance of the models depends on the quality of datasets. In this paper, we propose a novel two-stage data preprocessing approach which incorporates both feature selection and instance reduction. Specifically, in the feature selection stage, we first perform relevance analysis, and then propose a threshold-based clustering method, called novel threshold-based clustering algorithm, to conduct redundancy control. In the instance reduction stage, we apply random under-sampling to keep the balance between the faulty and non-faulty instances. In empirical studies, we chose datasets from real-world software projects, such as Eclipse and NASA. Then we compared our approach with some classical baseline methods, and further investigated the influencing factors in our approach. The final results demonstrate the effectiveness of our approach, and provide a guideline for achieving cost-effective data preprocessing when using our two-stage approach. [ABSTRACT FROM PUBLISHER]

Most existing metrics that we call white-box metrics, such as coverage metrics, require white-box information, like program structure information, and historical runtime information, to evaluate the fault detection capability of a test set. In practice, such white-box information is usually unavailable or difficult to obtain, which means they often cannot be used. In this paper, we propose a black-box metric, distance entropy, based on the diversification idea behind many published diversity-based techniques. Distance entropy provides a possible solution for test set evaluation when white-box information is not available. The empirical study illustrates that distance entropy can effectively evaluate test sets if the distance metric between tests is well defined. Meanwhile, distance entropy outperforms simple diversity metrics without increasing time complexity. [ABSTRACT FROM PUBLISHER]

With the rapid proliferation of RFID technologies, RFID has been introduced into applications such as inventory and sampling inspection. Conventionally, in RFID systems, the reader usually identifies all the RFID tags in the interrogation region with the maximum power. However, some applications may only need to identify the tags in a specified area, which is usually smaller than the reader’s default interrogation region. An example could be identifying the tags in a box, while ignoring the tags out of the box. In this paper, we respectively present two solutions to identify the tags in the specified area. The principle of the solutions can be compared to the picture-taking process of an auto-focus camera, which firstly focuses on the target automatically and then takes the picture. Similarly, our solutions first focus on the specified area and then shoot the tags. The design of the two solutions is based on the extensive empirical study on RFID tags. Realistic experiment results show that our solutions can reduce the execution time by $44$ percent compared to the baseline solution, which identifies the tags with maximum power. Furthermore, we improve the proposed solutions to make them work well in more complex environments. [ABSTRACT FROM AUTHOR]

This paper reports empirical evidence on having students use AR-SaBEr, a simulation tool based on augmented reality (AR), to discover the basic principles of electricity through a series of experiments. AR-SaBEr was enhanced with knowledge-based support and inquiry-based scaffolding mechanisms, which proved useful for discovery learning in Web-based simulation environments. Learning performance factors evaluated included students' learning behavior while interacting with the system, AR-SaBEr's learning effectiveness, and students' motivation in using the AR-based simulation environment. The study suggests that AR simulators can be exploited as effective learning environments for learning the basic principles of electricity. It would, however, be necessary to consider a number of scaffolding options to improve the learning effectiveness of AR-based simulation environments when used with students who have low levels of either: 1) self-regulation skills, or 2) motivation to engage in AR-based simulation activities. [ABSTRACT FROM AUTHOR]

Business Intelligence (BI) is recognized as one of the most important IT applications in the coming big data era. In recent years, non-uniform memory access (NUMA) has become the de-facto architecture of multiprocessors on the new generation of enterprise servers. Such new architecture brings new challenges to optimization techniques on traditional operators in BI. Aggregation, for example, is one of the basic building blocks of BI, while its processing performance with existing hash-based algorithms scales poorly in terms of the number of cores under NUMA architecture. In this paper, we provide new solutions to tackle the problem of parallel hash-based aggregation, especially targeting at domains of extremely large cardinality. We propose a NUMA-aware radix partitioning (NaRP) method which divides the original huge relation table into subsets, without invoking expensive remote memory access between nodes of the cores. We also present a new efficient aggregation algorithm (EAA), to aggregate the partitioned data in parallel with low cache coherence miss and locking costs. Theoretical analysis as well as empirical study on an IBM X5 server prove that our proposals are at least two times faster than existing methods. [ABSTRACT FROM PUBLISHER]

In this paper, new expressions are introduced for the determination of the maximum power point (MPP) of photovoltaic (PV) systems as explicit functions of the five parameters of the single-diode model employing the Lambert W function. These equations provide the voltage and current at MPP in a direct and straightforward manner, thus dispensing with any need for iterative solution. They are initially derived for a PV system operating under uniform conditions, and subsequently extended for mismatched conditions at the PV string level. The novelty of these formulae lies in their solid theoretical foundation, which supports their validity in the general case and offers a well-founded symbolic formulation for the MPP evaluation problem. Extended simulations and experimental validation are performed to verify the accuracy and computational efficiency of the proposed equations compared with other methods available in the literature. [ABSTRACT FROM AUTHOR]

Accurate modeling of electrothermal effects of GaN electronic devices is critical for reliability design and assessment. In this paper, an electrothermal model for large signal equivalent circuit modeling of AlGaN/GaN HEMTs including self-heating and ambient temperature effects is presented. To accurately describe the effect of ambient temperature, two separate electrothermal networks (Idiss, Rdiss, and Cdiss for self-heating, and Iamb, Ramb, and Camb for ambient temperature effect) are used to describe drain-source current slump due to self-heating and ambient temperature effects, respectively. A temperature-dependent thermal resistance and thermal capacitance model is proposed and implemented in the electrothermal network. The extraction of the thermal parameters is fulfilled by using numerical finite-element method. Single tone on wafer load-pull measurements at two operating frequencies (3 and 14 GHz) are carried out for verification purposes. The results show that good agreements on fundamental output power, the second and third harmonics output power, and power added efficiency have been achieved between simulations and measurements over a wide range of -55 °C to 175 °C. [ABSTRACT FROM AUTHOR]

Exponential growth of information generated by online social networks demands effective and scalable recommender systems to give useful results. Traditional techniques become unqualified because they ignore social relation data; existing social recommendation approaches consider social network structure, but social contextual information has not been fully considered. It is significant and challenging to fuse social contextual factors which are derived from users’ motivation of social behaviors into social recommendation. In this paper, we investigate the social recommendation problem on the basis of psychology and sociology studies, which exhibit two important factors: individual preference and interpersonal influence. We first present the particular importance of these two factors in online behavior prediction. Then we propose a novel probabilistic matrix factorization method to fuse them in latent space. We further provide a scalable algorithm which can incrementally process the large scale data. We conduct experiments on both Facebook style bidirectional and Twitter style unidirectional social network data sets. The empirical results and analysis on these two large data sets demonstrate that our method significantly outperforms the existing approaches. [ABSTRACT FROM AUTHOR]

Finding \mbi k shortest simple paths in a directed graph is a fundamental problem in many engineering applications. Most existing algorithms such as Yen’s algorithm and its variants have polynomial worst-case time complexity, but their average-case running time is very high. The heuristic algorithm MPS can run significantly faster in practice. However, it requires an excessive amount of memory space. In this paper, we provide a new heuristic algorithm that achieves high space efficiency while maintaining similar average-case running time. We first propose a sidetrack representation of path, with which a path can be stored in \mbi O\bf (1) space. We then show how to categorize a candidate path as either partial or complete, and restrict the number of paths added to the queue. In addition, we provide an empirical equation that can very accurately predict the \mbi kth shortest path length, provided that a much smaller number of shortest paths have been found. Extensive experiments prove that our algorithm can achieve an \mbi O(n) speedup in practice over Yen’s algorithm. In comparison with MPS, it runs up to three times faster and uses less space by an order of magnitude. [ABSTRACT FROM AUTHOR]

In this paper, we first propose a very interesting and practical problem, pattern matching in a distributed mobile environment. Pattern matching is a well-known problem and extensive research has been conducted for performing effective and efficient search. However, previous proposed approaches assume that data are centrally stored, which is not the case in a mobile environment (e.g., mobile phone networks), where one person's pattern could be separately stored in a number of different stations, and such a local pattern is incomplete compared with the global pattern. A simple solution to pattern matching over a mobile environment is to collect all the data distributed in base stations to a data center and conduct pattern matching at the data center afterwards. Clearly, such a simple solution will raise huge amount of communication traffic, which could cause the communication bottleneck brought by the limited wireless bandwidth to be even worse. Therefore, a communication efficient and search effective solution is necessary. In our work, we present a novel solution which is based on our well-designed weighted bloom filter (WBF), called, D istributed Incomplete pattern matching ( DI-matching), to find target patterns over a distributed mobile environment. Specifically, to save communication cost and ensure pattern matching in distributed incomplete patterns, we use WBF to encode a query pattern and disseminate the encoded data to each base station. Each base station conducts a local pattern search according to the received WBF. Only qualified IDs and corresponding weights in each base station are sent to the data center for aggregation and verification. Through non-trivial theoretical analysis and extensive empirical experiments on a real city-scale mobile networks data set, we demonstrate the effectiveness and efficiency of our proposed solutions. [ABSTRACT FROM PUBLISHER]

Human action recognition in video is often approached by means of sequential probabilistic models as they offer a natural match to the temporal dimension of the actions. However, effective estimation of the models' parameters is critical if one wants to achieve significant recognition accuracy. Parameter estimation is typically performed over a set of training data by maximizing objective functions such as the data likelihood or the conditional likelihood. However, such functions are nonconvex in nature and subject to local maxima. This problem is major since any solution algorithm (expectation-maximization, gradient ascent, variational methods and others) requires an arbitrary initialization and can only find a corresponding local maximum. Exhaustive search is otherwise impossible since the number of local maxima is unknown. While no theoretical solutions are available for this problem, the only practicable mollification is to repeat training with different initializations until satisfactory cross-validation accuracy is attained. Such a process is overall empirical and highly time-consuming. In this paper, we propose two methods for one-off initialization of hidden Markov models achieving interesting tradeoffs between accuracy and training time. Experiments over three challenging human action video datasets (Weizmann, MuHAVi and Hollywood Human Actions) and with various feature sets measured from the frames (STIP descriptors, projection histograms, notable contour points) prove that the proposed one-off initializations are capable of achieving accuracy above the average of repeated random initializations and comparable to the best. In addition, the methods proposed are not restricted solely to human action recognition as they suit time series classification as a general problem. [ABSTRACT FROM AUTHOR]

Domain ontology, as a conceptual model, provides a meaningful framework for semantic representation of textual information. In this paper, we explore the feasibility of using the ontology in solving multi-document summarization problems in the domain of disaster management. We provide an empirical study of different approaches in which the ontology has been used for summarization tasks. Extensive experiments on a collection of press releases relevant to Hurricane Wilma in 2005 demonstrate that ontology-based multi-document summarization methods outperform other baselines in terms of the summary quality. [ABSTRACT FROM AUTHOR]

In this paper, we use cross wavelet transform (XWT) for the analysis and classification of electrocardiogram (ECG) signals. The cross-correlation between two time-domain signals gives a measure of similarity between two waveforms. The application of the continuous wavelet transform to two time series and the cross examination of the two decompositions reveal localized similarities in time and frequency. Application of the XWT to a pair of data yields wavelet cross spectrum (WCS) and wavelet coherence (WCOH). The proposed algorithm analyzes ECG data utilizing XWT and explores the resulting spectral differences. A pathologically varying pattern from the normal pattern in the QT zone of the inferior leads shows the presence of inferior myocardial infarction. A normal beat ensemble is selected as the absolute normal ECG pattern template, and the coherence between various other normal and abnormal subjects is computed. The WCS and WCOH of various ECG patterns show distinguishing characteristics over two specific regions R1 and R2, where R1 is the QRS complex area and R2 is the T-wave region. The Physikalisch-Technische Bundesanstalt diagnostic ECG database is used for evaluation of the methods. A heuristically determined mathematical formula extracts the parameter(s) from the WCS and WCOH. Empirical tests establish that the parameter(s) are relevant for classification of normal and abnormal cardiac patterns. The overall accuracy, sensitivity, and specificity after combining the three leads are obtained as 97.6%, 97.3%, and 98.8%, respectively. [ABSTRACT FROM AUTHOR]

A new method for video watermarking is presented in this paper. In the proposed method, data are embedded in the LL subband of wavelet coefficients, and decoding is performed based on the comparison among the elements of the first principal component resulting from empirical principal component analysis (PCA). The locations for data embedding are selected such that they offer the most robust PCA-based decoding. Data are inserted in the LL subband in an adaptive manner based on the energy of high frequency subbands and visual saliency. Extensive testing was performed under various types of attacks, such as spatial attacks (uniform and Gaussian noise and median filtering), compression attacks (MPEG-2, H. 263, and H. 264), and temporal attacks (frame repetition, frame averaging, frame swapping, and frame rate conversion). The results show that the proposed method offers improved performance compared with several methods from the literature, especially under additive noise and compression attacks. [ABSTRACT FROM AUTHOR]