Trace Segments Research Articles

A network analysis based approach to characterizing periodic sharp wave complexes in electroencephalograms of patients with sporadic CJD

Creutzfeldt–Jacob disease (CJD) is a rapidly progressive, uniformly fatal transmissible spongiform encephalopathy. Sporadic CJD (sCJD) is the most common form of CJD. Electroencephalography (EEG) is one of the main methods to perform clinical diagnosis of CJD, mainly because of periodic sharp wave complexes (PSWCs). In this paper, we propose a network analysis based approach to characterizing PSWCs in EEGs of patients with sCJD. Our approach associates a network with each EEG at disposal and defines a new numerical coefficient and some network motifs, which characterize the presence of PSWCs in an EEG tracing. The new coefficient, called connection coefficient, and the detected network motifs are capable of characterizing the EEG tracing segments with PSWCs. Furthermore, network motifs are able to detect what are the most active and/or connected brain areas in the tracing segments with PSWCs. The results obtained show that, analogously to what happens for other neurological diseases, network analysis can be successfully exploited to investigate sCJD.

Changes in the Spectral Characteristics of the Electroencephalogram during Biocontrol of Heart Rate Variability Parameters in Healthy Subjects

Objectives. To assess changes in the spectral characteristics of the electroencephalogram (EEG) in humans during single sessions of biocontrol of hart rate variability parameters using biological feedback (BFB). Materials and Methods. A total of 150 adolescents aged 16–17 years were studied. Adolescents of the BFB group (n = 110) took part in biocontrol sessions to increase vagal influences on heart rate; the control group (n = 40) remained in the state of calm wakefulness. EEG recordings were made with the eyes closed before and after BFB training. EEG characteristics were studied in terms of changes in absolute power levels (μV2) in artifact-free trace segments in each frequency range. Results and conclusions. The ability of human subjects to change the activity of heart rate parameters determines the level of action on the functions of the central autonomic regulatory structures. This biocontrol method promotes improvements in cortical stability, with increases in α and decreases in β EEG activity. Brain activity synchronization processes during biocontrol were more clearly apparent in the right hemisphere, often involving the prefrontal areas.

Towards systematic, data-driven validation of a collaborative, multi-scale model of Caenorhabditis elegans.

The OpenWorm Project is an international open-source collaboration to create a multi-scale model of the organism Caenorhabditis elegans At each scale, including subcellular, cellular, network and behaviour, this project employs one or more computational models that aim to recapitulate the corresponding biological system at that scale. This requires that the simulated behaviour of each model be compared with experimental data both as the model is continuously refined and as new experimental data become available. Here we report the use of SciUnit, a software framework for model validation, to attempt to achieve these goals. During project development, each model is continuously subjected to data-driven 'unit tests' that quantitatively summarize model-data agreement, identifying modelling progress and highlighting particular aspects of each model that fail to adequately reproduce known features of the biological organism and its components. This workflow is publicly visible via both GitHub and a web application and accepts community contributions to ensure that modelling goals are transparent and well-informed.This article is part of a discussion meeting issue 'Connectome to behaviour: modelling C. elegans at cellular resolution'.

Hierarchical Cooperative Discovery of Personal Places from Location Traces

It is becoming increasingly important to accurately detect a user's presence at certain locations during certain times of the day, e.g., to study the user's patterns with respect to mobility, behavior, or social interactions and to enable the delivery of targeted services. However, instead of geographic locations, it is often more important to determine a locale that is relevant to the user, e.g., the place of work, home, homes of family and friends, social gathering places, etc. These significant personal places can be determined through analysis, e.g., via segmentation of location traces into a discrete sequence of places. However, segmentation of traces with many gaps (e.g., due to loss of network connectivity or GPS signal) results in a large number of small segments, where many of these segments actually belong together. This work proposes a novel segmentation approach that opportunistically fills gaps in a user's location trace by borrowing location data from other co-located users utilizing the power of mobile crowd sensing and computing (MCSC) paradigm. Through our analysis of four separate large-scale crowd sensing study datasets, we show that our approach yields more and larger segments than the state-of-the-art, where each segment accurately represents the presence of a user at a significant personal place.

Longitudinal Assessment of Hippocampal Atrophy in Midlife and Early Old Age: Contrasting Manual Tracing and Semi-automated Segmentation (FreeSurfer).

It is important to have accurate estimates of normal age-related brain structure changes and to understand how the choice of measurement technique may bias those estimates. We compared longitudinal change in hippocampal volume, laterality and atrophy measured by manual tracing and FreeSurfer (version 5.3) in middle age (n = 244, 47.2[1.4] years) and older age (n = 199, 67.0[1.4] years) individuals over 8years. The proportion of overlap (Dice coefficient) between the segmented hippocampi was calculated and we hypothesised that the proportion of overlap would be higher for older individuals as a consequence of higher atrophy. Hippocampal volumes produced by FreeSurfer were larger than manually traced volumes. Both methods produced a left less than right volume laterality difference. Over time this laterality difference increased for manual tracing and decreased for FreeSurfer leading to laterality differences in left and right estimated atrophy rates. The overlap proportion between methods was not significantly different for older individuals, but was greater for the right hippocampus. Estimated middle age annualised atrophy rates were - 0.39(1.0) left, 0.07(1.01) right, - 0.17(0.88) total for manual tracing and - 0.15(0.69) left, - 0.20(0.63) right, - 0.18(0.57) total for FreeSurfer. Older age atrophy rates were - 0.43(1.32) left, - 0.15(1.41) right, - 0.30 (1.23) total for manual tracing and - 0.34(0.79) left, - 0.68(0.78) right, - 0.51(0.65) total for FreeSurfer. FreeSurfer reliably segments the hippocampus producing atrophy rates that are comparable to manual tracing with some biases that need to be considered in study design. FreeSurfer is suited for use in large longitudinal studies where it is not cost effective to use manual tracing.

A Method for Extracting Road Boundary Information from Crowdsourcing Vehicle GPS Trajectories.

Crowdsourcing trajectory data is an important approach for accessing and updating road information. In this paper, we present a novel approach for extracting road boundary information from crowdsourcing vehicle traces based on Delaunay triangulation (DT). First, an optimization and interpolation method is proposed to filter abnormal trace segments from raw global positioning system (GPS) traces and interpolate the optimization segments adaptively to ensure there are enough tracking points. Second, constructing the DT and the Voronoi diagram within interpolated tracking lines to calculate road boundary descriptors using the area of Voronoi cell and the length of triangle edge. Then, the road boundary detection model is established integrating the boundary descriptors and trajectory movement features (e.g., direction) by DT. Third, using the boundary detection model to detect road boundary from the DT constructed by trajectory lines, and a regional growing method based on seed polygons is proposed to extract the road boundary. Experiments were conducted using the GPS traces of taxis in Beijing, China, and the results show that the proposed method is suitable for extracting the road boundary from low-frequency GPS traces, multi-type road structures, and different time intervals. Compared with two existing methods, the automatically extracted boundary information was proved to be of higher quality.

Application of Machine Learning Techniques in Post-Silicon Debugging and Bug Localization

As the size of hardware (HW) design increases significantly, a huge amount of data is generated during the design simulation, emulation or prototyping. Debugging large HW designs becomes a tedious, time consuming and a bottleneck task within the function verification activities. This paper proposes the utilization of machine learning techniques to automate the diagnosis of design trace dump as well as helping in bug localization during post-silicon validation. Our framework starts by signal selection algorithm that identifies which signals to monitor during design execution. Signal selection depends on signal types as well as their connectivity network. The design is then executed and the trace dump is saved for offline analysis. Big-Data processing technique, namely, Map-Reduce is used to overcome the challenge of processing huge trace dump resulted from design running on FPGA prototype. K-means Clustering method is applied to group trace segments that are very similar and to identify the ones with a rare occurrence during the design execution. Additionally, we propose a bug localization framework in which X-means clustering is used to group the passing regression tests in clusters such that buggy tests can be detected when they fail to be assigned to any of the trained clusters. Our experimental results demonstrate the feasibility of the proposed approach in guiding the debugging effort using a group of industrial HW designs and its ability to detect multiple design injected defects using mutation-based-testing method.

Design of a Novel Miniaturized Frequency Selective Surface Based on 2.5‐Dimensional Jerusalem Cross for 5G Applications

A compact frequency selective surface (FSS) for 5G applications has been designed based on 2.5‐dimensional Jerusalem cross. The proposed element consists of two main parts: the successive segments of the metal traces placed alternately on the two surfaces of the substrate and the vertical vias connecting traces. Compared with previous published two‐dimensional miniaturized elements, the transmission curves indicate a significant size reduction (1/26 wavelengths at the resonant frequency) and exhibit good angular and polarization stabilities. Furthermore, a general equivalent circuit model is established to provide direct physical insight into the operating principle of this FSS. A prototype of the proposed FSS has been fabricated and measured, and the results validate this design.

Actigraphy in Holter Monitoring for Assessment of Sleep Parameters and Periods

An automated algorithm for identifying periods of sleep and waking using data from the motor activity of the subject’s thoracic cage is proposed. The algorithm is based on accelerometry and is intended as a component of systems used for prolonged monitoring of cardiovascular system functions. Use of this algorithm allows trace segments to be defined as “sleep” or “waking” with accuracies of up to 77.6% in the overall cohort, 83.7% when there are no sleep disorders, and 70.5% in patients with respiratory abnormalities during sleep. Use of the algorithm in Holter monitoring systems in routine practice will help improve the accuracy of diagnostic results in cardiovascular diseases and the detection of sleep disorders.

Traffic-related trace elements in soils along six highway segments on the Tibetan Plateau: Influence factors and spatial variation.

The accumulation of traffic-related trace elements in soil as the result of anthropogenic activities raises serious concerns about environmental pollution and public health. Traffic is the main source of trace elements in roadside soil on the Tibetan Plateau, an area otherwise devoid of industrial emissions. Indeed, the rapid development of tourism and transportation in this region means it is becoming increasingly important to identify the accumulation levels, influence distance, spatial distribution, and other relevant factors influencing trace elements. In this study, 229 soil samples along six segments of the major transportation routes on the Tibetan Plateau (highways G214, S308, and G109), were collected for analysis of eight trace elements (Cr, Co, Ni, As, Cu, Zn, Cd, and Pb). The results of statistical analyses showed that of the eight trace elements in soils, Cu, Zn, Cd, and Pb were primarily derived from traffic. The relationship between the trace element accumulation levels and the distance from the roadside followed an exponential decline, with the exception of Segment 3, the only unpaved gravel road studied. In addition, the distance of influence from the roadside varied by trace element and segment, ranging from 16m to 144m. Background values for each segment were different because of soil heterogeneity, while a number of other potential influencing factors (including traffic volume, road surface material, roadside distance, land cover, terrain, and altitude) all had significant effects on trace-element concentrations. Overall, however, concentrations along most of the road segments investigated were at, or below, levels defined as low on the Nemero Synthesis index.

Metal Artifact Reduction in Dental Computed Tomography Images Based on Sinogram Segmentation Using Curvelet Transform Followed by Hough Transform

In X-ray computed tomography (CT), the presence of metal objects in a patient's body produces streak artifacts in the reconstructed images. During the past decades, many different methods were proposed for the reduction or elimination of the streaking artifacts. When scanning a patient, the projection data affected by metal objects (missing projections) appear as regions with high intensities in the sinogram. In spiral fan beam CT, these regions are sinusoid-like curves on sinogram. During the first time, if the metal curves are detected carefully, then, they can be replaced by corresponding unaffected projections using other slices or opposite views; therefore, the CT slices regenerated by the modified sonogram will be imaged with high quality. In this paper, a new method of the segmentation of metal traces in spiral fan-beam CT sinogram is proposed. This method is based on a sinogram curve detection using a curvelet transform followed by 2D Hough transform. The initial enhancement of the sinogram using modified curvelet transform coefficients is performed by suppressing all the coefficients of one band and applying 2D Hough transform to detect more precisely metal curves. To evaluate the performance of the proposed method for the detection of metal curves in a sinogram, precision and recall metrics are calculated. Compared with other methods, the results show that the proposed method is capable of detecting metal curves, with better precision and good recovery.

Variable complexity of the US Continental Divide trace

Map-view complexity of 65 contiguous segments of the conterminous US Continental Divide trace was evaluated by the divider method, with each segment’s geometry approximated by a fractal dimension (D) value, or two D values applying to fine and coarse scales of inspection. Results support a generalization that D values in the range 1.05–1.12 are typical for drainage divides viewed at regional to national scales. However about 20% of cases reviewed in this study are smoother (D = 1.01–1.04) or more complex (D = 1.13–1.18) traces. Complexities of these watershed boundary segments show no significant correlation with physiographic province or local relief. The primary influence of bedrock lithology and surface process is seen in zones of Quaternary volcanism, where uncommonly smooth and highly complex divide trace segments are more the rule than exception. Uncommonly smooth divide traces are also found descending mountain pediments in areas of parallel drainage, and some of the most tortuous traces are associated with drainage patterns disrupted by non-fluvial processes. Divide trace segments crossing terrain with uniform lithology show decreased complexity, but traces with the broadest range of complexity, including highest D values, occur in areas with moderate, not extreme, degrees of bedrock diversity.

Recognizing the Operating Hand and the Hand-Changing Process for User Interface Adjustment on Smartphones.

As the size of smartphone touchscreens has become larger and larger in recent years, operability with a single hand is getting worse, especially for female users. We envision that user experience can be significantly improved if smartphones are able to recognize the current operating hand, detect the hand-changing process and then adjust the user interfaces subsequently. In this paper, we proposed, implemented and evaluated two novel systems. The first one leverages the user-generated touchscreen traces to recognize the current operating hand, and the second one utilizes the accelerometer and gyroscope data of all kinds of activities in the user’s daily life to detect the hand-changing process. These two systems are based on two supervised classifiers constructed from a series of refined touchscreen trace, accelerometer and gyroscope features. As opposed to existing solutions that all require users to select the current operating hand or confirm the hand-changing process manually, our systems follow much more convenient and practical methods and allow users to change the operating hand frequently without any harm to the user experience. We conduct extensive experiments on Samsung Galaxy S4 smartphones, and the evaluation results demonstrate that our proposed systems can recognize the current operating hand and detect the hand-changing process with 94.1% and 93.9% precision and 94.1% and 93.7% True Positive Rates (TPR) respectively, when deciding with a single touchscreen trace or accelerometer-gyroscope data segment, and the False Positive Rates (FPR) are as low as 2.6% and 0.7% accordingly. These two systems can either work completely independently and achieve pretty high accuracies or work jointly to further improve the recognition accuracy.

Segmenting large traces of inter-process communication with a focus on high performance computing systems

The understanding of the interactions among processes of a High Performance Computing (HPC) system can be made easier if trace analysis is used. Traces, however, can be quite large, making it difficult to analyze their content unless some abstraction is provided. This paper presents a novel trace abstraction approach that aims to facilitate the analysis of large execution traces generated from HPC applications. Our approach allows automatic segmentation of large traces into smaller and meaningful clusters that reflect the various execution phases of the traced scenarios. Our approach is based on the application of information theory principles to the analysis of sequences of communication patterns extracted from traces of HPC systems. This work is inspired by recent studies in the field of bioinformatics where several techniques have been proposed to segment DNA sequences into homogeneous sub-domains, where each sub-domain exhibits a certain degree of internal homogeneity. Trace segments can be used in a number of applications such as recovering high-level views of the system behavior and program understanding. We demonstrate the usefulness of our approach by applying it to different traces of hundreds of millions of events, generated from two HPC systems.

Hippocampus and amygdala volumes from magnetic resonance images in children: Assessing accuracy of FreeSurfer and FSL against manual segmentation

The volumetric quantification of brain structures is of great interest in pediatric populations because it allows the investigation of different factors influencing neurodevelopment. FreeSurfer and FSL both provide frequently used packages for automatic segmentation of brain structures. In this study, we examined the accuracy and consistency of those two automated protocols relative to manual segmentation, commonly considered as the “gold standard” technique, for estimating hippocampus and amygdala volumes in a sample of preadolescent children aged between 6 to 11years. The volumes obtained with FreeSurfer and FSL-FIRST were evaluated and compared with manual segmentations with respect to volume difference, spatial agreement and between- and within-method correlations.Results highlighted a tendency for both automated techniques to overestimate hippocampus and amygdala volumes, in comparison to manual segmentation. This was more pronounced when using FreeSurfer than FSL-FIRST and, for both techniques, the overestimation was more marked for the amygdala than the hippocampus. Pearson correlations support moderate associations between manual tracing and FreeSurfer for hippocampus (right r=0.69, p<0.001; left r=0.77, p<0.001) and amygdala (right r=0.61, p<0.001; left r=0.67, p<0.001) volumes. Correlation coefficients between manual segmentation and FSL-FIRST were statistically significant (right hippocampus r=0.59, p<0.001; left hippocampus r=0.51, p<0.001; right amygdala r=0.35, p<0.001; left amygdala r=0.31, p<0.001) but were significantly weaker, for all investigated structures. When computing intraclass correlation coefficients between manual tracing and automatic segmentation, all comparisons, except for left hippocampus volume estimated with FreeSurfer, failed to reach 0.70. When looking at each method separately, correlations between left and right hemispheric volumes showed strong associations between bilateral hippocampus and bilateral amygdala volumes when assessed using manual segmentation or FreeSurfer. These correlations were significantly weaker when volumes were assessed with FSL-FIRST. Finally, Bland–Altman plots suggest that the difference between manual and automatic segmentation might be influenced by the volume of the structure, because smaller volumes were associated with larger volume differences between techniques.These results demonstrate that, at least in a pediatric population, the agreement between amygdala and hippocampus volumes obtained with automated FSL-FIRST and FreeSurfer protocols and those obtained with manual segmentation is not strong. Visual inspection by an informed individual and, if necessary, manual correction of automated segmentation outputs are important to ensure validity of volumetric results and interpretation of related findings.

Problem Detection in Real-Time Systems by Trace Analysis

This paper focuses on the analysis of execution traces for real-time systems. Kernel tracing can provide useful information, without having to instrument the applications studied. However, the generated traces are often very large. The challenge is to retrieve only relevant data in order to find quickly complex or erratic real-time problems. We propose a new approach to help finding those problems. First, we provide a way to define the execution model of real-time tasks with the optional suggestions of a pattern discovery algorithm. Then, we show the resulting real-time jobs in a Comparison View, to highlight those that are problematic. Once some jobs that present irregularities are selected, different analyses are executed on the corresponding trace segments instead of the whole trace. This allows saving huge amount of time and execute more complex analyses. Our main contribution is to combine the critical path analysis with the scheduling information to detect scheduling problems. The efficiency of the proposed method is demonstrated with two test cases, where problems that were difficult to identify were found in a few minutes.

A new method for automated discontinuity trace mapping on rock mass 3D surface model

This paper presents an automated discontinuity trace mapping method on a 3D surface model of rock mass. Feature points of discontinuity traces are first detected using the Normal Tensor Voting Theory, which is robust to noisy point cloud data. Discontinuity traces are then extracted from feature points in four steps: (1) trace feature point grouping, (2) trace segment growth, (3) trace segment connection, and (4) redundant trace segment removal. A sensitivity analysis is conducted to identify optimal values for the parameters used in the proposed method. The optimal triangular mesh element size is between 5cm and 6cm; the angle threshold in the trace segment growth step is between 70° and 90°; the angle threshold in the trace segment connection step is between 50° and 70°, and the distance threshold should be at least 15 times the mean triangular mesh element size. The method is applied to the excavation face trace mapping of a drill-and-blast tunnel. The results show that the proposed discontinuity trace mapping method is fast and effective and could be used as a supplement to traditional direct measurement of discontinuity traces.

Object-based Dune Analysis: Automated dune mapping and pattern characterization for Ganges Chasma and Gale crater, Mars

A method that enables the automated mapping and characterization of dune fields on Mars is described. Using CTX image mosaics, the introduced Object-based Dune Analysis (OBDA) technique produces an objective and reproducible mapping of dune morphologies over extensive areas. The data set thus obtained integrates a large variety of data, allowing a simple cross-analysis of dune patterns, spectral and morphometric information, and mesoscale wind models.Two dune fields, located in Gale crater and Ganges Chasma, were used to test and validate the methodology. The segmentation of dune-related morphologies is highly efficient, reaching overall accuracies of 95%. In addition, we show that the automated segmentation of slipface traces is also possible with expected accuracies of 85–90%.A qualitative and quantitative comparison of the final outputs with photointerpretations is performed, and the precision of the directional characterization of the dune patterns is evaluated. We demonstrate a good agreement between the OBDA outputs and the photointerpreted dune morphologies, with local trend deviations below 45° for 80–95% of the mapped areas. Because the developed algorithm is tuned for the recognition of linear features from the imagery, the slipfaces of small barchans can be preferentially overlooked owing to their small extent at the spatial resolution of the CTX mosaics. Dune types composed of longer linear morphologies are much better represented, including correct mapping of secondary structures. Having proved the effectiveness and accuracy of the mapping procedure, we discuss its future applications for the improvement of dune catalogs on Mars.

Volumetric brain analysis in neurosurgery: Part 1. Particle filter segmentation of brain and cerebrospinal fluid growth dynamics from MRI and CT images.

Accurate edge tracing segmentation remains an incompletely solved problem in brain image analysis. The authors propose a novel algorithm using a particle filter to follow the boundary of the brain in the style often used in autonomous air and ground vehicle navigation. Their goals were to create a versatile tool to segment brain and fluid in MRI and CT images of the developing brain, lay the foundation for an intelligent automated edge tracker that is modality independent, and segment normative data from MRI that can be applied to both MRI and CT. Simulated MRI data sets were used to train and evaluate the particle filter segmentation algorithm. The method was then applied to produce normative growth curves for children and adolescents from 0 to 18 years of age for brain and fluid from MR images from the National Institutes of Health pediatric database and these data were compared to historical results. The authors further adapted this method for use with CT images of pediatric hydrocephalus and compared the results with hand-segmented data. Segmentation of simulated MRI data with varied levels of noise (0%-9%) and spatial inhomogeneity (0%-40%) resulted in percent errors ranging from 0.06% to 5.38% for brain volume and 2.45% to 22.3% for fluid volume. The authors used this tool to create normal brain and CSF growth curves from MR images. The calculated growth curves showed excellent consistency with historical data. Additionally, compared with manual segmentation the particle filter accurately segmented brain and fluid volumes from CT scans of 5 pediatric patients with hydrocephalus (p<0.001). The authors have produced the first normative brain and CSF growth curves for children and adolescents 0-18 years of age. In addition, this study includes the first use of a particle filter as an edge tracker in image segmentation and offers a semiautomatic method to segment both pediatric and adult brain data from MR and CT images. The particle filter has the potential to be further automated toward a clinical rather than research tool with both of these modalities. Because of its modality independence, it has the capability to allow CT to be a more effective diagnostic tool for neurological disorders, a task of substantial importance in emergency settings and in developing countries where CT is often the only available method of brain imaging.

SCAN: an approach to label and relate execution trace segments

ABSTRACTProgram comprehension is a prerequisite to any maintenance and evolution task. In particular, when performing feature location, developers perform program comprehension by abstracting software features and identifying the links between high‐level abstractions (features) and program elements.We present Segment Concept AssigNer (SCAN), an approach to support developers in feature location. SCAN uses a search‐based approach to split execution traces into cohesive segments. Then, it labels the segments with relevant keywords and, finally, uses formal concept analysis to identify relations among segments. In a first study, we evaluate the performances of SCAN on six Java programs by 31 participants. We report an average precision of 69% and a recall of 63% when comparing the manual and automatic labels and a precision of 63% regarding the relations among segments identified by SCAN. After that, we evaluate the usefulness of SCAN for the purpose of feature location on two Java programs. We provide evidence that SCAN (i) identifies 69% of the gold set methods and (ii) is effective in reducing the quantity of information that developers must process to locate features—reducing the number of methods to understand by an average of 43% compared to the entire execution traces. Copyright © 2014 John Wiley &amp; Sons, Ltd.