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The LLC resonant converter offers a promising solution for dc power conversion. The input-parallel output-parallel system offers low specification switches to use in high power applications. Moreover, the interleaving of the converters reduces the requirement of input and output passive filters, providing better dynamic performance and higher power density. However, the tolerances in the designed resonant components and the unavoidable mismatch in the circuit parameters lead to unbalanced estimated tank gain characteristics. Consequently, the zero-voltage-switching is lost, a serious imbalance in currents appears, and a thermal runaway is likely to occur. The proposed method utilizes the existing magnetics of resonant inductors and self-alleviates the imbalance in the output, input, and tank currents without increasing active or passive components, dedicated balancing controls, and expensive sensors. A 4 kW four-phase prototype is tested during steady state, transients, and phase-shedding modes to validate the performance of the proposed method. [ABSTRACT FROM AUTHOR]

Over the years, researchers have studied the evolution of Electrocardiogram (ECG) and the complex classification of cardiovascular diseases. This review focuses on the evolution of the ECG and covers the most recent signal processing schemes with milestones over the last 150 years systematically. Development phases of ECG, ECG leads, portable ECG monitors, Signal Processing schemes and Complex Transformations are discussed. This paper summarizes the development of ECG features detection for cardiac anomalies and the history of the development of ECG monitors, beginning from String Galvanometer. It also discusses the automated detections on ECG, beginning from 1960 to the most recent signal processing techniques. Additionally, this paper provides recommendations for future research directions.

By considering the application requirements of automatic welding of labels on the heads of bundled rebars in the iron and steel industry, an automatic label welding robot system is developed based on machine vision and image processing. The system consists of six modules: pickup unit, image unit, control unit, welding stud unit, label unit, and rebar unit. It can automatically pick up welding studs, pick up labels, perform welding, detect dropped labels, and detect dropped welding studs. The development process of label welding mainly includes the following steps. First, the Cascade RCNN object detection algorithm is used to detect the rebar head, so that the number of rebars and the pixel coordinates of the rebar head centers can be obtained. The detection accuracy of the number of rebars can reach 100%, and the Cascade RCNN average score is 0.9801. Then, an algorithm to identify the center of bundled rebar heads based on the variable-scale method (Davidon–Fletcher–Powell formula (DFP)) is proposed. In addition, limiting conditions, and a process for selection of weldable points for double-label welding are provided. And the coordinate conversion equations of weldable points in different coordinate systems are derived. Finally, a serious of functional verification tests of the robot system were carried out on an actual rebar production line, which verified the accuracy of the weldable point image recognition process, and the usability of the robot system. The results indicate that the accuracy of weldable point recognition can reach 98.99%.

Infants receiving care in the Neonatal Intensive Care Unit (NICU) experience several painful procedures during their hospitalization. Assessing neonatal pain is difficult because the current standard for assessment is subjective, inconsistent, and discontinuous. The intermittent and inconsistent assessment can induce poor treatment and, therefore, cause serious long-term outcomes. In this paper, we present a comprehensive pain assessment system that utilizes facial expressions along with crying sounds, body movement, and vital sign changes. The proposed automatic system generates a standardized pain assessment comparable to those obtained by conventional nurse-derived pain scores. The system achieved 95.56 percent accuracy using decision fusion of different pain responses that were recorded in a challenging clinical environment. In addition to the decision fusion, we present the performance of multimodal assessment using other fusion schemes as well as a unimodal assessment approach. We also discuss the impact of different factors (e.g., gestational age) on pain, propose several group-specific models for pain assessment (e.g., pre-term and full-term models), and compare the performance of these models with the performance of general models. While further research is needed, our results show that the automatic assessment of neonatal pain is a viable and more efficient alternative to the manual assessment. [ABSTRACT FROM AUTHOR]

For large-scale medical images, automatic pulmonary vessel segmentation is a fundamental and essential research for various pulmonary disease diagnoses. Most existing approaches have made much progress in vessel segmentation, but the accuracy still remains unsatisfactory due to the absence of discriminative features extracted from the images. To address this problem, we propose novel stagewise convolutional networks followed by an orientation-based region growing method. The stage-wise convolutional networks aim to learn discriminative features of pulmonary vessels automatically in a stageby-stage manner, where stage I is the lung segmentation module and stage II is the main vessel segmentation module. In stage I, the lung segmentation module extracts pulmonary regions based on the convolutional neural networks to preprocess computed tomography (CT) scans and provides a good initial value for subsequent work. In stage II, the main vessel segmentation module exploits refined fully convolutional networks to hierarchically learn rich representations for pulmonary vessels, which enables accurate vessel segmentation. In addition, we further propose an optimization module that refines the results from the previous module based on the orientation of vessels in 3D space. Extensive experiments demonstrate that the proposed method has achieved the best performance in pulmonary vessel segmentation compared with the state of the arts.

We propose an automatic framework to recover the illumination of indoor scenes based on a single RGB-D image. Unlike previous works, our method can recover spatially varying illumination without using any lighting capturing devices or HDR information. The recovered illumination can produce realistic rendering results. To model the geometry of the visible and invisible parts of scenes corresponding to the input RGB-D image, we assume that all objects shown in the image are located in a box with six faces and build a planar-based geometry model based on the input depth map. We then present a confidence-scoring based strategy to separate the light sources from the highlight areas. The positions of light sources both in and out of the camera's view are calculated based on the classification result and the recovered geometry model. Finally, an iterative procedure is proposed to calculate the colors of light sources and the materials in the scene. In addition, a data-driven method is used to set constraints on the light source intensities. Using the estimated light sources and geometry model, environment maps at different points in the scene are generated that can model the spatial variance of illumination. The experimental results demonstrate the validity and flexibility of our approach. [ABSTRACT FROM AUTHOR]

In this paper, we focus on traffic camera calibration and a visual speed measurement from a single monocular camera, which is an important task of visual traffic surveillance. Existing methods addressing this problem are difficult to compare due to a lack of a common data set with reliable ground truth. Therefore, it is not clear how the methods compare in various aspects and what factors are affecting their performance. We captured a new data set of 18 full-HD videos, each around 1 hr long, captured at six different locations. Vehicles in the videos (20 865 instances in total) are annotated with the precise speed measurements from optical gates using LiDAR and verified with several reference GPS tracks. We made the data set available for download and it contains the videos and metadata (calibration, lengths of features in image, annotations, and so on) for future comparison and evaluation. Camera calibration is the most crucial part of the speed measurement; therefore, we provide a brief overview of the methods and analyze a recently published method for fully automatic camera calibration and vehicle speed measurement and report the results on this data set in detail. [ABSTRACT FROM AUTHOR]

Noncontact assessment of heart rate (HR) in Zebrafish larvae, based on a video record of the organism, acquired using a camera mounted on a microscope, has gained enormous significance. Completely automatic and robust estimation of HR from videos of nontransgenic larvae requires the determination of an appropriate region of interest (ROI), followed by suitable signal processing steps. Toward such a goal, we develop a fully automatic and adaptive ROI enclosing a predominant portion of the beating heart, irrespective of the image resolution and zoom. The information within the ROI is used to get one or more time series, to be processed for extracting the signal containing information about the beating heart. Among the various possibilities, we show that the multichannel approach exploiting color information and based on independent component analysis to extract the cardiac signal—is desirable, due to several reasons, including its ability to handle noise, minor movements of the larvae or of the platform, and statistical performance. The proposed sequence of algorithms is validated on videos of 41 larvae (2 days and 4 days postfertilization). The computer estimated values of HR compared well with the ground truth obtained by visual-counting. We have also devised a method of tracking the ROI associated with drifting larvae and tested it on real data. In addition, an example of handling a type of arrhythmia is given. Note to Practitioners—Studying the Zebrafish larva as a model organism, is very useful in developmental biology, toxicology, and pharmacological testing—due to the similarity in the response to various drugs, easiness of breeding, high throughput, lesser cost involved, in vivo testing, and reduced bioethical concerns. The traditional approach of manually counting the heart beat of the larvae kept under a microscope is tedious. Automating the process using videos of the larvae placed under a microscope is therefore important. The existing approaches are either not completely automatic or involve expensive equipment and/or modifying the larvae (biological intervention) such that the heart expresses fluorescence protein, and are not designed to handle even minor motion. We consider completely automating the process of estimating the heart rate from nontransgenic zebrafish larvae—avoiding any kind of manual intervention. The solution to the preceding lies in automating: 1) the determination of the ROI that adapts to the location and size of the larvae, camera zoom, and image resolution; 2) the tracking of the ROI to handle drifting larvae; and 3) estimation of HR through appropriate signal processing, to add to the robustness of the method, including the handling of mild motion. We present a completely automatic and robust HR estimation algorithm, which works automatically to estimate an adaptive ROI, followed by independent component analysis. The method works even in the presence of minor motion of the larvae, including drift. We plan to extend this paper to studying heart rate variability—an important method of quantifying the neural control of the cardiovascular system as well as identifying several pathologies—and certain types of arrhythmia. [ABSTRACT FROM AUTHOR]

An automatic wideband 90° phase shifter for optically pumped cesium magnetometer has been investigated experimentally. As an important part of the self-oscillating optically pumped cesium magnetometer detecting circuit, the band and automation of the phase shifter directly affect the continuous measurement of the magnetic field. At the same time, a phase deviation of 1° will cause 1 Hz change of the resonant frequency. In order to meet the continuous measurement of the geomagnetic field, an automatic wideband 90° phase shifter based on the all-pass filter has been designed. It can be used to precisely make the phase of the detected signal shifted by 90° in a wide range of frequencies from 50 to 350 kHz. Through the actual test and analysis, the phase shift error of the automatic phase shifter is less than 0.1°, and the relative error less than 0.1%. It has been proved the feasibility of the automatic wideband 90° phase shifter applied to the cesium optically pumped magnetometer. [ABSTRACT FROM PUBLISHER]

This paper presents a method to improve the traditional mixture of deformable part models (MDPM) method from the learning perspective. First, an object part configuration learning algorithm based on group sparsity constraint is introduced to automatically discover the object part number, size, and location. The algorithm imposes two additional regularization terms in addition to the standard hinge loss function. The first term focuses on automatic part selection and the second term focuses on automatic part placement. Second, this paper introduces an improved MDPM training framework. The framework applies a learned transformation to normalize the prediction score from each individual deformable part model (DPM) into a pseudo probability such that the partition of the entire object appearance feature space becomes less sensitive to the prior distributions of different DPMs. Finally, the two proposed improvements are combined and formulated under the expectation-maximization framework. We evaluate our method mainly using the PASCAL VOC2007 and VOC2010 detection benchmarks and show that the proposed learning algorithms could increase the detection mean AP score by 2.4% and 0.9%, respectively, on these two data sets when using the proposed part selection method and the training algorithm. We also present further in-depth analysis of the proposed algorithm in the experiments. [ABSTRACT FROM PUBLISHER]

We present a novel technique to generate transformations between arbitrary volumes, providing both expressive distances and smooth interpolates. In contrast to conventional morphing or warping approaches, our technique requires no user guidance, intermediate representations (like extracted features), or blending, and imposes no restrictions regarding shape or structure. Our technique operates directly on the volumetric data representation, and while linear programming approaches could solve the underlying problem optimally, their polynomial complexity makes them infeasible for high-resolution volumes. We therefore propose a progressive refinement approach designed for parallel execution that is able to quickly deliver approximate results that are iteratively improved toward the optimum. On this basis, we further present a new approach for the streaming selection of time steps in temporal data that allows for the reconstruction of the full sequence with a user-specified error bound. We finally demonstrate the utility of our technique for different applications, compare our approach against alternatives, and evaluate its characteristics with a variety of different data sets. [ABSTRACT FROM PUBLISHER]

Regular expressions are routinely used in a variety of different application domains. But building a regular expression involves a considerable amount of skill, expertise, and creativity. In this work, the authors investigate whether a machine can surrogate these qualities and automatically construct regular expressions for tasks of realistic complexity. They discuss a large-scale experiment involving more than 1,700 users on 10 challenging tasks. The authors compare the solutions constructed by these users to those constructed by a tool based on genetic programming that they recently developed and made publicly available. The quality of automatically constructed solutions turned out to be similar to the quality of those constructed by the most skilled user group; the time for automatic construction was likewise similar to the time required by human users. [ABSTRACT FROM PUBLISHER]

Automatic segmentation of the primary object in a video clip is a challenging problem as there is no prior knowledge of the primary object. Most existing techniques thus adapt an iterative approach for foreground and background appearance modeling, i.e., fix the appearance model while optimizing the segmentation and fix the segmentation while optimizing the appearance model. However, these approaches may rely on good initialization and can be easily trapped in local optimal. In addition, they are usually time consuming for analyzing videos. To address these limitations, we propose a novel and efficient appearance modeling technique for automatic primary video object segmentation in the Markov random field (MRF) framework. It embeds the appearance constraint as auxiliary nodes and edges in the MRF structure, and can optimize both the segmentation and appearance model parameters simultaneously in one graph cut. The extensive experimental evaluations validate the superiority of the proposed approach over the state-of-the-art methods, in both efficiency and effectiveness. [ABSTRACT FROM PUBLISHER]

Some performance evaluation systems for building extraction techniques are manual in the sense that only visual results are provided or human judgment is employed. Many evaluation systems that employ one or more thresholds to ascertain whether an extracted building or roof plane is correct are subjective and cannot be applied in general. There are only a small number of automatic and threshold-free evaluation systems, but these do not necessarily consider all special cases, e.g., when over- and under-segmentation occurs during the extraction of roof planes. This paper proposes an automatic and threshold-free evaluation system that offers robust object-based evaluation of building extraction techniques. It makes one-to-one correspondences between extracted and reference entities using the maximum overlaps. Its application to the evaluation of a building extraction technique shows that it estimates different performance indicators including segmentation errors. Consequently, it can be employed for bias-free evaluation of other techniques whose outputs consist of polygonal entities. [ABSTRACT FROM PUBLISHER]

Ship–sea contrast can be improved significantly when the full polarimetric information is used, compared with the information provided by a single polarization channel. Therefore, a new automatic ship detection method, termed SPAN Wishart (SPWH), is proposed in this paper based on an unsupervised classification concept, which combines the SPAN of a polarimetric SAR (POLSAR) data with the complex Wishart classifier. The significant improvement of this technique is to utilize the SPAN of ship cluster center as an iterative termination criterion to realize the automatic ship detection. Then, another method based on multifrequency is proposed to discriminate between ships and their ambiguities to provide a substitute for the ground truth for the subsequent validation of the SPWH algorithm by using an AIRSAR polarimetric dataset, which consists of C- and L-band data covering Kojimawan in Tamano of Japan. After that, the SPWH is validated only using the L-band data with the defined ships by comparing the performance of the SPWH with a traditional CFAR detector. By contrast, the SPWH algorithm is more effective, robust, and completely automatic. [ABSTRACT FROM PUBLISHER]

We propose an automatic wavelength control method using Rayleigh backscattering in a feeder fiber for tunable lasers employed in a wavelength-division multiplexing passive optical network. We demonstrate two realization methods, maximizing backscattered power and maximizing the second harmonic component with wavelength modulation. We analyze their limiting factors and impacts on the system performance. We use a VCSEL as a low-cost tunable light source. [ABSTRACT FROM PUBLISHER]

This paper deals with distributed multiagent reconfigurable embedded-control systems following the component-based International Industrial Standard IEC61499 in which a function block (FB) is an event-triggered software component owning data and a control application is a distributed network of FBs. We define an architecture of reconfigurable multiagent systems, where a reconfiguration agent modeled by nested state machines is affected to each device of the execution environment to apply local automatic reconfigurations, and a coordination agent is proposed for any coordination between devices in order to guarantee safe and adequate distributed reconfigurations. A communication protocol is proposed in our research to handle coordinations between agents by using well-defined coordination matrices. We define, in addition, Extensible Markup Language (XML) based implementations for both kinds of agents, where XML code blocks are exchanged between devices. The contributions of the paper are applied to two benchmark production systems available in our laboratory. [ABSTRACT FROM PUBLISHER]

Abstract-Automated extraction of pulmonary anatomy provides a foundation for computerized analysis of computed tomography (CT) scans of the chest. A completely automatic method is presented to segment the lungs, lobes and pulmonary segments from volumetric CT chest scans. The method starts with lung segmentation based on region growing and standard image processing techniques. Next, the pulmonary fissures are extracted by a supervised filter. Subsequently the lung lobes are obtained by voxel classification where the position of voxels in the lung and relative to the fissures are used as features. Finally, each lobe is subdivided in its pulmonary segments by applying another voxel classification that employs features based on the detected fissures and the relative position of voxels in the lobe. The method was evaluated on 100 low-dose CT scans obtained from a lung cancer screening trial and compared to estimates of both interobserver and intraobserver agreement. The method was able to segment the pulmonary segments with high accuracy (77%), comparable to both interobserver and intraobserver accuracy (74% and 80%, respectively). [ABSTRACT FROM AUTHOR]

Optical disc recording technology using many types of write strategy (WS) has been developed for optical discs. The optimum WS for each disc is determined based on the material of recordable layer, recording speed, specification of the optical heads and so on. Parameters of WS should be determined by accurate tuning for each optical disc. It has been strongly desired to develop an actual Auto-Write Strategy (AWS) technology to reduce the time for preparing WSs. A new AWS system to optimize the WS for 2x-speed recording on DVD-R discs was developed. This system can detect the pulse widths and signal level of RF signals, and can get better parameters of WS by calculation. As a result, optimum WS can be obtained for optical discs. In this paper, we introduce our new AWS system and experimental result of the effectiveness of this system for the optical discs in the actual market. [ABSTRACT FROM AUTHOR]

Detailed reliability modeling and analysis of industrial plants provides an estimate of the frequency and duration of load point interruptions. The duration of repair and switching activities necessary to restore a unique power system configuration to a normal operating state from an outage state has a significant impact on the power system reliability levels of industrial power systems. This paper presents and discusses the significant variations in the frequency and duration of load point interruptions at an industrial plant due to manual and automatic switching activities. Three case studies with different percentages of open- and short-circuit failure modes of circuit breakers and fuses will also be presented and discussed for both manual and automatic switching restoration activities. [ABSTRACT FROM AUTHOR]