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Tight assembly of stator windings with no insulation paper damage is a manufacturing challenge. We evaluate different sets of parts according to the following parameters: magnet wire thickness, stator slot smoothness, length of the straight magnet wire after the slot end, and type and amount of insulation cap at the end of the slot. These parameters have discrete values with small differences between them. The damage criterion is the decrease of the insulation paper breakdown voltage after assembly/disassembly of parts, assembled in a small set of designed experiments. Parameter values, i.e., levels at individual experiments are set by an orthogonal experiment matrix. Repetition of each experiment provides statistical significance. Data analysis shows that the additive model alone is not sufficient due to the high correlation of the parameters' influences. We extend the model to include interparameter influences, which we model by adding a virtual parameter. The extended additive model generates parameter values that do not degrade the insulation paper breakdown voltage within the manufacturing process. These values are verified by repetitions of the control experiment.

The frequency, nature, and scope of Soft Error Events (SEEs) is becoming an increasingly significant reliability concern as transistors continuously scale down into ever smaller nanometer memory chip geometries. As part of the on-going research efforts required to address this steadily developing situation, two significant research questions were addressed by the research work presented within this paper. The first research question addressed the possibility of crafting a novel methodology whose application would consistently lead to the creation of viable Monte Carlo SEE Simulators. Each of these Monte Carlo SEE Simulators would need to be capable of adequately profiling SEEs as well as simulating and modeling their impacts on the reliability of a variety of simulated 45-nm SRAM computer memories. The Standard 2-Parameter Weibull Soft Error Event (S2P-WSE) Simulator proposed within this paper as a direct application of this novel methodology successfully produced simulation results that were comparatively equivalent both directly and statistically to the results calculated by the empirically validated Weibull Row-Clustered Soft Error Reliability (WRCSER) model. This WRCSER Model was independently developed and empirically validated against proprietary reliability datasets obtained from testing a CISCO 45-nm SRAM computer memory device in a laboratory setting. The S2P-WSE Simulator itself was validated by means of a Kruskal–Wallis ANOVA test and by direct comparison of both the model and the simulator as a function of their respective failure probability and reliability results on 45-nm SRAM simulated computer memory devices subjected to the same SEE impacts over the course of an entire simulation run. The second research question sought to determine the significance of SEE topography versus a Row-Depth-Only bit error severity assumption on the reliability of simulated 45-nm SRAM computer memories shielded from the accumulating impacts of SEEs by variable error-correction codes, row-depth interleaving distances, and periodic scrubbing time intervals. The simulation runs for both the S2P-WSE Simulator and the T2P-WSE Simulator were carefully setup to ensure that only the potentially divergent factor between the results generated by the two simulators was the T2P-WSE Simulator's stochastically estimated topographical shape for each SEE impacting the simulated computer memory device. The work within this paper successfully demonstrated through direct plots of the two simulators’ failure probability and reliability results and the results of the Kruskal–Wallis ANOVA tests that the topography of SEEs does represent a significant impact on the overall failure probability and reliability of 45-nm SRAM computer memory devices. [ABSTRACT FROM PUBLISHER]

Prior research has shown that autocorrelation and variance in voltage measurements tend to increase as power systems approach instability. This paper seeks to identify the conditions under which these statistical indicators provide reliable early warning of instability in power systems. First, the paper derives and validates a semi-analytical method for quickly calculating the expected variance and autocorrelation of all voltages and currents in an arbitrary power system model. Building on this approach, the paper describes the conditions under which filtering can be used to detect these signs in the presence of measurement noise. Finally, several experiments show which types of measurements are good indicators of proximity to instability for particular types of state changes. For example, increased variance in voltages can reliably indicate both proximity to a bifurcation and the location of increased stress. On the other hand, growth of autocorrelation in certain line currents is related less to a specific location of stress but, rather, is a reliable indicator of stress occurring somewhere in the system; in particular, it would be a clear indicator of approaching instability when many nodes in an area are under stress. [ABSTRACT FROM AUTHOR]

Traffic flow (TF) prediction is an important and yet a challenging task in transportation systems, since the TF involves high nonlinearities and is affected by many elements. Recently, neural networks have attracted much attention for TF prediction, but they are commonly black boxes with complex architectures and difficult to be interpreted, e.g., the contributions of specific traffic elements are not explicit, hardly providing informative guidance. In this paper, we aim at addressing more interpretable short-term TF prediction with joint consideration to high accuracy, and thus introduces a pragmatic method by applying the efficient hinging hyperplanes neural network (EHHNN) simply built upon sparse neuron connections. In the proposed method, different traffic factors are incorporated into the inputs, including their spatial-temporal information. Besides the pursuit of accuracy, we further extend the ANOVA decomposition of EHHNNs to the interpretation analysis with specifications to traffic data, in which the contributions concerning specific traffic variables are detected quantitatively. As such, the proposed method firstly applies the EHHNN to filter out more important traffic variables for dimensionality reduction while maintaining accurate prediction. Then, variable interpretation analysis is performed from different perspectives, e.g. to quantitatively investigate the influence of traffic factors and also their spatial-temporal impacts. Therefore, a predictor and an analyzing tool can both be attained for the TF by exerting the flexibility and extending the interpretability of EHHNNs, which is promising to provide informative guidance to future traffic control. Numerical experiments verify the effectiveness and potential of the proposed method in TF prediction and analysis. [ABSTRACT FROM AUTHOR]

Variability is a challenge for future scaling as process dimensions reduce. The emerging 3-D sequential stacking technology is more than Moore’s scaling alternative. The 3-D design flow requires the partitioning of the netlist between the tiers. This paper presents the variability analysis of circuits partitioned into different levels. A comparison among local and global variations effects on ring oscillators (ROs) and SRAM is demonstrated. The across-chip variations and correlation range are shown as a critical point for the 3-D very large-scale integrated circuits, where the local variability is dominant. The correlations between devices due to the distances or the allocation into different tiers are directly taken into account in the SPICE model due to a statically unified model applied to 3-D circuits based on Monte Carlo simulations. Design wise, the 3-D integration can further decrease the circuit variability as shown in RO output frequency and SRAM static noise margin. [ABSTRACT FROM AUTHOR]

The study on pattern recognition (PR) is one of the fundamental steps for prosthetic hand control to improve the life quality of amputees. However, achieving a proper and functional PR has challenges due to the limitations of bio-signals. To this end, this paper proposes a novel hybrid approach to PR of finger movements and grasping gestures simultaneously based on mechanomyography (MMG) and force-myography (FMG) signals. In this method, first, time-domain features, time-frequency-domain features, and acoustic features are extracted from signal obtained by novel hybrid MMG-FMG sensor; second, analysis of variance (ANOVA) is used to select the best features separately, and then sequential forward feature selection (SFFS) is used to select the optimal combination of the selected features by ANOVA; and finally, the selected features are classified by neural network (ANN). To evaluate the performance of the multimode PR method by hybrid MMG-FMG signals for the accurate simultaneous classification of finger motions and grasping gestures, twelve motion patterns, including seven finger motions and five grasping gestures, from twelve participants including six transracial amputees and six healthy, are tested in the experiments. The real-time experimental results show that the hybrid MMG-FMG signals lead to high resolution, with an average classification accuracy of 91.44% ±0.77 for amputees and 92.19% ±1.12 for healthies. The results illustrate that the proposed approach reduces the difference accuracy between healthy subjects and amputees in classification. The outcomes of this study have great potential to perform onto amputees with different muscle conditions. [ABSTRACT FROM AUTHOR]

Global sensitivity analysis (GSA) of distribution system with respect to stochastic PV and load variations plays an important role in designing optimal voltage control schemes. This paper proposes a data-driven framework for GSA of distribution system. In particular, two representative surrogate modeling-based approaches are developed, including the traditional Gaussian process-based and the analysis of variance (ANOVA) kernel ones. The key idea is to develop a surrogate model that captures the hidden global relationship between voltage and real and reactive power injections from the historical data. With the surrogate model, the Sobol indices can be conveniently calculated through either the sampling-based method or the analytical method to assess the global sensitivity of voltage to variations of load and PV power injections. The sampling-based method estimates the Sobol indices using Monte Carlo simulations while the analytical method calculates them by resorting to the ANOVA expansion framework. Comparison results with other model-based GSA methods on the unbalanced three-phase IEEE 37-bus and 123-bus distribution systems show that the proposed framework can achieve much higher computational efficiency with negligible loss of accuracy. The results on a real 240-bus distribution system using actual smart meter data further validate the feasibility and scalability of the proposed framework. [ABSTRACT FROM AUTHOR]

The generalized polynomial chaos (gPC) method recently advocated in the literature, exhibits impressive efficiency and accuracy in probabilistic power flow (PPF) calculations of small-scale power systems. However, it suffers from the “curse of dimensionality” and can only be applied to systems with input random variables that follow a set of standard probability distributions. This paper overcome these weaknesses by developing a hierarchical polynomial chaos analysis of variance (ANOVA) method that shows excellent performances in terms of accuracy, efficiency, rationality, and adaptability, for small- to large-scale power systems. By proving the equivalence between the polynomial chaos expansion and the ANOVA decomposition, which is executed at no extra computational cost, the dimensionality of the polynomial chaos expansion can be adaptively reduced to improve the computational efficiency of the generalized polynomial chaos method in high-dimensional problems. Furthermore, by resorting to the Stieltjes procedure, it is extended to any assumed probability distributions of the input random variables. Simulation results carried out on the IEEE 118-bus system and the 1354-bus European high voltage system with correlated renewable energy generations reveal that the developed method outperforms the generalized polynomial chaos method and the Monte Carlo (MC) method while being compatible with real-time applications in power systems. [ABSTRACT FROM AUTHOR]

Counting on-road vehicles in the highway is fundamental for intelligent transportation management. This paper presents the first highway vehicle counting method in compressed domain, aiming at achieving comparable estimation performance with the pixel-domain methods. Counting in compressed domain is rather challenging due to limited information about vehicles and large variance in vehicle numbers. To address this problem, we develop new low-level features to mitigate the challenge from insufficient information in compressed videos. The new proposed features can be easily extracted from the coding-related metadata. Then, we propose a hierarchical classification-based regression (HCR) model to estimate the number of vehicles from the compressed-domain low-level features for individual frame. HCR hierarchically divides the traffic scenes into different cases according to the density of vehicles such that the large variance of traffic scenes can be effectively captured. Beside the spatial regression in each frame, we propose a locally temporal regression model to further refine the counting results, which exploits the continuous variation characteristics of the traffic flow. We extensively evaluate the proposed method on real highway surveillance videos. The experimental results consistently show that the proposed method is very competitive compared with the pixel-domain methods, which can reach similar performance with much lower computational cost. [ABSTRACT FROM AUTHOR]

This paper describes a new method of power-grid signal variation measurement, consisting of a demodulation algorithm and a set of measures (indices). The measures that will be presented are the abstract functionals, operating on the complex envelope of a signal. This paper presents a demodulation algorithm, definitions of proposed functionals, results of simulations, as well as laboratory experiments. [ABSTRACT FROM AUTHOR]

This paper deals with the problem of adaptive multidimensional/multichannel signal detection in homogeneous Gaussian disturbance with unknown covariance matrix and structured deterministic interference. The aforementioned problem corresponds to a generalization of the well-known Generalized Multivariate Analysis of Variance (GMANOVA). In this Part I of the paper, we formulate the considered problem in canonical form and, after identifying a desirable group of transformations for the considered hypothesis testing, we derive a Maximal Invariant Statistic (MIS) for the problem at hand. Furthermore, we provide the MIS distribution in the form of a stochastic representation. Finally, strong connections to the MIS obtained in the open literature in simpler scenarios are underlined. [ABSTRACT FROM AUTHOR]

This paper analyzes the variance of the estimated parametric best linear approximation (BLA) \mathhatGBLA(q, \theta) of a nonlinear system that is driven by random excitations. The estimated model \mathhatGBLA(q, \theta) varies not only due to the disturbing measurement and process noise but also over different realizations of the random excitation because the nonlinear distortions depend on the input realization. For the nonparametric frequency response function (FRF) estimate, it has been shown that the variance expression is still valid in the presence of nonlinear distortions, and the same formulas can be used in the linear as in the nonlinear case. This result does not hold for the variance \sigma\mathhat{GBLA(q, \theta)}^{2} on the parametric estimate \mathhatG \rm BLA(q, \theta). It is shown in this paper that it is still possible to upperbound the variance \sigma\mathhat{GBLA(q, \theta)}^{2} using linear expression by introducing an additional scaling factor that depends upon the maximal degree of nonlinearity. [ABSTRACT FROM PUBLISHER]

Milk adulteration is one of the most dishonest food preparation, which not only reduces the nutritional value but also causes various diseases to human. Some of the common adulterants are detergents, ammonium sulphate [(NH4)2SO4], sodium hydroxide (NaOH), sodium-bi-carbonate (NaHCO3), and common salt (NaCl). The aim of this paper is to identify the minimum detectable limit for the above five adulterants in milk. An impedance sensor is used, which is sensitive to the ionic concentration of the measuring medium and gives different phase angle of the impedance, when immersed in different adulterated/unadulterated milk. Signal transduction circuit is designed to convert the phase angle to voltage and for display in liquid crystal display. By this detection system, the limit of detection of adulterants in milk with different fat % (from 1.5% to 20%) is studied. Commercial packet milk, ultra high temperature milk, and raw-milk (directly collected from the milk collection center) are used for the experiment. Experimental results are plotted in origin software and statistical analysis method analysis of variance is applied on experimental data for verification of the result and to check the data consistency. [ABSTRACT FROM PUBLISHER]

Image processing circuits are expected to play an important role in Internet of Things electronic systems. For this type of circuits, errors (e.g., those due to wear out) might not be perceptible to us due to our insensitivity to small variances in images or colors. In the case that errors are acceptable (imperceptible), the systems are very likely to still be functional but with only minor performance degradation. The lifetime of the systems thus can be extended. Although there have been several attributes proposed in the literature to test acceptability of errors, these attributes do not consider human beings’ sensitivity to structural variances of erroneous images. This may result in acceptability misclassification of errors. In this paper, we take this into consideration and propose a new test method to more accurately evaluate acceptability of errors. Compared with the available attributes that can also extract the structural information of images, our method has much lower computation complexity, which is advantageous for shortening test time. This also makes our method hardware efficient. It is shown that the incurred area overhead of our method is low. The proposed method is to be applied periodically in-field to examine the reliability of the target circuit. We thus consider errors that may occur during in-field use. Single/multiple stuck-at faults and noises are considered, and a total of 161 460 erroneous images are generated. The experimental results on these diverse images show that on average the test accuracy of our method is more than 99%. [ABSTRACT FROM AUTHOR]

The Probability Hypothesis Density (PHD) and Cardinalized PHD (CPHD) filters are popular solutions to the multitarget tracking problem due to their low complexity and ability to estimate the number and states of targets in cluttered environments. The PHD filter propagates the first-order moment (i.e. mean) of the number of targets while the CPHD propagates the cardinality distribution in the number of targets, albeit for a greater computational cost. Introducing the Panjer point process, this paper proposes a Second-Order PHD (SO-PHD) filter, propagating the second-order moment (i.e., variance) of the number of targets alongside its mean. The resulting algorithm is more versatile in the modeling choices than the PHD filter, and its computational cost is significantly lower compared to the CPHD filter. This paper compares the three filters in statistical simulations which demonstrate that the proposed filter reacts more quickly to changes in the number of targets, i.e., target births and target deaths, than the CPHD filter. In addition, a new statistic for multiobject filters is introduced in order to study the correlation between the estimated number of targets in different regions of the state space, and propose a quantitative analysis of the spooky effect for the three filters. [ABSTRACT FROM PUBLISHER]

This paper proposes a novel motion reproduction method based on environmental disturbance (EnvDs) compensation. The preservation of skillful techniques is an important issue. Although the motion-copying system (MCS) was proposed to deal with this problem, a clear strategy still does not exist for adapting to the environmental variances between the motion saving and reproduction phases. Conventional methods cannot adapt to the difference properly because their control systems are just one-degree-of-freedom (DOF) control systems. In this paper, variances were defined as EnvDs, and the effect on the MCS was demonstrated. A 2-DOF control system structure was developed to compensate for EnvDs. The effectiveness of the proposed system was demonstrated through analysis and experimental results. [ABSTRACT FROM PUBLISHER]

For high-speed bearingless disk drives, certain topologies seem advantageous. The authors have published works on a bearingless disk drive for high speeds that is characterized by a slotless stator and a toroid winding set. Several different variations of this setup are imaginable. The ones which this paper focuses on are the variation of the number of phases, of the number of coils, and of the applied coil connection. A comparison of the constructed and tested setup with the possible variations is presented in the course of this paper. [ABSTRACT FROM AUTHOR]

Atomic clocks are essential elements in a variety of applications, such as global navigation satellite systems. Consequently, monitoring their performances is fundamental. The Allan variance is the key statistical tool for the performance characterization of atomic clocks. This paper proves that the Allan variance computed from frequency measurements with missing data is affected by a bias, which can make it dramatically different from the expected behavior in the full data case. Furthermore, it shows how to eliminate (or largely reduce) this bias by correcting the Allan variance. The corrected Allan variance is obtained for some of the most common atomic clock noise components, and it is validated through numerical simulations. [ABSTRACT FROM AUTHOR]

Semiconductors are essential components in many electronic devices. Because wafers are produced quickly and in large quantities, defects occur that adversely affect semiconductor properties. This makes it necessary to install powerful and robust inspection systems which use artificial intelligence techniques in the early stages of the manufacturing chain in order to detect and classify those defects. This paper proposes a method for defect detection and classification on images of semiconductor wafer materials obtained by means of a scanning electron microscope based in the following stages: (i) use of computer vision techniques to isolate the defect from the background; (ii) use of several descriptors based on shape, size, texture, histogram, and key-points to create a feature vector for the characterization of the defect; (iii) application of an exhaustive search as a feature selection method to determine the optimal subset of feature descriptors; and (iv) evaluation of the feature descriptors by using a support vector machine classifier providing the optimal set with highest F1-score metrics. Finally, the effectiveness of the proposed approach is compared with five popular feature selection methods, reporting better classification results than the latter. [ABSTRACT FROM AUTHOR]

The optimum design of the proportional-integral-derivative (PID) controller plays an important role in achieving a satisfactory response in the automatic voltage regulator (AVR) system. This paper presents a novel optimal design of the PID controller in the AVR system by using the Taguchi combined genetic algorithm (TCGA) method. A multiobjective design optimization is introduced to minimize the maximum percentage overshoot, the rise time, the settling time, and the steady-state error of the terminal voltage of the synchronous generator. The proportional gain, the integral gain, the derivative gain, and the saturation limit define the search space for the optimization problem. The approximate optimum values of the design variables are determined by the Taguchi method using analysis of means. Analysis of variance is used to select the two most influential design variables. A multiobjective GA is used to obtain the accurate optimum values of these two variables. MATLAB toolboxes are used for this paper. The effectiveness of the proposed method is then compared with that of the earlier GA method and the particle swarm optimization method. With this proposed TCGA method, the step response of the AVR system can be improved. [ABSTRACT FROM PUBLISHER]

The kernelized correlation filter (KCF) is one of the state-of-the-art object trackers. However, it does not reasonably model the distribution of correlation response during tracking process, which might cause the drifting problem, especially when targets undergo significant appearance changes due to occlusion, camera shaking, and/or deformation. In this paper, we propose an output constraint transfer (OCT) method that by modeling the distribution of correlation response in a Bayesian optimization framework is able to mitigate the drifting problem. OCT builds upon the reasonable assumption that the correlation response to the target image follows a Gaussian distribution, which we exploit to select training samples and reduce model uncertainty. OCT is rooted in a new theory which transfers data distribution to a constraint of the optimized variable, leading to an efficient framework to calculate correlation filters. Extensive experiments on a commonly used tracking benchmark show that the proposed method significantly improves KCF, and achieves better performance than other state-of-the-art trackers. To encourage further developments, the source code is made available. [ABSTRACT FROM AUTHOR]

An iterative technique for the computation of approximate performance indices of a class of stochastic Petri nets models is presented. The proposed technique is derived from the mean value analysis algorithm for product form solution stochastic Petri nets. In this paper, we apply the approximation technique to stochastic marked graphs. In principle, the proposed technique can be used for other stochastic Petri net subclasses in the paper, some of these possible applications are presented. Several examples are presented in order to validate the approximate results. [ABSTRACT FROM AUTHOR]

The degrees of freedom (DoFs) of the $K$ -user multiple-input multiple-output (MIMO) interference channel are studied when perfect, but delayed channel state information is available at the transmitter side (delayed CSIT). Recent works have proposed schemes improving the DoF knowledge of the interference channel, but at the cost of developing transmission involving many channel uses (long delay), thus increasing the complexity at both transmitter and receiver side. This paper proposes three linear precoding strategies, limited to at most three phases, based on the concept of interference alignment, and built upon three main ingredients: delayed CSIT precoding, user scheduling, and redundancy transmission. In this respect, the interference alignment is realized by exploiting delayed CSIT to align the interference at the non-intended receivers along the space-time domain. Moreover, a new framework is proposed where the number of transmitted symbols and duration of the phases is obtained as the solution of a maximization problem, and enabling the introduction of complexity constraints, which allows deriving the achievable DoF as a function of the transmission delay, i.e., the achievable DoF-delay trade-off. Finally, the latter part of this paper settles that the assumption of time-varying channels common along all the literature on delayed CSIT is indeed unnecessary. [ABSTRACT FROM PUBLISHER]

Qorvo is a global leader in scalable and dynamic RF solutions for mobile, infrastructure, and defense. To ensure quality and compliance to customer specifications, a large number of product devices and die on wafers are tested every day. Typical data sets will contain unusually large or small values when compared with others in that data set, which may or may not pass specification limits. Such outliers will have negative effects on data analyses such as cpk-analysis, ANOVA, and regressions. More important, outliers, particularly those passing specification limits, may indicate defective parts or pose a reliability concern. For engineering teams in development and production, it is standard practice to identify or label outliers in their data sets, so that they can be removed to improve data analyses and to protect customers against receiving defective parts. The aim for this paper is to provide product engineering teams at Qorvo with reliable outlier labeling methods particularly for skewed distributions. While the ultimate goal is to label and to remove outliers, we also need to balance this with unnecessary yield loss for the financial bottom line. [ABSTRACT FROM AUTHOR]

Activity recognition in videos in a deep-learning setting—or otherwise—uses both static and pre-computed motion components. The method of combining the two components, while keeping the burden on the deep network less, still remains uninvestigated. Moreover, it is not clear what the level of contribution of individual components is, and how to control the contribution. In this paper, we use a combination of convolutional-neural-network-generated static features and motion features in the form of motion tubes. We propose three schemas for combining static and motion components: based on a variance ratio, principal components, and Cholesky decomposition. The Cholesky-decomposition-based method allows the control of contributions. The ratio given by variance analysis of static and motion features matches well with the experimental optimal ratio used in the Cholesky decomposition-based method. The resulting activity recognition system is better or on par with the existing state-of-the-art when tested with three popular data sets. The findings also enable us to characterize a data set with respect to its richness in motion information. [ABSTRACT FROM AUTHOR]

Smart meters have been installed to report users’ real-time electricity consumption data to the utility supplier periodically, which enables fine-grained energy supply, as the utility supplier can adjust its supplement based on users’ consumptions. However, these real-time electricity data can also reveal the behaviors of the inhabitants; for example, the real-time electricity consumption data can reveal if the inhabitant is at home, if the television is working, and so on. People are reluctant to disclose these kinds of personal information. In this paper, we come up with a smart meter data aggregation scheme based on the Paillier homomorphic cryptosystem, this aggregation scheme enables a utility supplier to get the total consumption of all the smart meters, while the utility supplier is unable to get the consumption data of a single smart meter. In addition, the proposed scheme enables the smart meter to report multiple types of data in one reporting message, which makes it possible for the supplier to conduct the variance analysis and the one-way analysis of variance on the data. The formal security analysis shows that the proposed scheme is semantically secure. The experiment results show that the proposed scheme can reduce the computation cost both on the smart meter side and on the aggregator side. [ABSTRACT FROM AUTHOR]

A glitter pattern is a set of bright spots. To have a bright spot on the water, the slope of a point on the sea surface must provide a specular reflection direction of the sunlight to the detector where the image will be created. This interaction is visible for all of us on a clear day with appropriate wind velocities on the sea surface. In this paper, a mathematical methodology was deduced, which is suggested to apply in real images of the sea. First, the suitable 2-D geometry of the reflection situation was determined. Then, the range of the slopes that satisfies the specular reflection condition for each direction was obtained; in addition, the 2-D statistical relationships between the glitter patterns of the sea surface and the sea surface slopes for different wind velocities on the sea surface were deduced using a 2-D Rect and a Gaussian glitter function, considering Gaussian and non-Gaussian probability density functions. Finally, doing parallel computing, the results consistent with the extension of the 1-D case were obtained. This method could be applied on real images, instead of using pressure sensors and others expensive electronic dispositives to model ocean behavior and other various oceanographic studies. [ABSTRACT FROM AUTHOR]

As human visual attention is naturally biased toward foreground objects in a scene, it can be used to extract salient objects in video clips. In this paper, we proposed a weakly supervised learning-based video saliency detection algorithm utilizing eye fixations information from multiple subjects. Our main idea is to extend eye fixations to saliency regions step by step. First, visual seeds are collected using multiple color space geodesic distance-based seed region mapping with filtered and extended eye fixations. This operation helps raw fixation points spread to the most likely salient regions, namely, visual seed regions. Second, in order to seize the essential scene structure from video sequences, we introduce the total variance-based pairwise interaction model to learn the potential pairwise relationship between foreground and background within a frame or across video frames. In this vein, visual seed regions eventually grow into salient regions. Compared with previous approaches the generated saliency maps have two most outstanding properties: $integrity$ and $purity$ , which are conductive to segment the foreground and significant to the follow-up tasks. Extensive quantitative and qualitative experiments on various video sequences demonstrate that the proposed method outperforms the state-of-the-art image and video saliency detection algorithms. [ABSTRACT FROM AUTHOR]

A major goal of air traffic management is to strategically control the flow of traffic so that the demand at an airport meets but does not exceed the operational capacity in a dynamic environment. This paper uses the concept of receding horizon control (RHC) to conduct real-time planning for airport capacity management (ACM). It is shown that RHC provides a generic and flexible framework for developing real-time allocation algorithms for airport capacity in a dynamic and uncertain environment, and existing approaches such as the one step ahead adjustment can be considered as special cases of this approach. Robustness against the change of the environment and demands and computational efficiency are two advantages when applying RHC to the ACM problem, which are illustrated by a case study. [ABSTRACT FROM AUTHOR]

In this paper, several feature extraction and channel compensation techniques found in state-of-the-art speaker verification systems are analyzed and discussed. For the NIST SRE 2006 submission, cepstral mean subtraction, feature warping, RelAtive SpecTrAl (RASTA) filtering, heteroscedastic linear discriminant analysis (HLDA), feature mapping, and eigenchannel adaptation were incrementally added to minimize the system's error rate. This paper deals with eigenchannel adaptation in more detail and includes its theoretical background and implementation issues. The key part of the paper is, however, the post-evaluation analysis, undermining a common myth that "the more boxes in the scheme, the better the system." All results are presented on NIST Speaker Recognition Evaluation (SRE) 2005 and 2006 data. [ABSTRACT FROM AUTHOR]

In this paper, we present a methodology for the size optimization of an external magnetic system made of arc-shaped permanent magnets (ASMs). This magnetic system is able to remotely actuate a drug-release module embedded in a prototype of a capsule robot. The optimization of the magnetic system is carried out by using an accurate analytical model that is valid for any arbitrary dimensions of the ASMs. By using this analytical model, we perform parametric studies and conduct a statistical analysis [analysis of variance (ANOVA)] to investigate efficient ways to distribute the volume of the ASMs so that the dimensions and volume of the magnetic system are minimized while optimal flux densities and magnetic torques are obtained to actuate the drug delivery system (DDS). The ANOVA results, at 5% significance level, indicate that changes in the angular width followed by changes in the length of the ASMs have the highest impact on the magnetic linkage. Furthermore, our experimental results, which are in agreement with the analytical results, show that the size optimization of the magnetic system is effective for the actuation of the DDS in capsule robots. [ABSTRACT FROM AUTHOR]

This paper derives an effective rotor shape by the optimal design of hybrid magnetic torque converter using the multiple linear regression analysis (MLRA). In particular, using a non-linear finite-element method based on the 2-D numerical analysis, we conduct a correlation analysis between independent variables (selected by the Box-Behnken design) and reaction variable. In addition, we derive a regression equation for reaction variables according to the independent variables using the MLRA and analysis of variance. We assess the validity of the optimized design by comparing the characteristics of the optimized model derived from the response surface methodology and the initial model. [ABSTRACT FROM AUTHOR]

Global sensitivity analysis (GSA) of voltage to uncertain power injection variations plays an important role for appropriate Volt-VAR optimization. This paper proposes a data-driven GSA method for large-scale distribution systems with a large number of uncertain sources. Specifically, the deep Gaussian process is used to identify the mapping relationship between uncertain power injections and voltages. This allows resorting to the analysis of variance framework to calculate the Sobol indices for GSA. Unlike the existing polynomial chaos expansion and Gaussian process-based approaches, our proposed method has much better scalability. Test results on the EPRI 1747-node K1 circuit with different numbers of uncertain sources with various uncertain levels and different PV distributions demonstrate that the proposed method can achieve accurate GSA. [ABSTRACT FROM AUTHOR]

A magnetic gear that is capable of noncontact torque transmission has replaced mechanical gear, and has advantages of high efficiency and improved reliability. However, it also has the problem of a low torque density, which requires improvement. In this paper, a novel pole piece shape is proposed in order to improve the problem of low torque density of the magnetic gear. In order to identify the functional relationship between design variables of the proposed pole piece shape and response variables such as torque and torque ripple, response surface methodology is used, and the Box-Behnken design is used to establish the design of experiment. In addition, using variance analysis and regression analysis of design variables and response variables, response surface of the response variable to the design variable, and its response surface equation are estimated to derive optimal design variables for the proposed pole piece shape. The experiment data required for predicting the relationships among them are obtained using finite-element method based on two-dimensional (2-D) numerical analysis. Therefore, this paper derived an optimal model for the magnetic gear with the novel pole piece using the Box-Behnken design, and the validity of the optimal design of the proposed pole piece shape through variance analysis and regression analysis demonstrated. [ABSTRACT FROM AUTHOR]

This paper focuses on the performance of conductive polymer and conducting transparent conductive oxide coated on flexible substrate under tensile loads. Poly (3, 4-ethylenedioxythiophene) (PEDOT) thin films with two different resistances (i.e., 150 and 225 Ω/sq) and indium tin oxide (ITO) thin films with another two different resistances (i.e., 60 and 200 Ω/sq) were used in the experiment. All films were coated on polyethylene terephthalate. A mechanical stretching device was used to stretch all sheets up to 12% of the original length. The considered stretching speeds in terms of strain rate were 0.01, 0.1, and 1 min-1. Two types of cracks were noticed after stretching of the ITO sheet. The first type was noticed at 6% strain level initiated and propagated perpendicularly to the direction of the load toward the edges of the sample, whereas the second type was noticed at 9% strain level initiated from different points on the first type of cracks and propagated perpendicularly with them. No clear cracks or deformation was seen on the surface of PEDOT after stretching. The percentage change in electrical resistance (PCER) with strain was investigated under three different strain rates, and it was noticeable that PCER increases with strain for both PEDOT and ITO sheets. However, the increase was huge in the case of ITO. Strain and initial sheet resistance were significant factors in terms of affecting the PCER for ITO films under stretching, and all of strain, strain rate, and initial sheet resistance were significant factors in terms of affecting PCER for PEDOT films under stretching according to the analysis of variance study. As a final conclusion from this paper, the electrical performance of PEDOT films is greatly better than that of ITO films under stretching. [ABSTRACT FROM PUBLISHER]

This paper proposes a blanker with a threshold value that is adaptively updated based on average signal power in an impulsive noise environment. For conventional receivers, using a blanker as a preprocessor improves the bit error performance in an impulsive noise environment by removing received signal samples with large amplitudes in order to reduce the effect of impulsive noise. However, the conventional blanker shows a poor bit error performance in case of high signal-to-noise ratio (or large signal power) due to a fixed threshold. Therefore, the proposed blanker, called adaptive threshold blanker, overcomes this problem by adaptively updating the threshold and achieves an excellent bit error performance. In addition, this paper also introduces a simple method for calculating the quasi-optimal threshold value for the proposed blanker. This simple method allows an easy calculation of the threshold in practical systems. In order to evaluate the bit error performance of the proposed blanker, an approximated probability density function (PDF) using a sum of weighted Gaussian PDFs with different variances is also discussed. It is observed that simulation results agree well with the performance analysis. [ABSTRACT FROM AUTHOR]

This paper studies the degrees of freedom (DoF) of two-way multiple-input multiple-output (MIMO) butterfly networks, a class of two-way four-unicast networks, that consists of four source/destination nodes and three relay nodes. We first give upper and lower bounds on the sum DoF of such network with any number of antennas in each node. We also study the DoF of the two-way MIMO butterfly network with caching at the relays. For the special case that each source/destination node has $M$ antennas and each relay node has $N$ antennas, we obtain the exact DoF for some special cases. Specifically, if $N \leq M$ , then without caching, ${\rm DoF}=2N$ , which is equal to the DoF of a one-way MIMO butterfly network, and with relay caching, ${\rm DoF}=4N$. Moreover, if $N > 2M$ , then with or without relay caching, ${\rm DoF}=4M$ which doubles the DoF of a one-way MIMO butterfly network. Hence, for this MIMO butterfly network, when the number of relay antennas is large compared with the number of source/destination antennas, bidirectional transmission improves the DoF; and when the number of relay antennas is small, relay caching improves the DoF. [ABSTRACT FROM AUTHOR]

Given a collection of entities (or nodes) in a network and our intermittent observations of activities from each entity, an important problem is to learn the hidden edges depicting directional relationships among these entities. Here, we study causal relationships (excitations) that are realized by a multivariate Hawkes process (MHP). The MHP and its variations (spatio-temporal point processes) have been used to study contagion in earthquakes, crimes, neural spiking activities, the stock and foreign exchange markets, and so on. In this paper, we consider the MHP with gaps (MHPG) in observations. We propose a variational problem for detecting sparsely hidden relationships with an MHP that takes into account the gaps from each entity. We bypass the problem of dealing with a large amount of missing events by introducing a small number of unknown boundary conditions. In the case where our observations are sparse (e.g., from 10% to 30%), we show through numerical simulations that robust recovery with MHPG is still possible even if the lengths of the observed intervals are small but they are chosen accordingly. The numerical results also show that the knowledge of gaps and imposing the right boundary conditions are very crucial in discovering the underlying patterns and hidden relationships. [ABSTRACT FROM PUBLISHER]

This paper focuses on the application of the variance-based global sensitivity analysis for a topological derivative method in order to solve a stochastic nonlinear time-dependent magnetoquasi-static interface problem. To illustrate the approach a permanent magnet (PM) synchronous machine has been considered. Our key objective is to provide a robust design of the rotor poles and of the tooth base in a stator for the reduction of the torque ripple and electromagnetic losses, while taking material uncertainties into account. Input variations of material parameters are modeled using the polynomial chaos expansion technique, which is incorporated into the stochastic collocation method in order to provide a response surface model. Additionally, we can benefit from the variance-based sensitivity analysis. This allows us to reduce the dimensionality of the stochastic optimization problems, described by the random-dependent cost functional. Finally, to validate our approach, we provide the 2-D simulations and analysis, which confirm the usefulness of the proposed method and yield a novel topology of a PM synchronous machine. [ABSTRACT FROM AUTHOR]

Clinical pathways (CPs) are popular healthcare management tools to standardize care and ensure quality. Analyzing CP compliance levels and variances is known to be useful for training and CP redesign purposes. Flexible semantics of the business process model and notation (BPMN) language has been shown to be useful for the modeling and analysis of complex protocols. However, in practical cases one may want to exploit that CPs often have the form of task-time matrices. This paper presents a new method parsing complex BPMN models and aligning traces to the models heuristically. A case study on variance analysis is undertaken, where a CP from the practice and two large sets of patients data from an electronic medical record (EMR) database are used. The results demonstrate that automated variance analysis between BPMN task-time models and real-life EMR data are feasible, whereas that was not the case for the existing analysis techniques. We also provide meaningful insights for further improvement. [ABSTRACT FROM PUBLISHER]

We propose a structure of a semiparametric two-component mixture model when one component is parametric and the other is defined through linear constraints on either its distribution function or its quantile measure. Estimation of a two-component mixture model with an unknown component is very difficult when no particular assumption is made on the structure of the unknown component. A symmetry assumption was used in the literature to simplify the estimation. Such method has the advantage of producing consistent and asymptotically normal estimators, and identifiability of the semiparametric mixture model becomes tractable. Still, existing methods, which estimate a semiparametric mixture model have their limits when the parametric component has unknown parameters or the proportion of the parametric part is either very high or very low. We propose in this paper a method to incorporate a prior linear information about the unknown component in order to better estimate the model when existing estimation methods fail. This linear information is either translated by linear constraints, such as moment-type constraints or L-moments constraints (linear constraints over the quantile). The new method is based on $\varphi -$ divergences and has a non classical form since the minimization is carried over both arguments of the divergence. The resulting estimators are proved to be consistent and asymptotically normal under standard assumptions. We show that using the Pearson’s \chi ^{2} divergence our algorithm has a linear complexity when the constraints are moment-type. Simulations on univariate and multivariate mixtures demonstrate the viability and the interest of our novel approach. [ABSTRACT FROM AUTHOR]

For circular trace scanning synthetic aperture radar (CTSSAR) with a circular track, the conventional hyperbolic equation becomes inadequate to express the range history of a point target accurately, and when it comes to the wide swath observation and imaging, the range variance makes it even harder to focus the target on the edge of the scene. Thus, an expression with high-order terms is needed to approximate the range history and the range variance should also be considered in the imaging algorithm. In this paper, based on the method of series reversion, a fourth-order approximated range model is established for the CTSSAR processing and the 2-D spectrum is derived for the echo signal in CTSSAR with circular trajectory. At the same time, in order to deal with the range-variant range cell migration problem in large-area CTSSAR imaging, a modified chirp scaling algorithm is proposed to realize precise wide swath CTSSAR focusing. Experiments and analyses are performed to validate the effectiveness of the proposed algorithm. [ABSTRACT FROM PUBLISHER]

Objective: This paper describes the formulation of a surface electromyogram (EMG) model capable of representing the variance distribution of EMG signals. Methods: In the model, EMG signals are handled based on a Gaussian white noise process with a mean of zero for each variance value. EMG signal variance is taken as a random variable that follows inverse gamma distribution, allowing the representation of noise superimposed onto this variance. Variance distribution estimation based on marginal likelihood maximization is also outlined in this paper. The procedure can be approximated using rectified and smoothed EMG signals, thereby allowing the determination of distribution parameters in real time at low computational cost. Results: A simulation experiment was performed to evaluate the accuracy of distribution estimation using artificially generated EMG signals, with results demonstrating that the proposed model's accuracy is higher than that of maximum-likelihood-based estimation. Analysis of variance distribution using real EMG data also suggested a relationship between variance distribution and signal-dependent noise. Conclusion: The study reported here was conducted to examine the performance of a proposed surface EMG model capable of representing variance distribution and a related distribution parameter estimation method. Experiments using artificial and real EMG data demonstrated the validity of the model. Significance: Variance distribution estimated using the proposed model exhibits potential in the estimation of muscle force. [ABSTRACT FROM PUBLISHER]