Representation Of Segments Research Articles

Pole representation of the dislocation segment generalized susceptibility diagonal matrix elements

Diagonal elements of the generalized susceptibility matrix in the form of expansion in a series of poles are presented. Screw dislocation segment considered. The calculation of the series coefficients for different values of the dislocation segment normalized lengths has been carried out. The real and imaginary parts of the diagonal matrix elements are calculated. The results of the calculations are presented in the figures. The figures show a good agreement of the diagonal matrix elements direct calculation results with the calculation carried out in this work.

Maximum entropy PLDA for robust speaker recognition under speech coding distortion

The system combining i-vector and probabilistic linear discriminant analysis (PLDA) has been applied with great success in the speaker recognition task. The i-vector space gives a low-dimensional representation of a speech segment and training data of a PLDA model, which offers greater robustness under different conditions. In this paper, we propose a new framework based on i-vector/PLDA and Maximum Entropy (ME) to improve the performance of speaker identification system in the presence of speech coding distortion. The results are reported on TIMIT database and speech coding obtained by passing the speech test from TIMIT database through the AMR encoder/decoder. Our results show that the proposed methode achieves improved performance when compared with the i-vector/PLDA and MEGMM.

Segment representations with small resolution

A segment representation of a graph is an assignment of line segments in 2D to the vertices in such a way that two segments intersect if and only if the corresponding vertices are adjacent. Not all graphs have such segment representations, but they exist, for example, for all planar graphs.In this note, we study the resolution that can be achieved for segment representations, presuming the ends of segments must be on integer grid points. We show that any planar graph (and more generally, any graph that has a so-called L-representation) has a segment representation in a grid of width and height 4n.

Three-dimensional segmentation and symbolic representation of cerebral vessels on 3DRA images of arteriovenous malformations

BackgroundEndovascular embolization is a minimally invasive interventional method for the treatment of neurovascular pathologies such as aneurysms, arterial stenosis or arteriovenous malformations (AVMs). In this context, neuroradiologists need efficient tools for interventional planning and microcatheter embolization procedures optimization. Thus, the development of helpful methods is necessary to solve this challenging issue. MethodsA complete pipeline aiming to assist neuroradiologists in the visualization, interpretation and exploitation of three-dimensional rotational angiographic (3DRA) images for interventions planning in case of AVM is proposed. The developed method consists of two steps. First, an automated 3D region-based segmentation of the cerebral vessels which feed and drain the AVM is performed. From this, a graph-like tree representation of these connected vessels is then built. This symbolic representation provides a vascular network modelization with hierarchical and geometrical features that helps in the understanding of the complex angioarchitecture of the AVM. ResultsThe developed workflow achieves the segmentation of the vessels and of the malformation. It improves the 3D visualization of this complex network and highlights its three main components that are the arteries, the veins and the nidus. The symbolic representation then brings a better comprehension of the vessels angioarchitecture. It provides decomposition into topologically related vessels, offering the possibility to reduce the complexity due to the malformed vessels and also determine the optimal paths for AVM embolization during interventions planning. ConclusionsA relevant vascular network modelization has been developed that constitutes a breakthrough in the assistance of neuroradiologists for AVM endovascular embolization planning.

A Steganographic Approach to Sonar Tracking

Steganographic transmission waveforms are desirable for many applications, such as underwater acoustic communications, surveillance, detection, and tracking. In the context of active sonar tracking, an overall approach is presented that covers several topics including background sound modeling, steganographic security assessment of transmission waveforms, target motion modeling, and batch track detection. In shallow water, environment background sound dominated by snapping shrimp, the presented approach shows feasibility of steganographic sonar for target track detection. A background sound modeling approach is presented that utilizes symbolic time-series analysis with phase space representation and clustering of time-series segments. The time-evolving characteristics are captured with a hidden Markov model. Steganographic security of the transmission waveform can be assessed by measuring the Kullback-Leibler divergence between the cover model and the stego model. Conventional tracking approaches fail to perform reliably and existing track-before-detect approaches also fail due to overwhelming search computation when they use simplistic short timescale motion models. A long timescale target motion model with considerations for plausibility is presented and utilized in a batch track detection approach. A Monte-Carlo-based simulation is performed to find the scenarios that demonstrate the feasibility of steganographic sonar in a shallow water environment with snapping shrimp sound.

Automatic Seizure Detection Based on S-Transform and Deep Convolutional Neural Network

Automatic seizure detection is significant for the diagnosis of epilepsy and reducing the massive workload of reviewing continuous EEGs. In this work, a novel approach, combining Stockwell transform (S-transform) with deep Convolutional Neural Networks (CNN), is proposed to detect seizure onsets in long-term intracranial EEG recordings. Primarily, raw EEG data is filtered with wavelet decomposition. Then, S-transform is used to obtain a proper time-frequency representation of each EEG segment. After that, a 15-layer deep CNN using dropout and batch normalization serves as a robust feature extractor and classifier. Finally, smoothing and collar technique are applied to the outputs of CNN to improve the detection accuracy and reduce the false detection rate (FDR). The segment-based and event-based evaluation assessments and receiver operating characteristic (ROC) curves are employed for the performance evaluation on a public EEG database containing 21 patients. A segment-based sensitivity of 97.01% and a specificity of 98.12% are yielded. For the event-based assessment, this method achieves a sensitivity of 95.45% with an FDR of 0.36/h.

Somatotopic mapping of the human breast using 7 T functional MRI

How are tactile sensations in the breast represented in the female and male brain? Using ultra high-field 7 T MRI in ten females and ten males, we demonstrate that the representation of tactile breast information shows a somatotopic organization, with cortical magnification of the nipple. Furthermore, we show that the core representation of the breast is organized according to the specific nerve architecture that underlies breast sensation, where the medial and lateral sides of one breast are asymmetrically represented in bilateral primary somatosensory cortex. Finally, gradual selectivity signatures allude to a somatotopic organization of the breast area with overlapping, but distinctive, cortical representations of breast segments. Our univariate and multivariate analyses consistently showed similar somatosensory breast representations in males and females. The findings can guide future research on neuroplastic reorganization of the breast area, across reproductive life stages, and after breast surgery.

Modeling of a biped robot for investigating foot drop using MATLAB/Simulink

This study explores an alternative approach to the study and investigation of foot drop (weakness of the dorsiflexion muscles of the foot) problem which currently being treated by ankle foot orthosis, functional electrical stimulations, and rehabilitation exercises. This problem occurs due to various reasons, such as a nerve injury. The foot drop issue leads to the inability to lift the front part of the foot and thence it causes the toes to be dragged while walking. This study provides a model of a biped robot which can simulate human cases of foot drop geared towards improving its treatment. The model of the biped robot is described along with tools necessary for simulating contact with the ground and generating gait data using a simulated robot. Specifically and as a contribution of this study, a unique representation of the foot segment (ankle and toe joints) with a foot-drop has been simulated. A conventional feedback controller is used to correct the simulated case of foot drop. The process of setting up the controller parameters, depending on the type of foot drop, is documented, and the broad strategy for solving various foot drop cases is illustrated. The results from considering a study case of a foot-drop problem are represented and discussed that show the capability of this proposed model to simulated and correct the assigned foot-drop issue into the ankle joint. The overarching goal of this study is to produce the required torque data that will be converted into muscle signals to treat real foot drop cases.

From Embodiment of a Point-Light Display in Virtual Reality to Perception of One's Own Movements

Humans can recognize living organisms and understand their actions solely on the basis of a small animated set of well-positioned points of light, i.e. by recognizing biological motion. Our aim was to determine whether this type of recognition and integration also occurs during the perception of one's own movements. The participants (60 females) were immersed with a virtual reality headset in a virtual environment, either dark or illuminated, in which they could see a humanoid avatar from a first-person perspective. The avatar's forearms were either realistic or represented by three points of light. Embodiment was successfully achieved through a 1-min period during which either the realistic or point-light avatar's forearms faithfully reproduced voluntary flexion–extension movements. Then, the “virtual mirror paradigm” was used to evoke kinesthetic illusions. In this paradigm, a passive flexion–extension of the participant's left arm was coupled with the movements of the avatar's forearms. This combined visuo-proprioceptive stimulation, was compared with unimodal stimulation (either visual or proprioceptive stimulation only). We found that combined visuo-proprioceptive stimulation with realistic avatars evoked more vivid kinesthetic illusions of a moving right forearm than unimodal stimulations, regardless of whether the virtual environment was dark or illuminated. Kinesthetic illusions also occurred with point-light avatars, albeit less frequently and a little less intense, and only when the visual environment was optimal for slow motion detection of the point-light display (lit environment). We conclude that kinesthesia does not require visual access to an elaborate representation of a body segment. Access to biological movement can be sufficient.

Data Representations for Segmentation of Vascular Structures Using Convolutional Neural Networks with U-Net Architecture.

Convolutional neural networks (CNNs) produce promising results when applied to a wide range of medical imaging tasks including the segmentation of tissue structures. However, segmentation is particularly challenging when the target structures are small with respect to the complete image data and exhibit substantial curvature as in the case of coronary arteries in computed tomography angiography (CTA). Therefore, we evaluated the impact of data representation of tubular structures on the segmentation performance of CNNs with U-Net architecture in terms of the resulting Dice coefficients and Hausdorff distances. For this purpose, we considered 2D and 3D input data in cross-sectional and Cartesian representations. We found that the data representation can have a substantial impact on segmentation results with Dice coefficients ranging from 60% to 82% reaching values similar to those of human expert annotations used for training and Hausdorff distances ranging from 1.38 mm to 5.90 mm. Our results indicate that a 3D cross-sectional data representation is preferable for segmentation of thin tubular structures.

An Ontology-Based Contextual Knowledge Representation for Semantic Image Segmentation

International Journal of Computer Sciences and Engineering (A UGC Approved and indexed with DOI, ICI and Approved, DPI Digital Library) is one of the leading and growing open access, peer-reviewed, monthly, and scientific research journal for scientists, engineers, research scholars, and academicians, which gains a foothold in Asia and opens to the world, aims to publish original, theoretical and practical advances in Computer Science,Information Technology, Engineering (Software, Mechanical, Civil, Electronics & Electrical), and all interdisciplinary streams of Computing Sciences. It intends to disseminate original, scientific, theoretical or applied research in the field of Computer Sciences and allied fields. It provides a platform for publishing results and research with a strong empirical component. It aims to bridge the significant gap between research and practice by promoting the publication of original, novel, industry-relevant research.

Cross‐layer design of resource allocation and segment adaptation based on CPIQ model for DASH clients over LTE networks

With the rapid development of dynamic adaptive streaming over HTTP (DASH) services, how to satisfy the requirements of DASH clients has attracted more and more attention. This study was carried out to focus on the quality of experience (QoE) modelling and the model-based cross-layer design which consists of segment adaptation and resource allocation, improving the playback experience of the clients. First, the key factors are investigated, which can affect the clients’ subjective satisfaction. A characteristics and playback information of the segments-based QoE (CPIQ) model is established by employing the curve-fitting method based on a large amount of subjective experimental results of these factors. Then with the CPIQ model as the objective function, a cross-layer design CPIQ model-based joint algorithm of segment request and resource allocation (CJRRA) model is formulated to maximise the total QoE of all the clients subject to the network resource and segment representation constraints. Segment adaptation and resource allocation strategy can be determined jointly by the authors developed low complexity solution. Finally, the simulation results show that the overall performance of CPIQ model and CJRRA significantly outperforms other compared models or algorithms in terms of accuracy, linearity and stability.

Coupling image analysis and thermo-mechanical simulation results to produce a model of the fracture network in a nuclear glass canister

Understanding nuclear glass alteration in a fracture network of a block of nuclear glass is critical in predicting the release of the radionuclides in the subsurface and is relevant for the security of nuclear waste disposal. Here, we present an image processing-based fracture network characterization workflow that enables us to characterize a glass block fracture network, establish a link between a physical parameter representing an internal state of glass structural relaxation and an internal structure of the block fracture network, and generate multiple realizations of equivalent fracture networks by considering the variability of the fracture network parameters such as fracture distribution and apertures. The proposed workflow includes the following stages: (i) fracture network extraction, (ii) measurement of apertures of all distinct ramifications of the fracture network, (iii) image segmentation, (iv) anisotropy characterization, (v) passage to segment representation with the intention of modeling block equivalent fracture networks by using an algorithm of mesh growth, (vi) geostatistical modeling of average apertures and variables responsible for fracture spatial distribution, and (vii) simulation of equiprobable realizations of fracture network equivalent tessellations. Given this processing, the workflow is able to provide data for further steps including upscaling of diffusivity and permeability, and it facilitates reactive transport modeling at the glass block scale. As an illustrative example, we apply the proposed sequence of actions to a two-dimensional (2D) high-resolution image of two blocks of nuclear glass. This application brings into service both hard data obtained by direct measurement of the fracture network and soft physics-based explanatory data and considers the variability in the fracture network. The results show that implementation of the devised procedure presents an opportunity for better understanding of the impact of fracturing on aqueous alteration of glass.

EEG representation using multi-instance framework on the manifold of symmetric positive definite matrices

Objective. The generalization and robustness of an electroencephalogram (EEG)-based system are crucial requirements in actual practices. Approach. To reach these goals, we propose a new EEG representation that provides a more realistic view of brain functionality by applying multi-instance (MI) framework to consider the non-stationarity of the EEG signal. In this representation, the non-stationarity of EEG is considered by describing the signal as a bag of relevant and irrelevant concepts. The concepts are provided by a robust representation of homogeneous segments of EEG signal using spatial covariance matrices. Due to the nonlinear geometry of the space of covariance matrices, we determine the boundaries of the homogeneous segments based on adaptive segmentation of the signal in a Riemannian framework. Each subject is described as a bag of covariance matrices of homogeneous segments and the bag-level discriminative information is used for classification. Main results. To evaluate the performance of the proposed approach, we examine it in a cultural neuroscience application for classification Iranian versus Swiss normal subjects to discover if strongly differing cultures can result in distinguishing patterns in brain electrical activity of the subjects. To confirm the effectiveness of the proposed representation, we also evaluate the proposed representation in EEG-based mental disorder diagnosis application for attention deficit hyperactivity disorder (ADHD)/bipolar mood disorder (BMD), Schizophrenia/ normal, and Major Depression Disorder/normal diagnosis applications. Significance. Experimental results confirm the superiority of the proposed approach, which is gained due to the robustness of covariance descriptor, the effectiveness of Riemannian geometry, the benefits of considering the inherent non-stationary nature of the brain by applying bag-level discriminative information, and automatic handling the artifacts.

Hyperspectral Unmixing with Gaussian Mixture Model and Low-Rank Representation

Gaussian mixture model (GMM) has been one of the most representative models for hyperspectral unmixing while considering endmember variability. However, the GMM unmixing models only have proper smoothness and sparsity prior constraints on the abundances and thus do not take into account the possible local spatial correlation. When the pixels that lie on the boundaries of different materials or the inhomogeneous region, the abundances of the neighboring pixels do not have those prior constraints. Thus, we propose a novel GMM unmixing method based on superpixel segmentation (SS) and low-rank representation (LRR), which is called GMM-SS-LRR. we adopt the SS in the first principal component of HSI to get the homogeneous regions. Moreover, the HSI to be unmixed is partitioned into regions where the statistical property of the abundance coefficients have the underlying low-rank property. Then, to further exploit the spatial data structure, under the Bayesian framework, we use GMM to formulate the unmixing problem, and put the low-rank property into the objective function as a prior knowledge, using generalized expectation maximization to solve the objection function. Experiments on synthetic datasets and real HSIs demonstrated that the proposed GMM-SS-LRR is efficient compared with other current popular methods.

ИНТЕРТЕКСТУАЛЬНЫЕ СЕГМЕНТЫ В РЕЧЕВОМ ЖАНРЕ «ПОРТРЕТ ДЕЛОВОГО ЧЕЛОВЕКА» (НА ПРИМЕРЕ МЕДИЙНЫХ ИНТЕРНЕТ-ИЗДАНИЙ)

The article examines the representation of intertextual segments in the speech genre “Businessman’s Portrait”, which functions in media discourse. The author provides a brief theoretical survey of the “intertextuality” conception development, identifies the basic types of quotations depending on the functional component. Special attention is paid to allusive inclusions in the “Businessman’s Portrait” texture that immediately characterize the general cultural level of a modern information recipient in media discourse.

A Discriminative Colour-to-Grayscale Representation for Retinal Vessel Segmentation

Structural changes in retinal blood vessels are important indicators for many ocular diseases including diabetic retinopathy (DR), glaucoma, and hypertension. An automated vessel segmentation process almost always begins with acquired color fundus image, containing three layers of images (red, green and blue), and then quickly converted them to a single grayscale image. However, grayscale conversion is not unique and more than one grayscale representation can be obtained for a given color image. For the many currently existing automated vessel extraction methods, the green channel of the RGB color fundus image is routinely used as an input grey-scale representation to a pipeline of the segmentation process for the reason that it provides the best contrast among all three channels, namely red, green and blue. We hypothesize that vessel information contained in dropped channels, red and blue, will add to result in improved segmentation performance. In this paper, we propose a linear combination framework to utilize all three channels of the color fundus image based on their importance level rather than completely discarding some channels. We devised a Principal Component Analysis (PCA) method that provides appropriate weights for each color channel to realize a more discriminative grey-scale representation. The added information made available through PCA-based grayscale representation results in improved performance for Modautomated vessel segmentation algorithms. The performance of the framework is analyzed on two publically available databases (DRIVE, STARE) of fundus images quantifying improvements in all three aspects called accuracy, sensitivity, and specificity.

A Discriminative Colour-to-Grayscale Representation for Retinal Vessel Segmentation

A Discriminative Colour-to-Grayscale Representation for Retinal Vessel Segmentation

The representation of mature adults in TV commercials: A cross-cultural content analysis of portrayals in Poland and in the U.S.

The aim of the article is to examine the portrayals of mature adults (55+) in television commercials in Poland and in the United States. It is important to analyze the portrayal of this group as, on the one hand, they are a lucrative and growing consumer market, however, on the other hand, past studies have shown that this segment has been often stereotyped and underrepresented. It is hypothesized that due to clear differences in the value systems and socio-economic characteristics of both countries, the representation of the mature segment of consumers will differ significantly in terms of their overall portrayal. A purposive sample of 15 Polish commercials and 15 U.S. commercials was selected since the researchers wished to focus on particular portrayals rather than analyze a large sample of commercials. The method was a comparative content analysis. Each commercial was examined particularly for the social context, the activities performed by the individuals, gender representation, health status, race, image, role and product/service type. The findings showed that older people play various roles, however, cultural values and differences were evident in the commercials placed in both countries. This could be seen in the content and focus of the ads.

Random Forest with Learned Representations for Semantic Segmentation.

We present a random forest framework that learns the weights, shapes, and sparsities of feature representations for real-time semantic segmentation. Typical filters (kernels) have predetermined shapes and sparsities and learn only weights. A few feature extraction methods fix weights and learn only shapes and sparsities. These predetermined constraints restrict learning and extracting optimal features. To overcome this limitation, we propose an unconstrained representation that is able to extract optimal features by learning weights, shapes, and sparsities. We, then, present the random forest framework that learns the flexible filters using an iterative optimization algorithm and segments input images using the learned representations. We demonstrate the effectiveness of the proposed method using a hand segmentation dataset for hand-object interaction and using two semantic segmentation datasets. The results show that the proposed method achieves real-time semantic segmentation using limited computational and memory resources.