Multilabel user classification using the community structure of online networks.

PubMed

Rizos, Georgios; Papadopoulos, Symeon; Kompatsiaris, Yiannis

2017-01-01

We study the problem of semi-supervised, multi-label user classification of networked data in the online social platform setting. We propose a framework that combines unsupervised community extraction and supervised, community-based feature weighting before training a classifier. We introduce Approximate Regularized Commute-Time Embedding (ARCTE), an algorithm that projects the users of a social graph onto a latent space, but instead of packing the global structure into a matrix of predefined rank, as many spectral and neural representation learning methods do, it extracts local communities for all users in the graph in order to learn a sparse embedding. To this end, we employ an improvement of personalized PageRank algorithms for searching locally in each user's graph structure. Then, we perform supervised community feature weighting in order to boost the importance of highly predictive communities. We assess our method performance on the problem of user classification by performing an extensive comparative study among various recent methods based on graph embeddings. The comparison shows that ARCTE significantly outperforms the competition in almost all cases, achieving up to 35% relative improvement compared to the second best competing method in terms of F1-score.

Coupled binary embedding for large-scale image retrieval.

PubMed

Zheng, Liang; Wang, Shengjin; Tian, Qi

2014-08-01

Visual matching is a crucial step in image retrieval based on the bag-of-words (BoW) model. In the baseline method, two keypoints are considered as a matching pair if their SIFT descriptors are quantized to the same visual word. However, the SIFT visual word has two limitations. First, it loses most of its discriminative power during quantization. Second, SIFT only describes the local texture feature. Both drawbacks impair the discriminative power of the BoW model and lead to false positive matches. To tackle this problem, this paper proposes to embed multiple binary features at indexing level. To model correlation between features, a multi-IDF scheme is introduced, through which different binary features are coupled into the inverted file. We show that matching verification methods based on binary features, such as Hamming embedding, can be effectively incorporated in our framework. As an extension, we explore the fusion of binary color feature into image retrieval. The joint integration of the SIFT visual word and binary features greatly enhances the precision of visual matching, reducing the impact of false positive matches. Our method is evaluated through extensive experiments on four benchmark datasets (Ukbench, Holidays, DupImage, and MIR Flickr 1M). We show that our method significantly improves the baseline approach. In addition, large-scale experiments indicate that the proposed method requires acceptable memory usage and query time compared with other approaches. Further, when global color feature is integrated, our method yields competitive performance with the state-of-the-arts.

A consensus embedding approach for segmentation of high resolution in vivo prostate magnetic resonance imagery

NASA Astrophysics Data System (ADS)

Viswanath, Satish; Rosen, Mark; Madabhushi, Anant

2008-03-01

Current techniques for localization of prostatic adenocarcinoma (CaP) via blinded trans-rectal ultrasound biopsy are associated with a high false negative detection rate. While high resolution endorectal in vivo Magnetic Resonance (MR) prostate imaging has been shown to have improved contrast and resolution for CaP detection over ultrasound, similarity in intensity characteristics between benign and cancerous regions on MR images contribute to a high false positive detection rate. In this paper, we present a novel unsupervised segmentation method that employs manifold learning via consensus schemes for detection of cancerous regions from high resolution 1.5 Tesla (T) endorectal in vivo prostate MRI. A significant contribution of this paper is a method to combine multiple weak, lower-dimensional representations of high dimensional feature data in a way analogous to classifier ensemble schemes, and hence create a stable and accurate reduced dimensional representation. After correcting for MR image intensity artifacts, such as bias field inhomogeneity and intensity non-standardness, our algorithm extracts over 350 3D texture features at every spatial location in the MR scene at multiple scales and orientations. Non-linear dimensionality reduction schemes such as Locally Linear Embedding (LLE) and Graph Embedding (GE) are employed to create multiple low dimensional data representations of this high dimensional texture feature space. Our novel consensus embedding method is used to average object adjacencies from within the multiple low dimensional projections so that class relationships are preserved. Unsupervised consensus clustering is then used to partition the objects in this consensus embedding space into distinct classes. Quantitative evaluation on 18 1.5 T prostate MR data against corresponding histology obtained from the multi-site ACRIN trials show a sensitivity of 92.65% and a specificity of 82.06%, which suggests that our method is successfully able to detect suspicious regions in the prostate.

Image Description with Local Patterns: An Application to Face Recognition

NASA Astrophysics Data System (ADS)

Zhou, Wei; Ahrary, Alireza; Kamata, Sei-Ichiro

In this paper, we propose a novel approach for presenting the local features of digital image using 1D Local Patterns by Multi-Scans (1DLPMS). We also consider the extentions and simplifications of the proposed approach into facial images analysis. The proposed approach consists of three steps. At the first step, the gray values of pixels in image are represented as a vector giving the local neighborhood intensity distrubutions of the pixels. Then, multi-scans are applied to capture different spatial information on the image with advantage of less computation than other traditional ways, such as Local Binary Patterns (LBP). The second step is encoding the local features based on different encoding rules using 1D local patterns. This transformation is expected to be less sensitive to illumination variations besides preserving the appearance of images embedded in the original gray scale. At the final step, Grouped 1D Local Patterns by Multi-Scans (G1DLPMS) is applied to make the proposed approach computationally simpler and easy to extend. Next, we further formulate boosted algorithm to extract the most discriminant local features. The evaluated results demonstrate that the proposed approach outperforms the conventional approaches in terms of accuracy in applications of face recognition, gender estimation and facial expression.

Metric Optimization for Surface Analysis in the Laplace-Beltrami Embedding Space

PubMed Central

Lai, Rongjie; Wang, Danny J.J.; Pelletier, Daniel; Mohr, David; Sicotte, Nancy; Toga, Arthur W.

2014-01-01

In this paper we present a novel approach for the intrinsic mapping of anatomical surfaces and its application in brain mapping research. Using the Laplace-Beltrami eigen-system, we represent each surface with an isometry invariant embedding in a high dimensional space. The key idea in our system is that we realize surface deformation in the embedding space via the iterative optimization of a conformal metric without explicitly perturbing the surface or its embedding. By minimizing a distance measure in the embedding space with metric optimization, our method generates a conformal map directly between surfaces with highly uniform metric distortion and the ability of aligning salient geometric features. Besides pairwise surface maps, we also extend the metric optimization approach for group-wise atlas construction and multi-atlas cortical label fusion. In experimental results, we demonstrate the robustness and generality of our method by applying it to map both cortical and hippocampal surfaces in population studies. For cortical labeling, our method achieves excellent performance in a cross-validation experiment with 40 manually labeled surfaces, and successfully models localized brain development in a pediatric study of 80 subjects. For hippocampal mapping, our method produces much more significant results than two popular tools on a multiple sclerosis study of 109 subjects. PMID:24686245

A Novel CMOS Multi-band THz Detector with Embedded Ring Antenna

NASA Astrophysics Data System (ADS)

Xu, Lei-jun; Guan, Jia-ning; Bai, Xue; Li, Qin; Mao, Han-ping

2017-10-01

To overcome the large chip area occupation for the traditional terahertz multi-frequency detector by using the antenna elements in a different frequency, a novel structure for a multi-frequency detector is proposed and studied. Based on the ring antenna detector, an embedded multi-ring antenna with multi-port is proposed for the multi-frequency detector. A single-ring and dual-ring detectors are analyzed and designed in 0.18 μ m CMOS. For the single-ring detector, the best responsivity and NEP is 701 V/W and 261 pW/Hz0.5 at the frequency of 290 GHz. For the dual-ring detector, the best responsivity is 367 V/W and 297 V/W, NEP is 578 pW/Hz0.5 and 713pW/Hz0.5, at the frequency of 600 GHz and 806 GHz, respectively. This embedded multi-ring detector has a simple structure which can be expanded easily in a compact size.

Co Modeling and Co Synthesis of Safety Critical Multi threaded Embedded Software for Multi Core Embedded Platforms

DTIC Science & Technology

2017-03-20

computation, Prime Implicates, Boolean Abstraction, real- time embedded software, software synthesis, correct by construction software design , model...types for time -dependent data-flow networks". J.-P. Talpin, P. Jouvelot, S. Shukla. ACM-IEEE Conference on Methods and Models for System Design ...information is estimated to average 1 hour per response, including the time for reviewing instructions, searching existing   data sources, gathering and

The facile fabrication of tunable plasmonic gold nanostructure arrays using microwave plasma

NASA Astrophysics Data System (ADS)

Hsu, Chuen-Yuan; Huang, Jing-Wen; Gwo, Shangjr; Lin, Kuan-Jiuh

2010-01-01

Fabrication of isolated noble metal nanoparticles embedded in transparent substrates is the fasting growing demand for innovative plasmonic technologies. Here we report a simple and effective methodology for the preparation of highly stable plasmonic nanoparticles embedded in a glass surface. Size-controllable (10-70 nm) Au nanoparticles were rapidly prepared when subjected to the home-microwave plasma. Accordingly, the optical extinction maximum of the localized surface plasmon resonance (LSPR) can be systematically tuned in the range 532-586 nm. We find that the plasmonic structures are exceedingly stable toward immersion in ethanol solvents and pass successfully the adhesive tape test, which makes our system highly promising for efficient transmission-LSPR nanosensors. Besides, the attractive features of substrate-bound plasmonic nanostructures include its low cost, versatility, robustness, reusability and a promising ability to make a multi-arrayed LSPR biochip.

Self-recovery reversible image watermarking algorithm

PubMed Central

Sun, He; Gao, Shangbing; Jin, Shenghua

2018-01-01

The integrity of image content is essential, although most watermarking algorithms can achieve image authentication but not automatically repair damaged areas or restore the original image. In this paper, a self-recovery reversible image watermarking algorithm is proposed to recover the tampered areas effectively. First of all, the original image is divided into homogeneous blocks and non-homogeneous blocks through multi-scale decomposition, and the feature information of each block is calculated as the recovery watermark. Then, the original image is divided into 4×4 non-overlapping blocks classified into smooth blocks and texture blocks according to image textures. Finally, the recovery watermark generated by homogeneous blocks and error-correcting codes is embedded into the corresponding smooth block by mapping; watermark information generated by non-homogeneous blocks and error-correcting codes is embedded into the corresponding non-embedded smooth block and the texture block via mapping. The correlation attack is detected by invariant moments when the watermarked image is attacked. To determine whether a sub-block has been tampered with, its feature is calculated and the recovery watermark is extracted from the corresponding block. If the image has been tampered with, it can be recovered. The experimental results show that the proposed algorithm can effectively recover the tampered areas with high accuracy and high quality. The algorithm is characterized by sound visual quality and excellent image restoration. PMID:29920528

A multi-feature integration method for fatigue crack detection and crack length estimation in riveted lap joints using Lamb waves

NASA Astrophysics Data System (ADS)

He, Jingjing; Guan, Xuefei; Peng, Tishun; Liu, Yongming; Saxena, Abhinav; Celaya, Jose; Goebel, Kai

2013-10-01

This paper presents an experimental study of damage detection and quantification in riveted lap joints. Embedded lead zirconate titanate piezoelectric (PZT) ceramic wafer-type sensors are employed to perform in situ non-destructive evaluation (NDE) during fatigue cyclical loading. PZT wafers are used to monitor the wave reflection from the boundaries of the fatigue crack at the edge of bolt joints. The group velocity of the guided wave is calculated to select a proper time window in which the received signal contains the damage information. It is found that the fatigue crack lengths are correlated with three main features of the signal, i.e., correlation coefficient, amplitude change, and phase change. It was also observed that a single feature cannot be used to quantify the damage among different specimens since a considerable variability was observed in the response from different specimens. A multi-feature integration method based on a second-order multivariate regression analysis is proposed for the prediction of fatigue crack lengths using sensor measurements. The model parameters are obtained using training datasets from five specimens. The effectiveness of the proposed methodology is demonstrated using several lap joint specimens from different manufactures and under different loading conditions.

Flight State Identification of a Self-Sensing Wing via an Improved Feature Selection Method and Machine Learning Approaches.

PubMed

Chen, Xi; Kopsaftopoulos, Fotis; Wu, Qi; Ren, He; Chang, Fu-Kuo

2018-04-29

In this work, a data-driven approach for identifying the flight state of a self-sensing wing structure with an embedded multi-functional sensing network is proposed. The flight state is characterized by the structural vibration signals recorded from a series of wind tunnel experiments under varying angles of attack and airspeeds. A large feature pool is created by extracting potential features from the signals covering the time domain, the frequency domain as well as the information domain. Special emphasis is given to feature selection in which a novel filter method is developed based on the combination of a modified distance evaluation algorithm and a variance inflation factor. Machine learning algorithms are then employed to establish the mapping relationship from the feature space to the practical state space. Results from two case studies demonstrate the high identification accuracy and the effectiveness of the model complexity reduction via the proposed method, thus providing new perspectives of self-awareness towards the next generation of intelligent air vehicles.

Predicting multicellular function through multi-layer tissue networks

PubMed Central

Zitnik, Marinka; Leskovec, Jure

2017-01-01

Abstract Motivation: Understanding functions of proteins in specific human tissues is essential for insights into disease diagnostics and therapeutics, yet prediction of tissue-specific cellular function remains a critical challenge for biomedicine. Results: Here, we present OhmNet, a hierarchy-aware unsupervised node feature learning approach for multi-layer networks. We build a multi-layer network, where each layer represents molecular interactions in a different human tissue. OhmNet then automatically learns a mapping of proteins, represented as nodes, to a neural embedding-based low-dimensional space of features. OhmNet encourages sharing of similar features among proteins with similar network neighborhoods and among proteins activated in similar tissues. The algorithm generalizes prior work, which generally ignores relationships between tissues, by modeling tissue organization with a rich multiscale tissue hierarchy. We use OhmNet to study multicellular function in a multi-layer protein interaction network of 107 human tissues. In 48 tissues with known tissue-specific cellular functions, OhmNet provides more accurate predictions of cellular function than alternative approaches, and also generates more accurate hypotheses about tissue-specific protein actions. We show that taking into account the tissue hierarchy leads to improved predictive power. Remarkably, we also demonstrate that it is possible to leverage the tissue hierarchy in order to effectively transfer cellular functions to a functionally uncharacterized tissue. Overall, OhmNet moves from flat networks to multiscale models able to predict a range of phenotypes spanning cellular subsystems. Availability and implementation: Source code and datasets are available at http://snap.stanford.edu/ohmnet. Contact: jure@cs.stanford.edu PMID:28881986

Two layers LSTM with attention for multi-choice question answering in exams

NASA Astrophysics Data System (ADS)

Li, Yongbin

2018-03-01

Question Answering in Exams is typical question answering task that aims to test how accurately the model could answer the questions in exams. In this paper, we use general deep learning model to solve the multi-choice question answering task. Our approach is to build distributed word embedding of question and answers instead of manually extracting features or linguistic tools, meanwhile, for improving the accuracy, the external corpus is introduced. The framework uses a two layers LSTM with attention which get a significant result. By contrast, we introduce the simple long short-term memory (QA-LSTM) model and QA-LSTM-CNN model and QA-LSTM with attention model as the reference. Experiment demonstrate superior performance of two layers LSTM with attention compared to other models in question answering task.

Modeling activity recognition of multi resident using label combination of multi label classification in smart home

NASA Astrophysics Data System (ADS)

Mohamed, Raihani; Perumal, Thinagaran; Sulaiman, Md Nasir; Mustapha, Norwati; Zainudin, M. N. Shah

2017-10-01

Pertaining to the human centric concern and non-obtrusive way, the ambient sensor type technology has been selected, accepted and embedded in the environment in resilient style. Human activities, everyday are gradually becoming complex and thus complicate the inferences of activities when it involving the multi resident in the same smart environment. Current works solutions focus on separate model between the resident, activities and interactions. Some study use data association and extra auxiliary of graphical nodes to model human tracking information in an environment and some produce separate framework to incorporate the auxiliary for interaction feature model. Thus, recognizing the activities and which resident perform the activity at the same time in the smart home are vital for the smart home development and future applications. This paper will cater the above issue by considering the simplification and efficient method using the multi label classification framework. This effort eliminates time consuming and simplifies a lot of pre-processing tasks comparing with previous approach. Applications to the multi resident multi label learning in smart home problems shows the LC (Label Combination) using Decision Tree (DT) as base classifier can tackle the above problems.

A numerical multi-scale model to predict macroscopic material anisotropy of multi-phase steels from crystal plasticity material definitions

NASA Astrophysics Data System (ADS)

Ravi, Sathish Kumar; Gawad, Jerzy; Seefeldt, Marc; Van Bael, Albert; Roose, Dirk

2017-10-01

A numerical multi-scale model is being developed to predict the anisotropic macroscopic material response of multi-phase steel. The embedded microstructure is given by a meso-scale Representative Volume Element (RVE), which holds the most relevant features like phase distribution, grain orientation, morphology etc., in sufficient detail to describe the multi-phase behavior of the material. A Finite Element (FE) mesh of the RVE is constructed using statistical information from individual phases such as grain size distribution and ODF. The material response of the RVE is obtained for selected loading/deformation modes through numerical FE simulations in Abaqus. For the elasto-plastic response of the individual grains, single crystal plasticity based plastic potential functions are proposed as Abaqus material definitions. The plastic potential functions are derived using the Facet method for individual phases in the microstructure at the level of single grains. The proposed method is a new modeling framework and the results presented in terms of macroscopic flow curves are based on the building blocks of the approach, while the model would eventually facilitate the construction of an anisotropic yield locus of the underlying multi-phase microstructure derived from a crystal plasticity based framework.

EEG source space analysis of the supervised factor analytic approach for the classification of multi-directional arm movement

NASA Astrophysics Data System (ADS)

Shenoy Handiru, Vikram; Vinod, A. P.; Guan, Cuntai

2017-08-01

Objective. In electroencephalography (EEG)-based brain-computer interface (BCI) systems for motor control tasks the conventional practice is to decode motor intentions by using scalp EEG. However, scalp EEG only reveals certain limited information about the complex tasks of movement with a higher degree of freedom. Therefore, our objective is to investigate the effectiveness of source-space EEG in extracting relevant features that discriminate arm movement in multiple directions. Approach. We have proposed a novel feature extraction algorithm based on supervised factor analysis that models the data from source-space EEG. To this end, we computed the features from the source dipoles confined to Brodmann areas of interest (BA4a, BA4p and BA6). Further, we embedded class-wise labels of multi-direction (multi-class) source-space EEG to an unsupervised factor analysis to make it into a supervised learning method. Main Results. Our approach provided an average decoding accuracy of 71% for the classification of hand movement in four orthogonal directions, that is significantly higher (>10%) than the classification accuracy obtained using state-of-the-art spatial pattern features in sensor space. Also, the group analysis on the spectral characteristics of source-space EEG indicates that the slow cortical potentials from a set of cortical source dipoles reveal discriminative information regarding the movement parameter, direction. Significance. This study presents evidence that low-frequency components in the source space play an important role in movement kinematics, and thus it may lead to new strategies for BCI-based neurorehabilitation.

Comparison of coherently coupled multi-cavity and quantum dot embedded single cavity systems.

PubMed

Kocaman, Serdar; Sayan, Gönül Turhan

2016-12-12

Temporal group delays originating from the optical analogue to electromagnetically induced transparency (EIT) are compared in two systems. Similar transmission characteristics are observed between a coherently coupled high-Q multi-cavity array and a single quantum dot (QD) embedded cavity in the weak coupling regime. However, theoretically generated group delay values for the multi-cavity case are around two times higher. Both configurations allow direct scalability for chip-scale optical pulse trapping and coupled-cavity quantum electrodynamics (QED).

Transductive multi-view zero-shot learning.

PubMed

Fu, Yanwei; Hospedales, Timothy M; Xiang, Tao; Gong, Shaogang

2015-11-01

Most existing zero-shot learning approaches exploit transfer learning via an intermediate semantic representation shared between an annotated auxiliary dataset and a target dataset with different classes and no annotation. A projection from a low-level feature space to the semantic representation space is learned from the auxiliary dataset and applied without adaptation to the target dataset. In this paper we identify two inherent limitations with these approaches. First, due to having disjoint and potentially unrelated classes, the projection functions learned from the auxiliary dataset/domain are biased when applied directly to the target dataset/domain. We call this problem the projection domain shift problem and propose a novel framework, transductive multi-view embedding, to solve it. The second limitation is the prototype sparsity problem which refers to the fact that for each target class, only a single prototype is available for zero-shot learning given a semantic representation. To overcome this problem, a novel heterogeneous multi-view hypergraph label propagation method is formulated for zero-shot learning in the transductive embedding space. It effectively exploits the complementary information offered by different semantic representations and takes advantage of the manifold structures of multiple representation spaces in a coherent manner. We demonstrate through extensive experiments that the proposed approach (1) rectifies the projection shift between the auxiliary and target domains, (2) exploits the complementarity of multiple semantic representations, (3) significantly outperforms existing methods for both zero-shot and N-shot recognition on three image and video benchmark datasets, and (4) enables novel cross-view annotation tasks.

Trispectrum from co-dimension 2(n) Galileons

DOE Office of Scientific and Technical Information (OSTI.GOV)

Fasiello, Matteo, E-mail: mrf65@case.edu

2013-12-01

A generalized theory of multi-field Galileons has been recently put forward. This model stems from the ongoing effort to embed generic Galileon theories within brane constructions. Such an approach has proved very useful in connecting interesting and essential features of these theories with geometric properties of the branes embedding. We investigate the cosmological implications of a very restrictive multi-field Galileon theory whose leading interaction is solely quartic in the scalar field π and lends itself nicely to an interesting cosmology. The bispectrum is characterized by a naturally small amplitude (f{sub NL}∼<1) and an equilateral shape-function. The trispectrum of curvature fluctuationsmore » has features which are quite distinctive with respect to their P(X,φ) counterpart. We also show that, despite an absent cubic Lagrangian in the full theory, non-Gaussianities in this model cannot produce the combination of a small bispectrum alongside with a large trispectrum. We further expand on this point to draw a lesson on what having a symmetry in the full background independent theory entails at the level of fluctuations and vice-versa.« less

Fault Diagnosis for Rotating Machinery: A Method based on Image Processing

PubMed Central

Lu, Chen; Wang, Yang; Ragulskis, Minvydas; Cheng, Yujie

2016-01-01

Rotating machinery is one of the most typical types of mechanical equipment and plays a significant role in industrial applications. Condition monitoring and fault diagnosis of rotating machinery has gained wide attention for its significance in preventing catastrophic accident and guaranteeing sufficient maintenance. With the development of science and technology, fault diagnosis methods based on multi-disciplines are becoming the focus in the field of fault diagnosis of rotating machinery. This paper presents a multi-discipline method based on image-processing for fault diagnosis of rotating machinery. Different from traditional analysis method in one-dimensional space, this study employs computing method in the field of image processing to realize automatic feature extraction and fault diagnosis in a two-dimensional space. The proposed method mainly includes the following steps. First, the vibration signal is transformed into a bi-spectrum contour map utilizing bi-spectrum technology, which provides a basis for the following image-based feature extraction. Then, an emerging approach in the field of image processing for feature extraction, speeded-up robust features, is employed to automatically exact fault features from the transformed bi-spectrum contour map and finally form a high-dimensional feature vector. To reduce the dimensionality of the feature vector, thus highlighting main fault features and reducing subsequent computing resources, t-Distributed Stochastic Neighbor Embedding is adopt to reduce the dimensionality of the feature vector. At last, probabilistic neural network is introduced for fault identification. Two typical rotating machinery, axial piston hydraulic pump and self-priming centrifugal pumps, are selected to demonstrate the effectiveness of the proposed method. Results show that the proposed method based on image-processing achieves a high accuracy, thus providing a highly effective means to fault diagnosis for rotating machinery. PMID:27711246

Fault Diagnosis for Rotating Machinery: A Method based on Image Processing.

PubMed

Lu, Chen; Wang, Yang; Ragulskis, Minvydas; Cheng, Yujie

2016-01-01

Rotating machinery is one of the most typical types of mechanical equipment and plays a significant role in industrial applications. Condition monitoring and fault diagnosis of rotating machinery has gained wide attention for its significance in preventing catastrophic accident and guaranteeing sufficient maintenance. With the development of science and technology, fault diagnosis methods based on multi-disciplines are becoming the focus in the field of fault diagnosis of rotating machinery. This paper presents a multi-discipline method based on image-processing for fault diagnosis of rotating machinery. Different from traditional analysis method in one-dimensional space, this study employs computing method in the field of image processing to realize automatic feature extraction and fault diagnosis in a two-dimensional space. The proposed method mainly includes the following steps. First, the vibration signal is transformed into a bi-spectrum contour map utilizing bi-spectrum technology, which provides a basis for the following image-based feature extraction. Then, an emerging approach in the field of image processing for feature extraction, speeded-up robust features, is employed to automatically exact fault features from the transformed bi-spectrum contour map and finally form a high-dimensional feature vector. To reduce the dimensionality of the feature vector, thus highlighting main fault features and reducing subsequent computing resources, t-Distributed Stochastic Neighbor Embedding is adopt to reduce the dimensionality of the feature vector. At last, probabilistic neural network is introduced for fault identification. Two typical rotating machinery, axial piston hydraulic pump and self-priming centrifugal pumps, are selected to demonstrate the effectiveness of the proposed method. Results show that the proposed method based on image-processing achieves a high accuracy, thus providing a highly effective means to fault diagnosis for rotating machinery.

RapidIO as a multi-purpose interconnect

NASA Astrophysics Data System (ADS)

Baymani, Simaolhoda; Alexopoulos, Konstantinos; Valat, Sébastien

2017-10-01

RapidIO (http://rapidio.org/) technology is a packet-switched high-performance fabric, which has been under active development since 1997. Originally meant to be a front side bus, it developed into a system level interconnect which is today used in all 4G/LTE base stations world wide. RapidIO is often used in embedded systems that require high reliability, low latency and scalability in a heterogeneous environment - features that are highly interesting for several use cases, such as data analytics and data acquisition (DAQ) networks. We will present the results of evaluating RapidIO in a data analytics environment, from setup to benchmark. Specifically, we will share the experience of running ROOT and Hadoop on top of RapidIO. To demonstrate the multi-purpose characteristics of RapidIO, we will also present the results of investigating RapidIO as a technology for high-speed DAQ networks using a generic multi-protocol event-building emulation tool. In addition we will present lessons learned from implementing native ports of CERN applications to RapidIO.

EVIDENCE FOR QUASI-ADIABATIC MOTION OF CHARGED PARTICLES IN STRONG CURRENT SHEETS IN THE SOLAR WIND

DOE Office of Scientific and Technical Information (OSTI.GOV)

Malova, H. V.; Popov, V. Yu.; Grigorenko, E. E.

We investigate quasi-adiabatic dynamics of charged particles in strong current sheets (SCSs) in the solar wind, including the heliospheric current sheet (HCS), both theoretically and observationally. A self-consistent hybrid model of an SCS is developed in which ion dynamics is described at the quasi-adiabatic approximation, while the electrons are assumed to be magnetized, and their motion is described in the guiding center approximation. The model shows that the SCS profile is determined by the relative contribution of two currents: (i) the current supported by demagnetized protons that move along open quasi-adiabatic orbits, and (ii) the electron drift current. The simplestmore » modeled SCS is found to be a multi-layered structure that consists of a thin current sheet embedded into a much thicker analog of a plasma sheet. This result is in good agreement with observations of SCSs at ∼1 au. The analysis of fine structure of different SCSs, including the HCS, shows that an SCS represents a narrow current layer (with a thickness of ∼10{sup 4} km) embedded into a wider region of about 10{sup 5} km, independently of the SCS origin. Therefore, multi-scale structuring is very likely an intrinsic feature of SCSs in the solar wind.« less

Flight State Identification of a Self-Sensing Wing via an Improved Feature Selection Method and Machine Learning Approaches

PubMed Central

Chen, Xi; Wu, Qi; Ren, He; Chang, Fu-Kuo

2018-01-01

In this work, a data-driven approach for identifying the flight state of a self-sensing wing structure with an embedded multi-functional sensing network is proposed. The flight state is characterized by the structural vibration signals recorded from a series of wind tunnel experiments under varying angles of attack and airspeeds. A large feature pool is created by extracting potential features from the signals covering the time domain, the frequency domain as well as the information domain. Special emphasis is given to feature selection in which a novel filter method is developed based on the combination of a modified distance evaluation algorithm and a variance inflation factor. Machine learning algorithms are then employed to establish the mapping relationship from the feature space to the practical state space. Results from two case studies demonstrate the high identification accuracy and the effectiveness of the model complexity reduction via the proposed method, thus providing new perspectives of self-awareness towards the next generation of intelligent air vehicles. PMID:29710832

The sound of music: differentiating musicians using a fast, musical multi-feature mismatch negativity paradigm.

PubMed

Vuust, Peter; Brattico, Elvira; Seppänen, Miia; Näätänen, Risto; Tervaniemi, Mari

2012-06-01

Musicians' skills in auditory processing depend highly on instrument, performance practice, and on level of expertise. Yet, it is not known though whether the style/genre of music might shape auditory processing in the brains of musicians. Here, we aimed at tackling the role of musical style/genre on modulating neural and behavioral responses to changes in musical features. Using a novel, fast and musical sounding multi-feature paradigm, we measured the mismatch negativity (MMN), a pre-attentive brain response, to six types of musical feature change in musicians playing three distinct styles of music (classical, jazz, rock/pop) and in non-musicians. Jazz and classical musicians scored higher in the musical aptitude test than band musicians and non-musicians, especially with regards to tonal abilities. These results were extended by the MMN findings: jazz musicians had larger MMN-amplitude than all other experimental groups across the six different sound features, indicating a greater overall sensitivity to auditory outliers. In particular, we found enhanced processing of pith and sliding up to pitches in jazz musicians only. Furthermore, we observed a more frontal MMN to pitch and location compared to the other deviants in jazz musicians and left lateralization of the MMN to timbre in classical musicians. These findings indicate that the characteristics of the style/genre of music played by musicians influence their perceptual skills and the brain processing of sound features embedded in a musical context. Musicians' brain is hence shaped by the type of training, musical style/genre, and listening experiences. Copyright © 2012 Elsevier Ltd. All rights reserved.

Commentary on "How Task Features Impact Evidence from Assessments Embedded in Simulations and Games" by Almond et al.

ERIC Educational Resources Information Center

Timms, Mike

2014-01-01

In his commentary on "How Task Features Impact Evidence from Assessments Embedded in Simulations and Games" by Almond et al., Mike Timms writes that his own research has involved the use of embedded assessments using simulations in interactive learning environments, and the Evidence Centered Design (ECD) approach has provided a solid…

Lamb wave-based damage quantification and probability of detection modeling for fatigue life assessment of riveted lap joint

NASA Astrophysics Data System (ADS)

He, Jingjing; Wang, Dengjiang; Zhang, Weifang

2015-03-01

This study presents an experimental and modeling study for damage detection and quantification in riveted lap joints. Embedded lead zirconate titanate piezoelectric (PZT) ceramic wafer-type sensors are employed to perform in-situ non-destructive testing during fatigue cyclical loading. A multi-feature integration method is developed to quantify the crack size using signal features of correlation coefficient, amplitude change, and phase change. In addition, probability of detection (POD) model is constructed to quantify the reliability of the developed sizing method. Using the developed crack size quantification method and the resulting POD curve, probabilistic fatigue life prediction can be performed to provide comprehensive information for decision-making. The effectiveness of the overall methodology is demonstrated and validated using several aircraft lap joint specimens from different manufactures and under different loading conditions.

Multiview Locally Linear Embedding for Effective Medical Image Retrieval

PubMed Central

Shen, Hualei; Tao, Dacheng; Ma, Dianfu

2013-01-01

Content-based medical image retrieval continues to gain attention for its potential to assist radiological image interpretation and decision making. Many approaches have been proposed to improve the performance of medical image retrieval system, among which visual features such as SIFT, LBP, and intensity histogram play a critical role. Typically, these features are concatenated into a long vector to represent medical images, and thus traditional dimension reduction techniques such as locally linear embedding (LLE), principal component analysis (PCA), or laplacian eigenmaps (LE) can be employed to reduce the “curse of dimensionality”. Though these approaches show promising performance for medical image retrieval, the feature-concatenating method ignores the fact that different features have distinct physical meanings. In this paper, we propose a new method called multiview locally linear embedding (MLLE) for medical image retrieval. Following the patch alignment framework, MLLE preserves the geometric structure of the local patch in each feature space according to the LLE criterion. To explore complementary properties among a range of features, MLLE assigns different weights to local patches from different feature spaces. Finally, MLLE employs global coordinate alignment and alternating optimization techniques to learn a smooth low-dimensional embedding from different features. To justify the effectiveness of MLLE for medical image retrieval, we compare it with conventional spectral embedding methods. We conduct experiments on a subset of the IRMA medical image data set. Evaluation results show that MLLE outperforms state-of-the-art dimension reduction methods. PMID:24349277

Multiphysics Simulations of Hot-Spot Initiation in Shocked Insensitive High-Explosive

NASA Astrophysics Data System (ADS)

Najjar, Fady; Howard, W. M.; Fried, L. E.

2010-11-01

Solid plastic-bonded high-explosive materials consist of crystals with micron-sized pores embedded. Under mechanical or thermal insults, these voids increase the ease of shock initiation by generating high-temperature regions during their collapse that might lead to ignition. Understanding the mechanisms of hot-spot initiation has significant research interest due to safety, reliability and development of new insensitive munitions. Multi-dimensional high-resolution meso-scale simulations are performed using the multiphysics software, ALE3D, to understand the hot-spot initiation. The Cheetah code is coupled to ALE3D, creating multi-dimensional sparse tables for the HE properties. The reaction rates were obtained from MD Quantum computations. Our current predictions showcase several interesting features regarding hot spot dynamics including the formation of a "secondary" jet. We will discuss the results obtained with hydro-thermo-chemical processes leading to ignition growth for various pore sizes and different shock pressures.

Content-based audio authentication using a hierarchical patchwork watermark embedding

NASA Astrophysics Data System (ADS)

Gulbis, Michael; Müller, Erika

2010-05-01

Content-based audio authentication watermarking techniques extract perceptual relevant audio features, which are robustly embedded into the audio file to protect. Manipulations of the audio file are detected on the basis of changes between the original embedded feature information and the anew extracted features during verification. The main challenges of content-based watermarking are on the one hand the identification of a suitable audio feature to distinguish between content preserving and malicious manipulations. On the other hand the development of a watermark, which is robust against content preserving modifications and able to carry the whole authentication information. The payload requirements are significantly higher compared to transaction watermarking or copyright protection. Finally, the watermark embedding should not influence the feature extraction to avoid false alarms. Current systems still lack a sufficient alignment of watermarking algorithm and feature extraction. In previous work we developed a content-based audio authentication watermarking approach. The feature is based on changes in DCT domain over time. A patchwork algorithm based watermark was used to embed multiple one bit watermarks. The embedding process uses the feature domain without inflicting distortions to the feature. The watermark payload is limited by the feature extraction, more precisely the critical bands. The payload is inverse proportional to segment duration of the audio file segmentation. Transparency behavior was analyzed in dependence of segment size and thus the watermark payload. At a segment duration of about 20 ms the transparency shows an optimum (measured in units of Objective Difference Grade). Transparency and/or robustness are fast decreased for working points beyond this area. Therefore, these working points are unsuitable to gain further payload, needed for the embedding of the whole authentication information. In this paper we present a hierarchical extension of the watermark method to overcome the limitations given by the feature extraction. The approach is a recursive application of the patchwork algorithm onto its own patches, with a modified patch selection to ensure a better signal to noise ratio for the watermark embedding. The robustness evaluation was done by compression (mp3, ogg, aac), normalization, and several attacks of the stirmark benchmark for audio suite. Compared on the base of same payload and transparency the hierarchical approach shows improved robustness.

Carbon nanotube embedded PVDF membranes: Effect of solvent composition on the structural morphology for membrane distillation

NASA Astrophysics Data System (ADS)

Mapunda, Edgar C.; Mamba, Bhekie B.; Msagati, Titus A. M.

2017-08-01

Rapid population increase, growth in industrial and agricultural sectors and global climate change have added significant pressure on conventional freshwater resources. Tapping freshwater from non-conventional water sources such as desalination and wastewater recycling is considered as sustainable alternative to the fundamental challenges of water scarcity. However, affordable and sustainable technologies need to be applied for the communities to benefit from the treatment of non-conventional water source. Membrane distillation is a potential desalination technology which can be used sustainably for this purpose. In this work multi-walled carbon nanotube embedded polyvinylidene fluoride membranes for application in membrane distillation desalination were prepared via non-solvent induced phase separation method. The casting solution was prepared using mixed solvents (N, N-dimethylacetamide and triethyl phosphate) at varying ratios to study the effect of solvent composition on membrane morphological structures. Membrane morphological features were studied using a number of techniques including scanning electron microscope, atomic force microscope, SAXSpace tensile strength analysis, membrane thickness, porosity and contact angle measurements. It was revealed that membrane hydrophobicity, thickness, tensile strength and surface roughness were increasing as the composition of N, N-dimethylacetamide in the solvent was increasing with maximum values obtained between 40 and 60% N, N-dimethylacetamide. Internal morphological structures were changing from cellular structures to short finger-like and sponge-like pores and finally to large macro void type of pores when the amount of N, N-dimethylacetamide in the solvent was changed from low to high respectively. Multi-walled carbon nanotube embedded polyvinylidene fluoride membranes of desired morphological structures and physical properties can be synthesized by regulating the composition of solvents used to prepare the casting solution.

Feature Genes Selection Using Supervised Locally Linear Embedding and Correlation Coefficient for Microarray Classification

PubMed Central

Wang, Yun; Huang, Fangzhou

2018-01-01

The selection of feature genes with high recognition ability from the gene expression profiles has gained great significance in biology. However, most of the existing methods have a high time complexity and poor classification performance. Motivated by this, an effective feature selection method, called supervised locally linear embedding and Spearman's rank correlation coefficient (SLLE-SC2), is proposed which is based on the concept of locally linear embedding and correlation coefficient algorithms. Supervised locally linear embedding takes into account class label information and improves the classification performance. Furthermore, Spearman's rank correlation coefficient is used to remove the coexpression genes. The experiment results obtained on four public tumor microarray datasets illustrate that our method is valid and feasible. PMID:29666661

Feature Genes Selection Using Supervised Locally Linear Embedding and Correlation Coefficient for Microarray Classification.

PubMed

Xu, Jiucheng; Mu, Huiyu; Wang, Yun; Huang, Fangzhou

2018-01-01

The selection of feature genes with high recognition ability from the gene expression profiles has gained great significance in biology. However, most of the existing methods have a high time complexity and poor classification performance. Motivated by this, an effective feature selection method, called supervised locally linear embedding and Spearman's rank correlation coefficient (SLLE-SC 2 ), is proposed which is based on the concept of locally linear embedding and correlation coefficient algorithms. Supervised locally linear embedding takes into account class label information and improves the classification performance. Furthermore, Spearman's rank correlation coefficient is used to remove the coexpression genes. The experiment results obtained on four public tumor microarray datasets illustrate that our method is valid and feasible.

Recurrent neural networks with specialized word embeddings for health-domain named-entity recognition.

PubMed

Jauregi Unanue, Iñigo; Zare Borzeshi, Ehsan; Piccardi, Massimo

2017-12-01

Previous state-of-the-art systems on Drug Name Recognition (DNR) and Clinical Concept Extraction (CCE) have focused on a combination of text "feature engineering" and conventional machine learning algorithms such as conditional random fields and support vector machines. However, developing good features is inherently heavily time-consuming. Conversely, more modern machine learning approaches such as recurrent neural networks (RNNs) have proved capable of automatically learning effective features from either random assignments or automated word "embeddings". (i) To create a highly accurate DNR and CCE system that avoids conventional, time-consuming feature engineering. (ii) To create richer, more specialized word embeddings by using health domain datasets such as MIMIC-III. (iii) To evaluate our systems over three contemporary datasets. Two deep learning methods, namely the Bidirectional LSTM and the Bidirectional LSTM-CRF, are evaluated. A CRF model is set as the baseline to compare the deep learning systems to a traditional machine learning approach. The same features are used for all the models. We have obtained the best results with the Bidirectional LSTM-CRF model, which has outperformed all previously proposed systems. The specialized embeddings have helped to cover unusual words in DrugBank and MedLine, but not in the i2b2/VA dataset. We present a state-of-the-art system for DNR and CCE. Automated word embeddings has allowed us to avoid costly feature engineering and achieve higher accuracy. Nevertheless, the embeddings need to be retrained over datasets that are adequate for the domain, in order to adequately cover the domain-specific vocabulary. Copyright © 2017 Elsevier Inc. All rights reserved.

Robust kernel representation with statistical local features for face recognition.

PubMed

Yang, Meng; Zhang, Lei; Shiu, Simon Chi-Keung; Zhang, David

2013-06-01

Factors such as misalignment, pose variation, and occlusion make robust face recognition a difficult problem. It is known that statistical features such as local binary pattern are effective for local feature extraction, whereas the recently proposed sparse or collaborative representation-based classification has shown interesting results in robust face recognition. In this paper, we propose a novel robust kernel representation model with statistical local features (SLF) for robust face recognition. Initially, multipartition max pooling is used to enhance the invariance of SLF to image registration error. Then, a kernel-based representation model is proposed to fully exploit the discrimination information embedded in the SLF, and robust regression is adopted to effectively handle the occlusion in face images. Extensive experiments are conducted on benchmark face databases, including extended Yale B, AR (A. Martinez and R. Benavente), multiple pose, illumination, and expression (multi-PIE), facial recognition technology (FERET), face recognition grand challenge (FRGC), and labeled faces in the wild (LFW), which have different variations of lighting, expression, pose, and occlusions, demonstrating the promising performance of the proposed method.

Structure of Saturn's Rings from Cassini Diametric Radio Occultations

NASA Astrophysics Data System (ADS)

Marouf, E.; French, R.; Rappaport, N.; Kliore, A.; Flasar, M.; Nagy, A.; McGhee, C.; Schinder, P.; Anabtawi, A.; Asmar, S.; Barbinis, E.; Fleischman, D.; Goltz, G.; Johnston, D.; Rochblatt, D.; Thomson, F.; Wong, K.

2005-08-01

Cassini orbits around Saturn were designed to provide eight optimized radio occultation observations of Saturn's rings during summer, 2005. Three monochromatic radio signals (0.94, 3.6, and 13 cm-wavelength) were transmitted by Cassini through the rings and observed at multiple stations of the NASA Deep Space Network. A rich data set has been collected. Detailed structure of Ring B is revealed for the first time, including multi-feature dense ''core'' ˜ 6,000 km wide of normal optical depth > 4.3, a ˜ 5,500 km region of oscillations in optical depth ( ˜ 1.7 to ˜ 3.4) over characteristic radial scales of few hundred kilometers interior to the core, and a ˜ 5,000 km region exterior to the core of similar nature but smaller optical depth fluctuation ( ˜ 2.2 to ˜ 3.3). The innermost ˜ 7,000 km region is the thinnest (mean optical depth ˜ 1.2), and includes two unusually uniform regions and a prominent density wave. With few exceptions, the structure is nearly identical for the three radio signals (when detectable), indicating that Ring B is relatively devoid of centimeters and smaller size particles. The structure is largely circularly symmetric, except for radius > ˜ 116,600 km. In Ring A, numerous (> 40) density waves are clearly observed at multiple longitudes, different average background optical depth is observed among different occultations suggesting that the azimuthal asymmetry extends over most Ring A, and strong dependence of the observed structure on wavelength implies increase in the abundance of centimeter and smaller size particles with increasing radius. Multiple longitude observations of Ring C and the Cassini Division structure reveal remarkable variability of gaps and their embedded narrow eccentric ringlets, and a wake/wave like feature interior to the gap at ˜ 118,200 km (embedded moonlet?). Wavelength dependent structure of Ring C implies abundance of centimeter size particles everywhere and sorting by size within dense embedded features.

Low-Rank Discriminant Embedding for Multiview Learning.

PubMed

Li, Jingjing; Wu, Yue; Zhao, Jidong; Lu, Ke

2017-11-01

This paper focuses on the specific problem of multiview learning where samples have the same feature set but different probability distributions, e.g., different viewpoints or different modalities. Since samples lying in different distributions cannot be compared directly, this paper aims to learn a latent subspace shared by multiple views assuming that the input views are generated from this latent subspace. Previous approaches usually learn the common subspace by either maximizing the empirical likelihood, or preserving the geometric structure. However, considering the complementarity between the two objectives, this paper proposes a novel approach, named low-rank discriminant embedding (LRDE), for multiview learning by taking full advantage of both sides. By further considering the duality between data points and features of multiview scene, i.e., data points can be grouped based on their distribution on features, while features can be grouped based on their distribution on the data points, LRDE not only deploys low-rank constraints on both sample level and feature level to dig out the shared factors across different views, but also preserves geometric information in both the ambient sample space and the embedding feature space by designing a novel graph structure under the framework of graph embedding. Finally, LRDE jointly optimizes low-rank representation and graph embedding in a unified framework. Comprehensive experiments in both multiview manner and pairwise manner demonstrate that LRDE performs much better than previous approaches proposed in recent literatures.

An Embedded Multi-Agent Systems Based Industrial Wireless Sensor Network

PubMed Central

Brennan, Robert W.

2017-01-01

With the emergence of cyber-physical systems, there has been a growing interest in network-connected devices. One of the key requirements of a cyber-physical device is the ability to sense its environment. Wireless sensor networks are a widely-accepted solution for this requirement. In this study, an embedded multi-agent systems-managed wireless sensor network is presented. A novel architecture is proposed, along with a novel wireless sensor network architecture. Active and passive wireless sensor node types are defined, along with their communication protocols, and two application-specific examples are presented. A series of three experiments is conducted to evaluate the performance of the agent-embedded wireless sensor network. PMID:28906452

An Embedded Multi-Agent Systems Based Industrial Wireless Sensor Network.

PubMed

Taboun, Mohammed S; Brennan, Robert W

2017-09-14

With the emergence of cyber-physical systems, there has been a growing interest in network-connected devices. One of the key requirements of a cyber-physical device is the ability to sense its environment. Wireless sensor networks are a widely-accepted solution for this requirement. In this study, an embedded multi-agent systems-managed wireless sensor network is presented. A novel architecture is proposed, along with a novel wireless sensor network architecture. Active and passive wireless sensor node types are defined, along with their communication protocols, and two application-specific examples are presented. A series of three experiments is conducted to evaluate the performance of the agent-embedded wireless sensor network.

Lagrangian Modeling of Evaporating Sprays at Diesel Engine Conditions: Effects of Multi-Hole Injector Nozzles With JP-8 Surrogates

DTIC Science & Technology

2014-05-01

solver to treat the spray process. An Adaptive Mesh Refinement (AMR) and fixed embedding technique is employed to capture the gas - liquid interface with...Adaptive Mesh Refinement (AMR) and fixed embedding technique is employed to capture the gas - liquid interface with high fidelity while keeping the cell...in single and multi-hole nozzle configurations. The models were added to the present CONVERGE liquid fuel database and validated extensively

Multi-Component Current Sheets in the Martian Magnetotail. MAVEN Observations

NASA Astrophysics Data System (ADS)

Grigorenko, E.; Zelenyi, L. M.; Vaisberg, O. L.; Ermakov, V.; Dubinin, E.; Malova, H. V.

2016-12-01

Current sheets (CSs) are the wide-spread objects in space and laboratory plasmas. The capability of CSs to maintain their stability, efficiently store and convert energy is a challenge to space physicists for many decades. Extensive studies of the CSs showed that the presence of multi-component plasma distribution can significantly affect the CS structure and dynamics. Such features like CS thinning, embedding and bifurcation are often related to the anisotropy of particle velocity distribution functions and multi-component ion composition, and they can be a source for generation of plasma instabilities and current disruption/reconnection. The MAVEN mission equipped with comprehensive instrument suite allows the observations of plasma and magnetic field characteristics with a high time resolution and provides an opportunity to study different processes in the Martian plasma environment. In this work we present the analysis of the CSs observed by MAVEN in the Martian magnetotail and discuss the peculiarities of their structure in relation to the thermal/energy characteristics of different plasma components. The relation to the existing CS models is also discussed. This work is supported by Russian Science Foundation (grant Nr.16-42-01103)

Evidencing Learning Outcomes: A Multi-Level, Multi-Dimensional Course Alignment Model

ERIC Educational Resources Information Center

Sridharan, Bhavani; Leitch, Shona; Watty, Kim

2015-01-01

This conceptual framework proposes a multi-level, multi-dimensional course alignment model to implement a contextualised constructive alignment of rubric design that authentically evidences and assesses learning outcomes. By embedding quality control mechanisms at each level for each dimension, this model facilitates the development of an aligned…

Computer vision camera with embedded FPGA processing

NASA Astrophysics Data System (ADS)

Lecerf, Antoine; Ouellet, Denis; Arias-Estrada, Miguel

2000-03-01

Traditional computer vision is based on a camera-computer system in which the image understanding algorithms are embedded in the computer. To circumvent the computational load of vision algorithms, low-level processing and imaging hardware can be integrated in a single compact module where a dedicated architecture is implemented. This paper presents a Computer Vision Camera based on an open architecture implemented in an FPGA. The system is targeted to real-time computer vision tasks where low level processing and feature extraction tasks can be implemented in the FPGA device. The camera integrates a CMOS image sensor, an FPGA device, two memory banks, and an embedded PC for communication and control tasks. The FPGA device is a medium size one equivalent to 25,000 logic gates. The device is connected to two high speed memory banks, an IS interface, and an imager interface. The camera can be accessed for architecture programming, data transfer, and control through an Ethernet link from a remote computer. A hardware architecture can be defined in a Hardware Description Language (like VHDL), simulated and synthesized into digital structures that can be programmed into the FPGA and tested on the camera. The architecture of a classical multi-scale edge detection algorithm based on a Laplacian of Gaussian convolution has been developed to show the capabilities of the system.

An embedded multi-core parallel model for real-time stereo imaging

NASA Astrophysics Data System (ADS)

He, Wenjing; Hu, Jian; Niu, Jingyu; Li, Chuanrong; Liu, Guangyu

2018-04-01

The real-time processing based on embedded system will enhance the application capability of stereo imaging for LiDAR and hyperspectral sensor. The task partitioning and scheduling strategies for embedded multiprocessor system starts relatively late, compared with that for PC computer. In this paper, aimed at embedded multi-core processing platform, a parallel model for stereo imaging is studied and verified. After analyzing the computing amount, throughout capacity and buffering requirements, a two-stage pipeline parallel model based on message transmission is established. This model can be applied to fast stereo imaging for airborne sensors with various characteristics. To demonstrate the feasibility and effectiveness of the parallel model, a parallel software was designed using test flight data, based on the 8-core DSP processor TMS320C6678. The results indicate that the design performed well in workload distribution and had a speed-up ratio up to 6.4.

Word embeddings and recurrent neural networks based on Long-Short Term Memory nodes in supervised biomedical word sense disambiguation.

PubMed

Jimeno Yepes, Antonio

2017-09-01

Word sense disambiguation helps identifying the proper sense of ambiguous words in text. With large terminologies such as the UMLS Metathesaurus ambiguities appear and highly effective disambiguation methods are required. Supervised learning algorithm methods are used as one of the approaches to perform disambiguation. Features extracted from the context of an ambiguous word are used to identify the proper sense of such a word. The type of features have an impact on machine learning methods, thus affect disambiguation performance. In this work, we have evaluated several types of features derived from the context of the ambiguous word and we have explored as well more global features derived from MEDLINE using word embeddings. Results show that word embeddings improve the performance of more traditional features and allow as well using recurrent neural network classifiers based on Long-Short Term Memory (LSTM) nodes. The combination of unigrams and word embeddings with an SVM sets a new state of the art performance with a macro accuracy of 95.97 in the MSH WSD data set. Copyright © 2017 Elsevier Inc. All rights reserved.

Comparison of Feature Selection Techniques in Machine Learning for Anatomical Brain MRI in Dementia.

PubMed

Tohka, Jussi; Moradi, Elaheh; Huttunen, Heikki

2016-07-01

We present a comparative split-half resampling analysis of various data driven feature selection and classification methods for the whole brain voxel-based classification analysis of anatomical magnetic resonance images. We compared support vector machines (SVMs), with or without filter based feature selection, several embedded feature selection methods and stability selection. While comparisons of the accuracy of various classification methods have been reported previously, the variability of the out-of-training sample classification accuracy and the set of selected features due to independent training and test sets have not been previously addressed in a brain imaging context. We studied two classification problems: 1) Alzheimer's disease (AD) vs. normal control (NC) and 2) mild cognitive impairment (MCI) vs. NC classification. In AD vs. NC classification, the variability in the test accuracy due to the subject sample did not vary between different methods and exceeded the variability due to different classifiers. In MCI vs. NC classification, particularly with a large training set, embedded feature selection methods outperformed SVM-based ones with the difference in the test accuracy exceeding the test accuracy variability due to the subject sample. The filter and embedded methods produced divergent feature patterns for MCI vs. NC classification that suggests the utility of the embedded feature selection for this problem when linked with the good generalization performance. The stability of the feature sets was strongly correlated with the number of features selected, weakly correlated with the stability of classification accuracy, and uncorrelated with the average classification accuracy.

Tensor Train Neighborhood Preserving Embedding

NASA Astrophysics Data System (ADS)

Wang, Wenqi; Aggarwal, Vaneet; Aeron, Shuchin

2018-05-01

In this paper, we propose a Tensor Train Neighborhood Preserving Embedding (TTNPE) to embed multi-dimensional tensor data into low dimensional tensor subspace. Novel approaches to solve the optimization problem in TTNPE are proposed. For this embedding, we evaluate novel trade-off gain among classification, computation, and dimensionality reduction (storage) for supervised learning. It is shown that compared to the state-of-the-arts tensor embedding methods, TTNPE achieves superior trade-off in classification, computation, and dimensionality reduction in MNIST handwritten digits and Weizmann face datasets.

Tensor-driven extraction of developmental features from varying paediatric EEG datasets.

PubMed

Kinney-Lang, Eli; Spyrou, Loukianos; Ebied, Ahmed; Chin, Richard Fm; Escudero, Javier

2018-05-21

Constant changes in developing children's brains can pose a challenge in EEG dependant technologies. Advancing signal processing methods to identify developmental differences in paediatric populations could help improve function and usability of such technologies. Taking advantage of the multi-dimensional structure of EEG data through tensor analysis may offer a framework for extracting relevant developmental features of paediatric datasets. A proof of concept is demonstrated through identifying latent developmental features in resting-state EEG. Approach. Three paediatric datasets (n = 50, 17, 44) were analyzed using a two-step constrained parallel factor (PARAFAC) tensor decomposition. Subject age was used as a proxy measure of development. Classification used support vector machines (SVM) to test if PARAFAC identified features could predict subject age. The results were cross-validated within each dataset. Classification analysis was complemented by visualization of the high-dimensional feature structures using t-distributed Stochastic Neighbour Embedding (t-SNE) maps. Main Results. Development-related features were successfully identified for the developmental conditions of each dataset. SVM classification showed the identified features could accurately predict subject at a significant level above chance for both healthy and impaired populations. t-SNE maps revealed suitable tensor factorization was key in extracting the developmental features. Significance. The described methods are a promising tool for identifying latent developmental features occurring throughout childhood EEG. © 2018 IOP Publishing Ltd.

Development of an extensible dual-core wireless sensing node for cyber-physical systems

NASA Astrophysics Data System (ADS)

Kane, Michael; Zhu, Dapeng; Hirose, Mitsuhito; Dong, Xinjun; Winter, Benjamin; Häckell, Mortiz; Lynch, Jerome P.; Wang, Yang; Swartz, A.

2014-04-01

The introduction of wireless telemetry into the design of monitoring and control systems has been shown to reduce system costs while simplifying installations. To date, wireless nodes proposed for sensing and actuation in cyberphysical systems have been designed using microcontrollers with one computational pipeline (i.e., single-core microcontrollers). While concurrent code execution can be implemented on single-core microcontrollers, concurrency is emulated by splitting the pipeline's resources to support multiple threads of code execution. For many applications, this approach to multi-threading is acceptable in terms of speed and function. However, some applications such as feedback controls demand deterministic timing of code execution and maximum computational throughput. For these applications, the adoption of multi-core processor architectures represents one effective solution. Multi-core microcontrollers have multiple computational pipelines that can execute embedded code in parallel and can be interrupted independent of one another. In this study, a new wireless platform named Martlet is introduced with a dual-core microcontroller adopted in its design. The dual-core microcontroller design allows Martlet to dedicate one core to standard wireless sensor operations while the other core is reserved for embedded data processing and real-time feedback control law execution. Another distinct feature of Martlet is a standardized hardware interface that allows specialized daughter boards (termed wing boards) to be interfaced to the Martlet baseboard. This extensibility opens opportunity to encapsulate specialized sensing and actuation functions in a wing board without altering the design of Martlet. In addition to describing the design of Martlet, a few example wings are detailed, along with experiments showing the Martlet's ability to monitor and control physical systems such as wind turbines and buildings.

GPU surface extraction using the closest point embedding

NASA Astrophysics Data System (ADS)

Kim, Mark; Hansen, Charles

2015-01-01

Isosurface extraction is a fundamental technique used for both surface reconstruction and mesh generation. One method to extract well-formed isosurfaces is a particle system; unfortunately, particle systems can be slow. In this paper, we introduce an enhanced parallel particle system that uses the closest point embedding as the surface representation to speedup the particle system for isosurface extraction. The closest point embedding is used in the Closest Point Method (CPM), a technique that uses a standard three dimensional numerical PDE solver on two dimensional embedded surfaces. To fully take advantage of the closest point embedding, it is coupled with a Barnes-Hut tree code on the GPU. This new technique produces well-formed, conformal unstructured triangular and tetrahedral meshes from labeled multi-material volume datasets. Further, this new parallel implementation of the particle system is faster than any known methods for conformal multi-material mesh extraction. The resulting speed-ups gained in this implementation can reduce the time from labeled data to mesh from hours to minutes and benefits users, such as bioengineers, who employ triangular and tetrahedral meshes

Influence of the composite material thermal expansion on embedded highly birefringent polymer microstructured optical fibers

NASA Astrophysics Data System (ADS)

SzelÄ g, M.; Lesiak, P.; Kuczkowski, M.; Domański, A. W.; Woliński, T. R.

2013-05-01

Results of our research on embedded highly birefringent polymer microstructured fibers are presented. A composite material sample with fibers embedded between two layers of a multi-layer composite structure is fabricated and characterized. Temperature sensitivities of the polymer fibers are measured in a free space and compared with the fibers embedded in the composite material. It appeared that highly birefringent polymer microstructured fibers exhibit a strong increase in temperature sensitivity when embedded in the composite material, which is due to the stress-induced changes in birefringence created by thermally-induced strain.

Control of spontaneous emission from a microwave-field-driven four-level atom in an anisotropic photonic crystal

NASA Astrophysics Data System (ADS)

Zhang, Duo; Li, Jiahua; Ding, Chunling; Yang, Xiaoxue

2012-05-01

The spontaneous emission properties of a microwave-field-driven four-level atom embedded in anisotropic double-band photonic crystals (PCs) are investigated. We discuss the influences of the band-edge positions, Rabi frequency and detuning of the microwave field on the emission spectrum. It is found that several interesting features such as spectral-line enhancement, spectral-line suppression, spectral-line overlap, and multi-peak structures can be observed in the spectra. The proposed scheme can be achieved by use of a microwave-coupled field into hyperfine levels in rubidium atom confined in a photonic crystal. These theoretical investigations may provide more degrees of freedom to manipulate the atomic spontaneous emission.

On the crystallization of polymer composites with inorganic fullerene-like particles.

PubMed

Enyashin, Andrey N; Glazyrina, Polina Yu

2012-05-21

The effect of a sulfide fullerene-like particle embedded into a polymer has been studied by molecular dynamics simulations on the nanosecond time scale using a mesoscopic Van der Waals force field evaluated for the case of a spherical particle. Even in this approach, neglecting the atomistic features of the surface, the inorganic particle acts as a nucleation agent facilitating the crystallization of the polymeric sample. A consideration of the Van der Waals force field of multi-walled sulfide nanoparticles suggests that in the absence of chemical interactions the size of the nanoparticle is dominating for the adhesion strength, while the number of sulfide layers composing the cage does not play a role.

NOBAI: a web server for character coding of geometrical and statistical features in RNA structure

PubMed Central

Knudsen, Vegeir; Caetano-Anollés, Gustavo

2008-01-01

The Numeration of Objects in Biology: Alignment Inferences (NOBAI) web server provides a web interface to the applications in the NOBAI software package. This software codes topological and thermodynamic information related to the secondary structure of RNA molecules as multi-state phylogenetic characters, builds character matrices directly in NEXUS format and provides sequence randomization options. The web server is an effective tool that facilitates the search for evolutionary history embedded in the structure of functional RNA molecules. The NOBAI web server is accessible at ‘http://www.manet.uiuc.edu/nobai/nobai.php’. This web site is free and open to all users and there is no login requirement. PMID:18448469

Effective scheme for partitioning covalent bonds in density-functional embedding theory: From molecules to extended covalent systems.

PubMed

Huang, Chen; Muñoz-García, Ana Belén; Pavone, Michele

2016-12-28

Density-functional embedding theory provides a general way to perform multi-physics quantum mechanics simulations of large-scale materials by dividing the total system's electron density into a cluster's density and its environment's density. It is then possible to compute the accurate local electronic structures and energetics of the embedded cluster with high-level methods, meanwhile retaining a low-level description of the environment. The prerequisite step in the density-functional embedding theory is the cluster definition. In covalent systems, cutting across the covalent bonds that connect the cluster and its environment leads to dangling bonds (unpaired electrons). These represent a major obstacle for the application of density-functional embedding theory to study extended covalent systems. In this work, we developed a simple scheme to define the cluster in covalent systems. Instead of cutting covalent bonds, we directly split the boundary atoms for maintaining the valency of the cluster. With this new covalent embedding scheme, we compute the dehydrogenation energies of several different molecules, as well as the binding energy of a cobalt atom on graphene. Well localized cluster densities are observed, which can facilitate the use of localized basis sets in high-level calculations. The results are found to converge faster with the embedding method than the other multi-physics approach ONIOM. This work paves the way to perform the density-functional embedding simulations of heterogeneous systems in which different types of chemical bonds are present.

Prediction of enhancer-promoter interactions via natural language processing.

PubMed

Zeng, Wanwen; Wu, Mengmeng; Jiang, Rui

2018-05-09

Precise identification of three-dimensional genome organization, especially enhancer-promoter interactions (EPIs), is important to deciphering gene regulation, cell differentiation and disease mechanisms. Currently, it is a challenging task to distinguish true interactions from other nearby non-interacting ones since the power of traditional experimental methods is limited due to low resolution or low throughput. We propose a novel computational framework EP2vec to assay three-dimensional genomic interactions. We first extract sequence embedding features, defined as fixed-length vector representations learned from variable-length sequences using an unsupervised deep learning method in natural language processing. Then, we train a classifier to predict EPIs using the learned representations in supervised way. Experimental results demonstrate that EP2vec obtains F1 scores ranging from 0.841~ 0.933 on different datasets, which outperforms existing methods. We prove the robustness of sequence embedding features by carrying out sensitivity analysis. Besides, we identify motifs that represent cell line-specific information through analysis of the learned sequence embedding features by adopting attention mechanism. Last, we show that even superior performance with F1 scores 0.889~ 0.940 can be achieved by combining sequence embedding features and experimental features. EP2vec sheds light on feature extraction for DNA sequences of arbitrary lengths and provides a powerful approach for EPIs identification.

PolarBRDF: A general purpose Python package for visualization and quantitative analysis of multi-angular remote sensing measurements

NASA Astrophysics Data System (ADS)

Singh, Manoj K.; Gautam, Ritesh; Gatebe, Charles K.; Poudyal, Rajesh

2016-11-01

The Bidirectional Reflectance Distribution Function (BRDF) is a fundamental concept for characterizing the reflectance property of a surface, and helps in the analysis of remote sensing data from satellite, airborne and surface platforms. Multi-angular remote sensing measurements are required for the development and evaluation of BRDF models for improved characterization of surface properties. However, multi-angular data and the associated BRDF models are typically multidimensional involving multi-angular and multi-wavelength information. Effective visualization of such complex multidimensional measurements for different wavelength combinations is presently somewhat lacking in the literature, and could serve as a potentially useful research and teaching tool in aiding both interpretation and analysis of BRDF measurements. This article describes a newly developed software package in Python (PolarBRDF) to help visualize and analyze multi-angular data in polar and False Color Composite (FCC) forms. PolarBRDF also includes functionalities for computing important multi-angular reflectance/albedo parameters including spectral albedo, principal plane reflectance and spectral reflectance slope. Application of PolarBRDF is demonstrated using various case studies obtained from airborne multi-angular remote sensing measurements using NASA's Cloud Absorption Radiometer (CAR). Our visualization program also provides functionalities for untangling complex surface/atmosphere features embedded in pixel-based remote sensing measurements, such as the FCC imagery generation of BRDF measurements of grasslands in the presence of wildfire smoke and clouds. Furthermore, PolarBRDF also provides quantitative information of the angular distribution of scattered surface/atmosphere radiation, in the form of relevant BRDF variables such as sunglint, hotspot and scattering statistics.

PolarBRDF: A general purpose Python package for visualization and quantitative analysis of multi-angular remote sensing measurements

NASA Astrophysics Data System (ADS)

Poudyal, R.; Singh, M.; Gautam, R.; Gatebe, C. K.

2016-12-01

The Bidirectional Reflectance Distribution Function (BRDF) is a fundamental concept for characterizing the reflectance property of a surface, and helps in the analysis of remote sensing data from satellite, airborne and surface platforms. Multi-angular remote sensing measurements are required for the development and evaluation of BRDF models for improved characterization of surface properties. However, multi-angular data and the associated BRDF models are typically multidimensional involving multi-angular and multi-wavelength information. Effective visualization of such complex multidimensional measurements for different wavelength combinations is presently somewhat lacking in the literature, and could serve as a potentially useful research and teaching tool in aiding both interpretation and analysis of BRDF measurements. This article describes a newly developed software package in Python (PolarBRDF) to help visualize and analyze multi-angular data in polar and False Color Composite (FCC) forms. PolarBRDF also includes functionalities for computing important multi-angular reflectance/albedo parameters including spectral albedo, principal plane reflectance and spectral reflectance slope. Application of PolarBRDF is demonstrated using various case studies obtained from airborne multi-angular remote sensing measurements using NASA's Cloud Absorption Radiometer (CAR)- http://car.gsfc.nasa.gov/. Our visualization program also provides functionalities for untangling complex surface/atmosphere features embedded in pixel-based remote sensing measurements, such as the FCC imagery generation of BRDF measurements of grasslands in the presence of wildfire smoke and clouds. Furthermore, PolarBRDF also provides quantitative information of the angular distribution of scattered surface/atmosphere radiation, in the form of relevant BRDF variables such as sunglint, hotspot and scattering statistics.

Polarbrdf: A General Purpose Python Package for Visualization Quantitative Analysis of Multi-Angular Remote Sensing Measurements

NASA Technical Reports Server (NTRS)

Singh, Manoj K.; Gautam, Ritesh; Gatebe, Charles K.; Poudyal, Rajesh

2016-01-01

The Bidirectional Reflectance Distribution Function (BRDF) is a fundamental concept for characterizing the reflectance property of a surface, and helps in the analysis of remote sensing data from satellite, airborne and surface platforms. Multi-angular remote sensing measurements are required for the development and evaluation of BRDF models for improved characterization of surface properties. However, multi-angular data and the associated BRDF models are typically multidimensional involving multi-angular and multi-wavelength information. Effective visualization of such complex multidimensional measurements for different wavelength combinations is presently somewhat lacking in the literature, and could serve as a potentially useful research and teaching tool in aiding both interpretation and analysis of BRDF measurements. This article describes a newly developed software package in Python (PolarBRDF) to help visualize and analyze multi-angular data in polar and False Color Composite (FCC) forms. PolarBRDF also includes functionalities for computing important multi-angular reflectance/albedo parameters including spectral albedo, principal plane reflectance and spectral reflectance slope. Application of PolarBRDF is demonstrated using various case studies obtained from airborne multi-angular remote sensing measurements using NASA's Cloud Absorption Radiometer (CAR). Our visualization program also provides functionalities for untangling complex surface/atmosphere features embedded in pixel-based remote sensing measurements, such as the FCC imagery generation of BRDF measurements of grasslands in the presence of wild fire smoke and clouds. Furthermore, PolarBRDF also provides quantitative information of the angular distribution of scattered surface/atmosphere radiation, in the form of relevant BRDF variables such as sunglint, hotspot and scattering statistics.

Imaging of endodontic biofilms by combined microscopy (FISH/cLSM - SEM).

PubMed

Schaudinn, C; Carr, G; Gorur, A; Jaramillo, D; Costerton, J W; Webster, P

2009-08-01

Scanning electron microscopy is a useful imaging approach for the visualization of bacterial biofilms in their natural environments including their medical and dental habitats, because it allows for the exploration of large surfaces with excellent resolution of topographic features. Most biofilms in nature, however, are embedded in a thick layer of extracellular matrix that prevents a clear identification of individual bacteria by scanning electron microscopy. The use of confocal laser scanning microscopy on the other hand in combination with fluorescence in situ hybridization enables the visualization of matrix embedded bacteria in multi-layered biofilms. In our study, fluorescence in situ hybridization/confocal laser scanning microscopy and scanning electron microscopy were applied to visualize bacterial biofilm in endodontic root canals. The resulting fluorescence in situ hybridization /confocal laser scanning microscopy and scanning electron microscopy and pictures were subsequently combined into one single image to provide high-resolution information on the location of hidden bacteria. The combined use of scanning electron microscopy and fluorescence in situ hybridization / confocal laser scanning microscopy has the potential to overcome the limits of each single technique.

Integrated Micro-Optics for Microfluidic Detection.

PubMed

Kazama, Yuto; Hibara, Akihide

2016-01-01

A method of embedding micro-optics into a microfluidic device was proposed and demonstrated. First, the usefulness of embedded right-angle prisms was demonstrated in microscope observation. Lateral-view microscopic observation of an aqueous dye flow in a 100-μm-sized microchannel was demonstrated. Then, the embedded right-angle prisms were utilized for multi-beam laser spectroscopy. Here, crossed-beam thermal lens detection of a liquid sample was applied to glucose detection.

Carbon Nanotubes Embedded in Oriented Polymer Nanofibers by Electrospinning

NASA Astrophysics Data System (ADS)

Cohen, Yachin; Dror, Yael; Khalfin, Rafail L.; Salalha, Wael; Yarin, Alexander L.; Zussman, Eyal

2004-03-01

The electrospinning process was used successfully to fabricate nanofibers of poly(ethylene oxide) [PEO] in which carbon nanotubes, either multi-walled (MWCNT) or single-walled (SWCNT) are embedded. MWCNTs were dispersed in water using SDS or Gum Arabic - a highly branched polyelectrolyte. Aqueous dispersion of SWCNT's was achieved using an alternating copolymer of styrene and maleic anhydride, hydrolyzed with NaOH. The focus of this work is on the development of axial orientations in the multi-component nanofibers. The degree of orientation of polymers, surfactants and nanotubes was studied using X-ray diffraction and transmission electron microscopy. Individual nanotubes were successfully embedded in the polymer nanofibers with good axial alignment. A high degree of alignment of PEO crystals and SDS layers was also found in the electrospun nanofibers containing SWCNT's. Oriented ropes of the nanofibers were fabricated in a converging electric field by a rotating disc with a tapered edge. These results can lead to further usage of the nanofibers with embedded carbon nanotubes in applications such as nano-scale energy storage devices.

Raman Monte Carlo simulation for light propagation for tissue with embedded objects

NASA Astrophysics Data System (ADS)

Periyasamy, Vijitha; Jaafar, Humaira Bte; Pramanik, Manojit

2018-02-01

Monte Carlo (MC) stimulation is one of the prominent simulation technique and is rapidly becoming the model of choice to study light-tissue interaction. Monte Carlo simulation for light transport in multi-layered tissue (MCML) is adapted and modelled with different geometry by integrating embedded objects of various shapes (i.e., sphere, cylinder, cuboid and ellipsoid) into the multi-layered structure. These geometries would be useful in providing a realistic tissue structure such as modelling for lymph nodes, tumors, blood vessels, head and other simulation medium. MC simulations were performed on various geometric medium. Simulation of MCML with embedded object (MCML-EO) was improvised for propagation of the photon in the defined medium with Raman scattering. The location of Raman photon generation is recorded. Simulations were experimented on a modelled breast tissue with tumor (spherical and ellipsoidal) and blood vessels (cylindrical). Results were presented in both A-line and B-line scans for embedded objects to determine spatial location where Raman photons were generated. Studies were done for different Raman probabilities.

A 32-channel photon counting module with embedded auto/cross-correlators for real-time parallel fluorescence correlation spectroscopy

DOE Office of Scientific and Technical Information (OSTI.GOV)

Gong, S.; Labanca, I.; Rech, I.

2014-10-15

Fluorescence correlation spectroscopy (FCS) is a well-established technique to study binding interactions or the diffusion of fluorescently labeled biomolecules in vitro and in vivo. Fast FCS experiments require parallel data acquisition and analysis which can be achieved by exploiting a multi-channel Single Photon Avalanche Diode (SPAD) array and a corresponding multi-input correlator. This paper reports a 32-channel FPGA based correlator able to perform 32 auto/cross-correlations simultaneously over a lag-time ranging from 10 ns up to 150 ms. The correlator is included in a 32 × 1 SPAD array module, providing a compact and flexible instrument for high throughput FCS experiments.more » However, some inherent features of SPAD arrays, namely afterpulsing and optical crosstalk effects, may introduce distortions in the measurement of auto- and cross-correlation functions. We investigated these limitations to assess their impact on the module and evaluate possible workarounds.« less

Shape priors for segmentation of the cervix region within uterine cervix images

NASA Astrophysics Data System (ADS)

Lotenberg, Shelly; Gordon, Shiri; Greenspan, Hayit

2008-03-01

The work focuses on a unique medical repository of digital Uterine Cervix images ("Cervigrams") collected by the National Cancer Institute (NCI), National Institute of Health, in longitudinal multi-year studies. NCI together with the National Library of Medicine is developing a unique web-based database of the digitized cervix images to study the evolution of lesions related to cervical cancer. Tools are needed for the automated analysis of the cervigram content to support the cancer research. In recent works, a multi-stage automated system for segmenting and labeling regions of medical and anatomical interest within the cervigrams was developed. The current paper concentrates on incorporating prior-shape information in the cervix region segmentation task. In accordance with the fact that human experts mark the cervix region as circular or elliptical, two shape models (and corresponding methods) are suggested. The shape models are embedded within an active contour framework that relies on image features. Experiments indicate that incorporation of the prior shape information augments previous results.

Intrinsic embedded sensors for polymeric mechatronics: flexure and force sensing.

PubMed

Jentoft, Leif P; Dollar, Aaron M; Wagner, Christopher R; Howe, Robert D

2014-02-25

While polymeric fabrication processes, including recent advances in additive manufacturing, have revolutionized manufacturing, little work has been done on effective sensing elements compatible with and embedded within polymeric structures. In this paper, we describe the development and evaluation of two important sensing modalities for embedding in polymeric mechatronic and robotic mechanisms: multi-axis flexure joint angle sensing utilizing IR phototransistors, and a small (12 mm), three-axis force sensing via embedded silicon strain gages with similar performance characteristics as an equally sized metal element based sensor.

Intrinsic Embedded Sensors for Polymeric Mechatronics: Flexure and Force Sensing

PubMed Central

Jentoft, Leif P.; Dollar, Aaron M.; Wagner, Christopher R.; Howe, Robert D.

2014-01-01

While polymeric fabrication processes, including recent advances in additive manufacturing, have revolutionized manufacturing, little work has been done on effective sensing elements compatible with and embedded within polymeric structures. In this paper, we describe the development and evaluation of two important sensing modalities for embedding in polymeric mechatronic and robotic mechanisms: multi-axis flexure joint angle sensing utilizing IR phototransistors, and a small (12 mm), three-axis force sensing via embedded silicon strain gages with similar performance characteristics as an equally sized metal element based sensor. PMID:24573310

Further Thoughts on "How Task Features Impact Evidence from Assessments Embedded in Simulations and Games"

ERIC Educational Resources Information Center

Oliveri, María Elena; Khan, Saad

2014-01-01

María Oliveri, and Saad Khan write that the article: "How Task Features Impact Evidence from Assessments Embedded in Simulations and Games" provided helpful illustrations regarding the implementation of evidence-centered assessment design (Mislevy & Haertel, 2006; Mislevy, Steinberg, & Almond, 1999) with games and simulations.…

Single-molecule RNA detection at depth by hybridization chain reaction and tissue hydrogel embedding and clearing.

PubMed

Shah, Sheel; Lubeck, Eric; Schwarzkopf, Maayan; He, Ting-Fang; Greenbaum, Alon; Sohn, Chang Ho; Lignell, Antti; Choi, Harry M T; Gradinaru, Viviana; Pierce, Niles A; Cai, Long

2016-08-01

Accurate and robust detection of mRNA molecules in thick tissue samples can reveal gene expression patterns in single cells within their native environment. Preserving spatial relationships while accessing the transcriptome of selected cells is a crucial feature for advancing many biological areas - from developmental biology to neuroscience. However, because of the high autofluorescence background of many tissue samples, it is difficult to detect single-molecule fluorescence in situ hybridization (smFISH) signals robustly in opaque thick samples. Here, we draw on principles from the emerging discipline of dynamic nucleic acid nanotechnology to develop a robust method for multi-color, multi-RNA imaging in deep tissues using single-molecule hybridization chain reaction (smHCR). Using this approach, single transcripts can be imaged using epifluorescence, confocal or selective plane illumination microscopy (SPIM) depending on the imaging depth required. We show that smHCR has high sensitivity in detecting mRNAs in cell culture and whole-mount zebrafish embryos, and that combined with SPIM and PACT (passive CLARITY technique) tissue hydrogel embedding and clearing, smHCR can detect single mRNAs deep within thick (0.5 mm) brain slices. By simultaneously achieving ∼20-fold signal amplification and diffraction-limited spatial resolution, smHCR offers a robust and versatile approach for detecting single mRNAs in situ, including in thick tissues where high background undermines the performance of unamplified smFISH. © 2016. Published by The Company of Biologists Ltd.

Beyond Low-Rank Representations: Orthogonal clustering basis reconstruction with optimized graph structure for multi-view spectral clustering.

PubMed

Wang, Yang; Wu, Lin

2018-07-01

Low-Rank Representation (LRR) is arguably one of the most powerful paradigms for Multi-view spectral clustering, which elegantly encodes the multi-view local graph/manifold structures into an intrinsic low-rank self-expressive data similarity embedded in high-dimensional space, to yield a better graph partition than their single-view counterparts. In this paper we revisit it with a fundamentally different perspective by discovering LRR as essentially a latent clustered orthogonal projection based representation winged with an optimized local graph structure for spectral clustering; each column of the representation is fundamentally a cluster basis orthogonal to others to indicate its members, which intuitively projects the view-specific feature representation to be the one spanned by all orthogonal basis to characterize the cluster structures. Upon this finding, we propose our technique with the following: (1) We decompose LRR into latent clustered orthogonal representation via low-rank matrix factorization, to encode the more flexible cluster structures than LRR over primal data objects; (2) We convert the problem of LRR into that of simultaneously learning orthogonal clustered representation and optimized local graph structure for each view; (3) The learned orthogonal clustered representations and local graph structures enjoy the same magnitude for multi-view, so that the ideal multi-view consensus can be readily achieved. The experiments over multi-view datasets validate its superiority, especially over recent state-of-the-art LRR models. Copyright © 2018 Elsevier Ltd. All rights reserved.

Deep embedding convolutional neural network for synthesizing CT image from T1-Weighted MR image.

PubMed

Xiang, Lei; Wang, Qian; Nie, Dong; Zhang, Lichi; Jin, Xiyao; Qiao, Yu; Shen, Dinggang

2018-07-01

Recently, more and more attention is drawn to the field of medical image synthesis across modalities. Among them, the synthesis of computed tomography (CT) image from T1-weighted magnetic resonance (MR) image is of great importance, although the mapping between them is highly complex due to large gaps of appearances of the two modalities. In this work, we aim to tackle this MR-to-CT synthesis task by a novel deep embedding convolutional neural network (DECNN). Specifically, we generate the feature maps from MR images, and then transform these feature maps forward through convolutional layers in the network. We can further compute a tentative CT synthesis from the midway of the flow of feature maps, and then embed this tentative CT synthesis result back to the feature maps. This embedding operation results in better feature maps, which are further transformed forward in DECNN. After repeating this embedding procedure for several times in the network, we can eventually synthesize a final CT image in the end of the DECNN. We have validated our proposed method on both brain and prostate imaging datasets, by also comparing with the state-of-the-art methods. Experimental results suggest that our DECNN (with repeated embedding operations) demonstrates its superior performances, in terms of both the perceptive quality of the synthesized CT image and the run-time cost for synthesizing a CT image. Copyright © 2018. Published by Elsevier B.V.

Active Spread-Spectrum Steganalysis for Hidden Data Extraction

DTIC Science & Technology

2011-09-01

steganalysis. In particular, we aim to recover blindly se- cret data hidden in image hosts via (multi-signature) direct- sequence SS embedding [18]-[25...access (CDMA) communica- tion systems. Under the assumption that the embedded se- cret messages are independent identically distributed (i.i.d.) random

L2 Word Recognition: Influence of L1 Orthography on Multi-syllabic Word Recognition.

PubMed

Hamada, Megumi

2017-10-01

L2 reading research suggests that L1 orthographic experience influences L2 word recognition. Nevertheless, the findings on multi-syllabic words in English are still limited despite the fact that a vast majority of words are multi-syllabic. The study investigated whether L1 orthography influences the recognition of multi-syllabic words, focusing on the position of an embedded word. The participants were Arabic ESL learners, Chinese ESL learners, and native speakers of English. The task was a word search task, in which the participants identified a target word embedded in a pseudoword at the initial, middle, or final position. The search accuracy and speed indicated that all groups showed a strong preference for the initial position. The accuracy data further indicated group differences. The Arabic group showed higher accuracy in the final than middle, while the Chinese group showed the opposite and the native speakers showed no difference between the two positions. The findings suggest that L2 multi-syllabic word recognition involves unique processes.

Content-independent embedding scheme for multi-modal medical image watermarking.

PubMed

Nyeem, Hussain; Boles, Wageeh; Boyd, Colin

2015-02-04

As the increasing adoption of information technology continues to offer better distant medical services, the distribution of, and remote access to digital medical images over public networks continues to grow significantly. Such use of medical images raises serious concerns for their continuous security protection, which digital watermarking has shown great potential to address. We present a content-independent embedding scheme for medical image watermarking. We observe that the perceptual content of medical images varies widely with their modalities. Recent medical image watermarking schemes are image-content dependent and thus they may suffer from inconsistent embedding capacity and visual artefacts. To attain the image content-independent embedding property, we generalise RONI (region of non-interest, to the medical professionals) selection process and use it for embedding by utilising RONI's least significant bit-planes. The proposed scheme thus avoids the need for RONI segmentation that incurs capacity and computational overheads. Our experimental results demonstrate that the proposed embedding scheme performs consistently over a dataset of 370 medical images including their 7 different modalities. Experimental results also verify how the state-of-the-art reversible schemes can have an inconsistent performance for different modalities of medical images. Our scheme has MSSIM (Mean Structural SIMilarity) larger than 0.999 with a deterministically adaptable embedding capacity. Our proposed image-content independent embedding scheme is modality-wise consistent, and maintains a good image quality of RONI while keeping all other pixels in the image untouched. Thus, with an appropriate watermarking framework (i.e., with the considerations of watermark generation, embedding and detection functions), our proposed scheme can be viable for the multi-modality medical image applications and distant medical services such as teleradiology and eHealth.

Fiber Optic Sensor Embedment Study for Multi-Parameter Strain Sensing

PubMed Central

Drissi-Habti, Monssef; Raman, Venkadesh; Khadour, Aghiad; Timorian, Safiullah

2017-01-01

The fiber optic sensors (FOSs) are commonly used for large-scale structure monitoring systems for their small size, noise free and low electrical risk characteristics. Embedded fiber optic sensors (FOSs) lead to micro-damage in composite structures. This damage generation threshold is based on the coating material of the FOSs and their diameter. In addition, embedded FOSs are aligned parallel to reinforcement fibers to avoid micro-damage creation. This linear positioning of distributed FOS fails to provide all strain parameters. We suggest novel sinusoidal sensor positioning to overcome this issue. This method tends to provide multi-parameter strains in a large surface area. The effectiveness of sinusoidal FOS positioning over linear FOS positioning is studied under both numerical and experimental methods. This study proves the advantages of the sinusoidal positioning method for FOS in composite material’s bonding. PMID:28333117

Structural Integration of Sensors/Actuators by Laser Beam Melting for Tailored Smart Components

NASA Astrophysics Data System (ADS)

Töppel, Thomas; Lausch, Holger; Brand, Michael; Hensel, Eric; Arnold, Michael; Rotsch, Christian

2018-03-01

Laser beam melting (LBM), an additive laser powder bed fusion technology, enables the structural integration of temperature-sensitive sensors and actuators in complex monolithic metallic structures. The objective is to embed a functional component inside a metal part without losing its functionality by overheating. The first part of this paper addresses the development of a new process chain for bonded embedding of temperature-sensitive sensor/actuator systems by LBM. These systems are modularly built and coated by a multi-material/multi-layer thermal protection system of ceramic and metallic compounds. The characteristic of low global heat input in LBM is utilized for the functional embedding. In the second part, the specific functional design and optimization for tailored smart components with embedded functionalities are addressed. Numerical and experimental validated results are demonstrated on a smart femoral hip stem.

Detection of fibrils associated with Rickettsia rickettsii.

PubMed

Todd, W J; Burgdorfer, W; Wray, G P

1983-09-01

The ultrastructural appearance of the "halozone" formed at the interface between the spotted fever agent Rickettsia rickettsii and the cytoplasm of persistently infected cultured vole cells (Microtus pennsylvanicus) was studied by transmission electron microscopy. In sections of epoxy-embedded specimens stained with uranyl acetate and lead citrate, the halozone appeared clear and devoid of ultrastructural features. However, when unembedded preparations of whole infected cells were examined at 1,000 kV, fine structural features were observed within the halozone. These features, associated with the rickettsial outer membrane, were more clearly detectable when the infected cells were extracted with the detergent Triton X-100 before fixation. Under such conditions, long extensions of the rickettsial outer membrane, microfilament-like structures attached to that membrane, and extensive attachments between adjacent rickettsiae were seen. The fine structural features within the rickettsial halozone were also seen at 75 kV when unembedded sections were prepared from polyethylene glycol-embedded specimens. Thus, epoxy-embedding medium obscures the fine structural features within the halozone surrounding the rickettsiae in infected cells.

Detection of fibrils associated with Rickettsia rickettsii.

PubMed Central

Todd, W J; Burgdorfer, W; Wray, G P

1983-01-01

The ultrastructural appearance of the "halozone" formed at the interface between the spotted fever agent Rickettsia rickettsii and the cytoplasm of persistently infected cultured vole cells (Microtus pennsylvanicus) was studied by transmission electron microscopy. In sections of epoxy-embedded specimens stained with uranyl acetate and lead citrate, the halozone appeared clear and devoid of ultrastructural features. However, when unembedded preparations of whole infected cells were examined at 1,000 kV, fine structural features were observed within the halozone. These features, associated with the rickettsial outer membrane, were more clearly detectable when the infected cells were extracted with the detergent Triton X-100 before fixation. Under such conditions, long extensions of the rickettsial outer membrane, microfilament-like structures attached to that membrane, and extensive attachments between adjacent rickettsiae were seen. The fine structural features within the rickettsial halozone were also seen at 75 kV when unembedded sections were prepared from polyethylene glycol-embedded specimens. Thus, epoxy-embedding medium obscures the fine structural features within the halozone surrounding the rickettsiae in infected cells. Images PMID:6411620

DOE Office of Scientific and Technical Information (OSTI.GOV)

Dall'Anese, Emiliano; Baker, Kyri; Summers, Tyler

The paper focuses on distribution systems featuring renewable energy sources and energy storage devices, and develops an optimal power flow (OPF) approach to optimize the system operation in spite of forecasting errors. The proposed method builds on a chance-constrained multi-period AC OPF formulation, where probabilistic constraints are utilized to enforce voltage regulation with a prescribed probability. To enable a computationally affordable solution approach, a convex reformulation of the OPF task is obtained by resorting to i) pertinent linear approximations of the power flow equations, and ii) convex approximations of the chance constraints. Particularly, the approximate chance constraints provide conservative boundsmore » that hold for arbitrary distributions of the forecasting errors. An adaptive optimization strategy is then obtained by embedding the proposed OPF task into a model predictive control framework.« less

Hierarchical Regularity in Multi-Basin Dynamics on Protein Landscapes

NASA Astrophysics Data System (ADS)

Matsunaga, Yasuhiro; Kostov, Konstatin S.; Komatsuzaki, Tamiki

2004-04-01

We analyze time series of potential energy fluctuations and principal components at several temperatures for two kinds of off-lattice 46-bead models that have two distinctive energy landscapes. The less-frustrated "funnel" energy landscape brings about stronger nonstationary behavior of the potential energy fluctuations at the folding temperature than the other, rather frustrated energy landscape at the collapse temperature. By combining principal component analysis with an embedding nonlinear time-series analysis, it is shown that the fast fluctuations with small amplitudes of 70-80% of the principal components cause the time series to become almost "random" in only 100 simulation steps. However, the stochastic feature of the principal components tends to be suppressed through a wide range of degrees of freedom at the transition temperature.

Virtual network embedding in cross-domain network based on topology and resource attributes

NASA Astrophysics Data System (ADS)

Zhu, Lei; Zhang, Zhizhong; Feng, Linlin; Liu, Lilan

2018-03-01

Aiming at the network architecture ossification and the diversity of access technologies issues, this paper researches the cross-domain virtual network embedding algorithm. By analysing the topological attribute from the local and global perspective of nodes in the virtual network and the physical network, combined with the local network resource property, we rank the embedding priority of the nodes with PCA and TOPSIS methods. Besides, the link load distribution is considered. Above all, We proposed an cross-domain virtual network embedding algorithm based on topology and resource attributes. The simulation results depicts that our algorithm increases the acceptance rate of multi-domain virtual network requests, compared with the existing virtual network embedding algorithm.

Low Power Multi-Hop Networking Analysis in Intelligent Environments.

PubMed

Etxaniz, Josu; Aranguren, Gerardo

2017-05-19

Intelligent systems are driven by the latest technological advances in many different areas such as sensing, embedded systems, wireless communications or context recognition. This paper focuses on some of those areas. Concretely, the paper deals with wireless communications issues in embedded systems. More precisely, the paper combines the multi-hop networking with Bluetooth technology and a quality of service (QoS) metric, the latency. Bluetooth is a radio license-free worldwide communication standard that makes low power multi-hop wireless networking available. It establishes piconets (point-to-point and point-to-multipoint links) and scatternets (multi-hop networks). As a result, many Bluetooth nodes can be interconnected to set up ambient intelligent networks. Then, this paper presents the results of the investigation on multi-hop latency with park and sniff Bluetooth low power modes conducted over the hardware test bench previously implemented. In addition, the empirical models to estimate the latency of multi-hop communications over Bluetooth Asynchronous Connectionless Links (ACL) in park and sniff mode are given. The designers of devices and networks for intelligent systems will benefit from the estimation of the latency in Bluetooth multi-hop communications that the models provide.

Low Power Multi-Hop Networking Analysis in Intelligent Environments

PubMed Central

Etxaniz, Josu; Aranguren, Gerardo

2017-01-01

Intelligent systems are driven by the latest technological advances in many different areas such as sensing, embedded systems, wireless communications or context recognition. This paper focuses on some of those areas. Concretely, the paper deals with wireless communications issues in embedded systems. More precisely, the paper combines the multi-hop networking with Bluetooth technology and a quality of service (QoS) metric, the latency. Bluetooth is a radio license-free worldwide communication standard that makes low power multi-hop wireless networking available. It establishes piconets (point-to-point and point-to-multipoint links) and scatternets (multi-hop networks). As a result, many Bluetooth nodes can be interconnected to set up ambient intelligent networks. Then, this paper presents the results of the investigation on multi-hop latency with park and sniff Bluetooth low power modes conducted over the hardware test bench previously implemented. In addition, the empirical models to estimate the latency of multi-hop communications over Bluetooth Asynchronous Connectionless Links (ACL) in park and sniff mode are given. The designers of devices and networks for intelligent systems will benefit from the estimation of the latency in Bluetooth multi-hop communications that the models provide. PMID:28534847

Multi-scale Gaussian representation and outline-learning based cell image segmentation.

PubMed

Farhan, Muhammad; Ruusuvuori, Pekka; Emmenlauer, Mario; Rämö, Pauli; Dehio, Christoph; Yli-Harja, Olli

2013-01-01

High-throughput genome-wide screening to study gene-specific functions, e.g. for drug discovery, demands fast automated image analysis methods to assist in unraveling the full potential of such studies. Image segmentation is typically at the forefront of such analysis as the performance of the subsequent steps, for example, cell classification, cell tracking etc., often relies on the results of segmentation. We present a cell cytoplasm segmentation framework which first separates cell cytoplasm from image background using novel approach of image enhancement and coefficient of variation of multi-scale Gaussian scale-space representation. A novel outline-learning based classification method is developed using regularized logistic regression with embedded feature selection which classifies image pixels as outline/non-outline to give cytoplasm outlines. Refinement of the detected outlines to separate cells from each other is performed in a post-processing step where the nuclei segmentation is used as contextual information. We evaluate the proposed segmentation methodology using two challenging test cases, presenting images with completely different characteristics, with cells of varying size, shape, texture and degrees of overlap. The feature selection and classification framework for outline detection produces very simple sparse models which use only a small subset of the large, generic feature set, that is, only 7 and 5 features for the two cases. Quantitative comparison of the results for the two test cases against state-of-the-art methods show that our methodology outperforms them with an increase of 4-9% in segmentation accuracy with maximum accuracy of 93%. Finally, the results obtained for diverse datasets demonstrate that our framework not only produces accurate segmentation but also generalizes well to different segmentation tasks.

Multi-scale Gaussian representation and outline-learning based cell image segmentation

PubMed Central

2013-01-01

Background High-throughput genome-wide screening to study gene-specific functions, e.g. for drug discovery, demands fast automated image analysis methods to assist in unraveling the full potential of such studies. Image segmentation is typically at the forefront of such analysis as the performance of the subsequent steps, for example, cell classification, cell tracking etc., often relies on the results of segmentation. Methods We present a cell cytoplasm segmentation framework which first separates cell cytoplasm from image background using novel approach of image enhancement and coefficient of variation of multi-scale Gaussian scale-space representation. A novel outline-learning based classification method is developed using regularized logistic regression with embedded feature selection which classifies image pixels as outline/non-outline to give cytoplasm outlines. Refinement of the detected outlines to separate cells from each other is performed in a post-processing step where the nuclei segmentation is used as contextual information. Results and conclusions We evaluate the proposed segmentation methodology using two challenging test cases, presenting images with completely different characteristics, with cells of varying size, shape, texture and degrees of overlap. The feature selection and classification framework for outline detection produces very simple sparse models which use only a small subset of the large, generic feature set, that is, only 7 and 5 features for the two cases. Quantitative comparison of the results for the two test cases against state-of-the-art methods show that our methodology outperforms them with an increase of 4-9% in segmentation accuracy with maximum accuracy of 93%. Finally, the results obtained for diverse datasets demonstrate that our framework not only produces accurate segmentation but also generalizes well to different segmentation tasks. PMID:24267488

System and method for the detection of anomalies in an image

DOEpatents

Prasad, Lakshman; Swaminarayan, Sriram

2013-09-03

Preferred aspects of the present invention can include receiving a digital image at a processor; segmenting the digital image into a hierarchy of feature layers comprising one or more fine-scale features defining a foreground object embedded in one or more coarser-scale features defining a background to the one or more fine-scale features in the segmentation hierarchy; detecting a first fine-scale foreground feature as an anomaly with respect to a first background feature within which it is embedded; and constructing an anomalous feature layer by synthesizing spatially contiguous anomalous fine-scale features. Additional preferred aspects of the present invention can include detecting non-pervasive changes between sets of images in response at least in part to one or more difference images between the sets of images.

Hardware-software face detection system based on multi-block local binary patterns

NASA Astrophysics Data System (ADS)

Acasandrei, Laurentiu; Barriga, Angel

2015-03-01

Face detection is an important aspect for biometrics, video surveillance and human computer interaction. Due to the complexity of the detection algorithms any face detection system requires a huge amount of computational and memory resources. In this communication an accelerated implementation of MB LBP face detection algorithm targeting low frequency, low memory and low power embedded system is presented. The resulted implementation is time deterministic and uses a customizable AMBA IP hardware accelerator. The IP implements the kernel operations of the MB-LBP algorithm and can be used as universal accelerator for MB LBP based applications. The IP employs 8 parallel MB-LBP feature evaluators cores, uses a deterministic bandwidth, has a low area profile and the power consumption is ~95 mW on a Virtex5 XC5VLX50T. The resulted implementation acceleration gain is between 5 to 8 times, while the hardware MB-LBP feature evaluation gain is between 69 and 139 times.

Model-based Robotic Dynamic Motion Control for the Robonaut 2 Humanoid Robot

NASA Technical Reports Server (NTRS)

Badger, Julia M.; Hulse, Aaron M.; Taylor, Ross C.; Curtis, Andrew W.; Gooding, Dustin R.; Thackston, Allison

2013-01-01

Robonaut 2 (R2), an upper-body dexterous humanoid robot, has been undergoing experimental trials on board the International Space Station (ISS) for more than a year. R2 will soon be upgraded with two climbing appendages, or legs, as well as a new integrated model-based control system. This control system satisfies two important requirements; first, that the robot can allow humans to enter its workspace during operation and second, that the robot can move its large inertia with enough precision to attach to handrails and seat track while climbing around the ISS. This is achieved by a novel control architecture that features an embedded impedance control law on the motor drivers called Multi-Loop control which is tightly interfaced with a kinematic and dynamic coordinated control system nicknamed RoboDyn that resides on centralized processors. This paper presents the integrated control algorithm as well as several test results that illustrate R2's safety features and performance.

Bearing Fault Diagnosis Based on Statistical Locally Linear Embedding

PubMed Central

Wang, Xiang; Zheng, Yuan; Zhao, Zhenzhou; Wang, Jinping

2015-01-01

Fault diagnosis is essentially a kind of pattern recognition. The measured signal samples usually distribute on nonlinear low-dimensional manifolds embedded in the high-dimensional signal space, so how to implement feature extraction, dimensionality reduction and improve recognition performance is a crucial task. In this paper a novel machinery fault diagnosis approach based on a statistical locally linear embedding (S-LLE) algorithm which is an extension of LLE by exploiting the fault class label information is proposed. The fault diagnosis approach first extracts the intrinsic manifold features from the high-dimensional feature vectors which are obtained from vibration signals that feature extraction by time-domain, frequency-domain and empirical mode decomposition (EMD), and then translates the complex mode space into a salient low-dimensional feature space by the manifold learning algorithm S-LLE, which outperforms other feature reduction methods such as PCA, LDA and LLE. Finally in the feature reduction space pattern classification and fault diagnosis by classifier are carried out easily and rapidly. Rolling bearing fault signals are used to validate the proposed fault diagnosis approach. The results indicate that the proposed approach obviously improves the classification performance of fault pattern recognition and outperforms the other traditional approaches. PMID:26153771

Impact of Video Self-Monitoring with Graduated Training on Implementation of Embedded Instructional Learning Trials

ERIC Educational Resources Information Center

Bishop, Crystal D.; Snyder, Patricia A.; Crow, Robert E.

2015-01-01

We used a multi-component single-subject experimental design across three preschool teachers to examine the effects of video self-monitoring with graduated training and feedback on the accuracy with which teachers monitored their implementation of embedded instructional learning trials. We also examined changes in teachers' implementation of…

A new strategy on utilizing nitrogen doped TiO{sub 2} in nanostructured solar cells: Embedded multifunctional N-TiO{sub 2} scattering particles in mesoporous photoanode

DOE Office of Scientific and Technical Information (OSTI.GOV)

Shogh, Shiva; Mohammadpour, Raheleh; Iraji zad, Azam, E-mail: Iraji@sharif.edu

2015-12-15

Highlights: • N-doped TiO{sub 2} scattering particles were synthesized for embedding into commercial photoanode of dye sensitized solar cells. • Embedded scatterers improved optical and electrical features of the cells. • These multifunctional scatterers increased cell performance up to 17%. - Abstract: Aggregated sub-micron size nitrogen doped TiO{sub 2} (N-TiO{sub 2}) particles with superior optical and electrical features were successfully synthesized for embedding into commercial mesoporous TiO{sub 2} photoelectrode of dye sensitized solar cells (DSSCs) as the light scattering particles compared to undoped one. X-ray photoelectron spectroscopy and absorption spectra confirmed that the titanium dioxide is sufficiently doped by nitrogenmore » in N-TiO{sub 2} sample. Employing these high-surface N-TiO{sub 2} in mesoporous photoelectrode of solar cells, the power conversion efficiency of 8% has been achieved which shows 17% improvement for the optimum embedded level of doping (30 wt%) compared to commercial photoelectrode without additive; while enhanced efficiency is only 3% embedding undoped sub-micron size TiO{sub 2} particles. These results can introduce the novel multifunctional photoelectrode for nanostructured solar cells with enhanced values of scattering efficiency and improved electrical features including trap states density reduction in comparison to commercial mesoporous photoelectrodes.« less

Securely Partitioning Spacecraft Computing Resources: Validation of a Separation Kernel

NASA Astrophysics Data System (ADS)

Bremer, Leon; Schreutelkamp, Erwin

2011-08-01

The F-35 Lightning II, also known as the Joint Strike Fighter, will be the first operational fighter aircraft equipped with an operational MultiShip Embedded Training capability. This onboard training system allows teams of fighter pilots to jointly operate their F-35 in flight against virtual threats, avoiding the need for real adversary air threats and surface threat systems in their training. The European Real-time Operations Simulator (EuroSim) framework is well known in the space domain, particularly in support of engineering and test phases of space system development. In the MultiShip Embedded Training project, EuroSim is not only the essential tool for development and verification throughout the project but is also the engine of the final embedded simulator on board of the F-35 aircraft. The novel ways in which EuroSim is applied in the project in relation to distributed simulation problems, team collaboration, tool chains and embedded systems can benefit many projects and applications. The paper describes the application of EuroSim as the simulation engine of the F-35 Embedded Training solution, the extensions to the EuroSim product that enable this application, and its usage in development and verification of the whole project as carried out at the sites of Dutch Space and the National Aerospace Laboratory (NLR).

Software defined radio (SDR) architecture for concurrent multi-satellite communications

NASA Astrophysics Data System (ADS)

Maheshwarappa, Mamatha R.

SDRs have emerged as a viable approach for space communications over the last decade by delivering low-cost hardware and flexible software solutions. The flexibility introduced by the SDR concept not only allows the realisation of concurrent multiple standards on one platform, but also promises to ease the implementation of one communication standard on differing SDR platforms by signal porting. This technology would facilitate implementing reconfigurable nodes for parallel satellite reception in Mobile/Deployable Ground Segments and Distributed Satellite Systems (DSS) for amateur radio/university satellite operations. This work outlines the recent advances in embedded technologies that can enable new communication architectures for concurrent multi-satellite or satellite-to-ground missions where multi-link challenges are associated. This research proposes a novel concept to run advanced parallelised SDR back-end technologies in a Commercial-Off-The-Shelf (COTS) embedded system that can support multi-signal processing for multi-satellite scenarios simultaneously. The initial SDR implementation could support only one receiver chain due to system saturation. However, the design was optimised to facilitate multiple signals within the limited resources available on an embedded system at any given time. This was achieved by providing a VHDL solution to the existing Python and C/C++ programming languages along with parallelisation so as to accelerate performance whilst maintaining the flexibility. The improvement in the performance was validated at every stage through profiling. Various cases of concurrent multiple signals with different standards such as frequency (with Doppler effect) and symbol rates were simulated in order to validate the novel architecture proposed in this research. Also, the architecture allows the system to be reconfigurable by providing the opportunity to change the communication standards in soft real-time. The chosen COTS solution provides a generic software methodology for both ground and space applications that will remain unaltered despite new evolutions in hardware, and supports concurrent multi-standard, multi-channel and multi-rate telemetry signals.

T and D-Bench--Innovative Combined Support for Education and Research in Computer Architecture and Embedded Systems

ERIC Educational Resources Information Center

Soares, S. N.; Wagner, F. R.

2011-01-01

Teaching and Design Workbench (T&D-Bench) is a framework aimed at education and research in the areas of computer architecture and embedded systems. It includes a set of features not found in other educational environments. This set of features is the result of an original combination of design requirements for T&D-Bench: that the…

Review of battery powered embedded systems design for mission-critical low-power applications

NASA Astrophysics Data System (ADS)

Malewski, Matthew; Cowell, David M. J.; Freear, Steven

2018-06-01

The applications and uses of embedded systems is increasingly pervasive. Mission and safety critical systems relying on embedded systems pose specific challenges. Embedded systems is a multi-disciplinary domain, involving both hardware and software. Systems need to be designed in a holistic manner so that they are able to provide the desired reliability and minimise unnecessary complexity. The large problem landscape means that there is no one solution that fits all applications of embedded systems. With the primary focus of these mission and safety critical systems being functionality and reliability, there can be conflicts with business needs, and this can introduce pressures to reduce cost at the expense of reliability and functionality. This paper examines the challenges faced by battery powered systems, and then explores at more general problems, and several real-world embedded systems.

Application-oriented programming model for sensor networks embedded in the human body.

PubMed

Barbosa, Talles M G de A; Sene, Iwens G; da Rocha, Adson F; Nascimento, Fransisco A de O; Carvalho, Hervaldo S; Camapum, Juliana F

2006-01-01

This work presents a new programming model for sensor networks embedded in the human body which is based on the concept of multi-programming application-oriented software. This model was conceived with a top-down approach of four layers and its main goal is to allow the healthcare professionals to program and to reconfigure the network locally or by the Internet. In order to evaluate this hypothesis, a benchmarking was executed in order to allow the assessment of the mean time spent in the programming of a multi-functional sensor node used for the measurement and transmission of the electrocardiogram.

Two novel motion-based algorithms for surveillance video analysis on embedded platforms

NASA Astrophysics Data System (ADS)

Vijverberg, Julien A.; Loomans, Marijn J. H.; Koeleman, Cornelis J.; de With, Peter H. N.

2010-05-01

This paper proposes two novel motion-vector based techniques for target detection and target tracking in surveillance videos. The algorithms are designed to operate on a resource-constrained device, such as a surveillance camera, and to reuse the motion vectors generated by the video encoder. The first novel algorithm for target detection uses motion vectors to construct a consistent motion mask, which is combined with a simple background segmentation technique to obtain a segmentation mask. The second proposed algorithm aims at multi-target tracking and uses motion vectors to assign blocks to targets employing five features. The weights of these features are adapted based on the interaction between targets. These algorithms are combined in one complete analysis application. The performance of this application for target detection has been evaluated for the i-LIDS sterile zone dataset and achieves an F1-score of 0.40-0.69. The performance of the analysis algorithm for multi-target tracking has been evaluated using the CAVIAR dataset and achieves an MOTP of around 9.7 and MOTA of 0.17-0.25. On a selection of targets in videos from other datasets, the achieved MOTP and MOTA are 8.8-10.5 and 0.32-0.49 respectively. The execution time on a PC-based platform is 36 ms. This includes the 20 ms for generating motion vectors, which are also required by the video encoder.

A Multi-Scale Algorithm for Graffito Advertisement Detection from Images of Real Estate

NASA Astrophysics Data System (ADS)

Yang, Jun; Zhu, Shi-Jiao

There is a significant need to detect and extract the graffito advertisement embedded in the housing images automatically. However, it is a hard job to separate the advertisement region well since housing images generally have complex background. In this paper, a detecting algorithm which uses multi-scale Gabor filters to identify graffito regions is proposed. Firstly, multi-scale Gabor filters with different directions are applied to housing images, then the approach uses these frequency data to find likely graffito regions using the relationship of different channels, it exploits the ability of different filters technique to solve the detection problem with low computational efforts. Lastly, the method is tested on several real estate images which are embedded graffito advertisement to verify its robustness and efficiency. The experiments demonstrate graffito regions can be detected quite well.

Embedding EfS in Teacher Education through a Multi-Level Systems Approach: Lessons from Queensland

ERIC Educational Resources Information Center

Evans, Neus; Ferreira, Jo-Anne; Davis, Julie; Stevenson, Robert B.

2016-01-01

This article reports on the fourth stage of an evolving study to develop a systems model for embedding education for sustainability (EfS) into preservice teacher education. The fourth stage trialled the extension of the model to a comprehensive state-wide systems approach involving representatives from all eight Queensland teacher education…

Implicit Learning of Recursive Context-Free Grammars

PubMed Central

Rohrmeier, Martin; Fu, Qiufang; Dienes, Zoltan

2012-01-01

Context-free grammars are fundamental for the description of linguistic syntax. However, most artificial grammar learning experiments have explored learning of simpler finite-state grammars, while studies exploring context-free grammars have not assessed awareness and implicitness. This paper explores the implicit learning of context-free grammars employing features of hierarchical organization, recursive embedding and long-distance dependencies. The grammars also featured the distinction between left- and right-branching structures, as well as between centre- and tail-embedding, both distinctions found in natural languages. People acquired unconscious knowledge of relations between grammatical classes even for dependencies over long distances, in ways that went beyond learning simpler relations (e.g. n-grams) between individual words. The structural distinctions drawn from linguistics also proved important as performance was greater for tail-embedding than centre-embedding structures. The results suggest the plausibility of implicit learning of complex context-free structures, which model some features of natural languages. They support the relevance of artificial grammar learning for probing mechanisms of language learning and challenge existing theories and computational models of implicit learning. PMID:23094021

Named Entity Recognition in Chinese Clinical Text Using Deep Neural Network.

PubMed

Wu, Yonghui; Jiang, Min; Lei, Jianbo; Xu, Hua

2015-01-01

Rapid growth in electronic health records (EHRs) use has led to an unprecedented expansion of available clinical data in electronic formats. However, much of the important healthcare information is locked in the narrative documents. Therefore Natural Language Processing (NLP) technologies, e.g., Named Entity Recognition that identifies boundaries and types of entities, has been extensively studied to unlock important clinical information in free text. In this study, we investigated a novel deep learning method to recognize clinical entities in Chinese clinical documents using the minimal feature engineering approach. We developed a deep neural network (DNN) to generate word embeddings from a large unlabeled corpus through unsupervised learning and another DNN for the NER task. The experiment results showed that the DNN with word embeddings trained from the large unlabeled corpus outperformed the state-of-the-art CRF's model in the minimal feature engineering setting, achieving the highest F1-score of 0.9280. Further analysis showed that word embeddings derived through unsupervised learning from large unlabeled corpus remarkably improved the DNN with randomized embedding, denoting the usefulness of unsupervised feature learning.

Regular and singular pulse and front solutions and possible isochronous behavior in the short-pulse equation: Phase-plane, multi-infinite series and variational approaches

NASA Astrophysics Data System (ADS)

Gambino, G.; Tanriver, U.; Guha, P.; Choudhury, A. Ghose; Choudhury, S. Roy

2015-02-01

In this paper we employ three recent analytical approaches to investigate the possible classes of traveling wave solutions of some members of a family of so-called short-pulse equations (SPE). A recent, novel application of phase-plane analysis is first employed to show the existence of breaking kink wave solutions in certain parameter regimes. Secondly, smooth traveling waves are derived using a recent technique to derive convergent multi-infinite series solutions for the homoclinic (heteroclinic) orbits of the traveling-wave equations for the SPE equation, as well as for its generalized version with arbitrary coefficients. These correspond to pulse (kink or shock) solutions respectively of the original PDEs. We perform many numerical tests in different parameter regime to pinpoint real saddle equilibrium points of the corresponding traveling-wave equations, as well as ensure simultaneous convergence and continuity of the multi-infinite series solutions for the homoclinic/heteroclinic orbits anchored by these saddle points. Unlike the majority of unaccelerated convergent series, high accuracy is attained with relatively few terms. And finally, variational methods are employed to generate families of both regular and embedded solitary wave solutions for the SPE PDE. The technique for obtaining the embedded solitons incorporates several recent generalizations of the usual variational technique and it is thus topical in itself. One unusual feature of the solitary waves derived here is that we are able to obtain them in analytical form (within the assumed ansatz for the trial functions). Thus, a direct error analysis is performed, showing the accuracy of the resulting solitary waves. Given the importance of solitary wave solutions in wave dynamics and information propagation in nonlinear PDEs, as well as the fact that not much is known about solutions of the family of generalized SPE equations considered here, the results obtained are both new and timely.

Disease named entity recognition from biomedical literature using a novel convolutional neural network.

PubMed

Zhao, Zhehuan; Yang, Zhihao; Luo, Ling; Wang, Lei; Zhang, Yin; Lin, Hongfei; Wang, Jian

2017-12-28

Automatic disease named entity recognition (DNER) is of utmost importance for development of more sophisticated BioNLP tools. However, most conventional CRF based DNER systems rely on well-designed features whose selection is labor intensive and time-consuming. Though most deep learning methods can solve NER problems with little feature engineering, they employ additional CRF layer to capture the correlation information between labels in neighborhoods which makes them much complicated. In this paper, we propose a novel multiple label convolutional neural network (MCNN) based disease NER approach. In this approach, instead of the CRF layer, a multiple label strategy (MLS) first introduced by us, is employed. First, the character-level embedding, word-level embedding and lexicon feature embedding are concatenated. Then several convolutional layers are stacked over the concatenated embedding. Finally, MLS strategy is applied to the output layer to capture the correlation information between neighboring labels. As shown by the experimental results, MCNN can achieve the state-of-the-art performance on both NCBI and CDR corpora. The proposed MCNN based disease NER method achieves the state-of-the-art performance with little feature engineering. And the experimental results show the MLS strategy's effectiveness of capturing the correlation information between labels in the neighborhood.

FOCUSR: Feature Oriented Correspondence using Spectral Regularization–A Method for Precise Surface Matching

PubMed Central

Lombaert, Herve; Grady, Leo; Polimeni, Jonathan R.; Cheriet, Farida

2013-01-01

Existing methods for surface matching are limited by the trade-off between precision and computational efficiency. Here we present an improved algorithm for dense vertex-to-vertex correspondence that uses direct matching of features defined on a surface and improves it by using spectral correspondence as a regularization. This algorithm has the speed of both feature matching and spectral matching while exhibiting greatly improved precision (distance errors of 1.4%). The method, FOCUSR, incorporates implicitly such additional features to calculate the correspondence and relies on the smoothness of the lowest-frequency harmonics of a graph Laplacian to spatially regularize the features. In its simplest form, FOCUSR is an improved spectral correspondence method that nonrigidly deforms spectral embeddings. We provide here a full realization of spectral correspondence where virtually any feature can be used as additional information using weights on graph edges, but also on graph nodes and as extra embedded coordinates. As an example, the full power of FOCUSR is demonstrated in a real case scenario with the challenging task of brain surface matching across several individuals. Our results show that combining features and regularizing them in a spectral embedding greatly improves the matching precision (to a sub-millimeter level) while performing at much greater speed than existing methods. PMID:23868776

A Wavelet Polarization Decomposition Net Model for Polarimetric SAR Image Classification

NASA Astrophysics Data System (ADS)

He, Chu; Ou, Dan; Yang, Teng; Wu, Kun; Liao, Mingsheng; Chen, Erxue

2014-11-01

In this paper, a deep model based on wavelet texture has been proposed for Polarimetric Synthetic Aperture Radar (PolSAR) image classification inspired by recent successful deep learning method. Our model is supposed to learn powerful and informative representations to improve the generalization ability for the complex scene classification tasks. Given the influence of speckle noise in Polarimetric SAR image, wavelet polarization decomposition is applied first to obtain basic and discriminative texture features which are then embedded into a Deep Neural Network (DNN) in order to compose multi-layer higher representations. We demonstrate that the model can produce a powerful representation which can capture some untraceable information from Polarimetric SAR images and show a promising achievement in comparison with other traditional SAR image classification methods for the SAR image dataset.

A new approach for embedding causal sets into Minkowski space

NASA Astrophysics Data System (ADS)

Liu, He; Reid, David D.

2018-06-01

This paper reports on recent work toward an approach for embedding causal sets into two-dimensional Minkowski space. The main new feature of the present scheme is its use of the spacelike distance measure to construct an ordering of causal set elements within anti-chains of a causal set as an aid to the embedding procedure.

Enhancement web proxy cache performance using Wrapper Feature Selection methods with NB and J48

NASA Astrophysics Data System (ADS)

Mahmoud Al-Qudah, Dua'a.; Funke Olanrewaju, Rashidah; Wong Azman, Amelia

2017-11-01

Web proxy cache technique reduces response time by storing a copy of pages between client and server sides. If requested pages are cached in the proxy, there is no need to access the server. Due to the limited size and excessive cost of cache compared to the other storages, cache replacement algorithm is used to determine evict page when the cache is full. On the other hand, the conventional algorithms for replacement such as Least Recently Use (LRU), First in First Out (FIFO), Least Frequently Use (LFU), Randomized Policy etc. may discard important pages just before use. Furthermore, using conventional algorithm cannot be well optimized since it requires some decision to intelligently evict a page before replacement. Hence, most researchers propose an integration among intelligent classifiers and replacement algorithm to improves replacement algorithms performance. This research proposes using automated wrapper feature selection methods to choose the best subset of features that are relevant and influence classifiers prediction accuracy. The result present that using wrapper feature selection methods namely: Best First (BFS), Incremental Wrapper subset selection(IWSS)embedded NB and particle swarm optimization(PSO)reduce number of features and have a good impact on reducing computation time. Using PSO enhance NB classifier accuracy by 1.1%, 0.43% and 0.22% over using NB with all features, using BFS and using IWSS embedded NB respectively. PSO rises J48 accuracy by 0.03%, 1.91 and 0.04% over using J48 classifier with all features, using IWSS-embedded NB and using BFS respectively. While using IWSS embedded NB fastest NB and J48 classifiers much more than BFS and PSO. However, it reduces computation time of NB by 0.1383 and reduce computation time of J48 by 2.998.

Research and Design of Embedded Wireless Meal Ordering System Based on SQLite

NASA Astrophysics Data System (ADS)

Zhang, Jihong; Chen, Xiaoquan

The paper describes features and internal architecture and developing method of SQLite. And then it gives a design and program of meal ordering system. The system realizes the information interaction among the users and embedded devices with SQLite as database system. The embedded database SQLite manages the data and achieves wireless communication by using Bluetooth. A system program based on Qt/Embedded and Linux drivers realizes the local management of environmental data.

Contributions of individual face features to face discrimination.

PubMed

Logan, Andrew J; Gordon, Gael E; Loffler, Gunter

2017-08-01

Faces are highly complex stimuli that contain a host of information. Such complexity poses the following questions: (a) do observers exhibit preferences for specific information? (b) how does sensitivity to individual face parts compare? These questions were addressed by quantifying sensitivity to different face features. Discrimination thresholds were determined for synthetic faces under the following conditions: (i) 'full face': all face features visible; (ii) 'isolated feature': single feature presented in isolation; (iii) 'embedded feature': all features visible, but only one feature modified. Mean threshold elevations for isolated features, relative to full-faces, were 0.84x, 1.08, 2.12, 3.34, 4.07 and 4.47 for head-shape, hairline, nose, mouth, eyes and eyebrows respectively. Hence, when two full faces can be discriminated at threshold, the difference between the eyes is about four times less than what is required when discriminating between isolated eyes. In all cases, sensitivity was higher when features were presented in isolation than when they were embedded within a face context (threshold elevations of 0.94x, 1.74, 2.67, 2.90, 5.94 and 9.94). This reveals a specific pattern of sensitivity to face information. Observers are between two and four times more sensitive to external than internal features. The pattern for internal features (higher sensitivity for the nose, compared to mouth, eyes and eyebrows) is consistent with lower sensitivity for those parts affected by facial dynamics (e.g. facial expressions). That isolated features are easier to discriminate than embedded features supports a holistic face processing mechanism which impedes extraction of information about individual features from full faces. Copyright © 2017 Elsevier Ltd. All rights reserved.

Experience with 3-D composite grids

NASA Technical Reports Server (NTRS)

Benek, J. A.; Donegan, T. L.; Suhs, N. E.

1987-01-01

Experience with the three-dimensional (3-D), chimera grid embedding scheme is described. Applications of the inviscid version to a multiple-body configuration, a wind/body/tail configuration, and an estimate of wind tunnel wall interference are described. Applications to viscous flows include a 3-D cavity and another multi-body configuration. A variety of grid generators is used, and several embedding strategies are described.

NanoRelease: Pilot interlaboratory comparison of a weathering protocol applied to resilient and labile polymers with and without embedded carbon nanotubes

EPA Science Inventory

A major use of multi-walled carbon nanotubes (MWCNTs) is as functional fillers embedded in a solid matrix, such as plastics or coatings. Weathering and abrasion of the solid matrix during use can lead to environmental releases of the MWCNTs. Here we focus on a protocol to identif...

Recognition tunneling measurement of the conductance of DNA bases embedded in self-assembled monolayers.

PubMed

Huang, Shuo; Chang, Shuai; He, Jin; Zhang, Peiming; Liang, Feng; Tuchband, Michael; Li, Shengqing; Lindsay, Stuart

2010-12-09

The DNA bases interact strongly with gold electrodes, complicating efforts to measure the tunneling conductance through hydrogen-bonded Watson Crick base pairs. When bases are embedded in a self-assembled alkane-thiol monolayer to minimize these interactions, new features appear in the tunneling data. These new features track the predictions of density-functional calculations quite well, suggesting that they reflect tunnel conductance through hydrogen-bonded base pairs.

Recognition tunneling measurement of the conductance of DNA bases embedded in self-assembled monolayers

PubMed Central

Huang, Shuo; Chang, Shuai; He, Jin; Zhang, Peiming; Liang, Feng; Tuchband, Michael; Li, Shengqing; Lindsay, Stuart

2010-01-01

The DNA bases interact strongly with gold electrodes, complicating efforts to measure the tunneling conductance through hydrogen-bonded Watson Crick base pairs. When bases are embedded in a self-assembled alkane-thiol monolayer to minimize these interactions, new features appear in the tunneling data. These new features track the predictions of density-functional calculations quite well, suggesting that they reflect tunnel conductance through hydrogen-bonded base pairs. PMID:21197382

Anomalous thermal hysteresis in the high-field magnetic moments of magnetic nanoparticles embedded in multi-walled carbon nanotubes

NASA Astrophysics Data System (ADS)

Zhao, Guo-Meng; Wang, Jun; Ren, Yang; Beeli, Pieder

2012-02-01

We report high-temperature (300-1120 K) magnetic properties of Fe and Fe3O4 nanoparticles embedded in multi-walled carbon nanotubes. We unambiguously show that the magnetic moments of Fe and Fe3O4 nanoparticles are seemingly enhanced by a factor of about 3 compared with what they would be expected to have for free (unembedded) magnetic nanoparticles. What is more intriguing is that the enhanced moments were completely lost when the sample was heated up to 1120 K and the lost moments at 1120 K were completely recovered through several thermal cycles below 1020 K. The anomalous thermal hysteresis of the high-field magnetic moments is unlikely to be explained by existing physical models except for the high-field paramagnetic Meissner effect due to the existence of ultrahigh temperature superconductivity in the multi-walled carbon nanotubes.

Safe and Efficient Support for Embeded Multi-Processors in ADA

NASA Astrophysics Data System (ADS)

Ruiz, Jose F.

2010-08-01

New software demands increasing processing power, and multi-processor platforms are spreading as the answer to achieve the required performance. Embedded real-time systems are also subject to this trend, but in the case of real-time mission-critical systems, the properties of reliability, predictability and analyzability are also paramount. The Ada 2005 language defined a subset of its tasking model, the Ravenscar profile, that provides the basis for the implementation of deterministic and time analyzable applications on top of a streamlined run-time system. This Ravenscar tasking profile, originally designed for single processors, has proven remarkably useful for modelling verifiable real-time single-processor systems. This paper proposes a simple extension to the Ravenscar profile to support multi-processor systems using a fully partitioned approach. The implementation of this scheme is simple, and it can be used to develop applications amenable to schedulability analysis.

Image segmentation-based robust feature extraction for color image watermarking

NASA Astrophysics Data System (ADS)

Li, Mianjie; Deng, Zeyu; Yuan, Xiaochen

2018-04-01

This paper proposes a local digital image watermarking method based on Robust Feature Extraction. The segmentation is achieved by Simple Linear Iterative Clustering (SLIC) based on which an Image Segmentation-based Robust Feature Extraction (ISRFE) method is proposed for feature extraction. Our method can adaptively extract feature regions from the blocks segmented by SLIC. This novel method can extract the most robust feature region in every segmented image. Each feature region is decomposed into low-frequency domain and high-frequency domain by Discrete Cosine Transform (DCT). Watermark images are then embedded into the coefficients in the low-frequency domain. The Distortion-Compensated Dither Modulation (DC-DM) algorithm is chosen as the quantization method for embedding. The experimental results indicate that the method has good performance under various attacks. Furthermore, the proposed method can obtain a trade-off between high robustness and good image quality.

Classification of molecular structure images by using ANN, RF, LBP, HOG, and size reduction methods for early stomach cancer detection

NASA Astrophysics Data System (ADS)

Aytaç Korkmaz, Sevcan; Binol, Hamidullah

2018-03-01

Patients who die from stomach cancer are still present. Early diagnosis is crucial in reducing the mortality rate of cancer patients. Therefore, computer aided methods have been developed for early detection in this article. Stomach cancer images were obtained from Fırat University Medical Faculty Pathology Department. The Local Binary Patterns (LBP) and Histogram of Oriented Gradients (HOG) features of these images are calculated. At the same time, Sammon mapping, Stochastic Neighbor Embedding (SNE), Isomap, Classical multidimensional scaling (MDS), Local Linear Embedding (LLE), Linear Discriminant Analysis (LDA), t-Distributed Stochastic Neighbor Embedding (t-SNE), and Laplacian Eigenmaps methods are used for dimensional the reduction of the features. The high dimension of these features has been reduced to lower dimensions using dimensional reduction methods. Artificial neural networks (ANN) and Random Forest (RF) classifiers were used to classify stomach cancer images with these new lower feature sizes. New medical systems have developed to measure the effects of these dimensions by obtaining features in different dimensional with dimensional reduction methods. When all the methods developed are compared, it has been found that the best accuracy results are obtained with LBP_MDS_ANN and LBP_LLE_ANN methods.

Sequence2Vec: a novel embedding approach for modeling transcription factor binding affinity landscape.

PubMed

Dai, Hanjun; Umarov, Ramzan; Kuwahara, Hiroyuki; Li, Yu; Song, Le; Gao, Xin

2017-11-15

An accurate characterization of transcription factor (TF)-DNA affinity landscape is crucial to a quantitative understanding of the molecular mechanisms underpinning endogenous gene regulation. While recent advances in biotechnology have brought the opportunity for building binding affinity prediction methods, the accurate characterization of TF-DNA binding affinity landscape still remains a challenging problem. Here we propose a novel sequence embedding approach for modeling the transcription factor binding affinity landscape. Our method represents DNA binding sequences as a hidden Markov model which captures both position specific information and long-range dependency in the sequence. A cornerstone of our method is a novel message passing-like embedding algorithm, called Sequence2Vec, which maps these hidden Markov models into a common nonlinear feature space and uses these embedded features to build a predictive model. Our method is a novel combination of the strength of probabilistic graphical models, feature space embedding and deep learning. We conducted comprehensive experiments on over 90 large-scale TF-DNA datasets which were measured by different high-throughput experimental technologies. Sequence2Vec outperforms alternative machine learning methods as well as the state-of-the-art binding affinity prediction methods. Our program is freely available at https://github.com/ramzan1990/sequence2vec. xin.gao@kaust.edu.sa or lsong@cc.gatech.edu. Supplementary data are available at Bioinformatics online. © The Author(s) 2017. Published by Oxford University Press.

Bluetooth Low Power Modes Applied to the Data Transportation Network in Home Automation Systems.

PubMed

Etxaniz, Josu; Aranguren, Gerardo

2017-04-30

Even though home automation is a well-known research and development area, recent technological improvements in different areas such as context recognition, sensing, wireless communications or embedded systems have boosted wireless smart homes. This paper focuses on some of those areas related to home automation. The paper draws attention to wireless communications issues on embedded systems. Specifically, the paper discusses the multi-hop networking together with Bluetooth technology and latency, as a quality of service (QoS) metric. Bluetooth is a worldwide standard that provides low power multi-hop networking. It is a radio license free technology and establishes point-to-point and point-to-multipoint links, known as piconets, or multi-hop networks, known as scatternets. This way, many Bluetooth nodes can be interconnected to deploy ambient intelligent networks. This paper introduces the research on multi-hop latency done with park and sniff low power modes of Bluetooth over the test platform developed. Besides, an empirical model is obtained to calculate the latency of Bluetooth multi-hop communications over asynchronous links when links in scatternets are always in sniff or the park mode. Smart home devices and networks designers would take advantage of the models and the estimation of the delay they provide in communications along Bluetooth multi-hop networks.

Bluetooth Low Power Modes Applied to the Data Transportation Network in Home Automation Systems

PubMed Central

Etxaniz, Josu; Aranguren, Gerardo

2017-01-01

Even though home automation is a well-known research and development area, recent technological improvements in different areas such as context recognition, sensing, wireless communications or embedded systems have boosted wireless smart homes. This paper focuses on some of those areas related to home automation. The paper draws attention to wireless communications issues on embedded systems. Specifically, the paper discusses the multi-hop networking together with Bluetooth technology and latency, as a quality of service (QoS) metric. Bluetooth is a worldwide standard that provides low power multi-hop networking. It is a radio license free technology and establishes point-to-point and point-to-multipoint links, known as piconets, or multi-hop networks, known as scatternets. This way, many Bluetooth nodes can be interconnected to deploy ambient intelligent networks. This paper introduces the research on multi-hop latency done with park and sniff low power modes of Bluetooth over the test platform developed. Besides, an empirical model is obtained to calculate the latency of Bluetooth multi-hop communications over asynchronous links when links in scatternets are always in sniff or the park mode. Smart home devices and networks designers would take advantage of the models and the estimation of the delay they provide in communications along Bluetooth multi-hop networks. PMID:28468294

Video2vec Embeddings Recognize Events When Examples Are Scarce.

PubMed

Habibian, Amirhossein; Mensink, Thomas; Snoek, Cees G M

2017-10-01

This paper aims for event recognition when video examples are scarce or even completely absent. The key in such a challenging setting is a semantic video representation. Rather than building the representation from individual attribute detectors and their annotations, we propose to learn the entire representation from freely available web videos and their descriptions using an embedding between video features and term vectors. In our proposed embedding, which we call Video2vec, the correlations between the words are utilized to learn a more effective representation by optimizing a joint objective balancing descriptiveness and predictability. We show how learning the Video2vec embedding using a multimodal predictability loss, including appearance, motion and audio features, results in a better predictable representation. We also propose an event specific variant of Video2vec to learn a more accurate representation for the words, which are indicative of the event, by introducing a term sensitive descriptiveness loss. Our experiments on three challenging collections of web videos from the NIST TRECVID Multimedia Event Detection and Columbia Consumer Videos datasets demonstrate: i) the advantages of Video2vec over representations using attributes or alternative embeddings, ii) the benefit of fusing video modalities by an embedding over common strategies, iii) the complementarity of term sensitive descriptiveness and multimodal predictability for event recognition. By its ability to improve predictability of present day audio-visual video features, while at the same time maximizing their semantic descriptiveness, Video2vec leads to state-of-the-art accuracy for both few- and zero-example recognition of events in video.

Detailed Analysis of the Interoccurrence Time Statistics in Seismic Activity

NASA Astrophysics Data System (ADS)

Tanaka, Hiroki; Aizawa, Yoji

2017-02-01

The interoccurrence time statistics of seismiciry is studied theoretically as well as numerically by taking into account the conditional probability and the correlations among many earthquakes in different magnitude levels. It is known so far that the interoccurrence time statistics is well approximated by the Weibull distribution, but the more detailed information about the interoccurrence times can be obtained from the analysis of the conditional probability. Firstly, we propose the Embedding Equation Theory (EET), where the conditional probability is described by two kinds of correlation coefficients; one is the magnitude correlation and the other is the inter-event time correlation. Furthermore, the scaling law of each correlation coefficient is clearly determined from the numerical data-analysis carrying out with the Preliminary Determination of Epicenter (PDE) Catalog and the Japan Meteorological Agency (JMA) Catalog. Secondly, the EET is examined to derive the magnitude dependence of the interoccurrence time statistics and the multi-fractal relation is successfully formulated. Theoretically we cannot prove the universality of the multi-fractal relation in seismic activity; nevertheless, the theoretical results well reproduce all numerical data in our analysis, where several common features or the invariant aspects are clearly observed. Especially in the case of stationary ensembles the multi-fractal relation seems to obey an invariant curve, furthermore in the case of non-stationary (moving time) ensembles for the aftershock regime the multi-fractal relation seems to satisfy a certain invariant curve at any moving times. It is emphasized that the multi-fractal relation plays an important role to unify the statistical laws of seismicity: actually the Gutenberg-Richter law and the Weibull distribution are unified in the multi-fractal relation, and some universality conjectures regarding the seismicity are briefly discussed.

The Readiness of Lecturers in Embedding Soft Skills in the Bachelor's Degree Program in Malaysian Institutes of Teacher Education

ERIC Educational Resources Information Center

Hassan, Aminuddin; Maharoff, Marina; Abiddin, Norhasni Zainal

2014-01-01

This is a preliminary research to obtain information to formulate a problem statement for an overall study of the embedding of soft skills in the program courses in higher learning institutions. This research was conducted in the form of single case and multi-case studies. The research data was attained through mixed methods; the quantitative…

Detecting spam comments on Indonesia’s Instagram posts

NASA Astrophysics Data System (ADS)

Septiandri, Ali Akbar; Wibisono, Okiriza

2017-01-01

In this paper we experimented with several feature sets for detecting spam comments in social media contents authored by Indonesian public figures. We define spam comments as comments which have promotional purposes (e.g. referring other users to products and services) and thus not related to the content to which the comments are posted. Three sets of features are evaluated for detecting spams: (1) hand-engineered features such as comment length, number of capital letters, and number of emojis, (2) keyword features such as whether the comment contains advertising words or product-related words, and (3) text features, namely, bag-of-words, TF-IDF, and fastText embeddings, each combined with latent semantic analysis. With 24,000 manually-annotated comments scraped from Instagram posts authored by more than 100 Indonesian public figures, we compared the performance of these feature sets and their combinations using 3 popular classification algorithms: Na¨ıve Bayes, SVM, and XGBoost. We find that using all three feature sets (with fastText embedding for the text features) gave the best F 1-score of 0.9601 on a holdout dataset. More interestingly, fastText embedding combined with hand-engineered features (i.e. without keyword features) yield similar F 1-score of 0.9523, and McNemar’s test failed to reject the hypothesis that the two results are not significantly different. This result is important as keyword features are largely dependent on the dataset and may not be as generalisable as the other feature sets when applied to new data. For future work, we hope to collect bigger and more diverse dataset of Indonesian spam comments, improve our model’s performance and generalisability, and publish a programming package for others to reliably detect spam comments.

Multi-channel MRI segmentation of eye structures and tumors using patient-specific features

PubMed Central

Ciller, Carlos; De Zanet, Sandro; Kamnitsas, Konstantinos; Maeder, Philippe; Glocker, Ben; Munier, Francis L.; Rueckert, Daniel; Thiran, Jean-Philippe

2017-01-01

Retinoblastoma and uveal melanoma are fast spreading eye tumors usually diagnosed by using 2D Fundus Image Photography (Fundus) and 2D Ultrasound (US). Diagnosis and treatment planning of such diseases often require additional complementary imaging to confirm the tumor extend via 3D Magnetic Resonance Imaging (MRI). In this context, having automatic segmentations to estimate the size and the distribution of the pathological tissue would be advantageous towards tumor characterization. Until now, the alternative has been the manual delineation of eye structures, a rather time consuming and error-prone task, to be conducted in multiple MRI sequences simultaneously. This situation, and the lack of tools for accurate eye MRI analysis, reduces the interest in MRI beyond the qualitative evaluation of the optic nerve invasion and the confirmation of recurrent malignancies below calcified tumors. In this manuscript, we propose a new framework for the automatic segmentation of eye structures and ocular tumors in multi-sequence MRI. Our key contribution is the introduction of a pathological eye model from which Eye Patient-Specific Features (EPSF) can be computed. These features combine intensity and shape information of pathological tissue while embedded in healthy structures of the eye. We assess our work on a dataset of pathological patient eyes by computing the Dice Similarity Coefficient (DSC) of the sclera, the cornea, the vitreous humor, the lens and the tumor. In addition, we quantitatively show the superior performance of our pathological eye model as compared to the segmentation obtained by using a healthy model (over 4% DSC) and demonstrate the relevance of our EPSF, which improve the final segmentation regardless of the classifier employed. PMID:28350816

Dimension reduction and multiscaling law through source extraction

NASA Astrophysics Data System (ADS)

Capobianco, Enrico

2003-04-01

Through the empirical analysis of financial return generating processes one may find features that are common to other research fields, such as internet data from network traffic, physiological studies about human heart beat, speech and sleep recorded time series, geophysics signals, just to mention well-known cases of study. In particular, long range dependence, intermittency, heteroscedasticity are clearly appearing, and consequently power laws and multi-scaling behavior result typical signatures of either the spectral or the time correlation diagnostics. We study these features and the dynamics underlying financial volatility, which can respectively be detected and inferred from high frequency realizations of stock index returns, and show that they vary according to the resolution levels used for both the analysis and the synthesis of the available information. Discovering whether the volatility dynamics are subject to changes in scaling regimes requires the consideration of a model embedding scale-dependent information packets, thus accounting for possible heterogeneous activity occurring in financial markets. Independent component analysis result to be an important tool for reducing the dimension of the problem and calibrating greedy approximation techniques aimed to learn the structure of the underlying volatility.

A-Train Observations of Deep Convective Storm Tops

NASA Technical Reports Server (NTRS)

Setvak, Martin; Bedka, Kristopher; Lindsey, Daniel T.; Sokol, Alois; Charvat, Zdenek; Stastka, Jindrich; Wang, Pao K.

2013-01-01

The paper highlights simultaneous observations of tops of deep convective clouds from several space-borne instruments including the Moderate Resolution Imaging Spectroradiometer (MODIS) of the Aqua satellite, Cloud Profiling Radar (CPR) of the CloudSat satellite, and Cloud-Aerosol Lidar with Orthogonal Polarization (CALIOP) flown on the CALIPSO satellite. These satellites share very close orbits, thus together with several other satellites they are referred to as the "A-Train" constellation. Though the primary responsibility of these satellites and their instrumentation is much broader than observations of fine-scale processes atop convective storms, in this study we document how data from the A-Train can contribute to a better understanding and interpretation of various storm-top features, such as overshooting tops, cold-U/V and cold ring features with their coupled embedded warm areas, above anvil ice plumes and jumping cirrus. The relationships between MODIS multi-spectral brightness temperature difference (BTD) fields and cloud top signatures observed by the CPR and CALIOP are also examined in detail to highlight the variability in BTD signals across convective storm events.

Genomic, Pathway Network, and Immunologic Features Distinguishing Squamous Carcinomas.

PubMed

Campbell, Joshua D; Yau, Christina; Bowlby, Reanne; Liu, Yuexin; Brennan, Kevin; Fan, Huihui; Taylor, Alison M; Wang, Chen; Walter, Vonn; Akbani, Rehan; Byers, Lauren Averett; Creighton, Chad J; Coarfa, Cristian; Shih, Juliann; Cherniack, Andrew D; Gevaert, Olivier; Prunello, Marcos; Shen, Hui; Anur, Pavana; Chen, Jianhong; Cheng, Hui; Hayes, D Neil; Bullman, Susan; Pedamallu, Chandra Sekhar; Ojesina, Akinyemi I; Sadeghi, Sara; Mungall, Karen L; Robertson, A Gordon; Benz, Christopher; Schultz, Andre; Kanchi, Rupa S; Gay, Carl M; Hegde, Apurva; Diao, Lixia; Wang, Jing; Ma, Wencai; Sumazin, Pavel; Chiu, Hua-Sheng; Chen, Ting-Wen; Gunaratne, Preethi; Donehower, Larry; Rader, Janet S; Zuna, Rosemary; Al-Ahmadie, Hikmat; Lazar, Alexander J; Flores, Elsa R; Tsai, Kenneth Y; Zhou, Jane H; Rustgi, Anil K; Drill, Esther; Shen, Ronglei; Wong, Christopher K; Stuart, Joshua M; Laird, Peter W; Hoadley, Katherine A; Weinstein, John N; Peto, Myron; Pickering, Curtis R; Chen, Zhong; Van Waes, Carter

2018-04-03

This integrated, multiplatform PanCancer Atlas study co-mapped and identified distinguishing molecular features of squamous cell carcinomas (SCCs) from five sites associated with smoking and/or human papillomavirus (HPV). SCCs harbor 3q, 5p, and other recurrent chromosomal copy-number alterations (CNAs), DNA mutations, and/or aberrant methylation of genes and microRNAs, which are correlated with the expression of multi-gene programs linked to squamous cell stemness, epithelial-to-mesenchymal differentiation, growth, genomic integrity, oxidative damage, death, and inflammation. Low-CNA SCCs tended to be HPV(+) and display hypermethylation with repression of TET1 demethylase and FANCF, previously linked to predisposition to SCC, or harbor mutations affecting CASP8, RAS-MAPK pathways, chromatin modifiers, and immunoregulatory molecules. We uncovered hypomethylation of the alternative promoter that drives expression of the ΔNp63 oncogene and embedded miR944. Co-expression of immune checkpoint, T-regulatory, and Myeloid suppressor cells signatures may explain reduced efficacy of immune therapy. These findings support possibilities for molecular classification and therapeutic approaches. Published by Elsevier Inc.

Robust pattern decoding in shape-coded structured light

NASA Astrophysics Data System (ADS)

Tang, Suming; Zhang, Xu; Song, Zhan; Song, Lifang; Zeng, Hai

2017-09-01

Decoding is a challenging and complex problem in a coded structured light system. In this paper, a robust pattern decoding method is proposed for the shape-coded structured light in which the pattern is designed as grid shape with embedded geometrical shapes. In our decoding method, advancements are made at three steps. First, a multi-template feature detection algorithm is introduced to detect the feature point which is the intersection of each two orthogonal grid-lines. Second, pattern element identification is modelled as a supervised classification problem and the deep neural network technique is applied for the accurate classification of pattern elements. Before that, a training dataset is established, which contains a mass of pattern elements with various blurring and distortions. Third, an error correction mechanism based on epipolar constraint, coplanarity constraint and topological constraint is presented to reduce the false matches. In the experiments, several complex objects including human hand are chosen to test the accuracy and robustness of the proposed method. The experimental results show that our decoding method not only has high decoding accuracy, but also owns strong robustness to surface color and complex textures.

A multi-wavelength census of star formation activity in the young embedded cluster around Serpens/G3-G6

NASA Astrophysics Data System (ADS)

Djupvik, A. A.; André, Ph.; Bontemps, S.; Motte, F.; Olofsson, G.; Gålfalk, M.; Florén, H.-G.

2006-11-01

Aims.The aim of this paper is to characterise the star formation activity in the poorly studied embedded cluster Serpens/G3-G6, located ~45 arcmin (3 pc) to the south of the Serpens Cloud Core, and to determine the luminosity and mass functions of its population of Young Stellar Objects (YSOs). Methods: .Multi-wavelength broadband photometry was obtained to sample the near and mid-IR spectral energy distributions to separate YSOs from field stars and classify the YSO evolutionary stage. ISOCAM mapping in the two filters LW2 (5-8.5 μm) and LW3 (12-18 μm) of a 19 arcmin × 16 arcmin field was combined with JHKS data from 2MASS, KS data from Arnica/NOT, and L arcmin data from SIRCA/NOT. Continuum emission at 1.3 mm (IRAM) and 3.6 cm (VLA) was mapped to study the cloud structure and the coldest/youngest sources. Deep narrow band imaging at the 2.12 μm S(1) line of H2 from NOTCam/NOT was obtained to search for signs of bipolar outflows. Results: .We have strong evidence for a stellar population of 31 Class II sources, 5 flat-spectrum sources, 5 Class I sources, and two Class 0 sources. Our method does not sample the Class III sources. The cloud is composed of two main dense clumps aligned along a ridge over ~0.5 pc plus a starless core coinciding with absorption features seen in the ISOCAM maps. We find two S-shaped bipolar collimated flows embedded in the NE clump, and propose the two driving sources to be a Class 0 candidate (MMS3) and a double Class I (MMS2). For the Class II population we find a best age of ~2 Myr and compatibility with recent Initial Mass Functions (IMFs) by comparing the observed Class II luminosity function (LF), which is complete to 0.08 L⊙, to various model LFs with different star formation scenarios and input IMFs.

Conductive polymer sensor arrays for smart orthopaedic implants

NASA Astrophysics Data System (ADS)

Micolini, Carolina; Holness, F. B.; Johnson, James A.; Price, Aaron D.

2017-04-01

This study proposes and demonstrates the design, implementation, and characterization of a 3D-printed smartpolymer sensor array using conductive polyaniline (PANI) structures embedded in a polymeric substrate. The piezoresistive characteristics of PANI were studied to evaluate the efficacy of the manufacturing of an embedded pressure sensor. PANI's stability throughout loading and unloading cycles together with the response to incremental loading cycles was investigated. It is demonstrated that this specially developed multi-material additive manufacturing process for polyaniline is a good candidate for the manufacture of implant components with smart-polymer sensors embedded for the analysis of joint loads in orthopaedic implants.

Optimal Power Flow for Distribution Systems under Uncertain Forecasts: Preprint

DOE Office of Scientific and Technical Information (OSTI.GOV)

Dall'Anese, Emiliano; Baker, Kyri; Summers, Tyler

2016-12-01

The paper focuses on distribution systems featuring renewable energy sources and energy storage devices, and develops an optimal power flow (OPF) approach to optimize the system operation in spite of forecasting errors. The proposed method builds on a chance-constrained multi-period AC OPF formulation, where probabilistic constraints are utilized to enforce voltage regulation with a prescribed probability. To enable a computationally affordable solution approach, a convex reformulation of the OPF task is obtained by resorting to i) pertinent linear approximations of the power flow equations, and ii) convex approximations of the chance constraints. Particularly, the approximate chance constraints provide conservative boundsmore » that hold for arbitrary distributions of the forecasting errors. An adaptive optimization strategy is then obtained by embedding the proposed OPF task into a model predictive control framework.« less

Design and Experimental Validation of a Simple Controller for a Multi-Segment Magnetic Crawler Robot

DTIC Science & Technology

2015-04-01

Ave, Cambridge, MA USA 02139; bSpace and Naval Warfare (SPAWAR) Systems Center Pacific, San Diego, CA USA 92152 ABSTRACT A novel, multi-segmented...high-level, autonomous control computer. A low-level, embedded microcomputer handles the commands to the driving motors. This paper presents the...to be demonstrated.14 The Unmanned Systems Group at SPAWAR Systems Center Pacific has developed a multi-segment magnetic crawler robot (MSMR

Development of Field Excavator with Embedded Force Measurement

NASA Technical Reports Server (NTRS)

Johnson, K.; Creager, C.; Izadnegahdar, A.; Bauman, S.; Gallo, C.; Abel, P.

2012-01-01

A semi-intelligent excavation mechanism was developed for use with the NASA-built Centaur 2 rover prototype. The excavator features a continuously rotatable large bucket supported between two parallel arms, both of which share a single pivot axis near the excavator base attached to the rover. The excavator is designed to simulate the collection of regolith, such as on the Moon, and to dump the collected soil into a hopper up to one meter tall for processing to extract oxygen. Because the vehicle can be autonomous and the terrain is generally unknown, there is risk of damaging equipment or using excessive power when attempting to extract soil from dense or rocky terrain. To minimize these risks, it is critical for the rover to sense the digging forces and adjust accordingly. It is also important to understand the digging capabilities and limitations of the excavator. This paper discusses the implementation of multiple strain gages as an embedded force measurement system in the excavator's arms. These strain gages can accurately measure and resolve multi-axial forces on the excavator. In order to validate these sensors and characterize the load capabilities, a series of controlled excavation tests were performed at Glenn Research Center with the excavator at various depths and cut angles while supported by a six axis load cell. The results of these tests are both compared to a force estimation model and used for calibration of the embedded strain gages. In addition, excavation forces generated using two different types of bucket edge (straight vs. with teeth) were compared.

Speculation detection for Chinese clinical notes: Impacts of word segmentation and embedding models.

PubMed

Zhang, Shaodian; Kang, Tian; Zhang, Xingting; Wen, Dong; Elhadad, Noémie; Lei, Jianbo

2016-04-01

Speculations represent uncertainty toward certain facts. In clinical texts, identifying speculations is a critical step of natural language processing (NLP). While it is a nontrivial task in many languages, detecting speculations in Chinese clinical notes can be particularly challenging because word segmentation may be necessary as an upstream operation. The objective of this paper is to construct a state-of-the-art speculation detection system for Chinese clinical notes and to investigate whether embedding features and word segmentations are worth exploiting toward this overall task. We propose a sequence labeling based system for speculation detection, which relies on features from bag of characters, bag of words, character embedding, and word embedding. We experiment on a novel dataset of 36,828 clinical notes with 5103 gold-standard speculation annotations on 2000 notes, and compare the systems in which word embeddings are calculated based on word segmentations given by general and by domain specific segmenters respectively. Our systems are able to reach performance as high as 92.2% measured by F score. We demonstrate that word segmentation is critical to produce high quality word embedding to facilitate downstream information extraction applications, and suggest that a domain dependent word segmenter can be vital to such a clinical NLP task in Chinese language. Copyright © 2016 Elsevier Inc. All rights reserved.

Large Margin Multi-Modal Multi-Task Feature Extraction for Image Classification.

PubMed

Yong Luo; Yonggang Wen; Dacheng Tao; Jie Gui; Chao Xu

2016-01-01

The features used in many image analysis-based applications are frequently of very high dimension. Feature extraction offers several advantages in high-dimensional cases, and many recent studies have used multi-task feature extraction approaches, which often outperform single-task feature extraction approaches. However, most of these methods are limited in that they only consider data represented by a single type of feature, even though features usually represent images from multiple modalities. We, therefore, propose a novel large margin multi-modal multi-task feature extraction (LM3FE) framework for handling multi-modal features for image classification. In particular, LM3FE simultaneously learns the feature extraction matrix for each modality and the modality combination coefficients. In this way, LM3FE not only handles correlated and noisy features, but also utilizes the complementarity of different modalities to further help reduce feature redundancy in each modality. The large margin principle employed also helps to extract strongly predictive features, so that they are more suitable for prediction (e.g., classification). An alternating algorithm is developed for problem optimization, and each subproblem can be efficiently solved. Experiments on two challenging real-world image data sets demonstrate the effectiveness and superiority of the proposed method.

Bubbling and on-off intermittency in bailout embeddings.

PubMed

Cartwright, Julyan H E; Magnasco, Marcelo O; Piro, Oreste; Tuval, Idan

2003-07-01

We establish and investigate the conceptual connection between the dynamics of the bailout embedding of a Hamiltonian system and the dynamical regimes associated with the occurrence of bubbling and blowout bifurcations. The roles of the invariant manifold and the dynamics restricted to it, required in bubbling and blowout bifurcating systems, are played in the bailout embedding by the embedded Hamiltonian dynamical system. The Hamiltonian nature of the dynamics is precisely the distinctive feature of this instance of a bubbling or blowout bifurcation. The detachment of the embedding trajectories from the original ones can thus be thought of as transient on-off intermittency, and noise-induced avoidance of some regions of the embedded phase space can be recognized as Hamiltonian bubbling.

An integrated compact airborne multispectral imaging system using embedded computer

NASA Astrophysics Data System (ADS)

Zhang, Yuedong; Wang, Li; Zhang, Xuguo

2015-08-01

An integrated compact airborne multispectral imaging system using embedded computer based control system was developed for small aircraft multispectral imaging application. The multispectral imaging system integrates CMOS camera, filter wheel with eight filters, two-axis stabilized platform, miniature POS (position and orientation system) and embedded computer. The embedded computer has excellent universality and expansibility, and has advantages in volume and weight for airborne platform, so it can meet the requirements of control system of the integrated airborne multispectral imaging system. The embedded computer controls the camera parameters setting, filter wheel and stabilized platform working, image and POS data acquisition, and stores the image and data. The airborne multispectral imaging system can connect peripheral device use the ports of the embedded computer, so the system operation and the stored image data management are easy. This airborne multispectral imaging system has advantages of small volume, multi-function, and good expansibility. The imaging experiment results show that this system has potential for multispectral remote sensing in applications such as resource investigation and environmental monitoring.

Research on Multi - Person Parallel Modeling Method Based on Integrated Model Persistent Storage

NASA Astrophysics Data System (ADS)

Qu, MingCheng; Wu, XiangHu; Tao, YongChao; Liu, Ying

2018-03-01

This paper mainly studies the multi-person parallel modeling method based on the integrated model persistence storage. The integrated model refers to a set of MDDT modeling graphics system, which can carry out multi-angle, multi-level and multi-stage description of aerospace general embedded software. Persistent storage refers to converting the data model in memory into a storage model and converting the storage model into a data model in memory, where the data model refers to the object model and the storage model is a binary stream. And multi-person parallel modeling refers to the need for multi-person collaboration, the role of separation, and even real-time remote synchronization modeling.

Network dynamics of 3D engineered neuronal cultures: a new experimental model for in-vitro electrophysiology.

PubMed

Frega, Monica; Tedesco, Mariateresa; Massobrio, Paolo; Pesce, Mattia; Martinoia, Sergio

2014-06-30

Despite the extensive use of in-vitro models for neuroscientific investigations and notwithstanding the growing field of network electrophysiology, all studies on cultured cells devoted to elucidate neurophysiological mechanisms and computational properties, are based on 2D neuronal networks. These networks are usually grown onto specific rigid substrates (also with embedded electrodes) and lack of most of the constituents of the in-vivo like environment: cell morphology, cell-to-cell interaction and neuritic outgrowth in all directions. Cells in a brain region develop in a 3D space and interact with a complex multi-cellular environment and extracellular matrix. Under this perspective, 3D networks coupled to micro-transducer arrays, represent a new and powerful in-vitro model capable of better emulating in-vivo physiology. In this work, we present a new experimental paradigm constituted by 3D hippocampal networks coupled to Micro-Electrode-Arrays (MEAs) and we show how the features of the recorded network dynamics differ from the corresponding 2D network model. Further development of the proposed 3D in-vitro model by adding embedded functionalized scaffolds might open new prospects for manipulating, stimulating and recording the neuronal activity to elucidate neurophysiological mechanisms and to design bio-hybrid microsystems.

Network dynamics of 3D engineered neuronal cultures: a new experimental model for in-vitro electrophysiology

PubMed Central

Frega, Monica; Tedesco, Mariateresa; Massobrio, Paolo; Pesce, Mattia; Martinoia, Sergio

2014-01-01

Despite the extensive use of in-vitro models for neuroscientific investigations and notwithstanding the growing field of network electrophysiology, all studies on cultured cells devoted to elucidate neurophysiological mechanisms and computational properties, are based on 2D neuronal networks. These networks are usually grown onto specific rigid substrates (also with embedded electrodes) and lack of most of the constituents of the in-vivo like environment: cell morphology, cell-to-cell interaction and neuritic outgrowth in all directions. Cells in a brain region develop in a 3D space and interact with a complex multi-cellular environment and extracellular matrix. Under this perspective, 3D networks coupled to micro-transducer arrays, represent a new and powerful in-vitro model capable of better emulating in-vivo physiology. In this work, we present a new experimental paradigm constituted by 3D hippocampal networks coupled to Micro-Electrode-Arrays (MEAs) and we show how the features of the recorded network dynamics differ from the corresponding 2D network model. Further development of the proposed 3D in-vitro model by adding embedded functionalized scaffolds might open new prospects for manipulating, stimulating and recording the neuronal activity to elucidate neurophysiological mechanisms and to design bio-hybrid microsystems. PMID:24976386

The fictitious force method for efficient calculation of vibration from a tunnel embedded in a multi-layered half-space

NASA Astrophysics Data System (ADS)

Hussein, M. F. M.; François, S.; Schevenels, M.; Hunt, H. E. M.; Talbot, J. P.; Degrande, G.

2014-12-01

This paper presents an extension of the Pipe-in-Pipe (PiP) model for calculating vibrations from underground railways that allows for the incorporation of a multi-layered half-space geometry. The model is based on the assumption that the tunnel displacement is not influenced by the existence of a free surface or ground layers. The displacement at the tunnel-soil interface is calculated using a model of a tunnel embedded in a full space with soil properties corresponding to the soil in contact with the tunnel. Next, a full space model is used to determine the equivalent loads that produce the same displacements at the tunnel-soil interface. The soil displacements are calculated by multiplying these equivalent loads by Green's functions for a layered half-space. The results and the computation time of the proposed model are compared with those of an alternative coupled finite element-boundary element model that accounts for a tunnel embedded in a multi-layered half-space. While the overall response of the multi-layered half-space is well predicted, spatial shifts in the interference patterns are observed that result from the superposition of direct waves and waves reflected on the free surface and layer interfaces. The proposed model is much faster and can be run on a personal computer with much less use of memory. Therefore, it is a promising design tool to predict vibration from underground tunnels and to assess the performance of vibration countermeasures in an early design stage.

Geometrically robust image watermarking by sector-shaped partitioning of geometric-invariant regions.

PubMed

Tian, Huawei; Zhao, Yao; Ni, Rongrong; Cao, Gang

2009-11-23

In a feature-based geometrically robust watermarking system, it is a challenging task to detect geometric-invariant regions (GIRs) which can survive a broad range of image processing operations. Instead of commonly used Harris detector or Mexican hat wavelet method, a more robust corner detector named multi-scale curvature product (MSCP) is adopted to extract salient features in this paper. Based on such features, disk-like GIRs are found, which consists of three steps. First, robust edge contours are extracted. Then, MSCP is utilized to detect the centers for GIRs. Third, the characteristic scale selection is performed to calculate the radius of each GIR. A novel sector-shaped partitioning method for the GIRs is designed, which can divide a GIR into several sector discs with the help of the most important corner (MIC). The watermark message is then embedded bit by bit in each sector by using Quantization Index Modulation (QIM). The GIRs and the divided sector discs are invariant to geometric transforms, so the watermarking method inherently has high robustness against geometric attacks. Experimental results show that the scheme has a better robustness against various image processing operations including common processing attacks, affine transforms, cropping, and random bending attack (RBA) than the previous approaches.

The Embedded Ring-like Feature and Star Formation Activities in G35.673-00.847

NASA Astrophysics Data System (ADS)

Dewangan, L. K.; Devaraj, R.; Ojha, D. K.

2018-02-01

We present a multiwavelength study to probe the star formation (SF) process in the molecular cloud linked with the G35.673-00.847 site (hereafter MCG35.6), which is traced in a velocity range of 53–62 km s‑1. Multiwavelength images reveal a semi-ring-like feature (associated with ionized gas emission) and an embedded face-on ring-like feature (without the NVSS 1.4 GHz radio emission, where 1σ ∼ 0.45 mJy beam‑1) in MCG35.6. The semi-ring-like feature is originated by the ionizing feedback from a star with spectral type B0.5V–B0V. The central region of the ring-like feature does not contain detectable ionized gas emission, indicating that the ring-like feature is unlikely to be produced by the ionizing feedback from a massive star. Several embedded Herschel clumps and young stellar objects (YSOs) are identified in MCG35.6, tracing the ongoing SF activities within the cloud. The polarization information from the Planck and GPIPS data trace the plane-of-sky magnetic field, which is oriented parallel to the major axis of the ring-like feature. At least five clumps (having M clump ∼ 740–1420 M ⊙) seem to be distributed in an almost regularly spaced manner along the ring-like feature and contain noticeable YSOs. Based on the analysis of the polarization and molecular line data, three subregions containing the clumps are found to be magnetically supercritical in the ring-like feature. Altogether, the existence of the ring-like feature and the SF activities on its edges can be explained by the magnetic field mediated process as simulated by Li & Nakamura.

Multi-functional surface acoustic wave sensor for monitoring enviromental and structural condition

NASA Astrophysics Data System (ADS)

Furuya, Y.; Kon, T.; Okazaki, T.; Saigusa, Y.; Nomura, T.

2006-03-01

As a first step to develop a health monitoring system with active and embedded nondestructive evaluation devices for the machineries and structures, multi-functional SAW (surface acoustic wave) device was developed. A piezoelectric LiNbO3(x-y cut) materials were used as a SAW substrate on which IDT(20μm pitch) was produced by lithography. On the surface of a path of SAW between IDTs, environmentally active material films of shape memory Ti50Ni41Cu(at%) with non-linear hysteresis and superelastic Ti48Ni43Cu(at%) with linear deformation behavior were formed by magnetron-sputtering technique. In this study, these two kinds of shape memory alloys SMA) system were used to measure 1) loading level, 2) phase transformation and 3)stress-strain hysteresis under cyclic loading by utilizing their linearity and non-linearity deformation behaviors. Temperature and stress dependencies of SAW signal were also investigated in the non-sputtered film state. Signal amplitude and phase change of SAW were chosen to measure as the sensing parameters. As a result, temperature, stress level, phase transformation in SMA depending on temperature and mechanical damage accumulation could be measured by the proposed multi-functional SAW sensor. Moreover, the wireless SAW sensing system which has a unique feature of no supplying electric battery was constructed, and the same characteristic evaluation is confirmed in comparison with wired case.

PET image reconstruction using multi-parametric anato-functional priors

NASA Astrophysics Data System (ADS)

Mehranian, Abolfazl; Belzunce, Martin A.; Niccolini, Flavia; Politis, Marios; Prieto, Claudia; Turkheimer, Federico; Hammers, Alexander; Reader, Andrew J.

2017-08-01

In this study, we investigate the application of multi-parametric anato-functional (MR-PET) priors for the maximum a posteriori (MAP) reconstruction of brain PET data in order to address the limitations of the conventional anatomical priors in the presence of PET-MR mismatches. In addition to partial volume correction benefits, the suitability of these priors for reconstruction of low-count PET data is also introduced and demonstrated, comparing to standard maximum-likelihood (ML) reconstruction of high-count data. The conventional local Tikhonov and total variation (TV) priors and current state-of-the-art anatomical priors including the Kaipio, non-local Tikhonov prior with Bowsher and Gaussian similarity kernels are investigated and presented in a unified framework. The Gaussian kernels are calculated using both voxel- and patch-based feature vectors. To cope with PET and MR mismatches, the Bowsher and Gaussian priors are extended to multi-parametric priors. In addition, we propose a modified joint Burg entropy prior that by definition exploits all parametric information in the MAP reconstruction of PET data. The performance of the priors was extensively evaluated using 3D simulations and two clinical brain datasets of [18F]florbetaben and [18F]FDG radiotracers. For simulations, several anato-functional mismatches were intentionally introduced between the PET and MR images, and furthermore, for the FDG clinical dataset, two PET-unique active tumours were embedded in the PET data. Our simulation results showed that the joint Burg entropy prior far outperformed the conventional anatomical priors in terms of preserving PET unique lesions, while still reconstructing functional boundaries with corresponding MR boundaries. In addition, the multi-parametric extension of the Gaussian and Bowsher priors led to enhanced preservation of edge and PET unique features and also an improved bias-variance performance. In agreement with the simulation results, the clinical results also showed that the Gaussian prior with voxel-based feature vectors, the Bowsher and the joint Burg entropy priors were the best performing priors. However, for the FDG dataset with simulated tumours, the TV and proposed priors were capable of preserving the PET-unique tumours. Finally, an important outcome was the demonstration that the MAP reconstruction of a low-count FDG PET dataset using the proposed joint entropy prior can lead to comparable image quality to a conventional ML reconstruction with up to 5 times more counts. In conclusion, multi-parametric anato-functional priors provide a solution to address the pitfalls of the conventional priors and are therefore likely to increase the diagnostic confidence in MR-guided PET image reconstructions.

3D Printing Multi-Functionality: Embedded RF Antennas and Components

NASA Technical Reports Server (NTRS)

Shemelya, C. M.; Zemba, M.; Liang, M.; Espalin, D.; Kief, C.; Xin, H.; Wicker, R. B.; MacDonald, E. W.

2015-01-01

Significant research and press has recently focused on the fabrication freedom of Additive Manufacturing (AM) to create both conceptual models and final end-use products. This flexibility allows design modifications to be immediately reflected in 3D printed structures, creating new paradigms within the manufacturing process. 3D printed products will inevitably be fabricated locally, with unit-level customization, optimized to unique mission requirements. However, for the technology to be universally adopted, the processes must be enhanced to incorporate additional technologies; such as electronics, actuation, and electromagnetics. Recently, a novel 3D printing platform, Multi3D manufacturing, was funded by the presidential initiative for revitalizing manufacturing in the USA using 3D printing (America Makes - also known as the National Additive Manufacturing Innovation Institute). The Multi3D system specifically targets 3D printed electronics in arbitrary form; and building upon the potential of this system, this paper describes RF antennas and components fabricated through the integration of material extrusion 3D printing with embedded wire, mesh, and RF elements.

Behavior-aware cache hierarchy optimization for low-power multi-core embedded systems

NASA Astrophysics Data System (ADS)

Zhao, Huatao; Luo, Xiao; Zhu, Chen; Watanabe, Takahiro; Zhu, Tianbo

2017-07-01

In modern embedded systems, the increasing number of cores requires efficient cache hierarchies to ensure data throughput, but such cache hierarchies are restricted by their tumid size and interference accesses which leads to both performance degradation and wasted energy. In this paper, we firstly propose a behavior-aware cache hierarchy (BACH) which can optimally allocate the multi-level cache resources to many cores and highly improved the efficiency of cache hierarchy, resulting in low energy consumption. The BACH takes full advantage of the explored application behaviors and runtime cache resource demands as the cache allocation bases, so that we can optimally configure the cache hierarchy to meet the runtime demand. The BACH was implemented on the GEM5 simulator. The experimental results show that energy consumption of a three-level cache hierarchy can be saved from 5.29% up to 27.94% compared with other key approaches while the performance of the multi-core system even has a slight improvement counting in hardware overhead.

Beyond assemblies: system convergence and multi-materiality.

PubMed

Wiscombe, Tom

2012-03-01

The architectural construction industry has become increasingly more specialized over the past 50 years, creating a culture of layer thinking over part-to-whole thinking. Building systems and technologies are often cobbled together in conflicting and uncorrelated ways, even when referred to as 'integrated', such as by way of building information modeling. True integration of building systems requires rethinking how systems and architectural morphologies can push and pull on one another, creating not only innovation in technology but in aesthetics. The revolution in composite materials, with unprecedented plasticity and performance features, opens up a huge range of possibilities for achieving this kind of convergence. Composites by nature fuse envelope and structure, but through various types of inflections, they can also be made to conduct air and fluids through cavities and de-laminations, as well as integrate lighting and energy systems. Assembly as we know it moves away from mineral materials and hardware and toward polymers and 'healing'. Further, when projected into the near-future realm of multi-materiality and 3D manufacturing, possibilities for embedding systems and creating gradients of rigidity and opacity open up, pointing to an entirely new realm of architectural thinking.

A novel visual saliency detection method for infrared video sequences

NASA Astrophysics Data System (ADS)

Wang, Xin; Zhang, Yuzhen; Ning, Chen

2017-12-01

Infrared video applications such as target detection and recognition, moving target tracking, and so forth can benefit a lot from visual saliency detection, which is essentially a method to automatically localize the ;important; content in videos. In this paper, a novel visual saliency detection method for infrared video sequences is proposed. Specifically, for infrared video saliency detection, both the spatial saliency and temporal saliency are considered. For spatial saliency, we adopt a mutual consistency-guided spatial cues combination-based method to capture the regions with obvious luminance contrast and contour features. For temporal saliency, a multi-frame symmetric difference approach is proposed to discriminate salient moving regions of interest from background motions. Then, the spatial saliency and temporal saliency are combined to compute the spatiotemporal saliency using an adaptive fusion strategy. Besides, to highlight the spatiotemporal salient regions uniformly, a multi-scale fusion approach is embedded into the spatiotemporal saliency model. Finally, a Gestalt theory-inspired optimization algorithm is designed to further improve the reliability of the final saliency map. Experimental results demonstrate that our method outperforms many state-of-the-art saliency detection approaches for infrared videos under various backgrounds.

A near-infrared SETI experiment: A multi-time resolution data analysis

NASA Astrophysics Data System (ADS)

Tallis, Melisa; Maire, Jerome; Wright, Shelley; Drake, Frank D.; Duenas, Andres; Marcy, Geoffrey W.; Stone, Remington P. S.; Treffers, Richard R.; Werthimer, Dan; NIROSETI

2016-06-01

We present new post-processing routines which are used to detect very fast optical and near-infrared pulsed signals using the latest NIROSETI (Near-Infrared Optical Search for Extraterrestrial Intelligence) instrument. NIROSETI was commissioned in 2015 at Lick Observatory and searches for near-infrared (0.95 to 1.65μ) nanosecond pulsed laser signals transmitted by distant civilizations. Traditional optical SETI searches rely on analysis of coincidences that occur between multiple detectors at a fixed time resolution. We present a multi-time resolution data analysis that extends our search from the 1ns to 1ms range. This new feature greatly improves the versatility of the instrument and its search parameters for near-infrared SETI. We aim to use these algorithms to assist us in our search for signals that have varying duty cycles and pulse widths. We tested the fidelity and robustness of our algorithms using both synthetic embedded pulsed signals, as well as data from a near-infrared pulsed laser installed on the instrument. Applications of NIROSETI are widespread in time domain astrophysics, especially for high time resolution transients, and astronomical objects that emit short-duration high-energy pulses such as pulsars.

A Formal Approach to the Provably Correct Synthesis of Mission Critical Embedded Software for Multi Core Embedded Platforms

DTIC Science & Technology

2014-04-01

synchronization primitives based on preset templates can result in over synchronization if unchecked, possibly creating deadlock situations. Further...inputs rather than enforcing synchronization with a global clock. MRICDF models software as a network of communicating actors. Four primitive actors...control wants to send interrupt or not. Since this is shared buffer, a semaphore mechanism is assumed to synchronize the read/write of this buffer. The

HERA: A New Platform for Embedding Agents in Heterogeneous Wireless Sensor Networks

NASA Astrophysics Data System (ADS)

Alonso, Ricardo S.; de Paz, Juan F.; García, Óscar; Gil, Óscar; González, Angélica

Ambient Intelligence (AmI) based systems require the development of innovative solutions that integrate distributed intelligent systems with context-aware technologies. In this sense, Multi-Agent Systems (MAS) and Wireless Sensor Networks (WSN) are two key technologies for developing distributed systems based on AmI scenarios. This paper presents the new HERA (Hardware-Embedded Reactive Agents) platform, that allows using dynamic and self-adaptable heterogeneous WSNs on which agents are directly embedded on the wireless nodes This approach facilitates the inclusion of context-aware capabilities in AmI systems to gather data from their surrounding environments, achieving a higher level of ubiquitous and pervasive computing.

Single and Multiple Object Tracking Using a Multi-Feature Joint Sparse Representation.

PubMed

Hu, Weiming; Li, Wei; Zhang, Xiaoqin; Maybank, Stephen

2015-04-01

In this paper, we propose a tracking algorithm based on a multi-feature joint sparse representation. The templates for the sparse representation can include pixel values, textures, and edges. In the multi-feature joint optimization, noise or occlusion is dealt with using a set of trivial templates. A sparse weight constraint is introduced to dynamically select the relevant templates from the full set of templates. A variance ratio measure is adopted to adaptively adjust the weights of different features. The multi-feature template set is updated adaptively. We further propose an algorithm for tracking multi-objects with occlusion handling based on the multi-feature joint sparse reconstruction. The observation model based on sparse reconstruction automatically focuses on the visible parts of an occluded object by using the information in the trivial templates. The multi-object tracking is simplified into a joint Bayesian inference. The experimental results show the superiority of our algorithm over several state-of-the-art tracking algorithms.

Adaptable, modular, multi-purpose space vehicle backplane

DOE Office of Scientific and Technical Information (OSTI.GOV)

Judd, Stephen; Dallmann, Nicholas; McCabe, Kevin

An adaptable, modular, multi-purpose (AMM) space vehicle backplane may accommodate boards and components for various missions. The AMM backplane may provide a common hardware interface and common board-to-board communications. Components, connectors, test points, and sensors may be embedded directly into the backplane to provide additional functionality, diagnostics, and system access. Other space vehicle sections may plug directly into the backplane.

Improving resolution of dynamic communities in human brain networks through targeted node removal

PubMed Central

Turner, Benjamin O.; Miller, Michael B.; Carlson, Jean M.

2017-01-01

Current approaches to dynamic community detection in complex networks can fail to identify multi-scale community structure, or to resolve key features of community dynamics. We propose a targeted node removal technique to improve the resolution of community detection. Using synthetic oscillator networks with well-defined “ground truth” communities, we quantify the community detection performance of a common modularity maximization algorithm. We show that the performance of the algorithm on communities of a given size deteriorates when these communities are embedded in multi-scale networks with communities of different sizes, compared to the performance in a single-scale network. We demonstrate that targeted node removal during community detection improves performance on multi-scale networks, particularly when removing the most functionally cohesive nodes. Applying this approach to network neuroscience, we compare dynamic functional brain networks derived from fMRI data taken during both repetitive single-task and varied multi-task experiments. After the removal of regions in visual cortex, the most coherent functional brain area during the tasks, community detection is better able to resolve known functional brain systems into communities. In addition, node removal enables the algorithm to distinguish clear differences in brain network dynamics between these experiments, revealing task-switching behavior that was not identified with the visual regions present in the network. These results indicate that targeted node removal can improve spatial and temporal resolution in community detection, and they demonstrate a promising approach for comparison of network dynamics between neuroscientific data sets with different resolution parameters. PMID:29261662

Latest generation interconnect technologies in APEnet+ networking infrastructure

NASA Astrophysics Data System (ADS)

Ammendola, Roberto; Biagioni, Andrea; Cretaro, Paolo; Frezza, Ottorino; Lo Cicero, Francesca; Lonardo, Alessandro; Martinelli, Michele; Stanislao Paolucci, Pier; Pastorelli, Elena; Rossetti, Davide; Simula, Francesco; Vicini, Piero

2017-10-01

In this paper we present the status of the 3rd generation design of the APEnet board (V5) built upon the 28nm Altera Stratix V FPGA; it features a PCIe Gen3 x8 interface and enhanced embedded transceivers with a maximum capability of 12.5Gbps each. The network architecture is designed in accordance to the Remote DMA paradigm. The APEnet+ V5 prototype is built upon the Stratix V DevKit with the addition of a proprietary, third party IP core implementing multi-DMA engines. Support for zero-copy communication is assured by the possibility of DMA-accessing either host and GPU memory, offloading the CPU from the chore of data copying. The current implementation plateaus to a bandwidth for memory read of 4.8GB/s. Here we describe the hardware optimization to the memory write process which relies on the use of two independent DMA engines and an improved TLB.

Fast and accurate metrology of multi-layered ceramic materials by an automated boundary detection algorithm developed for optical coherence tomography data

PubMed Central

Ekberg, Peter; Su, Rong; Chang, Ernest W.; Yun, Seok Hyun; Mattsson, Lars

2014-01-01

Optical coherence tomography (OCT) is useful for materials defect analysis and inspection with the additional possibility of quantitative dimensional metrology. Here, we present an automated image-processing algorithm for OCT analysis of roll-to-roll multilayers in 3D manufacturing of advanced ceramics. It has the advantage of avoiding filtering and preset modeling, and will, thus, introduce a simplification. The algorithm is validated for its capability of measuring the thickness of ceramic layers, extracting the boundaries of embedded features with irregular shapes, and detecting the geometric deformations. The accuracy of the algorithm is very high, and the reliability is better than 1 µm when evaluating with the OCT images using the same gauge block step height reference. The method may be suitable for industrial applications to the rapid inspection of manufactured samples with high accuracy and robustness. PMID:24562018

Multi-task feature selection in microarray data by binary integer programming.

PubMed

Lan, Liang; Vucetic, Slobodan

2013-12-20

A major challenge in microarray classification is that the number of features is typically orders of magnitude larger than the number of examples. In this paper, we propose a novel feature filter algorithm to select the feature subset with maximal discriminative power and minimal redundancy by solving a quadratic objective function with binary integer constraints. To improve the computational efficiency, the binary integer constraints are relaxed and a low-rank approximation to the quadratic term is applied. The proposed feature selection algorithm was extended to solve multi-task microarray classification problems. We compared the single-task version of the proposed feature selection algorithm with 9 existing feature selection methods on 4 benchmark microarray data sets. The empirical results show that the proposed method achieved the most accurate predictions overall. We also evaluated the multi-task version of the proposed algorithm on 8 multi-task microarray datasets. The multi-task feature selection algorithm resulted in significantly higher accuracy than when using the single-task feature selection methods.

Method of assessing the state of a rolling bearing based on the relative compensation distance of multiple-domain features and locally linear embedding

NASA Astrophysics Data System (ADS)

Kang, Shouqiang; Ma, Danyang; Wang, Yujing; Lan, Chaofeng; Chen, Qingguo; Mikulovich, V. I.

2017-03-01

To effectively assess different fault locations and different degrees of performance degradation of a rolling bearing with a unified assessment index, a novel state assessment method based on the relative compensation distance of multiple-domain features and locally linear embedding is proposed. First, for a single-sample signal, time-domain and frequency-domain indexes can be calculated for the original vibration signal and each sensitive intrinsic mode function obtained by improved ensemble empirical mode decomposition, and the singular values of the sensitive intrinsic mode function matrix can be extracted by singular value decomposition to construct a high-dimensional hybrid-domain feature vector. Second, a feature matrix can be constructed by arranging each feature vector of multiple samples, the dimensions of each row vector of the feature matrix can be reduced by the locally linear embedding algorithm, and the compensation distance of each fault state of the rolling bearing can be calculated using the support vector machine. Finally, the relative distance between different fault locations and different degrees of performance degradation and the normal-state optimal classification surface can be compensated, and on the basis of the proposed relative compensation distance, the assessment model can be constructed and an assessment curve drawn. Experimental results show that the proposed method can effectively assess different fault locations and different degrees of performance degradation of the rolling bearing under certain conditions.

Dynamically Reconfigurable Systolic Array Accelorators

NASA Technical Reports Server (NTRS)

Dasu, Aravind (Inventor); Barnes, Robert C. (Inventor)

2014-01-01

A polymorphic systolic array framework that works in conjunction with an embedded microprocessor on an FPGA, that allows for dynamic and complimentary scaling of acceleration levels of two algorithms active concurrently on the FPGA. Use is made of systolic arrays and hardware-software co-design to obtain an efficient multi-application acceleration system. The flexible and simple framework allows hosting of a broader range of algorithms and extendable to more complex applications in the area of aerospace embedded systems.

Categorizing biomedicine images using novel image features and sparse coding representation

PubMed Central

2013-01-01

Background Images embedded in biomedical publications carry rich information that often concisely summarize key hypotheses adopted, methods employed, or results obtained in a published study. Therefore, they offer valuable clues for understanding main content in a biomedical publication. Prior studies have pointed out the potential of mining images embedded in biomedical publications for automatically understanding and retrieving such images' associated source documents. Within the broad area of biomedical image processing, categorizing biomedical images is a fundamental step for building many advanced image analysis, retrieval, and mining applications. Similar to any automatic categorization effort, discriminative image features can provide the most crucial aid in the process. Method We observe that many images embedded in biomedical publications carry versatile annotation text. Based on the locations of and the spatial relationships between these text elements in an image, we thus propose some novel image features for image categorization purpose, which quantitatively characterize the spatial positions and distributions of text elements inside a biomedical image. We further adopt a sparse coding representation (SCR) based technique to categorize images embedded in biomedical publications by leveraging our newly proposed image features. Results we randomly selected 990 images of the JPG format for use in our experiments where 310 images were used as training samples and the rest were used as the testing cases. We first segmented 310 sample images following the our proposed procedure. This step produced a total of 1035 sub-images. We then manually labeled all these sub-images according to the two-level hierarchical image taxonomy proposed by [1]. Among our annotation results, 316 are microscopy images, 126 are gel electrophoresis images, 135 are line charts, 156 are bar charts, 52 are spot charts, 25 are tables, 70 are flow charts, and the remaining 155 images are of the type "others". A serial of experimental results are obtained. Firstly, each image categorizing results is presented, and next image categorizing performance indexes such as precision, recall, F-score, are all listed. Different features which include conventional image features and our proposed novel features indicate different categorizing performance, and the results are demonstrated. Thirdly, we conduct an accuracy comparison between support vector machine classification method and our proposed sparse representation classification method. At last, our proposed approach is compared with three peer classification method and experimental results verify our impressively improved performance. Conclusions Compared with conventional image features that do not exploit characteristics regarding text positions and distributions inside images embedded in biomedical publications, our proposed image features coupled with the SR based representation model exhibit superior performance for classifying biomedical images as demonstrated in our comparative benchmark study. PMID:24565470

Fabrication and characterization of novel microsphere-embedded optical devices for enhancing microscopy resolution

NASA Astrophysics Data System (ADS)

Darafsheh, Arash

2018-02-01

Microsphere-assisted imaging can be incorporated onto conventional light microscopes allowing wide-field and flourescence imaging with enhanced resolution. We demonstrated that imaging of specimens containing subdiffraction-limited features is achievable through high-index microspheres embedded in a transparent thin film placed over the specimen. We fabricated novel microsphere-embedded microscope slides composed of barium titanate glass microspheres (with diameter 10-100 μm and refractive index 1.9-2.2) embedded in a transparent polydimethylsiloxane (PDMS) elastomer layer with controllable thickness. We characterized the imaging performance of such microsphere-embedded devices in white-light microscopies, by measuring the imaging resolution, field-of-view, and magnification as a function of microsphere size. Our results inform on the design of novel optical devices, such as microsphere-embedded microscope slides for imaging applications.

A Versatile Embedded Platform for EMG Acquisition and Gesture Recognition.

PubMed

Benatti, Simone; Casamassima, Filippo; Milosevic, Bojan; Farella, Elisabetta; Schönle, Philipp; Fateh, Schekeb; Burger, Thomas; Huang, Qiuting; Benini, Luca

2015-10-01

Wearable devices offer interesting features, such as low cost and user friendliness, but their use for medical applications is an open research topic, given the limited hardware resources they provide. In this paper, we present an embedded solution for real-time EMG-based hand gesture recognition. The work focuses on the multi-level design of the system, integrating the hardware and software components to develop a wearable device capable of acquiring and processing EMG signals for real-time gesture recognition. The system combines the accuracy of a custom analog front end with the flexibility of a low power and high performance microcontroller for on-board processing. Our system achieves the same accuracy of high-end and more expensive active EMG sensors used in applications with strict requirements on signal quality. At the same time, due to its flexible configuration, it can be compared to the few wearable platforms designed for EMG gesture recognition available on market. We demonstrate that we reach similar or better performance while embedding the gesture recognition on board, with the benefit of cost reduction. To validate this approach, we collected a dataset of 7 gestures from 4 users, which were used to evaluate the impact of the number of EMG channels, the number of recognized gestures and the data rate on the recognition accuracy and on the computational demand of the classifier. As a result, we implemented a SVM recognition algorithm capable of real-time performance on the proposed wearable platform, achieving a classification rate of 90%, which is aligned with the state-of-the-art off-line results and a 29.7 mW power consumption, guaranteeing 44 hours of continuous operation with a 400 mAh battery.

Diverse power iteration embeddings: Theory and practice

DOE PAGES

Huang, Hao; Yoo, Shinjae; Yu, Dantong; ...

2015-11-09

Manifold learning, especially spectral embedding, is known as one of the most effective learning approaches on high dimensional data, but for real-world applications it raises a serious computational burden in constructing spectral embeddings for large datasets. To overcome this computational complexity, we propose a novel efficient embedding construction, Diverse Power Iteration Embedding (DPIE). DPIE shows almost the same effectiveness of spectral embeddings and yet is three order of magnitude faster than spectral embeddings computed from eigen-decomposition. Our DPIE is unique in that (1) it finds linearly independent embeddings and thus shows diverse aspects of dataset; (2) the proposed regularized DPIEmore » is effective if we need many embeddings; (3) we show how to efficiently orthogonalize DPIE if one needs; and (4) Diverse Power Iteration Value (DPIV) provides the importance of each DPIE like an eigen value. As a result, such various aspects of DPIE and DPIV ensure that our algorithm is easy to apply to various applications, and we also show the effectiveness and efficiency of DPIE on clustering, anomaly detection, and feature selection as our case studies.« less

Ultra-Wideband Multi-Dye-Sensitized Upconverting Nanoparticles for Information Security Application.

PubMed

Lee, Jongha; Yoo, Byeongjun; Lee, Hakyong; Cha, Gi Doo; Lee, Hee-Su; Cho, Youngho; Kim, Sang Yeon; Seo, Hyunseon; Lee, Woongchan; Son, Donghee; Kang, Myungjoo; Kim, Hyung Min; Park, Yong Il; Hyeon, Taeghwan; Kim, Dae-Hyeong

2017-01-01

Multi-dye-sensitized upconverting nanoparticles (UCNPs), which harvest photons of wide wavelength range (450-975 nm) are designed and synthesized. The UCNPs embedded in a photo-acid generating layer are integrated on destructible nonvolatile resistive memory device. Upon illumination of light, the system permanently erases stored data, achieving enhanced information security. © 2016 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim.

Effective Beginning Handwriting Instruction: Multi-Modal, Consistent Format for 2 Years, and Linked to Spelling and Composing

ERIC Educational Resources Information Center

Wolf, Beverly; Abbott, Robert D.; Berninger, Virginia W.

2017-01-01

In Study 1, the treatment group (N = 33 first graders, M = 6 years 10 months, 16 girls) received Slingerland multi-modal (auditory, visual, tactile, motor through hand, and motor through mouth) manuscript (unjoined) handwriting instruction embedded in systematic spelling, reading, and composing lessons; and the control group (N = 16 first graders,…

Multi-Core Processors: An Enabling Technology for Embedded Distributed Model-Based Control (Postprint)

DTIC Science & Technology

2008-07-01

generation of process partitioning, a thread pipelining becomes possible. In this paper we briefly summarize the requirements and trends for FADEC based... FADEC environment, presenting a hypothetical realization of an example application. Finally we discuss the application of Time-Triggered...based control applications of the future. 15. SUBJECT TERMS Gas turbine, FADEC , Multi-core processing technology, disturbed based control

Embedded Formative Assessment and Classroom Process Quality: How Do They Interact in Promoting Science Understanding?

ERIC Educational Resources Information Center

Decristan, Jasmin; Klieme, Eckhard; Kunter, Mareike; Hochweber, Jan; Büttner, Gerhard; Fauth, Benjamin; Hondrich, A. Lena; Rieser, Svenja; Hertel, Silke; Hardy, Ilonca

2015-01-01

In this study we examine the interplay between curriculum-embedded formative assessment--a well-known teaching practice--and general features of classroom process quality (i.e., cognitive activation, supportive climate, classroom management) and their combined effect on elementary school students' understanding of the scientific concepts of…

Effectiveness of Electronic Textbooks with Embedded Activities on Student Learning

ERIC Educational Resources Information Center

Porter, Paula L.

2010-01-01

Current versions of electronic textbooks mimic the format and structure of printed textbooks; however, the electronic capabilities of these new versions of textbooks offer the potential of embedding interactive features of web-based learning within the context of a textbook. This dissertation research study was conducted to determine if student…

A multiple distributed representation method based on neural network for biomedical event extraction.

PubMed

Wang, Anran; Wang, Jian; Lin, Hongfei; Zhang, Jianhai; Yang, Zhihao; Xu, Kan

2017-12-20

Biomedical event extraction is one of the most frontier domains in biomedical research. The two main subtasks of biomedical event extraction are trigger identification and arguments detection which can both be considered as classification problems. However, traditional state-of-the-art methods are based on support vector machine (SVM) with massive manually designed one-hot represented features, which require enormous work but lack semantic relation among words. In this paper, we propose a multiple distributed representation method for biomedical event extraction. The method combines context consisting of dependency-based word embedding, and task-based features represented in a distributed way as the input of deep learning models to train deep learning models. Finally, we used softmax classifier to label the example candidates. The experimental results on Multi-Level Event Extraction (MLEE) corpus show higher F-scores of 77.97% in trigger identification and 58.31% in overall compared to the state-of-the-art SVM method. Our distributed representation method for biomedical event extraction avoids the problems of semantic gap and dimension disaster from traditional one-hot representation methods. The promising results demonstrate that our proposed method is effective for biomedical event extraction.

Mycobacterium tuberculosis RsdA provides a conformational rationale for selective regulation of σ-factor activity by proteolysis

PubMed Central

Jaiswal, Ravi K.; Prabha, Tangirala Surya; Manjeera, Gowravaram; Gopal, Balasubramanian

2013-01-01

The relative levels of different σ factors dictate the expression profile of a bacterium. Extracytoplasmic function σ factors synchronize the transcriptional profile with environmental conditions. The cellular concentration of free extracytoplasmic function σ factors is regulated by the localization of this protein in a σ/anti-σ complex. Anti-σ factors are multi-domain proteins with a receptor to sense environmental stimuli and a conserved anti-σ domain (ASD) that binds a σ factor. Here we describe the structure of Mycobacterium tuberculosis anti-σD (RsdA) in complex with the -35 promoter binding domain of σD (σD4). We note distinct conformational features that enable the release of σD by the selective proteolysis of the ASD in RsdA. The structural and biochemical features of the σD/RsdA complex provide a basis to reconcile diverse regulatory mechanisms that govern σ/anti-σ interactions despite high overall structural similarity. Multiple regulatory mechanisms embedded in an ASD scaffold thus provide an elegant route to rapidly re-engineer the expression profile of a bacterium in response to an environmental stimulus. PMID:23314154

Cork Embedded Internal Features and Contrast Mechanisms with Del Using 18, 20, 30, 36 and 40 keV Synchrotron X-rays

DOE Office of Scientific and Technical Information (OSTI.GOV)

Rao, D.V.; Zhong, Z.; Akatsuka, T.

Images of the cork used for wine and other bottles are visualized with the use of diffraction-enhanced imaging (DEI) technique. Present experimental studies allowed us to identify the cracks, holes, porosity, and importance of soft-matter (soft-material) and associated biology by visualization of the embedded internal complex features of the biological material such as cork and its microstructure. Highlighted the contrast mechanisms above and below the K-absorption edge of iodine and studied the attenuation through a combination of weakly and strongly attenuating materials.

Cork Embedded Internal Features and Contrast Mechanisms with DEI using 18, 20, 30, 36, and 40 kev Synchrotron X-rays

DOE Office of Scientific and Technical Information (OSTI.GOV)

Donepudi, R.; Cesareo, R; Brunetti, A

Images of the cork used for wine and other bottles are visualized with the use of diffraction-enhanced imaging (DEI) technique. Present experimental studies allowed us to identify the cracks, holes, porosity, and importance of soft-matter (soft-material) and associated biology by visualization of the embedded internal complex features of the biological material such as cork and its microstructure. Highlighted the contrast mechanisms above and below the K-absorption edge of iodine and studied the attenuation through a combination of weakly and strongly attenuating materials.

Chromatin accessibility prediction via convolutional long short-term memory networks with k-mer embedding.

PubMed

Min, Xu; Zeng, Wanwen; Chen, Ning; Chen, Ting; Jiang, Rui

2017-07-15

Experimental techniques for measuring chromatin accessibility are expensive and time consuming, appealing for the development of computational approaches to predict open chromatin regions from DNA sequences. Along this direction, existing methods fall into two classes: one based on handcrafted k -mer features and the other based on convolutional neural networks. Although both categories have shown good performance in specific applications thus far, there still lacks a comprehensive framework to integrate useful k -mer co-occurrence information with recent advances in deep learning. We fill this gap by addressing the problem of chromatin accessibility prediction with a convolutional Long Short-Term Memory (LSTM) network with k -mer embedding. We first split DNA sequences into k -mers and pre-train k -mer embedding vectors based on the co-occurrence matrix of k -mers by using an unsupervised representation learning approach. We then construct a supervised deep learning architecture comprised of an embedding layer, three convolutional layers and a Bidirectional LSTM (BLSTM) layer for feature learning and classification. We demonstrate that our method gains high-quality fixed-length features from variable-length sequences and consistently outperforms baseline methods. We show that k -mer embedding can effectively enhance model performance by exploring different embedding strategies. We also prove the efficacy of both the convolution and the BLSTM layers by comparing two variations of the network architecture. We confirm the robustness of our model to hyper-parameters by performing sensitivity analysis. We hope our method can eventually reinforce our understanding of employing deep learning in genomic studies and shed light on research regarding mechanisms of chromatin accessibility. The source code can be downloaded from https://github.com/minxueric/ismb2017_lstm . tingchen@tsinghua.edu.cn or ruijiang@tsinghua.edu.cn. Supplementary materials are available at Bioinformatics online. © The Author 2017. Published by Oxford University Press. All rights reserved. For Permissions, please e-mail: journals.permissions@oup.com

Chromatin accessibility prediction via convolutional long short-term memory networks with k-mer embedding

PubMed Central

Min, Xu; Zeng, Wanwen; Chen, Ning; Chen, Ting; Jiang, Rui

2017-01-01

Abstract Motivation: Experimental techniques for measuring chromatin accessibility are expensive and time consuming, appealing for the development of computational approaches to predict open chromatin regions from DNA sequences. Along this direction, existing methods fall into two classes: one based on handcrafted k-mer features and the other based on convolutional neural networks. Although both categories have shown good performance in specific applications thus far, there still lacks a comprehensive framework to integrate useful k-mer co-occurrence information with recent advances in deep learning. Results: We fill this gap by addressing the problem of chromatin accessibility prediction with a convolutional Long Short-Term Memory (LSTM) network with k-mer embedding. We first split DNA sequences into k-mers and pre-train k-mer embedding vectors based on the co-occurrence matrix of k-mers by using an unsupervised representation learning approach. We then construct a supervised deep learning architecture comprised of an embedding layer, three convolutional layers and a Bidirectional LSTM (BLSTM) layer for feature learning and classification. We demonstrate that our method gains high-quality fixed-length features from variable-length sequences and consistently outperforms baseline methods. We show that k-mer embedding can effectively enhance model performance by exploring different embedding strategies. We also prove the efficacy of both the convolution and the BLSTM layers by comparing two variations of the network architecture. We confirm the robustness of our model to hyper-parameters by performing sensitivity analysis. We hope our method can eventually reinforce our understanding of employing deep learning in genomic studies and shed light on research regarding mechanisms of chromatin accessibility. Availability and implementation: The source code can be downloaded from https://github.com/minxueric/ismb2017_lstm. Contact: tingchen@tsinghua.edu.cn or ruijiang@tsinghua.edu.cn Supplementary information: Supplementary materials are available at Bioinformatics online. PMID:28881969

Frameworks and Tools for High-Confidence Design of Adaptive, Distributed Embedded Control Systems. Multi-University Research Initiative on High-Confidence Design for Distributed Embedded Systems

DTIC Science & Technology

2009-01-01

controllers (currently using the Robostix+Gumstix pair ). The interface between the plant simulator and the controller is ‘hard real-time’, and the xPC box... simulation ) on aerobatic maneuver design for the STARMAC quadrotor helicopter testbed. In related work, we have developed a new optimization scheme...for scheduling hybrid systems, and have demonstrated the results on an autonomous car simulation testbed. We are focusing efforts this summer for

Reconstructing latent dynamical noise for better forecasting observables

NASA Astrophysics Data System (ADS)

Hirata, Yoshito

2018-03-01

I propose a method for reconstructing multi-dimensional dynamical noise inspired by the embedding theorem of Muldoon et al. [Dyn. Stab. Syst. 13, 175 (1998)] by regarding multiple predictions as different observables. Then, applying the embedding theorem by Stark et al. [J. Nonlinear Sci. 13, 519 (2003)] for a forced system, I produce time series forecast by supplying the reconstructed past dynamical noise as auxiliary information. I demonstrate the proposed method on toy models driven by auto-regressive models or independent Gaussian noise.

Hamiltonian vs Lagrangian Embedding of a Massive Spin-One Theory Involving Two-Form Field

NASA Astrophysics Data System (ADS)

Harikumar, E.; Sivakumar, M.

We consider the Hamiltonian and Lagrangian embedding of a first-order, massive spin-one, gauge noninvariant theory involving antisymmetric tensor field. We apply the BFV-BRST generalized canonical approach to convert the model to a first class system and construct nilpotent BFV-BRST charge and a unitarizing Hamiltonian. The canonical analysis of the Stückelberg formulation of this model is presented. We bring out the contrasting feature in the constraint structure, specifically with respect to the reducibility aspect, of the Hamiltonian and the Lagrangian embedded model. We show that to obtain manifestly covariant Stückelberg Lagrangian from the BFV embedded Hamiltonian, phase space has to be further enlarged and show how the reducible gauge structure emerges in the embedded model.

Crack Damage Detection Method via Multiple Visual Features and Efficient Multi-Task Learning Model.

PubMed

Wang, Baoxian; Zhao, Weigang; Gao, Po; Zhang, Yufeng; Wang, Zhe

2018-06-02

This paper proposes an effective and efficient model for concrete crack detection. The presented work consists of two modules: multi-view image feature extraction and multi-task crack region detection. Specifically, multiple visual features (such as texture, edge, etc.) of image regions are calculated, which can suppress various background noises (such as illumination, pockmark, stripe, blurring, etc.). With the computed multiple visual features, a novel crack region detector is advocated using a multi-task learning framework, which involves restraining the variability for different crack region features and emphasizing the separability between crack region features and complex background ones. Furthermore, the extreme learning machine is utilized to construct this multi-task learning model, thereby leading to high computing efficiency and good generalization. Experimental results of the practical concrete images demonstrate that the developed algorithm can achieve favorable crack detection performance compared with traditional crack detectors.

Algorithm Design of CPCI Backboard's Interrupts Management Based on VxWorks' Multi-Tasks

NASA Astrophysics Data System (ADS)

Cheng, Jingyuan; An, Qi; Yang, Junfeng

2006-09-01

This paper begins with a brief introduction of the embedded real-time operating system VxWorks and CompactPCI standard, then gives the programming interfaces of Peripheral Controller Interface (PCI) configuring, interrupts handling and multi-tasks programming interface under VxWorks, and then emphasis is placed on the software frameworks of CPCI interrupt management based on multi-tasks. This method is sound in design and easy to adapt, ensures that all possible interrupts are handled in time, which makes it suitable for data acquisition systems with multi-channels, a high data rate, and hard real-time high energy physics.

Algorithms for Image Analysis and Combination of Pattern Classifiers with Application to Medical Diagnosis

NASA Astrophysics Data System (ADS)

Georgiou, Harris

2009-10-01

Medical Informatics and the application of modern signal processing in the assistance of the diagnostic process in medical imaging is one of the more recent and active research areas today. This thesis addresses a variety of issues related to the general problem of medical image analysis, specifically in mammography, and presents a series of algorithms and design approaches for all the intermediate levels of a modern system for computer-aided diagnosis (CAD). The diagnostic problem is analyzed with a systematic approach, first defining the imaging characteristics and features that are relevant to probable pathology in mammo-grams. Next, these features are quantified and fused into new, integrated radio-logical systems that exhibit embedded digital signal processing, in order to improve the final result and minimize the radiological dose for the patient. In a higher level, special algorithms are designed for detecting and encoding these clinically interest-ing imaging features, in order to be used as input to advanced pattern classifiers and machine learning models. Finally, these approaches are extended in multi-classifier models under the scope of Game Theory and optimum collective deci-sion, in order to produce efficient solutions for combining classifiers with minimum computational costs for advanced diagnostic systems. The material covered in this thesis is related to a total of 18 published papers, 6 in scientific journals and 12 in international conferences.

3D ion-scale dynamics of BBFs and their associated emissions in Earth's magnetotail using 3D hybrid simulations and MMS multi-spacecraft observations

NASA Astrophysics Data System (ADS)

Breuillard, H.; Aunai, N.; Le Contel, O.; Catapano, F.; Alexandrova, A.; Retino, A.; Cozzani, G.; Gershman, D. J.; Giles, B. L.; Khotyaintsev, Y. V.; Lindqvist, P. A.; Ergun, R.; Strangeway, R. J.; Russell, C. T.; Magnes, W.; Plaschke, F.; Nakamura, R.; Fuselier, S. A.; Turner, D. L.; Schwartz, S. J.; Torbert, R. B.; Burch, J.

2017-12-01

Transient and localized jets of hot plasma, also known as Bursty Bulk Flows (BBFs), play a crucial role in Earth's magnetotail dynamics because the energy input from the solar wind is partly dissipated in their vicinity, notably in their embedded dipolarization front (DF). This dissipation is in the form of strong low-frequency waves that can heat and accelerate energetic particles up to the high-latitude plasma sheet. The ion-scale dynamics of BBFs have been revealed by the Cluster and THEMIS multi-spacecraft missions. However, the dynamics of BBF propagation in the magnetotail are still under debate due to instrumental limitations and spacecraft separation distances, as well as simulation limitations. The NASA/MMS fleet, which features unprecedented high time resolution instruments and four spacecraft separated by kinetic-scale distances, has also shown recently that the DF normal dynamics and its associated emissions are below the ion gyroradius scale in this region. Large variations in the dawn-dusk direction were also observed. However, most of large-scale simulations are using the MHD approach and are assumed 2D in the XZ plane. Thus, in this study we take advantage of both multi-spacecraft observations by MMS and large-scale 3D hybrid simulations to investigate the 3D dynamics of BBFs and their associated emissions at ion-scale in Earth's magnetotail, and their impact on particle heating and acceleration.

An Embedded Systems Laboratory to Support Rapid Prototyping of Robotics and the Internet of Things

ERIC Educational Resources Information Center

Hamblen, J. O.; van Bekkum, G. M. E.

2013-01-01

This paper describes a new approach for a course and laboratory designed to allow students to develop low-cost prototypes of robotic and other embedded devices that feature Internet connectivity, I/O, networking, a real-time operating system (RTOS), and object-oriented C/C++. The application programming interface (API) libraries provided permit…

Time reversal optical tomography and decomposition methods for detection and localization of targets in highly scattering turbid media

NASA Astrophysics Data System (ADS)

Wu, Binlin

New near-infrared (NIR) diffuse optical tomography (DOT) approaches were developed to detect, locate, and image small targets embedded in highly scattering turbid media. The first approach, referred to as time reversal optical tomography (TROT), is based on time reversal (TR) imaging and multiple signal classification (MUSIC). The second approach uses decomposition methods of non-negative matrix factorization (NMF) and principal component analysis (PCA) commonly used in blind source separation (BSS) problems, and compare the outcomes with that of optical imaging using independent component analysis (OPTICA). The goal is to develop a safe, affordable, noninvasive imaging modality for detection and characterization of breast tumors in early growth stages when those are more amenable to treatment. The efficacy of the approaches was tested using simulated data, and experiments involving model media and absorptive, scattering, and fluorescent targets, as well as, "realistic human breast model" composed of ex vivo breast tissues with embedded tumors. The experimental arrangements realized continuous wave (CW) multi-source probing of samples and multi-detector acquisition of diffusely transmitted signal in rectangular slab geometry. A data matrix was generated using the perturbation in the transmitted light intensity distribution due to the presence of absorptive or scattering targets. For fluorescent targets the data matrix was generated using the diffusely transmitted fluorescence signal distribution from the targets. The data matrix was analyzed using different approaches to detect and characterize the targets. The salient features of the approaches include ability to: (a) detect small targets; (b) provide three-dimensional location of the targets with high accuracy (~within a millimeter or 2); and (c) assess optical strength of the targets. The approaches are less computation intensive and consequently are faster than other inverse image reconstruction methods that attempt to reconstruct the optical properties of every voxel of the sample volume. The location of a target was estimated to be the weighted center of the optical property of the target. Consequently, the locations of small targets were better specified than those of the extended targets. It was more difficult to retrieve the size and shape of a target. The fluorescent measurements seemed to provide better accuracy than the transillumination measurements. In the case of ex vivo detection of tumors embedded in human breast tissue, measurements using multiple wavelengths provided more robust results, and helped suppress artifacts (false positives) than that from single wavelength measurements. The ability to detect and locate small targets, speedier reconstruction, combined with fluorophore-specific multi-wavelength probing has the potential to make these approaches suitable for breast cancer detection and diagnosis.

Exploiting Many-Body Bus States for Multi-Qubit Entanglement

DTIC Science & Technology

2013-06-06

ancilla qubits . We studied electron-spin-photon coupling in a single-spin double quantum dot embedded in a superconducting stripline cavity. We... qubit to a superconducting stripline cavity,” Xuedong Hu, Yu-xi Liu, and Franco Nori, Phys. Rev. B 86, 035314 (2012). [9] “Controllable exchange...DARPA) EXPLOITING MANY-BODY BUS STATES FOR MULTI- QUBIT ENTANGLEMENT MARK FRIESEN UNIVERSITY OF WISCONSIN SYSTEM 06/06/2013 Final Report

How Task Features Impact Evidence from Assessments Embedded in Simulations and Games

ERIC Educational Resources Information Center

Almond, Russell G.; Kim, Yoon Jeon; Velasquez, Gertrudes; Shute, Valerie J.

2014-01-01

One of the key ideas of evidence-centered assessment design (ECD) is that task features can be deliberately manipulated to change the psychometric properties of items. ECD identifies a number of roles that task-feature variables can play, including determining the focus of evidence, guiding form creation, determining item difficulty and…

Embedded Incremental Feature Selection for Reinforcement Learning

DTIC Science & Technology

2012-05-01

Prior to this work, feature selection for reinforce- ment learning has focused on linear value function ap- proximation ( Kolter and Ng, 2009; Parr et al...InProceed- ings of the the 23rd International Conference on Ma- chine Learning, pages 449–456. Kolter , J. Z. and Ng, A. Y. (2009). Regularization and feature

Multi-modal demands of a smartphone used to place calls and enter addresses during highway driving relative to two embedded systems.

PubMed

Reimer, Bryan; Mehler, Bruce; Reagan, Ian; Kidd, David; Dobres, Jonathan

2016-12-01

There is limited research on trade-offs in demand between manual and voice interfaces of embedded and portable technologies. Mehler et al. identified differences in driving performance, visual engagement and workload between two contrasting embedded vehicle system designs (Chevrolet MyLink and Volvo Sensus). The current study extends this work by comparing these embedded systems with a smartphone (Samsung Galaxy S4). None of the voice interfaces eliminated visual demand. Relative to placing calls manually, both embedded voice interfaces resulted in less eyes-off-road time than the smartphone. Errors were most frequent when calling contacts using the smartphone. The smartphone and MyLink allowed addresses to be entered using compound voice commands resulting in shorter eyes-off-road time compared with the menu-based Sensus but with many more errors. Driving performance and physiological measures indicated increased demand when performing secondary tasks relative to 'just driving', but were not significantly different between the smartphone and embedded systems. Practitioner Summary: The findings show that embedded system and portable device voice interfaces place fewer visual demands on the driver than manual interfaces, but they also underscore how differences in system designs can significantly affect not only the demands placed on drivers, but also the successful completion of tasks.

In Vivo Microscopy Reveals Extensive Embedding of Capillaries within the Sarcolemma of Skeletal Muscle Fibers

PubMed Central

Glancy, Brian; Hsu, Li-Yueh; Dao, Lam; Bakalar, Matthew; French, Stephanie; Chess, David J.; Taylor, Joni L.; Picard, Martin; Aponte, Angel; Daniels, Mathew P.; Esfahani, Shervin; Cushman, Samuel; Balaban, Robert S.

2013-01-01

Objective To provide insight into mitochondrial function in vivo, we evaluated the 3D spatial relationship between capillaries, mitochondria, and muscle fibers in live mice. Methods 3D volumes of in vivo murine Tibialis anterior muscles were imaged by multi-photon microscopy (MPM). Muscle fiber type, mitochondrial distribution, number of capillaries, and capillary-to-fiber contact were assessed. The role of myoglobin-facilitated diffusion was examined in myoglobin knockout mice. Distribution of GLUT4 was also evaluated in the context of the capillary and mitochondrial network. Results MPM revealed that 43.6 ± 3.3% of oxidative fiber capillaries had ≥ 50% of their circumference embedded in a groove in the sarcolemma, in vivo. Embedded capillaries were tightly associated with dense mitochondrial populations lateral to capillary grooves and nearly absent below the groove. Mitochondrial distribution, number of embedded capillaries, and capillary-to-fiber contact were proportional to fiber oxidative capacity and unaffected by myoglobin knockout. GLUT4 did not preferentially localize to embedded capillaries. Conclusions Embedding capillaries in the sarcolemma may provide a regulatory mechanism to optimize delivery of oxygen to heterogeneous groups of muscle fibers. We hypothesize that mitochondria locate to paravascular regions due to myofibril voids created by embedded capillaries, not to enhance the delivery of oxygen to the mitochondria. PMID:25279425

High-rate serial interconnections for embedded and distributed systems with power and resource constraints

NASA Astrophysics Data System (ADS)

Sheynin, Yuriy; Shutenko, Felix; Suvorova, Elena; Yablokov, Evgenej

2008-04-01

High rate interconnections are important subsystems in modern data processing and control systems of many classes. They are especially important in prospective embedded and on-board systems that used to be multicomponent systems with parallel or distributed architecture, [1]. Modular architecture systems of previous generations were based on parallel busses that were widely used and standardised: VME, PCI, CompactPCI, etc. Busses evolution went in improvement of bus protocol efficiency (burst transactions, split transactions, etc.) and increasing operation frequencies. However, due to multi-drop bus nature and multi-wire skew problems the parallel bussing speedup became more and more limited. For embedded and on-board systems additional reason for this trend was in weight, size and power constraints of an interconnection and its components. Parallel interfaces have become technologically more challenging as their respective clock frequencies have increased to keep pace with the bandwidth requirements of their attached storage devices. Since each interface uses a data clock to gate and validate the parallel data (which is normally 8 bits or 16 bits wide), the clock frequency need only be equivalent to the byte rate or word rate being transmitted. In other words, for a given transmission frequency, the wider the data bus, the slower the clock. As the clock frequency increases, more high frequency energy is available in each of the data lines, and a portion of this energy is dissipated in radiation. Each data line not only transmits this energy but also receives some from its neighbours. This form of mutual interference is commonly called "cross-talk," and the signal distortion it produces can become another major contributor to loss of data integrity unless compensated by appropriate cable designs. Other transmission problems such as frequency-dependent attenuation and signal reflections, while also applicable to serial interfaces, are more troublesome in parallel interfaces due to the number of additional cable conductors involved. In order to compensate for these drawbacks, higher quality cables, shorter cable runs and fewer devices on the bus have been the norm. Finally, the physical bulk of the parallel cables makes them more difficult to route inside an enclosure, hinders cooling airflow and is incompatible with the trend toward smaller form-factor devices. Parallel busses worked in systems during the past 20 years, but the accumulated problems dictate the need for change and the technology is available to spur the transition. The general trend in high-rate interconnections turned from parallel bussing to scalable interconnections with a network architecture and high-rate point-to-point links. Analysis showed that data links with serial information transfer could achieve higher throughput and efficiency and it was confirmed in various research and practical design. Serial interfaces offer an improvement over older parallel interfaces: better performance, better scalability, and also better reliability as the parallel interfaces are at their limits of speed with reliable data transfers and others. The trend was implemented in major standards' families evolution: e.g. from PCI/PCI-X parallel bussing to PCIExpress interconnection architecture with serial lines, from CompactPCI parallel bus to ATCA (Advanced Telecommunications Architecture) specification with serial links and network topologies of an interconnection, etc. In the article we consider a general set of characteristics and features of serial interconnections, give a brief overview of serial interconnections specifications. In more details we present the SpaceWire interconnection technology. Have been developed for space on-board systems applications the SpaceWire has important features and characteristics that make it a prospective interconnection for wide range of embedded systems.

Hadoop neural network for parallel and distributed feature selection.

PubMed

Hodge, Victoria J; O'Keefe, Simon; Austin, Jim

2016-06-01

In this paper, we introduce a theoretical basis for a Hadoop-based neural network for parallel and distributed feature selection in Big Data sets. It is underpinned by an associative memory (binary) neural network which is highly amenable to parallel and distributed processing and fits with the Hadoop paradigm. There are many feature selectors described in the literature which all have various strengths and weaknesses. We present the implementation details of five feature selection algorithms constructed using our artificial neural network framework embedded in Hadoop YARN. Hadoop allows parallel and distributed processing. Each feature selector can be divided into subtasks and the subtasks can then be processed in parallel. Multiple feature selectors can also be processed simultaneously (in parallel) allowing multiple feature selectors to be compared. We identify commonalities among the five features selectors. All can be processed in the framework using a single representation and the overall processing can also be greatly reduced by only processing the common aspects of the feature selectors once and propagating these aspects across all five feature selectors as necessary. This allows the best feature selector and the actual features to select to be identified for large and high dimensional data sets through exploiting the efficiency and flexibility of embedding the binary associative-memory neural network in Hadoop. Copyright © 2015 The Authors. Published by Elsevier Ltd.. All rights reserved.

Multi-Source Multi-Target Dictionary Learning for Prediction of Cognitive Decline.

PubMed

Zhang, Jie; Li, Qingyang; Caselli, Richard J; Thompson, Paul M; Ye, Jieping; Wang, Yalin

2017-06-01

Alzheimer's Disease (AD) is the most common type of dementia. Identifying correct biomarkers may determine pre-symptomatic AD subjects and enable early intervention. Recently, Multi-task sparse feature learning has been successfully applied to many computer vision and biomedical informatics researches. It aims to improve the generalization performance by exploiting the shared features among different tasks. However, most of the existing algorithms are formulated as a supervised learning scheme. Its drawback is with either insufficient feature numbers or missing label information. To address these challenges, we formulate an unsupervised framework for multi-task sparse feature learning based on a novel dictionary learning algorithm. To solve the unsupervised learning problem, we propose a two-stage Multi-Source Multi-Target Dictionary Learning (MMDL) algorithm. In stage 1, we propose a multi-source dictionary learning method to utilize the common and individual sparse features in different time slots. In stage 2, supported by a rigorous theoretical analysis, we develop a multi-task learning method to solve the missing label problem. Empirical studies on an N = 3970 longitudinal brain image data set, which involves 2 sources and 5 targets, demonstrate the improved prediction accuracy and speed efficiency of MMDL in comparison with other state-of-the-art algorithms.

Multi-Wavelength Q-Switched Ytterbium-Doped Fiber Laser with Multi-Walled Carbon Nanotubes

NASA Astrophysics Data System (ADS)

Al-Masoodi, A. H. H.; Ahmed, M. H. M.; Arof, H.; Harun, S. W.

2018-03-01

We demonstrate a passively multi-wavelength Q-switched Ytterbium-doped fiber laser (YDFL) based on a multi-wall carbon nanotubes embedded in polyethylene oxide film as saturable absorber. The YDFL generates a stable multi-wavelength with spacing of 1.9 nm as the 980 nm pump power is fixed within 62. 4 mW and 78.0 mW. The repetition rate of the laser is tunable from 10.41 to 29.04 kHz by increasing the pump power from the threshold power of 62.4 mW to 78 mW. At 78 mW pump power, the maximum pulse energy of 38 nJ and the shortest pulse width of 8.87 µs are obtained.

Space Technology 5 Multi-Point Observations of Temporal Variability of Field-Aligned Currents

NASA Technical Reports Server (NTRS)

Le, Guan; Wang, Yongli; Slavin, James A.; Strangeway, Robert J.

2008-01-01

Space Technology 5 (ST5) is a three micro-satellite constellation deployed into a 300 x 4500 km, dawn-dusk, sun-synchronous polar orbit from March 22 to June 21, 2006, for technology validations. In this paper, we present a study of the temporal variability of field-aligned currents using multi-point magnetic field measurements from ST5. The data demonstrate that meso-scale current structures are commonly embedded within large-scale field-aligned current sheets. The meso-scale current structures are very dynamic with highly variable current density and/or polarity in time scales of approximately 10 min. They exhibit large temporal variations during both quiet and disturbed times in such time scales. On the other hand, the data also shown that the time scales for the currents to be relatively stable are approximately 1 min for meso-scale currents and approximately 10 min for large scale current sheets. These temporal features are obviously associated with dynamic variations of their particle carriers (mainly electrons) as they respond to the variations of the parallel electric field in auroral acceleration region. The characteristic time scales for the temporal variability of meso-scale field-aligned currents are found to be consistent with those of auroral parallel electric field.

Space Technology 5 Multi-point Observations of Field-aligned Currents: Temporal Variability of Meso-Scale Structures

NASA Technical Reports Server (NTRS)

Le, Guan; Wang, Yongli; Slavin, James A.; Strangeway, Robert J.

2007-01-01

Space Technology 5 (ST5) is a three micro-satellite constellation deployed into a 300 x 4500 km, dawn-dusk, sun-synchronous polar orbit from March 22 to June 21, 2006, for technology validations. In this paper, we present a study of the temporal variability of field-aligned currents using multi-point magnetic field measurements from ST5. The data demonstrate that meso-scale current structures are commonly embedded within large-scale field-aligned current sheets. The meso-scale current structures are very dynamic with highly variable current density and/or polarity in time scales of - 10 min. They exhibit large temporal variations during both quiet and disturbed times in such time scales. On the other hand, the data also shown that the time scales for the currents to be relatively stable are approx. 1 min for meso-scale currents and approx. 10 min for large scale current sheets. These temporal features are obviously associated with dynamic variations of their particle carriers (mainly electrons) as they respond to the variations of the parallel electric field in auroral acceleration region. The characteristic time scales for the temporal variability of meso-scale field-aligned currents are found to be consistent with those of auroral parallel electric field.

Detection of LSB+/-1 steganography based on co-occurrence matrix and bit plane clipping

NASA Astrophysics Data System (ADS)

Abolghasemi, Mojtaba; Aghaeinia, Hassan; Faez, Karim; Mehrabi, Mohammad Ali

2010-01-01

Spatial LSB+/-1 steganography changes smooth characteristics between adjoining pixels of the raw image. We present a novel steganalysis method for LSB+/-1 steganography based on feature vectors derived from the co-occurrence matrix in the spatial domain. We investigate how LSB+/-1 steganography affects the bit planes of an image and show that it changes more least significant bit (LSB) planes of it. The co-occurrence matrix is derived from an image in which some of its most significant bit planes are clipped. By this preprocessing, in addition to reducing the dimensions of the feature vector, the effects of embedding were also preserved. We compute the co-occurrence matrix in different directions and with different dependency and use the elements of the resulting co-occurrence matrix as features. This method is sensitive to the data embedding process. We use a Fisher linear discrimination (FLD) classifier and test our algorithm on different databases and embedding rates. We compare our scheme with the current LSB+/-1 steganalysis methods. It is shown that the proposed scheme outperforms the state-of-the-art methods in detecting the LSB+/-1 steganographic method for grayscale images.

Multi-channel EEG-based sleep stage classification with joint collaborative representation and multiple kernel learning.

PubMed

Shi, Jun; Liu, Xiao; Li, Yan; Zhang, Qi; Li, Yingjie; Ying, Shihui

2015-10-30

Electroencephalography (EEG) based sleep staging is commonly used in clinical routine. Feature extraction and representation plays a crucial role in EEG-based automatic classification of sleep stages. Sparse representation (SR) is a state-of-the-art unsupervised feature learning method suitable for EEG feature representation. Collaborative representation (CR) is an effective data coding method used as a classifier. Here we use CR as a data representation method to learn features from the EEG signal. A joint collaboration model is established to develop a multi-view learning algorithm, and generate joint CR (JCR) codes to fuse and represent multi-channel EEG signals. A two-stage multi-view learning-based sleep staging framework is then constructed, in which JCR and joint sparse representation (JSR) algorithms first fuse and learning the feature representation from multi-channel EEG signals, respectively. Multi-view JCR and JSR features are then integrated and sleep stages recognized by a multiple kernel extreme learning machine (MK-ELM) algorithm with grid search. The proposed two-stage multi-view learning algorithm achieves superior performance for sleep staging. With a K-means clustering based dictionary, the mean classification accuracy, sensitivity and specificity are 81.10 ± 0.15%, 71.42 ± 0.66% and 94.57 ± 0.07%, respectively; while with the dictionary learned using the submodular optimization method, they are 80.29 ± 0.22%, 71.26 ± 0.78% and 94.38 ± 0.10%, respectively. The two-stage multi-view learning based sleep staging framework outperforms all other classification methods compared in this work, while JCR is superior to JSR. The proposed multi-view learning framework has the potential for sleep staging based on multi-channel or multi-modality polysomnography signals. Copyright © 2015 Elsevier B.V. All rights reserved.

Run-time implementation issues for real-time embedded Ada

NASA Technical Reports Server (NTRS)

Maule, Ruth A.

1986-01-01

A motivating factor in the development of Ada as the department of defense standard language was the high cost of embedded system software development. It was with embedded system requirements in mind that many of the features of the language were incorporated. Yet it is the designers of embedded systems that seem to comprise the majority of the Ada community dissatisfied with the language. There are a variety of reasons for this dissatisfaction, but many seem to be related in some way to the Ada run-time support system. Some of the areas in which the inconsistencies were found to have the greatest impact on performance from the standpoint of real-time systems are presented. In particular, a large part of the duties of the tasking supervisor are subject to the design decisions of the implementer. These include scheduling, rendezvous, delay processing, and task activation and termination. Some of the more general issues presented include time and space efficiencies, generic expansions, memory management, pragmas, and tracing features. As validated compilers become available for bare computer targets, it is important for a designer to be aware that, at least for many real-time issues, all validated Ada compilers are not created equal.

Cell-Averaged discretization for incompressible Navier-Stokes with embedded boundaries and locally refined Cartesian meshes: a high-order finite volume approach

NASA Astrophysics Data System (ADS)

Bhalla, Amneet Pal Singh; Johansen, Hans; Graves, Dan; Martin, Dan; Colella, Phillip; Applied Numerical Algorithms Group Team

2017-11-01

We present a consistent cell-averaged discretization for incompressible Navier-Stokes equations on complex domains using embedded boundaries. The embedded boundary is allowed to freely cut the locally-refined background Cartesian grid. Implicit-function representation is used for the embedded boundary, which allows us to convert the required geometric moments in the Taylor series expansion (upto arbitrary order) of polynomials into an algebraic problem in lower dimensions. The computed geometric moments are then used to construct stencils for various operators like the Laplacian, divergence, gradient, etc., by solving a least-squares system locally. We also construct the inter-level data-transfer operators like prolongation and restriction for multi grid solvers using the same least-squares system approach. This allows us to retain high-order of accuracy near coarse-fine interface and near embedded boundaries. Canonical problems like Taylor-Green vortex flow and flow past bluff bodies will be presented to demonstrate the proposed method. U.S. Department of Energy, Office of Science, ASCR (Award Number DE-AC02-05CH11231).

Design and Implementation of Embedded Computer Vision Systems Based on Particle Filters

DTIC Science & Technology

2010-01-01

for hardware/software implementa- tion of multi-dimensional particle filter application and we explore this in the third application which is a 3D...methodology for hardware/software implementation of multi-dimensional particle filter application and we explore this in the third application which is a...and hence multiprocessor implementation of parti- cle filters is an important option to examine. A significant body of work exists on optimizing generic

Powering embedded electronics for wind turbine monitoring using multi-source energy harvesting techniques

NASA Astrophysics Data System (ADS)

Anton, S. R.; Taylor, S. G.; Raby, E. Y.; Farinholt, K. M.

2013-03-01

With a global interest in the development of clean, renewable energy, wind energy has seen steady growth over the past several years. Advances in wind turbine technology bring larger, more complex turbines and wind farms. An important issue in the development of these complex systems is the ability to monitor the state of each turbine in an effort to improve the efficiency and power generation. Wireless sensor nodes can be used to interrogate the current state and health of wind turbine structures; however, a drawback of most current wireless sensor technology is their reliance on batteries for power. Energy harvesting solutions present the ability to create autonomous power sources for small, low-power electronics through the scavenging of ambient energy; however, most conventional energy harvesting systems employ a single mode of energy conversion, and thus are highly susceptible to variations in the ambient energy. In this work, a multi-source energy harvesting system is developed to power embedded electronics for wind turbine applications in which energy can be scavenged simultaneously from several ambient energy sources. Field testing is performed on a full-size, residential scale wind turbine where both vibration and solar energy harvesting systems are utilized to power wireless sensing systems. Two wireless sensors are investigated, including the wireless impedance device (WID) sensor node, developed at Los Alamos National Laboratory (LANL), and an ultra-low power RF system-on-chip board that is the basis for an embedded wireless accelerometer node currently under development at LANL. Results indicate the ability of the multi-source harvester to successfully power both sensors.

Optimization of Selected Remote Sensing Algorithms for Embedded NVIDIA Kepler GPU Architecture

NASA Technical Reports Server (NTRS)

Riha, Lubomir; Le Moigne, Jacqueline; El-Ghazawi, Tarek

2015-01-01

This paper evaluates the potential of embedded Graphic Processing Units in the Nvidias Tegra K1 for onboard processing. The performance is compared to a general purpose multi-core CPU and full fledge GPU accelerator. This study uses two algorithms: Wavelet Spectral Dimension Reduction of Hyperspectral Imagery and Automated Cloud-Cover Assessment (ACCA) Algorithm. Tegra K1 achieved 51 for ACCA algorithm and 20 for the dimension reduction algorithm, as compared to the performance of the high-end 8-core server Intel Xeon CPU with 13.5 times higher power consumption.

Feature selection methods for big data bioinformatics: A survey from the search perspective.

PubMed

Wang, Lipo; Wang, Yaoli; Chang, Qing

2016-12-01

This paper surveys main principles of feature selection and their recent applications in big data bioinformatics. Instead of the commonly used categorization into filter, wrapper, and embedded approaches to feature selection, we formulate feature selection as a combinatorial optimization or search problem and categorize feature selection methods into exhaustive search, heuristic search, and hybrid methods, where heuristic search methods may further be categorized into those with or without data-distilled feature ranking measures. Copyright © 2016 Elsevier Inc. All rights reserved.

Multi-Level and Multi-Scale Feature Aggregation Using Pretrained Convolutional Neural Networks for Music Auto-Tagging

NASA Astrophysics Data System (ADS)

Lee, Jongpil; Nam, Juhan

2017-08-01

Music auto-tagging is often handled in a similar manner to image classification by regarding the 2D audio spectrogram as image data. However, music auto-tagging is distinguished from image classification in that the tags are highly diverse and have different levels of abstractions. Considering this issue, we propose a convolutional neural networks (CNN)-based architecture that embraces multi-level and multi-scaled features. The architecture is trained in three steps. First, we conduct supervised feature learning to capture local audio features using a set of CNNs with different input sizes. Second, we extract audio features from each layer of the pre-trained convolutional networks separately and aggregate them altogether given a long audio clip. Finally, we put them into fully-connected networks and make final predictions of the tags. Our experiments show that using the combination of multi-level and multi-scale features is highly effective in music auto-tagging and the proposed method outperforms previous state-of-the-arts on the MagnaTagATune dataset and the Million Song Dataset. We further show that the proposed architecture is useful in transfer learning.

Multi-Feature Based Information Extraction of Urban Green Space Along Road

NASA Astrophysics Data System (ADS)

Zhao, H. H.; Guan, H. Y.

2018-04-01

Green space along road of QuickBird image was studied in this paper based on multi-feature-marks in frequency domain. The magnitude spectrum of green along road was analysed, and the recognition marks of the tonal feature, contour feature and the road were built up by the distribution of frequency channels. Gabor filters in frequency domain were used to detect the features based on the recognition marks built up. The detected features were combined as the multi-feature-marks, and watershed based image segmentation were conducted to complete the extraction of green space along roads. The segmentation results were evaluated by Fmeasure with P = 0.7605, R = 0.7639, F = 0.7622.

Multi-Source Multi-Target Dictionary Learning for Prediction of Cognitive Decline

PubMed Central

Zhang, Jie; Li, Qingyang; Caselli, Richard J.; Thompson, Paul M.; Ye, Jieping; Wang, Yalin

2017-01-01

Alzheimer’s Disease (AD) is the most common type of dementia. Identifying correct biomarkers may determine pre-symptomatic AD subjects and enable early intervention. Recently, Multi-task sparse feature learning has been successfully applied to many computer vision and biomedical informatics researches. It aims to improve the generalization performance by exploiting the shared features among different tasks. However, most of the existing algorithms are formulated as a supervised learning scheme. Its drawback is with either insufficient feature numbers or missing label information. To address these challenges, we formulate an unsupervised framework for multi-task sparse feature learning based on a novel dictionary learning algorithm. To solve the unsupervised learning problem, we propose a two-stage Multi-Source Multi-Target Dictionary Learning (MMDL) algorithm. In stage 1, we propose a multi-source dictionary learning method to utilize the common and individual sparse features in different time slots. In stage 2, supported by a rigorous theoretical analysis, we develop a multi-task learning method to solve the missing label problem. Empirical studies on an N = 3970 longitudinal brain image data set, which involves 2 sources and 5 targets, demonstrate the improved prediction accuracy and speed efficiency of MMDL in comparison with other state-of-the-art algorithms. PMID:28943731

Network Receptive Field Modeling Reveals Extensive Integration and Multi-feature Selectivity in Auditory Cortical Neurons.

PubMed

Harper, Nicol S; Schoppe, Oliver; Willmore, Ben D B; Cui, Zhanfeng; Schnupp, Jan W H; King, Andrew J

2016-11-01

Cortical sensory neurons are commonly characterized using the receptive field, the linear dependence of their response on the stimulus. In primary auditory cortex neurons can be characterized by their spectrotemporal receptive fields, the spectral and temporal features of a sound that linearly drive a neuron. However, receptive fields do not capture the fact that the response of a cortical neuron results from the complex nonlinear network in which it is embedded. By fitting a nonlinear feedforward network model (a network receptive field) to cortical responses to natural sounds, we reveal that primary auditory cortical neurons are sensitive over a substantially larger spectrotemporal domain than is seen in their standard spectrotemporal receptive fields. Furthermore, the network receptive field, a parsimonious network consisting of 1-7 sub-receptive fields that interact nonlinearly, consistently better predicts neural responses to auditory stimuli than the standard receptive fields. The network receptive field reveals separate excitatory and inhibitory sub-fields with different nonlinear properties, and interaction of the sub-fields gives rise to important operations such as gain control and conjunctive feature detection. The conjunctive effects, where neurons respond only if several specific features are present together, enable increased selectivity for particular complex spectrotemporal structures, and may constitute an important stage in sound recognition. In conclusion, we demonstrate that fitting auditory cortical neural responses with feedforward network models expands on simple linear receptive field models in a manner that yields substantially improved predictive power and reveals key nonlinear aspects of cortical processing, while remaining easy to interpret in a physiological context.

Network Receptive Field Modeling Reveals Extensive Integration and Multi-feature Selectivity in Auditory Cortical Neurons

PubMed Central

Willmore, Ben D. B.; Cui, Zhanfeng; Schnupp, Jan W. H.; King, Andrew J.

2016-01-01

Cortical sensory neurons are commonly characterized using the receptive field, the linear dependence of their response on the stimulus. In primary auditory cortex neurons can be characterized by their spectrotemporal receptive fields, the spectral and temporal features of a sound that linearly drive a neuron. However, receptive fields do not capture the fact that the response of a cortical neuron results from the complex nonlinear network in which it is embedded. By fitting a nonlinear feedforward network model (a network receptive field) to cortical responses to natural sounds, we reveal that primary auditory cortical neurons are sensitive over a substantially larger spectrotemporal domain than is seen in their standard spectrotemporal receptive fields. Furthermore, the network receptive field, a parsimonious network consisting of 1–7 sub-receptive fields that interact nonlinearly, consistently better predicts neural responses to auditory stimuli than the standard receptive fields. The network receptive field reveals separate excitatory and inhibitory sub-fields with different nonlinear properties, and interaction of the sub-fields gives rise to important operations such as gain control and conjunctive feature detection. The conjunctive effects, where neurons respond only if several specific features are present together, enable increased selectivity for particular complex spectrotemporal structures, and may constitute an important stage in sound recognition. In conclusion, we demonstrate that fitting auditory cortical neural responses with feedforward network models expands on simple linear receptive field models in a manner that yields substantially improved predictive power and reveals key nonlinear aspects of cortical processing, while remaining easy to interpret in a physiological context. PMID:27835647

Gravitational energy in the framework of embedding and splitting theories

NASA Astrophysics Data System (ADS)

Grad, D. A.; Ilin, R. V.; Paston, S. A.; Sheykin, A. A.

We study various definitions of the gravitational field energy based on the usage of isometric embeddings in the Regge-Teitelboim approach. For the embedding theory, we consider the coordinate translations on the surface as well as the coordinate translations in the flat bulk. In the latter case, the independent definition of gravitational energy-momentum tensor appears as a Noether current corresponding to global inner symmetry. In the field-theoretic form of this approach (splitting theory), we consider Noether procedure and the alternative method of energy-momentum tensor defining by varying the action of the theory with respect to flat bulk metric. As a result, we obtain energy definition in field-theoretic form of embedding theory which, among the other features, gives a nontrivial result for the solutions of embedding theory which are also solutions of Einstein equations. The question of energy localization is also discussed.

Low contrast detection in abdominal CT: comparing single-slice and multi-slice tasks

NASA Astrophysics Data System (ADS)

Ba, Alexandre; Racine, Damien; Viry, Anaïs.; Verdun, Francis R.; Schmidt, Sabine; Bochud, François O.

2017-03-01

Image quality assessment is crucial for the optimization of computed tomography (CT) protocols. Human and mathematical model observers are increasingly used for the detection of low contrast signal in abdominal CT, but are frequently limited to the use of a single image slice. Another limitation is that most of them only consider the detection of a signal embedded in a uniform background phantom. The purpose of this paper was to test if human observer performance is significantly different in CT images read in single or multiple slice modes and if these differences are the same for anatomical and uniform clinical images. We investigated detection performance and scrolling trends of human observers of a simulated liver lesion embedded in anatomical and uniform CT backgrounds. Results show that observers don't take significantly benefit of additional information provided in multi-slice reading mode. Regarding the background, performances are moderately higher for uniform than for anatomical images. Our results suggest that for low contrast detection in abdominal CT, the use of multi-slice model observers would probably only add a marginal benefit. On the other hand, the quality of a CT image is more accurately estimated with clinical anatomical backgrounds.

Distributed digital signal processors for multi-body flexible structures

NASA Technical Reports Server (NTRS)

Lee, Gordon K. F.

1992-01-01

Multi-body flexible structures, such as those currently under investigation in spacecraft design, are large scale (high-order) dimensional systems. Controlling and filtering such structures is a computationally complex problem. This is particularly important when many sensors and actuators are located along the structure and need to be processed in real time. This report summarizes research activity focused on solving the signal processing (that is, information processing) issues of multi-body structures. A distributed architecture is developed in which single loop processors are employed for local filtering and control. By implementing such a philosophy with an embedded controller configuration, a supervising controller may be used to process global data and make global decisions as the local devices are processing local information. A hardware testbed, a position controller system for a servo motor, is employed to illustrate the capabilities of the embedded controller structure. Several filtering and control structures which can be modeled as rational functions can be implemented on the system developed in this research effort. Thus the results of the study provide a support tool for many Control/Structure Interaction (CSI) NASA testbeds such as the Evolutionary model and the nine-bay truss structure.

Multi-modal demands of a smartphone used to place calls and enter addresses during highway driving relative to two embedded systems

PubMed Central

Reimer, Bryan; Mehler, Bruce; Reagan, Ian; Kidd, David; Dobres, Jonathan

2016-01-01

Abstract There is limited research on trade-offs in demand between manual and voice interfaces of embedded and portable technologies. Mehler et al. identified differences in driving performance, visual engagement and workload between two contrasting embedded vehicle system designs (Chevrolet MyLink and Volvo Sensus). The current study extends this work by comparing these embedded systems with a smartphone (Samsung Galaxy S4). None of the voice interfaces eliminated visual demand. Relative to placing calls manually, both embedded voice interfaces resulted in less eyes-off-road time than the smartphone. Errors were most frequent when calling contacts using the smartphone. The smartphone and MyLink allowed addresses to be entered using compound voice commands resulting in shorter eyes-off-road time compared with the menu-based Sensus but with many more errors. Driving performance and physiological measures indicated increased demand when performing secondary tasks relative to ‘just driving’, but were not significantly different between the smartphone and embedded systems. Practitioner Summary: The findings show that embedded system and portable device voice interfaces place fewer visual demands on the driver than manual interfaces, but they also underscore how differences in system designs can significantly affect not only the demands placed on drivers, but also the successful completion of tasks. PMID:27110964

Micro-precise spatiotemporal delivery system embedded in 3D printing for complex tissue regeneration.

PubMed

Tarafder, Solaiman; Koch, Alia; Jun, Yena; Chou, Conrad; Awadallah, Mary R; Lee, Chang H

2016-04-25

Three dimensional (3D) printing has emerged as an efficient tool for tissue engineering and regenerative medicine, given its advantages for constructing custom-designed scaffolds with tunable microstructure/physical properties. Here we developed a micro-precise spatiotemporal delivery system embedded in 3D printed scaffolds. PLGA microspheres (μS) were encapsulated with growth factors (GFs) and then embedded inside PCL microfibers that constitute custom-designed 3D scaffolds. Given the substantial difference in the melting points between PLGA and PCL and their low heat conductivity, μS were able to maintain its original structure while protecting GF's bioactivities. Micro-precise spatial control of multiple GFs was achieved by interchanging dispensing cartridges during a single printing process. Spatially controlled delivery of GFs, with a prolonged release, guided formation of multi-tissue interfaces from bone marrow derived mesenchymal stem/progenitor cells (MSCs). To investigate efficacy of the micro-precise delivery system embedded in 3D printed scaffold, temporomandibular joint (TMJ) disc scaffolds were fabricated with micro-precise spatiotemporal delivery of CTGF and TGFβ3, mimicking native-like multiphase fibrocartilage. In vitro, TMJ disc scaffolds spatially embedded with CTGF/TGFβ3-μS resulted in formation of multiphase fibrocartilaginous tissues from MSCs. In vivo, TMJ disc perforation was performed in rabbits, followed by implantation of CTGF/TGFβ3-μS-embedded scaffolds. After 4 wks, CTGF/TGFβ3-μS embedded scaffolds significantly improved healing of the perforated TMJ disc as compared to the degenerated TMJ disc in the control group with scaffold embedded with empty μS. In addition, CTGF/TGFβ3-μS embedded scaffolds significantly prevented arthritic changes on TMJ condyles. In conclusion, our micro-precise spatiotemporal delivery system embedded in 3D printing may serve as an efficient tool to regenerate complex and inhomogeneous tissues.

Soft Somatosensitive Actuators via Embedded 3D Printing.

PubMed

Truby, Ryan L; Wehner, Michael; Grosskopf, Abigail K; Vogt, Daniel M; Uzel, Sebastien G M; Wood, Robert J; Lewis, Jennifer A

2018-04-01

Humans possess manual dexterity, motor skills, and other physical abilities that rely on feedback provided by the somatosensory system. Herein, a method is reported for creating soft somatosensitive actuators (SSAs) via embedded 3D printing, which are innervated with multiple conductive features that simultaneously enable haptic, proprioceptive, and thermoceptive sensing. This novel manufacturing approach enables the seamless integration of multiple ionically conductive and fluidic features within elastomeric matrices to produce SSAs with the desired bioinspired sensing and actuation capabilities. Each printed sensor is composed of an ionically conductive gel that exhibits both long-term stability and hysteresis-free performance. As an exemplar, multiple SSAs are combined into a soft robotic gripper that provides proprioceptive and haptic feedback via embedded curvature, inflation, and contact sensors, including deep and fine touch contact sensors. The multimaterial manufacturing platform enables complex sensing motifs to be easily integrated into soft actuating systems, which is a necessary step toward closed-loop feedback control of soft robots, machines, and haptic devices. © 2018 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim.

Deeply-Integrated Feature Tracking for Embedded Navigation

DTIC Science & Technology

2009-03-01

metric would result in increased feature strength, but a decrease in repeatability. The feature spacing also helped with repeatability of strong...locations in the second frame. This relationship is a constraint of projective geometry and states that the cross product of a point with itself (when...integrated refers to the incorporation of inertial information into the image processing, rather than just

Facile synthesis of enzyme-embedded magnetic metal-organic frameworks as a reusable mimic multi-enzyme system: mimetic peroxidase properties and colorimetric sensor.

PubMed

Hou, Chen; Wang, Yang; Ding, Qinghua; Jiang, Long; Li, Ming; Zhu, Weiwei; Pan, Duo; Zhu, Hao; Liu, Mingzhu

2015-11-28

This work reports a facile and easily-achieved approach for enzyme immobilization by embedding glucose oxidase (GOx) in magnetic zeolitic imidazolate framework 8 (mZIF-8) via a de novo approach. As a demonstration of the power of such materials, the resulting GOx embedded mZIF-8 (mZIF-8@GOx) was utilized as a colorimetric sensor for rapid detection of glucose. This method was constructed on the basis of metal-organic frameworks (MOFs), which possessed very fascinating peroxidase-like properties, and the cascade reaction for the visual detection of glucose was combined into one step through the mZIF-8@GOx based mimic multi-enzyme system. After characterization by electron microscopy, X-ray diffraction, nitrogen sorption, fourier transform infrared spectroscopy and vibrating sample magnetometry, the as-prepared mZIF-8@GOx was confirmed with the robust core-shell structure, the monodisperse nanoparticle had an average diameter of about 200 nm and displayed superparamagnetism with a saturation magnetization value of 40.5 emu g(-1), it also exhibited a large surface area of 396.10 m(2) g(-1). As a peroxidase mimic, mZIF-8 was verified to be highly stable and of low cost, and showed a strong affinity towards H2O2. Meanwhile, the mZIF-8 embedded GOx also exhibited improved activity, stability and greatly enhanced selectivity in glucose detection. Moreover, the mZIF-8@GOx had excellent recyclability with high activity (88.7% residual activity after 12 times reuse).

A real-time extension of density matrix embedding theory for non-equilibrium electron dynamics

NASA Astrophysics Data System (ADS)

Kretchmer, Joshua S.; Chan, Garnet Kin-Lic

2018-02-01

We introduce real-time density matrix embedding theory (DMET), a dynamical quantum embedding theory for computing non-equilibrium electron dynamics in strongly correlated systems. As in the previously developed static DMET, real-time DMET partitions the system into an impurity corresponding to the region of interest coupled to the surrounding environment, which is efficiently represented by a quantum bath of the same size as the impurity. In this work, we focus on a simplified single-impurity time-dependent formulation as a first step toward a multi-impurity theory. The equations of motion of the coupled impurity and bath embedding problem are derived using the time-dependent variational principle. The accuracy of real-time DMET is compared to that of time-dependent complete active space self-consistent field (TD-CASSCF) theory and time-dependent Hartree-Fock (TDHF) theory for a variety of quantum quenches in the single impurity Anderson model (SIAM), in which the Hamiltonian is suddenly changed (quenched) to induce a non-equilibrium state. Real-time DMET shows a marked improvement over the mean-field TDHF, converging to the exact answer even in the non-trivial Kondo regime of the SIAM. However, as expected from analogous behavior in static DMET, the constrained structure of the real-time DMET wavefunction leads to a slower convergence with respect to active space size, in the single-impurity formulation, relative to TD-CASSCF. Our initial results suggest that real-time DMET provides a promising framework to simulate non-equilibrium electron dynamics in which strong electron correlation plays an important role, and lays the groundwork for future multi-impurity formulations.

A real-time extension of density matrix embedding theory for non-equilibrium electron dynamics.

PubMed

Kretchmer, Joshua S; Chan, Garnet Kin-Lic

2018-02-07

We introduce real-time density matrix embedding theory (DMET), a dynamical quantum embedding theory for computing non-equilibrium electron dynamics in strongly correlated systems. As in the previously developed static DMET, real-time DMET partitions the system into an impurity corresponding to the region of interest coupled to the surrounding environment, which is efficiently represented by a quantum bath of the same size as the impurity. In this work, we focus on a simplified single-impurity time-dependent formulation as a first step toward a multi-impurity theory. The equations of motion of the coupled impurity and bath embedding problem are derived using the time-dependent variational principle. The accuracy of real-time DMET is compared to that of time-dependent complete active space self-consistent field (TD-CASSCF) theory and time-dependent Hartree-Fock (TDHF) theory for a variety of quantum quenches in the single impurity Anderson model (SIAM), in which the Hamiltonian is suddenly changed (quenched) to induce a non-equilibrium state. Real-time DMET shows a marked improvement over the mean-field TDHF, converging to the exact answer even in the non-trivial Kondo regime of the SIAM. However, as expected from analogous behavior in static DMET, the constrained structure of the real-time DMET wavefunction leads to a slower convergence with respect to active space size, in the single-impurity formulation, relative to TD-CASSCF. Our initial results suggest that real-time DMET provides a promising framework to simulate non-equilibrium electron dynamics in which strong electron correlation plays an important role, and lays the groundwork for future multi-impurity formulations.

Three dimensional time reversal optical tomography

NASA Astrophysics Data System (ADS)

Wu, Binlin; Cai, W.; Alrubaiee, M.; Xu, M.; Gayen, S. K.

2011-03-01

Time reversal optical tomography (TROT) approach is used to detect and locate absorptive targets embedded in a highly scattering turbid medium to assess its potential in breast cancer detection. TROT experimental arrangement uses multi-source probing and multi-detector signal acquisition and Multiple-Signal-Classification (MUSIC) algorithm for target location retrieval. Light transport from multiple sources through the intervening medium with embedded targets to the detectors is represented by a response matrix constructed using experimental data. A TR matrix is formed by multiplying the response matrix by its transpose. The eigenvectors with leading non-zero eigenvalues of the TR matrix correspond to embedded objects. The approach was used to: (a) obtain the location and spatial resolution of an absorptive target as a function of its axial position between the source and detector planes; and (b) study variation in spatial resolution of two targets at the same axial position but different lateral positions. The target(s) were glass sphere(s) of diameter ~9 mm filled with ink (absorber) embedded in a 60 mm-thick slab of Intralipid-20% suspension in water with an absorption coefficient μa ~ 0.003 mm-1 and a transport mean free path lt ~ 1 mm at 790 nm, which emulate the average values of those parameters for human breast tissue. The spatial resolution and accuracy of target location depended on axial position, and target contrast relative to the background. Both the targets could be resolved and located even when they were only 4-mm apart. The TROT approach is fast, accurate, and has the potential to be useful in breast cancer detection and localization.

MixDroid: A multi-features and multi-classifiers bagging system for Android malware detection

NASA Astrophysics Data System (ADS)

Huang, Weiqing; Hou, Erhang; Zheng, Liang; Feng, Weimiao

2018-05-01

In the past decade, Android platform has rapidly taken over the mobile market for its superior convenience and open source characteristics. However, with the popularity of Android, malwares targeting on Android devices are increasing rapidly, while the conventional rule-based and expert-experienced approaches are no longer able to handle such explosive growth. In this paper, combining with the theory of natural language processing and machine learning, we not only implement the basic feature extraction of permission application features, but also propose two innovative schemes of feature extraction: Dalvik opcode features and malicious code image, and implement an automatic Android malware detection system MixDroid which is based on multi-features and multi-classifiers. According to our experiment results on 20,000 Android applications, detection accuracy of MixDroid is 98.1%, which proves our schemes' effectiveness in Android malware detection.

Nanodiamond embedded ta-C composite film by pulsed filtered vacuum arc deposition from a single target

NASA Astrophysics Data System (ADS)

Iyer, Ajai; Etula, Jarkko; Ge, Yanling; Liu, Xuwen; Koskinen, Jari

2016-11-01

Detonation Nanodiamonds (DNDs) are known to have sp3 core, sp2 shell, small size (few nm) and are gaining importance as multi-functional nanoparticles. Diverse methods have been used to form composites, containing detonation nanodiamonds (DNDs) embedded in conductive and dielectric matrices for various applications. Here we show a method, wherein DND-ta-C composite film, consisting of DNDs embedded in ta-C matrix have been co-deposited from the same cathode by pulsed filtered cathodic vacuum arc method. Transmission Electron Microscope analysis of these films revel the presence of DNDs embedded in the matrix of amorphous carbon. Raman spectroscopy indicates that the presence of DNDs does not adversely affect the sp3 content of DND-ta-C composite film compared to ta-C film of same thickness. Nanoindentation and nanowear tests indicate that DND-ta-C composite films possess improved mechanical properties in comparison to ta-C films of similar thickness.

A 300MHz Embedded Flash Memory with Pipeline Architecture and Offset-Free Sense Amplifiers for Dual-Core Automotive Microcontrollers

NASA Astrophysics Data System (ADS)

Kajiyama, Shinya; Fujito, Masamichi; Kasai, Hideo; Mizuno, Makoto; Yamaguchi, Takanori; Shinagawa, Yutaka

A novel 300MHz embedded flash memory for dual-core microcontrollers with a shared ROM architecture is proposed. One of its features is a three-stage pipeline read operation, which enables reduced access pitch and therefore reduces performance penalty due to conflict of shared ROM accesses. Another feature is a highly sensitive sense amplifier that achieves efficient pipeline operation with two-cycle latency one-cycle pitch as a result of a shortened sense time of 0.63ns. The combination of the pipeline architecture and proposed sense amplifiers significantly reduces access-conflict penalties with shared ROM and enhances performance of 32-bit RISC dual-core microcontrollers by 30%.

The Process of Designing Task Features

ERIC Educational Resources Information Center

Bauer, Malcolm

2014-01-01

Malcolm Bauer, from Education Testing Services, provides his comments on the Focus article in this issue of "Measurement" entitled : "How Task Features Impact Evidence from Assessments Embedded in Simulations and Games" (Russell G. Almond, Yoon Jeon Kim, Gertrudes Velasquez, Valerie J. Shute). Bauer begins his remarks by noting…

NGC 5523: An isolated product of soft galaxy mergers?

NASA Astrophysics Data System (ADS)

Fulmer, Leah M.; Gallagher, John S.; Kotulla, Ralf

2017-02-01

Multi-band images of the very isolated spiral galaxy NGC 5523 show a number of unusual features consistent with NGC 5523 having experienced a significant merger. (1) Near-infrared images from the Spitzer Space Telescope (SST) and the WIYN 3.5-m telescope reveal a nucleated bulge-like structure embedded in a spiral disk; (2) the bulge is offset by 1.8 kpc from a brightness minimum at the center of the optically bright inner disk; (3) a tidal stream, possibly associated with an ongoing satellite interaction, extends from the nucleated bulge along the disk. We interpret these properties as the results of one or more non-disruptive mergers between NGC 5523 and companion galaxies or satellites, raising the possibility that some galaxies become isolated because they have merged with former companions. The reduced images (FITS files) are only available at the CDS via anonymous ftp to http://cdsarc.u-strasbg.fr (http://130.79.128.5) or via http://cdsarc.u-strasbg.fr/viz-bin/qcat?J/A+A/598/A119

A 3D Model for Eddy Current Inspection in Aeronautics: Application to Riveted Structures

NASA Astrophysics Data System (ADS)

Paillard, S.; Pichenot, G.; Lambert, M.; Voillaume, H.; Dominguez, N.

2007-03-01

Eddy current technique is currently an operational tool used for fastener inspection which is an important issue for the maintenance of aircraft structures. The industry calls for faster, more sensitive and reliable NDT techniques for the detection and characterization of potential flaws nearby rivet. In order to reduce the development time and to optimize the design and the performances assessment of an inspection procedure, the CEA and EADS have started a collaborative work aiming at extending the modeling features of the CIVA non destructive simulation plat-form in order to handle the configuration of a layered planar structure with a rivet and an embedded flaw nearby. Therefore, an approach based on the Volume Integral Method using the Green dyadic formalism which greatly increases computation efficiency has been developed. The first step, modeling the rivet without flaw as a hole in a multi-stratified structure, has been reached and validated in several configurations with experimental data.

Constitutive modeling of shock response of PTFE

DOE Office of Scientific and Technical Information (OSTI.GOV)

Brown, Eric N; Reanyansky, Anatoly D; Bourne, Neil K

2009-01-01

The PTFE (polytetrafluoroethylene) material is complex and attracts attention of the shock physics researchers because it has amorphous and crystalline components. In turn, the crystalline component has four known phases with the high pressure transition to phase III. At the same time, as has been recently studied using spectrometry, the crystalline region is growing with load. Stress and velocity shock-wave profiles acquired recently with embedded gauges demonstrate feature that may be related to impedance mismatches between the regions subjected to some transitions resulting in density and modulus variations. We consider the above mentioned amorphous-to-crystalline transition and the high pressure Phasemore » II-to-III transitions as possible candidates for the analysis. The present work utilizes a multi-phase rate sensitive model to describe shock response of the PTFE material. One-dimensional experimental shock wave profiles are compared with calculated profiles with the kinetics describing the transitions. The objective of this study is to understand the role of the various transitions in the shock response of PTFE.« less

Enriching the national map database for multi-scale use: Introducing the visibilityfilter attribution

USGS Publications Warehouse

Stauffer, Andrew J.; Webinger, Seth; Roche, Brittany

2016-01-01

The US Geological Survey’s (USGS) National Geospatial Technical Operations Center is prototyping and evaluating the ability to filter data through a range of scales using 1:24,000-scale The National Map (TNM) datasets as the source. A “VisibilityFilter” attribute is under evaluation that can be added to all TNM vector data themes and will permit filtering of data to eight target scales between 1:24,000 and 1:5,000,000, thus defining each feature’s smallest applicable scale-of-use. For a prototype implementation, map specifications for 1:100,000- and 1:250,000-scale USGS Topographic Map Series are being utilized to define feature content appropriate at fixed mapping scales to guide generalization decisions that are documented in a ScaleMaster diagram. This paper defines the VisibilityFilter attribute, the generalization decisions made for each TNM data theme, and how these decisions are embedded into the data to support efficient data filtering.

A software defined RTU multi-protocol automatic adaptation data transmission method

NASA Astrophysics Data System (ADS)

Jin, Huiying; Xu, Xingwu; Wang, Zhanfeng; Ma, Weijun; Li, Sheng; Su, Yong; Pan, Yunpeng

2018-02-01

Remote terminal unit (RTU) is the core device of the monitor system in hydrology and water resources. Different devices often have different communication protocols in the application layer, which results in the difficulty in information analysis and communication networking. Therefore, we introduced the idea of software defined hardware, and abstracted the common feature of mainstream communication protocols of RTU application layer, and proposed a uniformed common protocol model. Then, various communication protocol algorithms of application layer are modularized according to the model. The executable codes of these algorithms are labeled by the virtual functions and stored in the flash chips of embedded CPU to form the protocol stack. According to the configuration commands to initialize the RTU communication systems, it is able to achieve dynamic assembling and loading of various application layer communication protocols of RTU and complete the efficient transport of sensor data from RTU to central station when the data acquisition protocol of sensors and various external communication terminals remain unchanged.

Audio Classification in Speech and Music: A Comparison between a Statistical and a Neural Approach

NASA Astrophysics Data System (ADS)

Bugatti, Alessandro; Flammini, Alessandra; Migliorati, Pierangelo

2002-12-01

We focus the attention on the problem of audio classification in speech and music for multimedia applications. In particular, we present a comparison between two different techniques for speech/music discrimination. The first method is based on Zero crossing rate and Bayesian classification. It is very simple from a computational point of view, and gives good results in case of pure music or speech. The simulation results show that some performance degradation arises when the music segment contains also some speech superimposed on music, or strong rhythmic components. To overcome these problems, we propose a second method, that uses more features, and is based on neural networks (specifically a multi-layer Perceptron). In this case we obtain better performance, at the expense of a limited growth in the computational complexity. In practice, the proposed neural network is simple to be implemented if a suitable polynomial is used as the activation function, and a real-time implementation is possible even if low-cost embedded systems are used.

Multigrid calculation of internal flows in complex geometries

NASA Technical Reports Server (NTRS)

Smith, K. M.; Vanka, S. P.

1992-01-01

The development, validation, and application of a general purpose multigrid solution algorithm and computer program for the computation of elliptic flows in complex geometries is presented. This computer program combines several desirable features including a curvilinear coordinate system, collocated arrangement of the variables, and Full Multi-Grid/Full Approximation Scheme (FMG/FAS). Provisions are made for the inclusion of embedded obstacles and baffles inside the flow domain. The momentum and continuity equations are solved in a decoupled manner and a pressure corrective equation is used to update the pressures such that the fluxes at the cell faces satisfy local mass continuity. Despite the computational overhead required in the restriction and prolongation phases of the multigrid cycling, the superior convergence results in reduced overall CPU time. The numerical scheme and selected results of several validation flows are presented. Finally, the procedure is applied to study the flowfield in a side-inlet dump combustor and twin jet impingement from a simulated aircraft fuselage.

Broiler chickens can benefit from machine learning: support vector machine analysis of observational epidemiological data

PubMed Central

Hepworth, Philip J.; Nefedov, Alexey V.; Muchnik, Ilya B.; Morgan, Kenton L.

2012-01-01

Machine-learning algorithms pervade our daily lives. In epidemiology, supervised machine learning has the potential for classification, diagnosis and risk factor identification. Here, we report the use of support vector machine learning to identify the features associated with hock burn on commercial broiler farms, using routinely collected farm management data. These data lend themselves to analysis using machine-learning techniques. Hock burn, dermatitis of the skin over the hock, is an important indicator of broiler health and welfare. Remarkably, this classifier can predict the occurrence of high hock burn prevalence with accuracy of 0.78 on unseen data, as measured by the area under the receiver operating characteristic curve. We also compare the results with those obtained by standard multi-variable logistic regression and suggest that this technique provides new insights into the data. This novel application of a machine-learning algorithm, embedded in poultry management systems could offer significant improvements in broiler health and welfare worldwide. PMID:22319115

Automatic Generation of Cycle-Approximate TLMs with Timed RTOS Model Support

NASA Astrophysics Data System (ADS)

Hwang, Yonghyun; Schirner, Gunar; Abdi, Samar

This paper presents a technique for automatically generating cycle-approximate transaction level models (TLMs) for multi-process applications mapped to embedded platforms. It incorporates three key features: (a) basic block level timing annotation, (b) RTOS model integration, and (c) RTOS overhead delay modeling. The inputs to TLM generation are application C processes and their mapping to processors in the platform. A processor data model, including pipelined datapath, memory hierarchy and branch delay model is used to estimate basic block execution delays. The delays are annotated to the C code, which is then integrated with a generated SystemC RTOS model. Our abstract RTOS provides dynamic scheduling and inter-process communication (IPC) with processor- and RTOS-specific pre-characterized timing. Our experiments using a MP3 decoder and a JPEG encoder show that timed TLMs, with integrated RTOS models, can be automatically generated in less than a minute. Our generated TLMs simulated three times faster than real-time and showed less than 10% timing error compared to board measurements.

Multi-scale thermal stability of a hard thermoplastic protein-based material

NASA Astrophysics Data System (ADS)

Latza, Victoria; Guerette, Paul A.; Ding, Dawei; Amini, Shahrouz; Kumar, Akshita; Schmidt, Ingo; Keating, Steven; Oxman, Neri; Weaver, James C.; Fratzl, Peter; Miserez, Ali; Masic, Admir

2015-09-01

Although thermoplastic materials are mostly derived from petro-chemicals, it would be highly desirable, from a sustainability perspective, to produce them instead from renewable biopolymers. Unfortunately, biopolymers exhibiting thermoplastic behaviour and which preserve their mechanical properties post processing are essentially non-existent. The robust sucker ring teeth (SRT) from squid and cuttlefish are one notable exception of thermoplastic biopolymers. Here we describe thermoplastic processing of squid SRT via hot extrusion of fibres, demonstrating the potential suitability of these materials for large-scale thermal forming. Using high-resolution in situ X-ray diffraction and vibrational spectroscopy, we elucidate the molecular and nanoscale features responsible for this behaviour and show that SRT consist of semi-crystalline polymers, whereby heat-resistant, nanocrystalline β-sheets embedded within an amorphous matrix are organized into a hexagonally packed nanofibrillar lattice. This study provides key insights for the molecular design of biomimetic protein- and peptide-based thermoplastic structural biopolymers with potential biomedical and 3D printing applications.

Broiler chickens can benefit from machine learning: support vector machine analysis of observational epidemiological data.

PubMed

Hepworth, Philip J; Nefedov, Alexey V; Muchnik, Ilya B; Morgan, Kenton L

2012-08-07

Machine-learning algorithms pervade our daily lives. In epidemiology, supervised machine learning has the potential for classification, diagnosis and risk factor identification. Here, we report the use of support vector machine learning to identify the features associated with hock burn on commercial broiler farms, using routinely collected farm management data. These data lend themselves to analysis using machine-learning techniques. Hock burn, dermatitis of the skin over the hock, is an important indicator of broiler health and welfare. Remarkably, this classifier can predict the occurrence of high hock burn prevalence with accuracy of 0.78 on unseen data, as measured by the area under the receiver operating characteristic curve. We also compare the results with those obtained by standard multi-variable logistic regression and suggest that this technique provides new insights into the data. This novel application of a machine-learning algorithm, embedded in poultry management systems could offer significant improvements in broiler health and welfare worldwide.

Simulation of Attacks for Security in Wireless Sensor Network.

PubMed

Diaz, Alvaro; Sanchez, Pablo

2016-11-18

The increasing complexity and low-power constraints of current Wireless Sensor Networks (WSN) require efficient methodologies for network simulation and embedded software performance analysis of nodes. In addition, security is also a very important feature that has to be addressed in most WSNs, since they may work with sensitive data and operate in hostile unattended environments. In this paper, a methodology for security analysis of Wireless Sensor Networks is presented. The methodology allows designing attack-aware embedded software/firmware or attack countermeasures to provide security in WSNs. The proposed methodology includes attacker modeling and attack simulation with performance analysis (node's software execution time and power consumption estimation). After an analysis of different WSN attack types, an attacker model is proposed. This model defines three different types of attackers that can emulate most WSN attacks. In addition, this paper presents a virtual platform that is able to model the node hardware, embedded software and basic wireless channel features. This virtual simulation analyzes the embedded software behavior and node power consumption while it takes into account the network deployment and topology. Additionally, this simulator integrates the previously mentioned attacker model. Thus, the impact of attacks on power consumption and software behavior/execution-time can be analyzed. This provides developers with essential information about the effects that one or multiple attacks could have on the network, helping them to develop more secure WSN systems. This WSN attack simulator is an essential element of the attack-aware embedded software development methodology that is also introduced in this work.

Angular difference feature extraction for urban scene classification using ZY-3 multi-angle high-resolution satellite imagery

NASA Astrophysics Data System (ADS)

Huang, Xin; Chen, Huijun; Gong, Jianya

2018-01-01

Spaceborne multi-angle images with a high-resolution are capable of simultaneously providing spatial details and three-dimensional (3D) information to support detailed and accurate classification of complex urban scenes. In recent years, satellite-derived digital surface models (DSMs) have been increasingly utilized to provide height information to complement spectral properties for urban classification. However, in such a way, the multi-angle information is not effectively exploited, which is mainly due to the errors and difficulties of the multi-view image matching and the inaccuracy of the generated DSM over complex and dense urban scenes. Therefore, it is still a challenging task to effectively exploit the available angular information from high-resolution multi-angle images. In this paper, we investigate the potential for classifying urban scenes based on local angular properties characterized from high-resolution ZY-3 multi-view images. Specifically, three categories of angular difference features (ADFs) are proposed to describe the angular information at three levels (i.e., pixel, feature, and label levels): (1) ADF-pixel: the angular information is directly extrapolated by pixel comparison between the multi-angle images; (2) ADF-feature: the angular differences are described in the feature domains by comparing the differences between the multi-angle spatial features (e.g., morphological attribute profiles (APs)). (3) ADF-label: label-level angular features are proposed based on a group of urban primitives (e.g., buildings and shadows), in order to describe the specific angular information related to the types of primitive classes. In addition, we utilize spatial-contextual information to refine the multi-level ADF features using superpixel segmentation, for the purpose of alleviating the effects of salt-and-pepper noise and representing the main angular characteristics within a local area. The experiments on ZY-3 multi-angle images confirm that the proposed ADF features can effectively improve the accuracy of urban scene classification, with a significant increase in overall accuracy (3.8-11.7%) compared to using the spectral bands alone. Furthermore, the results indicated the superiority of the proposed ADFs in distinguishing between the spectrally similar and complex man-made classes, including roads and various types of buildings (e.g., high buildings, urban villages, and residential apartments).

Multi-dimension feature fusion for action recognition

NASA Astrophysics Data System (ADS)

Dong, Pei; Li, Jie; Dong, Junyu; Qi, Lin

2018-04-01

Typical human actions last several seconds and exhibit characteristic spatio-temporal structure. The challenge for action recognition is to capture and fuse the multi-dimension information in video data. In order to take into account these characteristics simultaneously, we present a novel method that fuses multiple dimensional features, such as chromatic images, depth and optical flow fields. We built our model based on the multi-stream deep convolutional networks with the help of temporal segment networks and extract discriminative spatial and temporal features by fusing ConvNets towers multi-dimension, in which different feature weights are assigned in order to take full advantage of this multi-dimension information. Our architecture is trained and evaluated on the currently largest and most challenging benchmark NTU RGB-D dataset. The experiments demonstrate that the performance of our method outperforms the state-of-the-art methods.

Functional feature embedded space mapping of fMRI data.

PubMed

Hu, Jin; Tian, Jie; Yang, Lei

2006-01-01

We have proposed a new method for fMRI data analysis which is called Functional Feature Embedded Space Mapping (FFESM). Our work mainly focuses on the experimental design with periodic stimuli which can be described by a number of Fourier coefficients in the frequency domain. A nonlinear dimension reduction technique Isomap is applied to the high dimensional features obtained from frequency domain of the fMRI data for the first time. Finally, the presence of activated time series is identified by the clustering method in which the information theoretic criterion of minimum description length (MDL) is used to estimate the number of clusters. The feasibility of our algorithm is demonstrated by real human experiments. Although we focus on analyzing periodic fMRI data, the approach can be extended to analyze non-periodic fMRI data (event-related fMRI) by replacing the Fourier analysis with a wavelet analysis.

MultiLIS: A Description of the System Design and Operational Features.

ERIC Educational Resources Information Center

Kelly, Glen J.; And Others

1988-01-01

Describes development, hardware requirements, and features of the MultiLIS integrated library software package. A system profile provides pricing information, operational characteristics, and technical specifications. Sidebars discuss MultiLIS integration structure, incremental architecture, and NCR Tower Computers. (4 references) (MES)

Multi-tube fuel nozzle with mixing features

DOEpatents

Hughes, Michael John

2014-04-22

A system includes a multi-tube fuel nozzle having an inlet plate and a plurality of tubes adjacent the inlet plate. The inlet plate includes a plurality of apertures, and each aperture includes an inlet feature. Each tube of the plurality of tubes is coupled to an aperture of the plurality of apertures. The multi-tube fuel nozzle includes a differential configuration of inlet features among the plurality of tubes.

Biometric feature embedding using robust steganography technique

NASA Astrophysics Data System (ADS)

Rashid, Rasber D.; Sellahewa, Harin; Jassim, Sabah A.

2013-05-01

This paper is concerned with robust steganographic techniques to hide and communicate biometric data in mobile media objects like images, over open networks. More specifically, the aim is to embed binarised features extracted using discrete wavelet transforms and local binary patterns of face images as a secret message in an image. The need for such techniques can arise in law enforcement, forensics, counter terrorism, internet/mobile banking and border control. What differentiates this problem from normal information hiding techniques is the added requirement that there should be minimal effect on face recognition accuracy. We propose an LSB-Witness embedding technique in which the secret message is already present in the LSB plane but instead of changing the cover image LSB values, the second LSB plane will be changed to stand as a witness/informer to the receiver during message recovery. Although this approach may affect the stego quality, it is eliminating the weakness of traditional LSB schemes that is exploited by steganalysis techniques for LSB, such as PoV and RS steganalysis, to detect the existence of secrete message. Experimental results show that the proposed method is robust against PoV and RS attacks compared to other variants of LSB. We also discussed variants of this approach and determine capacity requirements for embedding face biometric feature vectors while maintain accuracy of face recognition.

EOS: A project to investigate the design and construction of real-time distributed Embedded Operating Systems

NASA Technical Reports Server (NTRS)

Campbell, R. H.; Essick, Ray B.; Johnston, Gary; Kenny, Kevin; Russo, Vince

1987-01-01

Project EOS is studying the problems of building adaptable real-time embedded operating systems for the scientific missions of NASA. Choices (A Class Hierarchical Open Interface for Custom Embedded Systems) is an operating system designed and built by Project EOS to address the following specific issues: the software architecture for adaptable embedded parallel operating systems, the achievement of high-performance and real-time operation, the simplification of interprocess communications, the isolation of operating system mechanisms from one another, and the separation of mechanisms from policy decisions. Choices is written in C++ and runs on a ten processor Encore Multimax. The system is intended for use in constructing specialized computer applications and research on advanced operating system features including fault tolerance and parallelism.

Beyond Common Features: The Role of Roles in Determining Similarity

ERIC Educational Resources Information Center

Jones, Matt; Love, Bradley C.

2007-01-01

Historically, accounts of object representation and perceived similarity have focused on intrinsic features. Although more recent accounts have explored how objects, scenes, and situations containing common relational structures come to be perceived as similar, less is known about how the perceived similarity of parts or objects embedded within…

Embedded Ultrasonics for SHM of Space Applications

DTIC Science & Technology

2012-07-30

information on material properties and other forms of damage such as cracks, structural fatigue and/or impact events. This synergistic aspect of the embedded...larger the phase shift. However, high excitation levels could contribute to sensor fatigue and levels in a range 15 to 20 (110 to 130 volts) are...joints each featuring three bolts. Piezoelectric wafers ( PZT ) with UNF electrodes were bonded to the isogrid panels using 3M 2216 epoxy

A tool for multi-scale modelling of the renal nephron

PubMed Central

Nickerson, David P.; Terkildsen, Jonna R.; Hamilton, Kirk L.; Hunter, Peter J.

2011-01-01

We present the development of a tool, which provides users with the ability to visualize and interact with a comprehensive description of a multi-scale model of the renal nephron. A one-dimensional anatomical model of the nephron has been created and is used for visualization and modelling of tubule transport in various nephron anatomical segments. Mathematical models of nephron segments are embedded in the one-dimensional model. At the cellular level, these segment models use models encoded in CellML to describe cellular and subcellular transport kinetics. A web-based presentation environment has been developed that allows the user to visualize and navigate through the multi-scale nephron model, including simulation results, at the different spatial scales encompassed by the model description. The Zinc extension to Firefox is used to provide an interactive three-dimensional view of the tubule model and the native Firefox rendering of scalable vector graphics is used to present schematic diagrams for cellular and subcellular scale models. The model viewer is embedded in a web page that dynamically presents content based on user input. For example, when viewing the whole nephron model, the user might be presented with information on the various embedded segment models as they select them in the three-dimensional model view. Alternatively, the user chooses to focus the model viewer on a cellular model located in a particular nephron segment in order to view the various membrane transport proteins. Selecting a specific protein may then present the user with a description of the mathematical model governing the behaviour of that protein—including the mathematical model itself and various simulation experiments used to validate the model against the literature. PMID:22670210

Dominance of debonding defect of CFST on PZT sensor response considering the meso-scale structure of concrete with multi-scale simulation

NASA Astrophysics Data System (ADS)

Xu, Bin; Chen, Hongbing; Mo, Y.-L.; Zhou, Tianmin

2018-07-01

Piezoelectric-lead-zirconate-titanate(PZT)-based interface debonding defects detection for concrete filled steel tubulars (CFSTs) has been proposed and validated through experiments, and numerical study on its mechanism has been carried out recently by assuming that concrete material is homogenous. However, concrete is composed of coarse and fine aggregates, mortar and interface transition zones (ITZs) and even initial defects and is a typical nonhomogeneous material and its mesoscale structure might affect the wave propagation in the concrete core of CFST members. Therefore, it is significantly important to further investigate the influence of mesoscale structure of concrete on the stress wave propagation and the response of embedded PZT sensor for the interface debonding detection. In this study, multi-physical numerical simulation on the wave propagation and embedded PZT sensor response of rectangular CFST members with numerical concrete core considering the randomness in circular aggregate distribution, and coupled with surface-mounted PZT actuator and embedded PZT sensor is carried out. The effect of randomness in the circular aggregates distribution and the existence of ITZs are discussed. Both a local stress wave propagation behavior including transmission, reflection, and diffraction at the interface between concrete core and steel tube under a pulse signal excitation and a global wave field in the cross-section of the rectangular CFST models without and with interface debonding defects under sweep frequency excitation are simulated. The sensitivity of an evaluation index based on wavelet packet analysis on the embedded PZT sensor response on the variation of mesoscale parameters of concrete core without and with different interface debonding defects under sweep frequency voltage signal is investigated in details. The results show that the effect of the interface debondings on the embedded PZT measurement is dominant when compared to the meso-scale structures of concrete core. This study verified the feasibility of the PZT based debonding detection for rectangular CFST members even the meso-scale structure of concrete core is considered.

Nanomechanical Optical Fiber with Embedded Electrodes Actuated by Joule Heating.

PubMed

Lian, Zhenggang; Segura, Martha; Podoliak, Nina; Feng, Xian; White, Nicholas; Horak, Peter

2014-07-31

Nanomechanical optical fibers with metal electrodes embedded in the jacket were fabricated by a multi-material co-draw technique. At the center of the fibers, two glass cores suspended by thin membranes and surrounded by air form a directional coupler that is highly temperature-dependent. We demonstrate optical switching between the two fiber cores by Joule heating of the electrodes with as little as 0.4 W electrical power, thereby demonstrating an electrically actuated all-fiber microelectromechanical system (MEMS). Simulations show that the main mechanism for optical switching is the transverse thermal expansion of the fiber structure.

Feature selection method based on multi-fractal dimension and harmony search algorithm and its application

NASA Astrophysics Data System (ADS)

Zhang, Chen; Ni, Zhiwei; Ni, Liping; Tang, Na

2016-10-01

Feature selection is an important method of data preprocessing in data mining. In this paper, a novel feature selection method based on multi-fractal dimension and harmony search algorithm is proposed. Multi-fractal dimension is adopted as the evaluation criterion of feature subset, which can determine the number of selected features. An improved harmony search algorithm is used as the search strategy to improve the efficiency of feature selection. The performance of the proposed method is compared with that of other feature selection algorithms on UCI data-sets. Besides, the proposed method is also used to predict the daily average concentration of PM2.5 in China. Experimental results show that the proposed method can obtain competitive results in terms of both prediction accuracy and the number of selected features.

Multi-element germanium detectors for synchrotron applications

NASA Astrophysics Data System (ADS)

Rumaiz, A. K.; Kuczewski, A. J.; Mead, J.; Vernon, E.; Pinelli, D.; Dooryhee, E.; Ghose, S.; Caswell, T.; Siddons, D. P.; Miceli, A.; Baldwin, J.; Almer, J.; Okasinski, J.; Quaranta, O.; Woods, R.; Krings, T.; Stock, S.

2018-04-01

We have developed a series of monolithic multi-element germanium detectors, based on sensor arrays produced by the Forschungzentrum Julich, and on Application-specific integrated circuits (ASICs) developed at Brookhaven. Devices have been made with element counts ranging from 64 to 384. These detectors are being used at NSLS-II and APS for a range of diffraction experiments, both monochromatic and energy-dispersive. Compact and powerful readout systems have been developed, based on the new generation of FPGA system-on-chip devices, which provide closely coupled multi-core processors embedded in large gate arrays. We will discuss the technical details of the systems, and present some of the results from them.

An embedding structure of the cross-tail CSs and its relation to the ion composition according to MAVEN observations in the Martian magnetotai

NASA Astrophysics Data System (ADS)

Grigorenko, E. E.; Shuvalov, S. D.; Malova, H. V.; Zelenyi, L. M.

2017-12-01

The multilayered (embedded) Current Sheets (CS) are often observed in the Earth's magnetotail. Simulations based on quasi-adiabatic dynamics of different ion components showed that the observed embedding structures can be reconstructed by taking into account the net electric currents carried by ions with different masses and, thus, with different gyroradii. The last determines the spatial scales of the corresponding current layers. The embedding can be quantitatively described by the ratio of the magnetic field value at the edges of a thin embedded layer Bext to the value of the magnetic field outside a thick CS, B0. For the Earth's magnetotail it was shown that there is a relation between the Bext/B0 and the relative densities of heavy and light ion components. In the Martian magnetotail the embedding feature is also often observed in the cross-tail CS formed by the draping of the IMF field lines. The analysis of 100 CS crossings by MAVEN spacecraft showed that in the Martian magnetotail the relation between the embedding characteristics and ion composition is similar to the one observed in the Earth's magnetotail and the spatial scales of the embedded layers are defined by the gyroradii of the current carrying ion component.

Reimagining Building Sensing and Control (Presentation)

DOE Office of Scientific and Technical Information (OSTI.GOV)

Polese, L.

2014-06-01

Buildings are responsible for 40% of US energy consumption, and sensing and control technologies are an important element in creating a truly sustainable built environment. Motion-based occupancy sensors are often part of these control systems, but are usually altered or disabled in response to occupants' complaints, at the expense of energy savings. Can we leverage commodity hardware developed for other sectors and embedded software to produce more capable sensors for robust building controls? The National Renewable Energy Laboratory's (NREL) 'Image Processing Occupancy Sensor (IPOS)' is one example of leveraging embedded systems to create smarter, more reliable, multi-function sensors that openmore » the door to new control strategies for building heating, cooling, ventilation, and lighting control. In this keynote, we will discuss how cost-effective embedded systems are changing the state-of-the-art of building sensing and control.« less

The design of multi-core DSP parallel model based on message passing and multi-level pipeline

NASA Astrophysics Data System (ADS)

Niu, Jingyu; Hu, Jian; He, Wenjing; Meng, Fanrong; Li, Chuanrong

2017-10-01

Currently, the design of embedded signal processing system is often based on a specific application, but this idea is not conducive to the rapid development of signal processing technology. In this paper, a parallel processing model architecture based on multi-core DSP platform is designed, and it is mainly suitable for the complex algorithms which are composed of different modules. This model combines the ideas of multi-level pipeline parallelism and message passing, and summarizes the advantages of the mainstream model of multi-core DSP (the Master-Slave model and the Data Flow model), so that it has better performance. This paper uses three-dimensional image generation algorithm to validate the efficiency of the proposed model by comparing with the effectiveness of the Master-Slave and the Data Flow model.

Wearable Sensor-Based Human Activity Recognition Method with Multi-Features Extracted from Hilbert-Huang Transform.

PubMed

Xu, Huile; Liu, Jinyi; Hu, Haibo; Zhang, Yi

2016-12-02

Wearable sensors-based human activity recognition introduces many useful applications and services in health care, rehabilitation training, elderly monitoring and many other areas of human interaction. Existing works in this field mainly focus on recognizing activities by using traditional features extracted from Fourier transform (FT) or wavelet transform (WT). However, these signal processing approaches are suitable for a linear signal but not for a nonlinear signal. In this paper, we investigate the characteristics of the Hilbert-Huang transform (HHT) for dealing with activity data with properties such as nonlinearity and non-stationarity. A multi-features extraction method based on HHT is then proposed to improve the effect of activity recognition. The extracted multi-features include instantaneous amplitude (IA) and instantaneous frequency (IF) by means of empirical mode decomposition (EMD), as well as instantaneous energy density (IE) and marginal spectrum (MS) derived from Hilbert spectral analysis. Experimental studies are performed to verify the proposed approach by using the PAMAP2 dataset from the University of California, Irvine for wearable sensors-based activity recognition. Moreover, the effect of combining multi-features vs. a single-feature are investigated and discussed in the scenario of a dependent subject. The experimental results show that multi-features combination can further improve the performance measures. Finally, we test the effect of multi-features combination in the scenario of an independent subject. Our experimental results show that we achieve four performance indexes: recall, precision, F-measure, and accuracy to 0.9337, 0.9417, 0.9353, and 0.9377 respectively, which are all better than the achievements of related works.

Wearable Sensor-Based Human Activity Recognition Method with Multi-Features Extracted from Hilbert-Huang Transform

PubMed Central

Xu, Huile; Liu, Jinyi; Hu, Haibo; Zhang, Yi

2016-01-01

Wearable sensors-based human activity recognition introduces many useful applications and services in health care, rehabilitation training, elderly monitoring and many other areas of human interaction. Existing works in this field mainly focus on recognizing activities by using traditional features extracted from Fourier transform (FT) or wavelet transform (WT). However, these signal processing approaches are suitable for a linear signal but not for a nonlinear signal. In this paper, we investigate the characteristics of the Hilbert-Huang transform (HHT) for dealing with activity data with properties such as nonlinearity and non-stationarity. A multi-features extraction method based on HHT is then proposed to improve the effect of activity recognition. The extracted multi-features include instantaneous amplitude (IA) and instantaneous frequency (IF) by means of empirical mode decomposition (EMD), as well as instantaneous energy density (IE) and marginal spectrum (MS) derived from Hilbert spectral analysis. Experimental studies are performed to verify the proposed approach by using the PAMAP2 dataset from the University of California, Irvine for wearable sensors-based activity recognition. Moreover, the effect of combining multi-features vs. a single-feature are investigated and discussed in the scenario of a dependent subject. The experimental results show that multi-features combination can further improve the performance measures. Finally, we test the effect of multi-features combination in the scenario of an independent subject. Our experimental results show that we achieve four performance indexes: recall, precision, F-measure, and accuracy to 0.9337, 0.9417, 0.9353, and 0.9377 respectively, which are all better than the achievements of related works. PMID:27918414

Multi-elemental imaging of paraffin-embedded human samples by laser-induced breakdown spectroscopy

NASA Astrophysics Data System (ADS)

Moncayo, S.; Trichard, F.; Busser, B.; Sabatier-Vincent, M.; Pelascini, F.; Pinel, N.; Templier, I.; Charles, J.; Sancey, L.; Motto-Ros, V.

2017-07-01

Chemical elements play central roles for physiological homeostasis in human cells, and their dysregulation might lead to a certain number of pathologies. Novel imaging techniques that improve the work of pathologists for tissue analysis and diagnostics are continuously sought. We report the use of Laser-Induced Breakdown Spectroscopy (LIBS) to perform multi-elemental images of human paraffin-embedded skin samples on the entire biopsy scale in a complementary and compatible way with microscope histopathological examination. A specific instrumental configuration is proposed in order to detect most of the elements of medical interest (i.e. P, Al, Mg, Na, Zn, Si, Fe, and Cu). As an example of medical application, we selected and analysed skin biopsies, including healthy skin tissue, cutaneous metastasis of melanoma, Merkel-cell carcinoma and squamous cell carcinoma. Clear distinctions in the distribution of chemical elements are observed from the different samples investigated. This study demonstrates the high complementarity of LIBS elemental imaging with conventional histopathology, opening new opportunities for any medical application involving metals.

Development and Characterization of Embedded Sensory Particles Using Multi-Scale 3D Digital Image Correlation

NASA Technical Reports Server (NTRS)

Cornell, Stephen R.; Leser, William P.; Hochhalter, Jacob D.; Newman, John A.; Hartl, Darren J.

2014-01-01

A method for detecting fatigue cracks has been explored at NASA Langley Research Center. Microscopic NiTi shape memory alloy (sensory) particles were embedded in a 7050 aluminum alloy matrix to detect the presence of fatigue cracks. Cracks exhibit an elevated stress field near their tip inducing a martensitic phase transformation in nearby sensory particles. Detectable levels of acoustic energy are emitted upon particle phase transformation such that the existence and location of fatigue cracks can be detected. To test this concept, a fatigue crack was grown in a mode-I single-edge notch fatigue crack growth specimen containing sensory particles. As the crack approached the sensory particles, measurements of particle strain, matrix-particle debonding, and phase transformation behavior of the sensory particles were performed. Full-field deformation measurements were performed using a novel multi-scale optical 3D digital image correlation (DIC) system. This information will be used in a finite element-based study to determine optimal sensory material behavior and density.

Multi-harmonic quantum dot optomechanics in fused LiNbO3-(Al)GaAs hybrids

NASA Astrophysics Data System (ADS)

Nysten, Emeline D. S.; Huo, Yong Heng; Yu, Hailong; Song, Guo Feng; Rastelli, Armando; Krenner, Hubert J.

2017-11-01

We fabricated an acousto-optic semiconductor hybrid device for strong optomechanical coupling of individual quantum emitters and a surface acoustic wave. Our device comprises of a surface acoustic wave chip made from highly piezoelectric LiNbO3 and a GaAs-based semiconductor membrane with an embedded layer of quantum dots. Employing multi-harmonic transducers, we generated sound waves on LiNbO3 over a wide range of radio frequencies. We monitored their coupling to and propagation across the semiconductor membrane, both in the electrical and optical domain. We demonstrate the enhanced optomechanical tuning of the embedded quantum dots with increasing frequencies. This effect was verified by finite element modelling of our device geometry and attributed to an increased localization of the acoustic field within the semiconductor membrane. For moderately high acoustic frequencies, our simulations predict strong optomechanical coupling, making our hybrid device ideally suited for applications in semiconductor based quantum acoustics.

Development and realization of the open fault diagnosis system based on XPE

NASA Astrophysics Data System (ADS)

Deng, Hui; Wang, TaiYong; He, HuiLong; Xu, YongGang; Zeng, JuXiang

2005-12-01

To make the complex mechanical equipment work in good service, the technology for realizing an embedded open system is introduced systematically, including open hardware configuration, customized embedded operation system and open software structure. The ETX technology is adopted in this system, integrating the CPU main-board functions, and achieving the quick, real-time signal acquisition and intelligent data analysis with applying DSP and CPLD data acquisition card. Under the open configuration, the signal bus mode such as PCI, ISA and PC/104 can be selected and the styles of the signals can be chosen too. In addition, through customizing XPE system, adopting the EWF (Enhanced Write Filter), and realizing the open system authentically, the stability of the system is enhanced. Multi-thread and multi-task programming techniques are adopted in the software programming process. Interconnecting with the remote fault diagnosis center via the net interface, cooperative diagnosis is conducted and the intelligent degree of the fault diagnosis is improved.

Generalized Predictive Control of Dynamic Systems with Rigid-Body Modes

NASA Technical Reports Server (NTRS)

Kvaternik, Raymond G.

2013-01-01

Numerical simulations to assess the effectiveness of Generalized Predictive Control (GPC) for active control of dynamic systems having rigid-body modes are presented. GPC is a linear, time-invariant, multi-input/multi-output predictive control method that uses an ARX model to characterize the system and to design the controller. Although the method can accommodate both embedded (implicit) and explicit feedforward paths for incorporation of disturbance effects, only the case of embedded feedforward in which the disturbances are assumed to be unknown is considered here. Results from numerical simulations using mathematical models of both a free-free three-degree-of-freedom mass-spring-dashpot system and the XV-15 tiltrotor research aircraft are presented. In regulation mode operation, which calls for zero system response in the presence of disturbances, the simulations showed reductions of nearly 100%. In tracking mode operations, where the system is commanded to follow a specified path, the GPC controllers produced the desired responses, even in the presence of disturbances.

Diverse Power Iteration Embeddings and Its Applications

DOE Office of Scientific and Technical Information (OSTI.GOV)

Huang H.; Yoo S.; Yu, D.

2014-12-14

Abstract—Spectral Embedding is one of the most effective dimension reduction algorithms in data mining. However, its computation complexity has to be mitigated in order to apply it for real-world large scale data analysis. Many researches have been focusing on developing approximate spectral embeddings which are more efficient, but meanwhile far less effective. This paper proposes Diverse Power Iteration Embeddings (DPIE), which not only retains the similar efficiency of power iteration methods but also produces a series of diverse and more effective embedding vectors. We test this novel method by applying it to various data mining applications (e.g. clustering, anomaly detectionmore » and feature selection) and evaluating their performance improvements. The experimental results show our proposed DPIE is more effective than popular spectral approximation methods, and obtains the similar quality of classic spectral embedding derived from eigen-decompositions. Moreover it is extremely fast on big data applications. For example in terms of clustering result, DPIE achieves as good as 95% of classic spectral clustering on the complex datasets but 4000+ times faster in limited memory environment.« less

DOE Office of Scientific and Technical Information (OSTI.GOV)

Yu, Kuang; Libisch, Florian; Carter, Emily A., E-mail: eac@princeton.edu

We report a new implementation of the density functional embedding theory (DFET) in the VASP code, using the projector-augmented-wave (PAW) formalism. Newly developed algorithms allow us to efficiently perform optimized effective potential optimizations within PAW. The new algorithm generates robust and physically correct embedding potentials, as we verified using several test systems including a covalently bound molecule, a metal surface, and bulk semiconductors. We show that with the resulting embedding potential, embedded cluster models can reproduce the electronic structure of point defects in bulk semiconductors, thereby demonstrating the validity of DFET in semiconductors for the first time. Compared to ourmore » previous version, the new implementation of DFET within VASP affords use of all features of VASP (e.g., a systematic PAW library, a wide selection of functionals, a more flexible choice of U correction formalisms, and faster computational speed) with DFET. Furthermore, our results are fairly robust with respect to both plane-wave and Gaussian type orbital basis sets in the embedded cluster calculations. This suggests that the density functional embedding method is potentially an accurate and efficient way to study properties of isolated defects in semiconductors.« less

Isolation of Thermal and Strain Responses in Composites Using Embedded Fiber Bragg Grating Temperature Sensors

DTIC Science & Technology

2013-05-10

13. SUPPLEMENTARY NOTES 14. ABSTRACT In this research, fiber Bragg grating ( FBG ) optical temperature sensors are used for structural health...surface of a composite structure. FBG sensors also respond to axial strain in the optical fiber, thus any structural strain experienced by the composite...features. First, a three-dimensional array of FBG temperature sensors has been embedded in a carbon/epoxy composite structure, consisting of both in

Development of biocompatible and safe polyethersulfone hemodialysis membrane incorporated with functionalized multi-walled carbon nanotubes.

PubMed

Abidin, Muhammad Nidzhom Zainol; Goh, Pei Sean; Ismail, Ahmad Fauzi; Othman, Mohd Hafiz Dzarfan; Hasbullah, Hasrinah; Said, Noresah; Kadir, Siti Hamimah Sheikh Abdul; Kamal, Fatmawati; Abdullah, Mohd Sohaimi; Ng, Be Cheer

2017-08-01

A novel approach in the design of a safe, high performance hemodialysis membrane is of great demand. Despite many advantages, the employment of prodigious nanomaterials in hemodialysis membrane is often restricted by their potential threat to health. Hence, this work focusses on designing a biocompatible polyethersulfone (PES) hemodialysis membrane embedded with poly (citric acid)-grafted-multi walled carbon nanotubes (PCA-g-MWCNTs). Two important elements which could assure the safety of the nanocomposite membrane, i.e. (i) dispersion stability and (ii) leaching of MWCNTs were observed. The results showed the improved dispersion stability of MWCNTs in water and organic solvent due to the enriched ratio of oxygen-rich groups which subsequently enhanced membrane separation features. It was revealed that only 0.17% of MWCNTs was leached out during the membrane fabrication process (phase inversion) while no leaching was detected during permeation. In terms of biocompatibility, PES/PCA-g-MWCNT nanocomposite membrane exhibited lesser C3 and C5 activation (189.13 and 5.29ng/mL) and proteins adsorption (bovine serum albumin=4.5μg/cm 2 , fibrinogen=15.95μg/cm 2 ) as compared to the neat PES membrane, while keeping a normal blood coagulation time. Hence, the PES/PCA-g-MWCNT nanocomposite membrane is proven to have the prospect of becoming a safe and high performance hemodialysis membrane. Copyright © 2017 Elsevier B.V. All rights reserved.

DOE Office of Scientific and Technical Information (OSTI.GOV)

Jing, Yaqi; Meng, Qinghao, E-mail: qh-meng@tju.edu.cn; Qi, Peifeng

An electronic nose (e-nose) was designed to classify Chinese liquors of the same aroma style. A new method of feature reduction which combined feature selection with feature extraction was proposed. Feature selection method used 8 feature-selection algorithms based on information theory and reduced the dimension of the feature space to 41. Kernel entropy component analysis was introduced into the e-nose system as a feature extraction method and the dimension of feature space was reduced to 12. Classification of Chinese liquors was performed by using back propagation artificial neural network (BP-ANN), linear discrimination analysis (LDA), and a multi-linear classifier. The classificationmore » rate of the multi-linear classifier was 97.22%, which was higher than LDA and BP-ANN. Finally the classification of Chinese liquors according to their raw materials and geographical origins was performed using the proposed multi-linear classifier and classification rate was 98.75% and 100%, respectively.« less

The role of multi-target policy instruments in agri-environmental policy mixes.

PubMed

Schader, Christian; Lampkin, Nicholas; Muller, Adrian; Stolze, Matthias

2014-12-01

The Tinbergen Rule has been used to criticise multi-target policy instruments for being inefficient. The aim of this paper is to clarify the role of multi-target policy instruments using the case of agri-environmental policy. Employing an analytical linear optimisation model, this paper demonstrates that there is no general contradiction between multi-target policy instruments and the Tinbergen Rule, if multi-target policy instruments are embedded in a policy-mix with a sufficient number of targeted instruments. We show that the relation between cost-effectiveness of the instruments, related to all policy targets, is the key determinant for an economically sound choice of policy instruments. If economies of scope with respect to achieving policy targets are realised, a higher cost-effectiveness of multi-target policy instruments can be achieved. Using the example of organic farming support policy, we discuss several reasons why economies of scope could be realised by multi-target agri-environmental policy instruments. Copyright © 2014 Elsevier Ltd. All rights reserved.

Deep learning for pharmacovigilance: recurrent neural network architectures for labeling adverse drug reactions in Twitter posts.

PubMed

Cocos, Anne; Fiks, Alexander G; Masino, Aaron J

2017-07-01

Social media is an important pharmacovigilance data source for adverse drug reaction (ADR) identification. Human review of social media data is infeasible due to data quantity, thus natural language processing techniques are necessary. Social media includes informal vocabulary and irregular grammar, which challenge natural language processing methods. Our objective is to develop a scalable, deep-learning approach that exceeds state-of-the-art ADR detection performance in social media. We developed a recurrent neural network (RNN) model that labels words in an input sequence with ADR membership tags. The only input features are word-embedding vectors, which can be formed through task-independent pretraining or during ADR detection training. Our best-performing RNN model used pretrained word embeddings created from a large, non-domain-specific Twitter dataset. It achieved an approximate match F-measure of 0.755 for ADR identification on the dataset, compared to 0.631 for a baseline lexicon system and 0.65 for the state-of-the-art conditional random field model. Feature analysis indicated that semantic information in pretrained word embeddings boosted sensitivity and, combined with contextual awareness captured in the RNN, precision. Our model required no task-specific feature engineering, suggesting generalizability to additional sequence-labeling tasks. Learning curve analysis showed that our model reached optimal performance with fewer training examples than the other models. ADR detection performance in social media is significantly improved by using a contextually aware model and word embeddings formed from large, unlabeled datasets. The approach reduces manual data-labeling requirements and is scalable to large social media datasets. © The Author 2017. Published by Oxford University Press on behalf of the American Medical Informatics Association. All rights reserved. For Permissions, please email: journals.permissions@oup.com

Big Data Clustering via Community Detection and Hyperbolic Network Embedding in IoT Applications.

PubMed

Karyotis, Vasileios; Tsitseklis, Konstantinos; Sotiropoulos, Konstantinos; Papavassiliou, Symeon

2018-04-15

In this paper, we present a novel data clustering framework for big sensory data produced by IoT applications. Based on a network representation of the relations among multi-dimensional data, data clustering is mapped to node clustering over the produced data graphs. To address the potential very large scale of such datasets/graphs that test the limits of state-of-the-art approaches, we map the problem of data clustering to a community detection one over the corresponding data graphs. Specifically, we propose a novel computational approach for enhancing the traditional Girvan-Newman (GN) community detection algorithm via hyperbolic network embedding. The data dependency graph is embedded in the hyperbolic space via Rigel embedding, allowing more efficient computation of edge-betweenness centrality needed in the GN algorithm. This allows for more efficient clustering of the nodes of the data graph in terms of modularity, without sacrificing considerable accuracy. In order to study the operation of our approach with respect to enhancing GN community detection, we employ various representative types of artificial complex networks, such as scale-free, small-world and random geometric topologies, and frequently-employed benchmark datasets for demonstrating its efficacy in terms of data clustering via community detection. Furthermore, we provide a proof-of-concept evaluation by applying the proposed framework over multi-dimensional datasets obtained from an operational smart-city/building IoT infrastructure provided by the Federated Interoperable Semantic IoT/cloud Testbeds and Applications (FIESTA-IoT) testbed federation. It is shown that the proposed framework can be indeed used for community detection/data clustering and exploited in various other IoT applications, such as performing more energy-efficient smart-city/building sensing.

Joint kinematics estimation using a multi-body kinematics optimisation and an extended Kalman filter, and embedding a soft tissue artefact model.

PubMed

Bonnet, Vincent; Richard, Vincent; Camomilla, Valentina; Venture, Gentiane; Cappozzo, Aurelio; Dumas, Raphaël

2017-09-06

To reduce the impact of the soft tissue artefact (STA) on the estimate of skeletal movement using stereophotogrammetric and skin-marker data, multi-body kinematics optimisation (MKO) and extended Kalman filters (EKF) have been proposed. This paper assessed the feasibility and efficiency of these methods when they embed a mathematical model of the STA and simultaneously estimate the ankle, knee and hip joint kinematics and the model parameters. A STA model was used that provides an estimate of the STA affecting the marker-cluster located on a body segment as a function of the kinematics of the adjacent joints. The MKO and the EKF were implemented with and without the STA model. To assess these methods, intra-cortical pin and skin markers located on the thigh, shank, and foot of three subjects and tracked during the stance phase of running were used. Embedding the STA model in MKO and EKF reduced the average RMS of marker tracking from 12.6 to 1.6mm and from 4.3 to 1.9mm, respectively, showing that a STA model trial-specific calibration is feasible. Nevertheless, with the STA model embedded in MKO, the RMS difference between the estimated and the reference joint kinematics determined from the pin markers slightly increased (from 2.0 to 2.1deg) On the contrary, when the STA model was embedded in the EKF, this RMS difference was slightly reduced (from 2.0 to 1.7deg) thus showing a better potentiality of this method to attenuate STA effects and improve the accuracy of joint kinematics estimate. Copyright © 2017 Elsevier Ltd. All rights reserved.

Big Data Clustering via Community Detection and Hyperbolic Network Embedding in IoT Applications

PubMed Central

Sotiropoulos, Konstantinos

2018-01-01

In this paper, we present a novel data clustering framework for big sensory data produced by IoT applications. Based on a network representation of the relations among multi-dimensional data, data clustering is mapped to node clustering over the produced data graphs. To address the potential very large scale of such datasets/graphs that test the limits of state-of-the-art approaches, we map the problem of data clustering to a community detection one over the corresponding data graphs. Specifically, we propose a novel computational approach for enhancing the traditional Girvan–Newman (GN) community detection algorithm via hyperbolic network embedding. The data dependency graph is embedded in the hyperbolic space via Rigel embedding, allowing more efficient computation of edge-betweenness centrality needed in the GN algorithm. This allows for more efficient clustering of the nodes of the data graph in terms of modularity, without sacrificing considerable accuracy. In order to study the operation of our approach with respect to enhancing GN community detection, we employ various representative types of artificial complex networks, such as scale-free, small-world and random geometric topologies, and frequently-employed benchmark datasets for demonstrating its efficacy in terms of data clustering via community detection. Furthermore, we provide a proof-of-concept evaluation by applying the proposed framework over multi-dimensional datasets obtained from an operational smart-city/building IoT infrastructure provided by the Federated Interoperable Semantic IoT/cloud Testbeds and Applications (FIESTA-IoT) testbed federation. It is shown that the proposed framework can be indeed used for community detection/data clustering and exploited in various other IoT applications, such as performing more energy-efficient smart-city/building sensing. PMID:29662043

Experimental Methodology in English Teaching and Learning: Method Features, Validity Issues, and Embedded Experimental Design

ERIC Educational Resources Information Center

Lee, Jang Ho

2012-01-01

Experimental methods have played a significant role in the growth of English teaching and learning studies. The paper presented here outlines basic features of experimental design, including the manipulation of independent variables, the role and practicality of randomised controlled trials (RCTs) in educational research, and alternative methods…

Who Needs 3D When the Universe Is Flat?

ERIC Educational Resources Information Center

Eriksson, Urban; Linder, Cedric; Airey, John; Redfors, Andreas

2014-01-01

An overlooked feature in astronomy education is the need for students to learn to extrapolate three-dimensionality and the challenges that this may involve. Discerning critical features in the night sky that are embedded in dimensionality is a long-term learning process. Several articles have addressed the usefulness of three-dimensional (3D)…

Using Alternative Multiplication Algorithms to "Offload" Cognition

ERIC Educational Resources Information Center

Jazby, Dan; Pearn, Cath

2015-01-01

When viewed through a lens of embedded cognition, algorithms may enable aspects of the cognitive work of multi-digit multiplication to be "offloaded" to the environmental structure created by an algorithm. This study analyses four multiplication algorithms by viewing different algorithms as enabling cognitive work to be distributed…

Culture and Ethics in First Nations Educational Research

ERIC Educational Resources Information Center

Taylor, Josiah; Plaice, Evie; Perley, Imelda

2010-01-01

In this paper, we share phenomena experienced by a multi-cultural research team working collaboratively with Wolastoq (Maliseet) First Nations Elders to document rapidly disappearing Wolastoq language, culture, and knowledge. This knowledge will ultimately be stored in databanks for future educational, community, and heritage use. Embedded within…

Simulation of Attacks for Security in Wireless Sensor Network

PubMed Central

Diaz, Alvaro; Sanchez, Pablo

2016-01-01

The increasing complexity and low-power constraints of current Wireless Sensor Networks (WSN) require efficient methodologies for network simulation and embedded software performance analysis of nodes. In addition, security is also a very important feature that has to be addressed in most WSNs, since they may work with sensitive data and operate in hostile unattended environments. In this paper, a methodology for security analysis of Wireless Sensor Networks is presented. The methodology allows designing attack-aware embedded software/firmware or attack countermeasures to provide security in WSNs. The proposed methodology includes attacker modeling and attack simulation with performance analysis (node’s software execution time and power consumption estimation). After an analysis of different WSN attack types, an attacker model is proposed. This model defines three different types of attackers that can emulate most WSN attacks. In addition, this paper presents a virtual platform that is able to model the node hardware, embedded software and basic wireless channel features. This virtual simulation analyzes the embedded software behavior and node power consumption while it takes into account the network deployment and topology. Additionally, this simulator integrates the previously mentioned attacker model. Thus, the impact of attacks on power consumption and software behavior/execution-time can be analyzed. This provides developers with essential information about the effects that one or multiple attacks could have on the network, helping them to develop more secure WSN systems. This WSN attack simulator is an essential element of the attack-aware embedded software development methodology that is also introduced in this work. PMID:27869710

Multi-Feature Classification of Multi-Sensor Satellite Imagery Based on Dual-Polarimetric Sentinel-1A, Landsat-8 OLI, and Hyperion Images for Urban Land-Cover Classification.

PubMed

Zhou, Tao; Li, Zhaofu; Pan, Jianjun

2018-01-27

This paper focuses on evaluating the ability and contribution of using backscatter intensity, texture, coherence, and color features extracted from Sentinel-1A data for urban land cover classification and comparing different multi-sensor land cover mapping methods to improve classification accuracy. Both Landsat-8 OLI and Hyperion images were also acquired, in combination with Sentinel-1A data, to explore the potential of different multi-sensor urban land cover mapping methods to improve classification accuracy. The classification was performed using a random forest (RF) method. The results showed that the optimal window size of the combination of all texture features was 9 × 9, and the optimal window size was different for each individual texture feature. For the four different feature types, the texture features contributed the most to the classification, followed by the coherence and backscatter intensity features; and the color features had the least impact on the urban land cover classification. Satisfactory classification results can be obtained using only the combination of texture and coherence features, with an overall accuracy up to 91.55% and a kappa coefficient up to 0.8935, respectively. Among all combinations of Sentinel-1A-derived features, the combination of the four features had the best classification result. Multi-sensor urban land cover mapping obtained higher classification accuracy. The combination of Sentinel-1A and Hyperion data achieved higher classification accuracy compared to the combination of Sentinel-1A and Landsat-8 OLI images, with an overall accuracy of up to 99.12% and a kappa coefficient up to 0.9889. When Sentinel-1A data was added to Hyperion images, the overall accuracy and kappa coefficient were increased by 4.01% and 0.0519, respectively.

YamiPred: A Novel Evolutionary Method for Predicting Pre-miRNAs and Selecting Relevant Features.

PubMed

Kleftogiannis, Dimitrios; Theofilatos, Konstantinos; Likothanassis, Spiros; Mavroudi, Seferina

2015-01-01

MicroRNAs (miRNAs) are small non-coding RNAs, which play a significant role in gene regulation. Predicting miRNA genes is a challenging bioinformatics problem and existing experimental and computational methods fail to deal with it effectively. We developed YamiPred, an embedded classification method that combines the efficiency and robustness of support vector machines (SVM) with genetic algorithms (GA) for feature selection and parameters optimization. YamiPred was tested in a new and realistic human dataset and was compared with state-of-the-art computational intelligence approaches and the prevalent SVM-based tools for miRNA prediction. Experimental results indicate that YamiPred outperforms existing approaches in terms of accuracy and of geometric mean of sensitivity and specificity. The embedded feature selection component selects a compact feature subset that contributes to the performance optimization. Further experimentation with this minimal feature subset has achieved very high classification performance and revealed the minimum number of samples required for developing a robust predictor. YamiPred also confirmed the important role of commonly used features such as entropy and enthalpy, and uncovered the significance of newly introduced features, such as %A-U aggregate nucleotide frequency and positional entropy. The best model trained on human data has successfully predicted pre-miRNAs to other organisms including the category of viruses.

Locally Linear Embedding of Local Orthogonal Least Squares Images for Face Recognition

NASA Astrophysics Data System (ADS)

Hafizhelmi Kamaru Zaman, Fadhlan

2018-03-01

Dimensionality reduction is very important in face recognition since it ensures that high-dimensionality data can be mapped to lower dimensional space without losing salient and integral facial information. Locally Linear Embedding (LLE) has been previously used to serve this purpose, however, the process of acquiring LLE features requires high computation and resources. To overcome this limitation, we propose a locally-applied Local Orthogonal Least Squares (LOLS) model can be used as initial feature extraction before the application of LLE. By construction of least squares regression under orthogonal constraints we can preserve more discriminant information in the local subspace of facial features while reducing the overall features into a more compact form that we called LOLS images. LLE can then be applied on the LOLS images to maps its representation into a global coordinate system of much lower dimensionality. Several experiments carried out using publicly available face datasets such as AR, ORL, YaleB, and FERET under Single Sample Per Person (SSPP) constraint demonstrates that our proposed method can reduce the time required to compute LLE features while delivering better accuracy when compared to when either LLE or OLS alone is used. Comparison against several other feature extraction methods and more recent feature-learning method such as state-of-the-art Convolutional Neural Networks (CNN) also reveal the superiority of the proposed method under SSPP constraint.

SU-F-R-46: Predicting Distant Failure in Lung SBRT Using Multi-Objective Radiomics Model

DOE Office of Scientific and Technical Information (OSTI.GOV)

Zhou, Z; Folkert, M; Iyengar, P

2016-06-15

Purpose: To predict distant failure in lung stereotactic body radiation therapy (SBRT) in early stage non-small cell lung cancer (NSCLC) by using a new multi-objective radiomics model. Methods: Currently, most available radiomics models use the overall accuracy as the objective function. However, due to data imbalance, a single object may not reflect the performance of a predictive model. Therefore, we developed a multi-objective radiomics model which considers both sensitivity and specificity as the objective functions simultaneously. The new model is used to predict distant failure in lung SBRT using 52 patients treated at our institute. Quantitative imaging features of PETmore » and CT as well as clinical parameters are utilized to build the predictive model. Image features include intensity features (9), textural features (12) and geometric features (8). Clinical parameters for each patient include demographic parameters (4), tumor characteristics (8), treatment faction schemes (4) and pretreatment medicines (6). The modelling procedure consists of two steps: extracting features from segmented tumors in PET and CT; and selecting features and training model parameters based on multi-objective. Support Vector Machine (SVM) is used as the predictive model, while a nondominated sorting-based multi-objective evolutionary computation algorithm II (NSGA-II) is used for solving the multi-objective optimization. Results: The accuracy for PET, clinical, CT, PET+clinical, PET+CT, CT+clinical, PET+CT+clinical are 71.15%, 84.62%, 84.62%, 85.54%, 82.69%, 84.62%, 86.54%, respectively. The sensitivities for the above seven combinations are 41.76%, 58.33%, 50.00%, 50.00%, 41.67%, 41.67%, 58.33%, while the specificities are 80.00%, 92.50%, 90.00%, 97.50%, 92.50%, 97.50%, 97.50%. Conclusion: A new multi-objective radiomics model for predicting distant failure in NSCLC treated with SBRT was developed. The experimental results show that the best performance can be obtained by combining all features.« less

Facile synthesis of enzyme-embedded magnetic metal-organic frameworks as a reusable mimic multi-enzyme system: mimetic peroxidase properties and colorimetric sensor

NASA Astrophysics Data System (ADS)

Hou, Chen; Wang, Yang; Ding, Qinghua; Jiang, Long; Li, Ming; Zhu, Weiwei; Pan, Duo; Zhu, Hao; Liu, Mingzhu

2015-11-01

This work reports a facile and easily-achieved approach for enzyme immobilization by embedding glucose oxidase (GOx) in magnetic zeolitic imidazolate framework 8 (mZIF-8) via a de novo approach. As a demonstration of the power of such materials, the resulting GOx embedded mZIF-8 (mZIF-8@GOx) was utilized as a colorimetric sensor for rapid detection of glucose. This method was constructed on the basis of metal-organic frameworks (MOFs), which possessed very fascinating peroxidase-like properties, and the cascade reaction for the visual detection of glucose was combined into one step through the mZIF-8@GOx based mimic multi-enzyme system. After characterization by electron microscopy, X-ray diffraction, nitrogen sorption, fourier transform infrared spectroscopy and vibrating sample magnetometry, the as-prepared mZIF-8@GOx was confirmed with the robust core-shell structure, the monodisperse nanoparticle had an average diameter of about 200 nm and displayed superparamagnetism with a saturation magnetization value of 40.5 emu g-1, it also exhibited a large surface area of 396.10 m2 g-1. As a peroxidase mimic, mZIF-8 was verified to be highly stable and of low cost, and showed a strong affinity towards H2O2. Meanwhile, the mZIF-8 embedded GOx also exhibited improved activity, stability and greatly enhanced selectivity in glucose detection. Moreover, the mZIF-8@GOx had excellent recyclability with high activity (88.7% residual activity after 12 times reuse).This work reports a facile and easily-achieved approach for enzyme immobilization by embedding glucose oxidase (GOx) in magnetic zeolitic imidazolate framework 8 (mZIF-8) via a de novo approach. As a demonstration of the power of such materials, the resulting GOx embedded mZIF-8 (mZIF-8@GOx) was utilized as a colorimetric sensor for rapid detection of glucose. This method was constructed on the basis of metal-organic frameworks (MOFs), which possessed very fascinating peroxidase-like properties, and the cascade reaction for the visual detection of glucose was combined into one step through the mZIF-8@GOx based mimic multi-enzyme system. After characterization by electron microscopy, X-ray diffraction, nitrogen sorption, fourier transform infrared spectroscopy and vibrating sample magnetometry, the as-prepared mZIF-8@GOx was confirmed with the robust core-shell structure, the monodisperse nanoparticle had an average diameter of about 200 nm and displayed superparamagnetism with a saturation magnetization value of 40.5 emu g-1, it also exhibited a large surface area of 396.10 m2 g-1. As a peroxidase mimic, mZIF-8 was verified to be highly stable and of low cost, and showed a strong affinity towards H2O2. Meanwhile, the mZIF-8 embedded GOx also exhibited improved activity, stability and greatly enhanced selectivity in glucose detection. Moreover, the mZIF-8@GOx had excellent recyclability with high activity (88.7% residual activity after 12 times reuse). Electronic supplementary information (ESI) available. See DOI: 10.1039/c5nr04994f

Assessment of Embedded Conjugated Polymer Sensor Arrays for Potential Load Transmission Measurement in Orthopaedic Implants

PubMed Central

Micolini, Carolina; Holness, Frederick Benjamin; Johnson, James A.

2017-01-01

Load transfer through orthopaedic joint implants is poorly understood. The longer-term outcomes of these implants are just starting to be studied, making it imperative to monitor contact loads across the entire joint implant interface to elucidate the force transmission and distribution mechanisms exhibited by these implants in service. This study proposes and demonstrates the design, implementation, and characterization of a 3D-printed smart polymer sensor array using conductive polyaniline (PANI) structures embedded within a polymeric parent phase. The piezoresistive characteristics of PANI were investigated to characterize the sensing behaviour inherent to these embedded pressure sensor arrays, including the experimental determination of the stable response of PANI to continuous loading, stability throughout the course of loading and unloading cycles, and finally sensor repeatability and linearity in response to incremental loading cycles. This specially developed multi-material additive manufacturing process for PANI is shown be an attractive approach for the fabrication of implant components having embedded smart-polymer sensors, which could ultimately be employed for the measurement and analysis of joint loads in orthopaedic implants for in vitro testing. PMID:29186079

Time-frequency feature representation using multi-resolution texture analysis and acoustic activity detector for real-life speech emotion recognition.

PubMed

Wang, Kun-Ching

2015-01-14

The classification of emotional speech is mostly considered in speech-related research on human-computer interaction (HCI). In this paper, the purpose is to present a novel feature extraction based on multi-resolutions texture image information (MRTII). The MRTII feature set is derived from multi-resolution texture analysis for characterization and classification of different emotions in a speech signal. The motivation is that we have to consider emotions have different intensity values in different frequency bands. In terms of human visual perceptual, the texture property on multi-resolution of emotional speech spectrogram should be a good feature set for emotion classification in speech. Furthermore, the multi-resolution analysis on texture can give a clearer discrimination between each emotion than uniform-resolution analysis on texture. In order to provide high accuracy of emotional discrimination especially in real-life, an acoustic activity detection (AAD) algorithm must be applied into the MRTII-based feature extraction. Considering the presence of many blended emotions in real life, in this paper make use of two corpora of naturally-occurring dialogs recorded in real-life call centers. Compared with the traditional Mel-scale Frequency Cepstral Coefficients (MFCC) and the state-of-the-art features, the MRTII features also can improve the correct classification rates of proposed systems among different language databases. Experimental results show that the proposed MRTII-based feature information inspired by human visual perception of the spectrogram image can provide significant classification for real-life emotional recognition in speech.

On computing stress in polymer systems involving multi-body potentials from molecular dynamics simulation

NASA Astrophysics Data System (ADS)

Fu, Yao; Song, Jeong-Hoon

2014-08-01

Hardy stress definition has been restricted to pair potentials and embedded-atom method potentials due to the basic assumptions in the derivation of a symmetric microscopic stress tensor. Force decomposition required in the Hardy stress expression becomes obscure for multi-body potentials. In this work, we demonstrate the invariance of the Hardy stress expression for a polymer system modeled with multi-body interatomic potentials including up to four atoms interaction, by applying central force decomposition of the atomic force. The balance of momentum has been demonstrated to be valid theoretically and tested under various numerical simulation conditions. The validity of momentum conservation justifies the extension of Hardy stress expression to multi-body potential systems. Computed Hardy stress has been observed to converge to the virial stress of the system with increasing spatial averaging volume. This work provides a feasible and reliable linkage between the atomistic and continuum scales for multi-body potential systems.

TOPICAL REVIEW: Smart aggregates: multi-functional sensors for concrete structures—a tutorial and a review

NASA Astrophysics Data System (ADS)

Song, Gangbing; Gu, Haichang; Mo, Yi-Lung

2008-06-01

This paper summarizes the authors' recent pioneering research work in piezoceramic-based smart aggregates and their innovative applications in concrete civil structures. The basic operating principle of smart aggregates is first introduced. The proposed smart aggregate is formed by embedding a waterproof piezoelectric patch with lead wires into a small concrete block. The proposed smart aggregates are multi-functional and can perform three major tasks: early-age concrete strength monitoring, impact detection and structural health monitoring. The proposed smart aggregates are embedded into the desired location before the casting of the concrete structure. The concrete strength development is monitored by observing the high frequency harmonic wave response of the smart aggregate. Impact on the concrete structure is detected by observing the open-circuit voltage of the piezoceramic patch in the smart aggregate. For structural health monitoring purposes, a smart aggregate-based active sensing system is designed for the concrete structure. Wavelet packet analysis is used as a signal-processing tool to analyze the sensor signal. A damage index based on the wavelet packet analysis is used to determine the structural health status. To better describe the time-history and location information of damage, two types of damage index matrices are proposed: a sensor-history damage index matrix and an actuator-sensor damage index matrix. To demonstrate the multi-functionality of the proposed smart aggregates, different types of concrete structures have been used as test objects, including concrete bridge bent-caps, concrete cylinders and a concrete frame. Experimental results have verified the effectiveness and the multi-functionality of the proposed smart aggregates. The multi-functional smart aggregates have the potential to be applied to the comprehensive monitoring of concrete structures from their earliest stages and throughout their lifetime.

Fast and Efficient Feature Engineering for Multi-Cohort Analysis of EHR Data.

PubMed

Ozery-Flato, Michal; Yanover, Chen; Gottlieb, Assaf; Weissbrod, Omer; Parush Shear-Yashuv, Naama; Goldschmidt, Yaara

2017-01-01

We present a framework for feature engineering, tailored for longitudinal structured data, such as electronic health records (EHRs). To fast-track feature engineering and extraction, the framework combines general-use plug-in extractors, a multi-cohort management mechanism, and modular memoization. Using this framework, we rapidly extracted thousands of features from diverse and large healthcare data sources in multiple projects.

Neuro-symbolic representation learning on biological knowledge graphs.

PubMed

Alshahrani, Mona; Khan, Mohammad Asif; Maddouri, Omar; Kinjo, Akira R; Queralt-Rosinach, Núria; Hoehndorf, Robert

2017-09-01

Biological data and knowledge bases increasingly rely on Semantic Web technologies and the use of knowledge graphs for data integration, retrieval and federated queries. In the past years, feature learning methods that are applicable to graph-structured data are becoming available, but have not yet widely been applied and evaluated on structured biological knowledge. Results: We develop a novel method for feature learning on biological knowledge graphs. Our method combines symbolic methods, in particular knowledge representation using symbolic logic and automated reasoning, with neural networks to generate embeddings of nodes that encode for related information within knowledge graphs. Through the use of symbolic logic, these embeddings contain both explicit and implicit information. We apply these embeddings to the prediction of edges in the knowledge graph representing problems of function prediction, finding candidate genes of diseases, protein-protein interactions, or drug target relations, and demonstrate performance that matches and sometimes outperforms traditional approaches based on manually crafted features. Our method can be applied to any biological knowledge graph, and will thereby open up the increasing amount of Semantic Web based knowledge bases in biology to use in machine learning and data analytics. https://github.com/bio-ontology-research-group/walking-rdf-and-owl. robert.hoehndorf@kaust.edu.sa. Supplementary data are available at Bioinformatics online. © The Author(s) 2017. Published by Oxford University Press.

a Probabilistic Embedding Clustering Method for Urban Structure Detection

NASA Astrophysics Data System (ADS)

Lin, X.; Li, H.; Zhang, Y.; Gao, L.; Zhao, L.; Deng, M.

2017-09-01

Urban structure detection is a basic task in urban geography. Clustering is a core technology to detect the patterns of urban spatial structure, urban functional region, and so on. In big data era, diverse urban sensing datasets recording information like human behaviour and human social activity, suffer from complexity in high dimension and high noise. And unfortunately, the state-of-the-art clustering methods does not handle the problem with high dimension and high noise issues concurrently. In this paper, a probabilistic embedding clustering method is proposed. Firstly, we come up with a Probabilistic Embedding Model (PEM) to find latent features from high dimensional urban sensing data by "learning" via probabilistic model. By latent features, we could catch essential features hidden in high dimensional data known as patterns; with the probabilistic model, we can also reduce uncertainty caused by high noise. Secondly, through tuning the parameters, our model could discover two kinds of urban structure, the homophily and structural equivalence, which means communities with intensive interaction or in the same roles in urban structure. We evaluated the performance of our model by conducting experiments on real-world data and experiments with real data in Shanghai (China) proved that our method could discover two kinds of urban structure, the homophily and structural equivalence, which means clustering community with intensive interaction or under the same roles in urban space.

Nonlinear model identification and spectral submanifolds for multi-degree-of-freedom mechanical vibrations

NASA Astrophysics Data System (ADS)

Szalai, Robert; Ehrhardt, David; Haller, George

2017-06-01

In a nonlinear oscillatory system, spectral submanifolds (SSMs) are the smoothest invariant manifolds tangent to linear modal subspaces of an equilibrium. Amplitude-frequency plots of the dynamics on SSMs provide the classic backbone curves sought in experimental nonlinear model identification. We develop here, a methodology to compute analytically both the shape of SSMs and their corresponding backbone curves from a data-assimilating model fitted to experimental vibration signals. This model identification utilizes Taken's delay-embedding theorem, as well as a least square fit to the Taylor expansion of the sampling map associated with that embedding. The SSMs are then constructed for the sampling map using the parametrization method for invariant manifolds, which assumes that the manifold is an embedding of, rather than a graph over, a spectral subspace. Using examples of both synthetic and real experimental data, we demonstrate that this approach reproduces backbone curves with high accuracy.

Abstract for 1999 Rational Software User Conference

NASA Technical Reports Server (NTRS)

Dunphy, Julia; Rouquette, Nicolas; Feather, Martin; Tung, Yu-Wen

1999-01-01

We develop spacecraft fault-protection software at NASA/JPL. Challenges exemplified by our task: 1) high-quality systems - need for extensive validation & verification; 2) multi-disciplinary context - involves experts from diverse areas; 3) embedded systems - must adapt to external practices, notations, etc.; and 4) development pressures - NASA's mandate of "better, faster, cheaper".

Embedded 100 Gbps Photonic Components

DOE Office of Scientific and Technical Information (OSTI.GOV)

Kuznia, Charlie

This innovation to fiber optic component technology increases the performance, reduces the size and reduces the power consumption of optical communications within dense network systems, such as advanced distributed computing systems and data centers. VCSEL technology is enabling short-reach (< 100 m) and >100 Gbps optical interconnections over multi-mode fiber in commercial applications.

Multi-Class Classification for Identifying JPEG Steganography Embedding Methods

DTIC Science & Technology

2008-09-01

B.H. (2000). STEGANOGRAPHY: Hidden Images, A New Challenge in the Fight Against Child Porn . UPDATE, Volume 13, Number 2, pp. 1-4, Retrieved June 3...Other crimes involving the use of steganography include child pornography where the stego files are used to hide a predator’s location when posting

Microfluidic systems with embedded materials and structures and method thereof

DOEpatents

Morse, Jeffrey D [Martinez, CA; Rose, Klint A [Boston, MA; Maghribi, Mariam [Livermore, CA; Benett, William [Livermore, CA; Krulevitch, Peter [Pleasanton, CA; Hamilton, Julie [Tracy, CA; Graff, Robert T [Modesto, CA; Jankowski, Alan [Livermore, CA

2007-03-06

Described herein is a process for fabricating microfluidic systems with embedded components in which micron-scale features are molded into the polymeric material polydimethylsiloxane (PDMS). Micromachining is used to create a mold master and the liquid precursors for PDMS are poured over the mold and allowed to cure. The PDMS is then removed form the mold and bonded to another material such as PDMS, glass, or silicon after a simple surface preparation step to form sealed microchannels.

CVXPY: A Python-Embedded Modeling Language for Convex Optimization.

PubMed

Diamond, Steven; Boyd, Stephen

2016-04-01

CVXPY is a domain-specific language for convex optimization embedded in Python. It allows the user to express convex optimization problems in a natural syntax that follows the math, rather than in the restrictive standard form required by solvers. CVXPY makes it easy to combine convex optimization with high-level features of Python such as parallelism and object-oriented design. CVXPY is available at http://www.cvxpy.org/ under the GPL license, along with documentation and examples.

Advanced DC/DC Converters Towards Higher Volumetric Efficiencies For Space Applications

NASA Technical Reports Server (NTRS)

Shaw, Harry; Shue, Jack; Liu, David; Wang, Bright; Plante, Jeanette

2005-01-01

A new emphasis on planetary exploration by NASA drives the need for small, high power DC/DC converters which are functionally modular. NASA GSFC and other government space organizations are supporting technology development in the DC/DC converter area to both meet new needs and to promote more sources of supply. New technologies which enable miniaturization such as embedded passive technologies and thermal management using high thermal conductivity materials are features of the new designs. Construction of some simple DC/DC converter core circuits using embedded components was found to be successful for increasing volumetric efficiency to 37 W/inch. The embedded passives were also able to perform satisfactorily in this application in cryogenic temperatures.

Slice&Dice: Recognizing Food Preparation Activities Using Embedded Accelerometers

NASA Astrophysics Data System (ADS)

Pham, Cuong; Olivier, Patrick

Within the context of an endeavor to provide situated support for people with cognitive impairments in the kitchen, we developed and evaluated classifiers for recognizing 11 actions involved in food preparation. Data was collected from 20 lay subjects using four specially designed kitchen utensils incorporating embedded 3-axis accelerometers. Subjects were asked to prepare a mixed salad in our laboratory-based instrumented kitchen environment. Video of each subject's food preparation activities were independently annotated by three different coders. Several classifiers were trained and tested using these features. With an overall accuracy of 82.9% our investigation demonstrated that a broad set of food preparation actions can be reliably recognized using sensors embedded in kitchen utensils.

Multi-Sensor Registration of Earth Remotely Sensed Imagery

NASA Technical Reports Server (NTRS)

LeMoigne, Jacqueline; Cole-Rhodes, Arlene; Eastman, Roger; Johnson, Kisha; Morisette, Jeffrey; Netanyahu, Nathan S.; Stone, Harold S.; Zavorin, Ilya; Zukor, Dorothy (Technical Monitor)

2001-01-01

Assuming that approximate registration is given within a few pixels by a systematic correction system, we develop automatic image registration methods for multi-sensor data with the goal of achieving sub-pixel accuracy. Automatic image registration is usually defined by three steps; feature extraction, feature matching, and data resampling or fusion. Our previous work focused on image correlation methods based on the use of different features. In this paper, we study different feature matching techniques and present five algorithms where the features are either original gray levels or wavelet-like features, and the feature matching is based on gradient descent optimization, statistical robust matching, and mutual information. These algorithms are tested and compared on several multi-sensor datasets covering one of the EOS Core Sites, the Konza Prairie in Kansas, from four different sensors: IKONOS (4m), Landsat-7/ETM+ (30m), MODIS (500m), and SeaWIFS (1000m).

Extreme learning machine based optimal embedding location finder for image steganography

PubMed Central

Aljeroudi, Yazan

2017-01-01

In image steganography, determining the optimum location for embedding the secret message precisely with minimum distortion of the host medium remains a challenging issue. Yet, an effective approach for the selection of the best embedding location with least deformation is far from being achieved. To attain this goal, we propose a novel approach for image steganography with high-performance, where extreme learning machine (ELM) algorithm is modified to create a supervised mathematical model. This ELM is first trained on a part of an image or any host medium before being tested in the regression mode. This allowed us to choose the optimal location for embedding the message with best values of the predicted evaluation metrics. Contrast, homogeneity, and other texture features are used for training on a new metric. Furthermore, the developed ELM is exploited for counter over-fitting while training. The performance of the proposed steganography approach is evaluated by computing the correlation, structural similarity (SSIM) index, fusion matrices, and mean square error (MSE). The modified ELM is found to outperform the existing approaches in terms of imperceptibility. Excellent features of the experimental results demonstrate that the proposed steganographic approach is greatly proficient for preserving the visual information of an image. An improvement in the imperceptibility as much as 28% is achieved compared to the existing state of the art methods. PMID:28196080

An improved feature extraction algorithm based on KAZE for multi-spectral image

NASA Astrophysics Data System (ADS)

Yang, Jianping; Li, Jun

2018-02-01

Multi-spectral image contains abundant spectral information, which is widely used in all fields like resource exploration, meteorological observation and modern military. Image preprocessing, such as image feature extraction and matching, is indispensable while dealing with multi-spectral remote sensing image. Although the feature matching algorithm based on linear scale such as SIFT and SURF performs strong on robustness, the local accuracy cannot be guaranteed. Therefore, this paper proposes an improved KAZE algorithm, which is based on nonlinear scale, to raise the number of feature and to enhance the matching rate by using the adjusted-cosine vector. The experiment result shows that the number of feature and the matching rate of the improved KAZE are remarkably than the original KAZE algorithm.

Micromagnetics on high-performance workstation and mobile computational platforms

NASA Astrophysics Data System (ADS)

Fu, S.; Chang, R.; Couture, S.; Menarini, M.; Escobar, M. A.; Kuteifan, M.; Lubarda, M.; Gabay, D.; Lomakin, V.

2015-05-01

The feasibility of using high-performance desktop and embedded mobile computational platforms is presented, including multi-core Intel central processing unit, Nvidia desktop graphics processing units, and Nvidia Jetson TK1 Platform. FastMag finite element method-based micromagnetic simulator is used as a testbed, showing high efficiency on all the platforms. Optimization aspects of improving the performance of the mobile systems are discussed. The high performance, low cost, low power consumption, and rapid performance increase of the embedded mobile systems make them a promising candidate for micromagnetic simulations. Such architectures can be used as standalone systems or can be built as low-power computing clusters.

Embedded Relative Navigation Sensor Fusion Algorithms for Autonomous Rendezvous and Docking Missions

NASA Technical Reports Server (NTRS)

DeKock, Brandon K.; Betts, Kevin M.; McDuffie, James H.; Dreas, Christine B.

2008-01-01

bd Systems (a subsidiary of SAIC) has developed a suite of embedded relative navigation sensor fusion algorithms to enable NASA autonomous rendezvous and docking (AR&D) missions. Translational and rotational Extended Kalman Filters (EKFs) were developed for integrating measurements based on the vehicles' orbital mechanics and high-fidelity sensor error models and provide a solution with increased accuracy and robustness relative to any single relative navigation sensor. The filters were tested tinough stand-alone covariance analysis, closed-loop testing with a high-fidelity multi-body orbital simulation, and hardware-in-the-loop (HWIL) testing in the Marshall Space Flight Center (MSFC) Flight Robotics Laboratory (FRL).

Vertical transportation systems embedded on shuffled frog leaping algorithm for manufacturing optimisation problems in industries.

PubMed

Aungkulanon, Pasura; Luangpaiboon, Pongchanun

2016-01-01

Response surface methods via the first or second order models are important in manufacturing processes. This study, however, proposes different structured mechanisms of the vertical transportation systems or VTS embedded on a shuffled frog leaping-based approach. There are three VTS scenarios, a motion reaching a normal operating velocity, and both reaching and not reaching transitional motion. These variants were performed to simultaneously inspect multiple responses affected by machining parameters in multi-pass turning processes. The numerical results of two machining optimisation problems demonstrated the high performance measures of the proposed methods, when compared to other optimisation algorithms for an actual deep cut design.

Audio Watermark Embedding Technique Applying Auditory Stream Segregation: "G-encoder Mark" Able to Be Extracted by Mobile Phone

NASA Astrophysics Data System (ADS)

Modegi, Toshio

We are developing audio watermarking techniques which enable extraction of embedded data by cell phones. For that we have to embed data onto frequency ranges, where our auditory response is prominent, therefore data embedding will cause much auditory noises. Previously we have proposed applying a two-channel stereo play-back feature, where noises generated by a data embedded left-channel signal will be reduced by the other right-channel signal. However, this proposal has practical problems of restricting extracting terminal location. In this paper, we propose synthesizing the noise reducing right-channel signal with the left-signal and reduces noises completely by generating an auditory stream segregation phenomenon to users. This newly proposed makes the noise reducing right-channel signal unnecessary and supports monaural play-back operations. Moreover, we propose a wide-band embedding method causing dual auditory stream segregation phenomena, which enables data embedding on whole public phone frequency ranges and stable extractions with 3-G mobile phones. From these proposals, extraction precisions become higher than those by the previously proposed method whereas the quality damages of embedded signals become smaller. In this paper we present an abstract of our newly proposed method and experimental results comparing with those by the previously proposed method.

Variable importance in nonlinear kernels (VINK): classification of digitized histopathology.

PubMed

Ginsburg, Shoshana; Ali, Sahirzeeshan; Lee, George; Basavanhally, Ajay; Madabhushi, Anant

2013-01-01

Quantitative histomorphometry is the process of modeling appearance of disease morphology on digitized histopathology images via image-based features (e.g., texture, graphs). Due to the curse of dimensionality, building classifiers with large numbers of features requires feature selection (which may require a large training set) or dimensionality reduction (DR). DR methods map the original high-dimensional features in terms of eigenvectors and eigenvalues, which limits the potential for feature transparency or interpretability. Although methods exist for variable selection and ranking on embeddings obtained via linear DR schemes (e.g., principal components analysis (PCA)), similar methods do not yet exist for nonlinear DR (NLDR) methods. In this work we present a simple yet elegant method for approximating the mapping between the data in the original feature space and the transformed data in the kernel PCA (KPCA) embedding space; this mapping provides the basis for quantification of variable importance in nonlinear kernels (VINK). We show how VINK can be implemented in conjunction with the popular Isomap and Laplacian eigenmap algorithms. VINK is evaluated in the contexts of three different problems in digital pathology: (1) predicting five year PSA failure following radical prostatectomy, (2) predicting Oncotype DX recurrence risk scores for ER+ breast cancers, and (3) distinguishing good and poor outcome p16+ oropharyngeal tumors. We demonstrate that subsets of features identified by VINK provide similar or better classification or regression performance compared to the original high dimensional feature sets.

More on molecular excitations: Dark matter detection in ice

DOE PAGES

Va'vra, J.

2016-08-10

In this paper we investigate di-atomic molecules embedded in ice crystals under strain. In this environment coherent vibrations of many OH-bonds may be generated by one WIMP collision. The detection of such multiple-photon signals may provide a signature of a 100 GeV/c 2 WIMP. To do a proper lab test of “WIMP-induced” multi-photon emission is very difficult. As a result, we suggest that Ice Cube make a search for multi-photon events, and investigate whether the rate of such events exhibits yearly modulation.

Improving Teaching through Continuous Learning: The Inquiry Process John Wooden Used to Become Coach of the Century

ERIC Educational Resources Information Center

Ermeling, Bradley Alan

2012-01-01

Past and contemporary scholars have emphasized the importance of job-embedded, systematic instructional inquiry for educators. A recent review of the literature highlights four key features shared by several well documented inquiry approaches for classroom teachers. Interestingly, another line of research suggests that these key features also…

Delay differential analysis of time series.

PubMed

Lainscsek, Claudia; Sejnowski, Terrence J

2015-03-01

Nonlinear dynamical system analysis based on embedding theory has been used for modeling and prediction, but it also has applications to signal detection and classification of time series. An embedding creates a multidimensional geometrical object from a single time series. Traditionally either delay or derivative embeddings have been used. The delay embedding is composed of delayed versions of the signal, and the derivative embedding is composed of successive derivatives of the signal. The delay embedding has been extended to nonuniform embeddings to take multiple timescales into account. Both embeddings provide information on the underlying dynamical system without having direct access to all the system variables. Delay differential analysis is based on functional embeddings, a combination of the derivative embedding with nonuniform delay embeddings. Small delay differential equation (DDE) models that best represent relevant dynamic features of time series data are selected from a pool of candidate models for detection or classification. We show that the properties of DDEs support spectral analysis in the time domain where nonlinear correlation functions are used to detect frequencies, frequency and phase couplings, and bispectra. These can be efficiently computed with short time windows and are robust to noise. For frequency analysis, this framework is a multivariate extension of discrete Fourier transform (DFT), and for higher-order spectra, it is a linear and multivariate alternative to multidimensional fast Fourier transform of multidimensional correlations. This method can be applied to short or sparse time series and can be extended to cross-trial and cross-channel spectra if multiple short data segments of the same experiment are available. Together, this time-domain toolbox provides higher temporal resolution, increased frequency and phase coupling information, and it allows an easy and straightforward implementation of higher-order spectra across time compared with frequency-based methods such as the DFT and cross-spectral analysis.

An adaptive multi-feature segmentation model for infrared image

NASA Astrophysics Data System (ADS)

Zhang, Tingting; Han, Jin; Zhang, Yi; Bai, Lianfa

2016-04-01

Active contour models (ACM) have been extensively applied to image segmentation, conventional region-based active contour models only utilize global or local single feature information to minimize the energy functional to drive the contour evolution. Considering the limitations of original ACMs, an adaptive multi-feature segmentation model is proposed to handle infrared images with blurred boundaries and low contrast. In the proposed model, several essential local statistic features are introduced to construct a multi-feature signed pressure function (MFSPF). In addition, we draw upon the adaptive weight coefficient to modify the level set formulation, which is formed by integrating MFSPF with local statistic features and signed pressure function with global information. Experimental results demonstrate that the proposed method can make up for the inadequacy of the original method and get desirable results in segmenting infrared images.

Vibration and acoustic frequency spectra for industrial process modeling using selective fusion multi-condition samples and multi-source features

NASA Astrophysics Data System (ADS)

Tang, Jian; Qiao, Junfei; Wu, ZhiWei; Chai, Tianyou; Zhang, Jian; Yu, Wen

2018-01-01

Frequency spectral data of mechanical vibration and acoustic signals relate to difficult-to-measure production quality and quantity parameters of complex industrial processes. A selective ensemble (SEN) algorithm can be used to build a soft sensor model of these process parameters by fusing valued information selectively from different perspectives. However, a combination of several optimized ensemble sub-models with SEN cannot guarantee the best prediction model. In this study, we use several techniques to construct mechanical vibration and acoustic frequency spectra of a data-driven industrial process parameter model based on selective fusion multi-condition samples and multi-source features. Multi-layer SEN (MLSEN) strategy is used to simulate the domain expert cognitive process. Genetic algorithm and kernel partial least squares are used to construct the inside-layer SEN sub-model based on each mechanical vibration and acoustic frequency spectral feature subset. Branch-and-bound and adaptive weighted fusion algorithms are integrated to select and combine outputs of the inside-layer SEN sub-models. Then, the outside-layer SEN is constructed. Thus, "sub-sampling training examples"-based and "manipulating input features"-based ensemble construction methods are integrated, thereby realizing the selective information fusion process based on multi-condition history samples and multi-source input features. This novel approach is applied to a laboratory-scale ball mill grinding process. A comparison with other methods indicates that the proposed MLSEN approach effectively models mechanical vibration and acoustic signals.

Real-Time Visual Tracking through Fusion Features

PubMed Central

Ruan, Yang; Wei, Zhenzhong

2016-01-01

Due to their high-speed, correlation filters for object tracking have begun to receive increasing attention. Traditional object trackers based on correlation filters typically use a single type of feature. In this paper, we attempt to integrate multiple feature types to improve the performance, and we propose a new DD-HOG fusion feature that consists of discriminative descriptors (DDs) and histograms of oriented gradients (HOG). However, fusion features as multi-vector descriptors cannot be directly used in prior correlation filters. To overcome this difficulty, we propose a multi-vector correlation filter (MVCF) that can directly convolve with a multi-vector descriptor to obtain a single-channel response that indicates the location of an object. Experiments on the CVPR2013 tracking benchmark with the evaluation of state-of-the-art trackers show the effectiveness and speed of the proposed method. Moreover, we show that our MVCF tracker, which uses the DD-HOG descriptor, outperforms the structure-preserving object tracker (SPOT) in multi-object tracking because of its high-speed and ability to address heavy occlusion. PMID:27347951

EMIR, the NIR MOS and Imager for the GTC

NASA Astrophysics Data System (ADS)

Garzón, F.; EMIR Team

2016-10-01

EMIR is one of the first common-user instruments for the GTC, the 10-meter telescope operating at the Roque de los Muchachos Observatory (La Palma, Canary Islands, Spain). EMIR is being built by a Consortium of Spanish and French institutes led by the Instituto de Astrofísica de Canarias (IAC). EMIR is primarily designed to be operated as a MOS in the near-IR band, but offers a wide range of observing modes, including imaging and spectroscopy, both long slit and multi-object, in the wavelength range 0.9 to 2.5 μm. This contribution reports on the results achieved so far during the verification phase at the IAC prior to the shipment of the instrument to the GTC for being commissioned, which is due by mid 2015. EMIR is equipped with a set of three dispersive elements, one for each of the atmospheric windows J,H & K, formed by the combination of a high quality transmission grating embedded in between of two large prisms of ZnSe; plus a low resolution standard replicated grism, functional in the HK and ZJ windows in first and second dispersion orders respectively. The multi-object capability is achieved by means of the Cold Slit Unit (CSU), a cryogenic robotic reconfigurable multi-slit mask system capable of making user specified patterns with 55 different slitlets distributed across the EMIR focal plane. We will describe the principal units and features of the EMIR instrument and the main results of the verification performed so far with special emphasis on the NIR MOS capabilities. The development and fabrication of EMIR is funded by GRANTECAN and the Plan Nacional de Astronomía y Astrofísica (National Plan for Astronomy and Astrophysics, Spain).

Discriminative graph embedding for label propagation.

PubMed

Nguyen, Canh Hao; Mamitsuka, Hiroshi

2011-09-01

In many applications, the available information is encoded in graph structures. This is a common problem in biological networks, social networks, web communities and document citations. We investigate the problem of classifying nodes' labels on a similarity graph given only a graph structure on the nodes. Conventional machine learning methods usually require data to reside in some Euclidean spaces or to have a kernel representation. Applying these methods to nodes on graphs would require embedding the graphs into these spaces. By embedding and then learning the nodes on graphs, most methods are either flexible with different learning objectives or efficient enough for large scale applications. We propose a method to embed a graph into a feature space for a discriminative purpose. Our idea is to include label information into the embedding process, making the space representation tailored to the task. We design embedding objective functions that the following learning formulations become spectral transforms. We then reformulate these spectral transforms into multiple kernel learning problems. Our method, while being tailored to the discriminative tasks, is efficient and can scale to massive data sets. We show the need of discriminative embedding on some simulations. Applying to biological network problems, our method is shown to outperform baselines.

Compositionally modulated multilayer diamond-like carbon coatings with AlTiSi multi-doping by reactive high power impulse magnetron sputtering

NASA Astrophysics Data System (ADS)

Dai, Wei; Gao, Xiang; Liu, Jingmao; Kwon, Se-Hun; Wang, Qimin

2017-12-01

Diamond-like carbon (DLC) coatings with AlTiSi multi-doping were prepared by a reactive high power impulse magnetron sputtering with using a gas mixture of Ar and C2H2 as precursor. The composition, microstructure, compressive stress, and mechanical property of the as-deposited DLC coatings were studied systemically by using SEM, XPS, TEM, Raman spectrum, stress-tester, and nanoindentation as a function of the Ar fraction. The results show that the doping concentrations of the Al, Ti and Si atoms increased as the Ar fraction increased. The doped Ti and Si preferred to bond with C while the doped Al mainly existed in oxidation state without bonding with C. As the doping concentrations increased, TiC carbide nanocrystals were formed in the DLC matrix. The microstructure of coatings changed from an amorphous feature dominant AlTiSi-DLC to a carbide nanocomposite AlTiSi-DLC with TiC nanoparticles embedding. In addition, the coatings exhibited the compositionally modulated multilayer consisting of alternate Al-rich layer and Al-poor layer due to the rotation of the substrate holder and the diffusion behavior of the doped Al which tended to separate from C and diffuse towards the DLC matrix surface owing to its weak interactions with C. The periodic Al-rich layer can effectively release the compressive stress of the coatings. On the other hand, the hard TiC nanoparticles were conducive to the hardness of the coatings. Consequently, the DLC coatings with relatively low residual stress and high hardness could be acquired successfully through AlTiSi multi-doping. It is believed that the AlCrSi multi-doping may be a good way for improving the comprehensive properties of the DLC coatings. In addition, we believe that the DLC coatings with Al-rich multilayered structure have a high oxidation resistance, which allows the DLC coatings application in high temperature environment.

Density-based clustering: A 'landscape view' of multi-channel neural data for inference and dynamic complexity analysis.

PubMed

Baglietto, Gabriel; Gigante, Guido; Del Giudice, Paolo

2017-01-01

Two, partially interwoven, hot topics in the analysis and statistical modeling of neural data, are the development of efficient and informative representations of the time series derived from multiple neural recordings, and the extraction of information about the connectivity structure of the underlying neural network from the recorded neural activities. In the present paper we show that state-space clustering can provide an easy and effective option for reducing the dimensionality of multiple neural time series, that it can improve inference of synaptic couplings from neural activities, and that it can also allow the construction of a compact representation of the multi-dimensional dynamics, that easily lends itself to complexity measures. We apply a variant of the 'mean-shift' algorithm to perform state-space clustering, and validate it on an Hopfield network in the glassy phase, in which metastable states are largely uncorrelated from memories embedded in the synaptic matrix. In this context, we show that the neural states identified as clusters' centroids offer a parsimonious parametrization of the synaptic matrix, which allows a significant improvement in inferring the synaptic couplings from the neural activities. Moving to the more realistic case of a multi-modular spiking network, with spike-frequency adaptation inducing history-dependent effects, we propose a procedure inspired by Boltzmann learning, but extending its domain of application, to learn inter-module synaptic couplings so that the spiking network reproduces a prescribed pattern of spatial correlations; we then illustrate, in the spiking network, how clustering is effective in extracting relevant features of the network's state-space landscape. Finally, we show that the knowledge of the cluster structure allows casting the multi-dimensional neural dynamics in the form of a symbolic dynamics of transitions between clusters; as an illustration of the potential of such reduction, we define and analyze a measure of complexity of the neural time series.

[Research Progress of Multi-Model Medical Image Fusion at Feature Level].

PubMed

Zhang, Junjie; Zhou, Tao; Lu, Huiling; Wang, Huiqun

2016-04-01

Medical image fusion realizes advantage integration of functional images and anatomical images.This article discusses the research progress of multi-model medical image fusion at feature level.We firstly describe the principle of medical image fusion at feature level.Then we analyze and summarize fuzzy sets,rough sets,D-S evidence theory,artificial neural network,principal component analysis and other fusion methods’ applications in medical image fusion and get summery.Lastly,we in this article indicate present problems and the research direction of multi-model medical images in the future.

Feature selection for the classification of traced neurons.

PubMed

López-Cabrera, José D; Lorenzo-Ginori, Juan V

2018-06-01

The great availability of computational tools to calculate the properties of traced neurons leads to the existence of many descriptors which allow the automated classification of neurons from these reconstructions. This situation determines the necessity to eliminate irrelevant features as well as making a selection of the most appropriate among them, in order to improve the quality of the classification obtained. The dataset used contains a total of 318 traced neurons, classified by human experts in 192 GABAergic interneurons and 126 pyramidal cells. The features were extracted by means of the L-measure software, which is one of the most used computational tools in neuroinformatics to quantify traced neurons. We review some current feature selection techniques as filter, wrapper, embedded and ensemble methods. The stability of the feature selection methods was measured. For the ensemble methods, several aggregation methods based on different metrics were applied to combine the subsets obtained during the feature selection process. The subsets obtained applying feature selection methods were evaluated using supervised classifiers, among which Random Forest, C4.5, SVM, Naïve Bayes, Knn, Decision Table and the Logistic classifier were used as classification algorithms. Feature selection methods of types filter, embedded, wrappers and ensembles were compared and the subsets returned were tested in classification tasks for different classification algorithms. L-measure features EucDistanceSD, PathDistanceSD, Branch_pathlengthAve, Branch_pathlengthSD and EucDistanceAve were present in more than 60% of the selected subsets which provides evidence about their importance in the classification of this neurons. Copyright © 2018 Elsevier B.V. All rights reserved.

Label-aligned Multi-task Feature Learning for Multimodal Classification of Alzheimer’s Disease and Mild Cognitive Impairment

PubMed Central

Zu, Chen; Jie, Biao; Liu, Mingxia; Chen, Songcan

2015-01-01

Multimodal classification methods using different modalities of imaging and non-imaging data have recently shown great advantages over traditional single-modality-based ones for diagnosis and prognosis of Alzheimer’s disease (AD), as well as its prodromal stage, i.e., mild cognitive impairment (MCI). However, to the best of our knowledge, most existing methods focus on mining the relationship across multiple modalities of the same subjects, while ignoring the potentially useful relationship across different subjects. Accordingly, in this paper, we propose a novel learning method for multimodal classification of AD/MCI, by fully exploring the relationships across both modalities and subjects. Specifically, our proposed method includes two subsequent components, i.e., label-aligned multi-task feature selection and multimodal classification. In the first step, the feature selection learning from multiple modalities are treated as different learning tasks and a group sparsity regularizer is imposed to jointly select a subset of relevant features. Furthermore, to utilize the discriminative information among labeled subjects, a new label-aligned regularization term is added into the objective function of standard multi-task feature selection, where label-alignment means that all multi-modality subjects with the same class labels should be closer in the new feature-reduced space. In the second step, a multi-kernel support vector machine (SVM) is adopted to fuse the selected features from multi-modality data for final classification. To validate our method, we perform experiments on the Alzheimer’s Disease Neuroimaging Initiative (ADNI) database using baseline MRI and FDG-PET imaging data. The experimental results demonstrate that our proposed method achieves better classification performance compared with several state-of-the-art methods for multimodal classification of AD/MCI. PMID:26572145

Multi-stage classification method oriented to aerial image based on low-rank recovery and multi-feature fusion sparse representation.

PubMed

Ma, Xu; Cheng, Yongmei; Hao, Shuai

2016-12-10

Automatic classification of terrain surfaces from an aerial image is essential for an autonomous unmanned aerial vehicle (UAV) landing at an unprepared site by using vision. Diverse terrain surfaces may show similar spectral properties due to the illumination and noise that easily cause poor classification performance. To address this issue, a multi-stage classification algorithm based on low-rank recovery and multi-feature fusion sparse representation is proposed. First, color moments and Gabor texture feature are extracted from training data and stacked as column vectors of a dictionary. Then we perform low-rank matrix recovery for the dictionary by using augmented Lagrange multipliers and construct a multi-stage terrain classifier. Experimental results on an aerial map database that we prepared verify the classification accuracy and robustness of the proposed method.

CVXPY: A Python-Embedded Modeling Language for Convex Optimization

PubMed Central

Diamond, Steven; Boyd, Stephen

2016-01-01

CVXPY is a domain-specific language for convex optimization embedded in Python. It allows the user to express convex optimization problems in a natural syntax that follows the math, rather than in the restrictive standard form required by solvers. CVXPY makes it easy to combine convex optimization with high-level features of Python such as parallelism and object-oriented design. CVXPY is available at http://www.cvxpy.org/ under the GPL license, along with documentation and examples. PMID:27375369

Microfluidic fuel cell systems with embedded materials and structures and method thereof

DOEpatents

Morse, Jeffrey D.; Rose, Klint A; Maghribi, Mariam; Benett, William; Krulevitch, Peter; Hamilton, Julie; Graff, Robert T.; Jankowski, Alan

2005-07-26

Described herein is a process for fabricating microfluidic systems with embedded components in which micron-scale features are molded into the polymeric material polydimethylsiloxane (PDMS). Micromachining is used to create a mold master and the liquid precursors for PDMS are poured over the mold and allowed to cure. The PDMS is then removed form the mold and bonded to another material such as PDMS, glass, or silicon after a simple surface preparation step to form sealed microchannels.

Selective, Embedded, Just-In-Time Specialization (SEJITS): Portable Parallel Performance from Sequential, Productive, Embedded Domain-Specific Languages

DTIC Science & Technology

2012-12-01

identity operation SIMD Single instruction, multiple datastream parallel computing Scala A byte-compiled programming language featuring dynamic type...Specific Languages 5a. CONTRACT NUMBER FA8750-10-1-0191 5b. GRANT NUMBER N/A 5c. PROGRAM ELEMENT NUMBER 61101E 6. AUTHOR(S) Armando Fox 5d...application performance, but usually must rely on efficiency programmers who are experts in explicit parallel programming to achieve it. Since such efficiency

Time-Frequency Feature Representation Using Multi-Resolution Texture Analysis and Acoustic Activity Detector for Real-Life Speech Emotion Recognition

PubMed Central

Wang, Kun-Ching

2015-01-01

The classification of emotional speech is mostly considered in speech-related research on human-computer interaction (HCI). In this paper, the purpose is to present a novel feature extraction based on multi-resolutions texture image information (MRTII). The MRTII feature set is derived from multi-resolution texture analysis for characterization and classification of different emotions in a speech signal. The motivation is that we have to consider emotions have different intensity values in different frequency bands. In terms of human visual perceptual, the texture property on multi-resolution of emotional speech spectrogram should be a good feature set for emotion classification in speech. Furthermore, the multi-resolution analysis on texture can give a clearer discrimination between each emotion than uniform-resolution analysis on texture. In order to provide high accuracy of emotional discrimination especially in real-life, an acoustic activity detection (AAD) algorithm must be applied into the MRTII-based feature extraction. Considering the presence of many blended emotions in real life, in this paper make use of two corpora of naturally-occurring dialogs recorded in real-life call centers. Compared with the traditional Mel-scale Frequency Cepstral Coefficients (MFCC) and the state-of-the-art features, the MRTII features also can improve the correct classification rates of proposed systems among different language databases. Experimental results show that the proposed MRTII-based feature information inspired by human visual perception of the spectrogram image can provide significant classification for real-life emotional recognition in speech. PMID:25594590

A data-driven dynamics simulation framework for railway vehicles

NASA Astrophysics Data System (ADS)

Nie, Yinyu; Tang, Zhao; Liu, Fengjia; Chang, Jian; Zhang, Jianjun

2018-03-01

The finite element (FE) method is essential for simulating vehicle dynamics with fine details, especially for train crash simulations. However, factors such as the complexity of meshes and the distortion involved in a large deformation would undermine its calculation efficiency. An alternative method, the multi-body (MB) dynamics simulation provides satisfying time efficiency but limited accuracy when highly nonlinear dynamic process is involved. To maintain the advantages of both methods, this paper proposes a data-driven simulation framework for dynamics simulation of railway vehicles. This framework uses machine learning techniques to extract nonlinear features from training data generated by FE simulations so that specific mesh structures can be formulated by a surrogate element (or surrogate elements) to replace the original mechanical elements, and the dynamics simulation can be implemented by co-simulation with the surrogate element(s) embedded into a MB model. This framework consists of a series of techniques including data collection, feature extraction, training data sampling, surrogate element building, and model evaluation and selection. To verify the feasibility of this framework, we present two case studies, a vertical dynamics simulation and a longitudinal dynamics simulation, based on co-simulation with MATLAB/Simulink and Simpack, and a further comparison with a popular data-driven model (the Kriging model) is provided. The simulation result shows that using the legendre polynomial regression model in building surrogate elements can largely cut down the simulation time without sacrifice in accuracy.

Street Viewer: An Autonomous Vision Based Traffic Tracking System.

PubMed

Bottino, Andrea; Garbo, Alessandro; Loiacono, Carmelo; Quer, Stefano

2016-06-03

The development of intelligent transportation systems requires the availability of both accurate traffic information in real time and a cost-effective solution. In this paper, we describe Street Viewer, a system capable of analyzing the traffic behavior in different scenarios from images taken with an off-the-shelf optical camera. Street Viewer operates in real time on embedded hardware architectures with limited computational resources. The system features a pipelined architecture that, on one side, allows one to exploit multi-threading intensively and, on the other side, allows one to improve the overall accuracy and robustness of the system, since each layer is aimed at refining for the following layers the information it receives as input. Another relevant feature of our approach is that it is self-adaptive. During an initial setup, the application runs in learning mode to build a model of the flow patterns in the observed area. Once the model is stable, the system switches to the on-line mode where the flow model is used to count vehicles traveling on each lane and to produce a traffic information summary. If changes in the flow model are detected, the system switches back autonomously to the learning mode. The accuracy and the robustness of the system are analyzed in the paper through experimental results obtained on several different scenarios and running the system for long periods of time.

Change Detection of High-Resolution Remote Sensing Images Based on Adaptive Fusion of Multiple Features

NASA Astrophysics Data System (ADS)

Wang, G. H.; Wang, H. B.; Fan, W. F.; Liu, Y.; Chen, C.

2018-04-01

In view of the traditional change detection algorithm mainly depends on the spectral information image spot, failed to effectively mining and fusion of multi-image feature detection advantage, the article borrows the ideas of object oriented analysis proposed a multi feature fusion of remote sensing image change detection algorithm. First by the multi-scale segmentation of image objects based; then calculate the various objects of color histogram and linear gradient histogram; utilizes the color distance and edge line feature distance between EMD statistical operator in different periods of the object, using the adaptive weighted method, the color feature distance and edge in a straight line distance of combination is constructed object heterogeneity. Finally, the curvature histogram analysis image spot change detection results. The experimental results show that the method can fully fuse the color and edge line features, thus improving the accuracy of the change detection.

Feature-based Alignment of Volumetric Multi-modal Images

PubMed Central

Toews, Matthew; Zöllei, Lilla; Wells, William M.

2014-01-01

This paper proposes a method for aligning image volumes acquired from different imaging modalities (e.g. MR, CT) based on 3D scale-invariant image features. A novel method for encoding invariant feature geometry and appearance is developed, based on the assumption of locally linear intensity relationships, providing a solution to poor repeatability of feature detection in different image modalities. The encoding method is incorporated into a probabilistic feature-based model for multi-modal image alignment. The model parameters are estimated via a group-wise alignment algorithm, that iteratively alternates between estimating a feature-based model from feature data, then realigning feature data to the model, converging to a stable alignment solution with few pre-processing or pre-alignment requirements. The resulting model can be used to align multi-modal image data with the benefits of invariant feature correspondence: globally optimal solutions, high efficiency and low memory usage. The method is tested on the difficult RIRE data set of CT, T1, T2, PD and MP-RAGE brain images of subjects exhibiting significant inter-subject variability due to pathology. PMID:24683955

A High Performance Computer Architecture for Embedded And/Or Multi-Computer Applications

DTIC Science & Technology

1990-09-01

commercially available, real - time operating system . CHOICES and ARTS are real-time operating systems developed at the University of Illinois and CMU...respectively. Selection of a real - time operating system will be made in the next phase of the project. U BIBLIOGRAPHY U Wulf, Wm. A. The WM Computer

An educational video game for nutrition of young people: Theory and design

USDA-ARS?s Scientific Manuscript database

Playing Escape from Diab (DIAB) and Nanoswarm (NANO), epic video game adventures, increased fruit and vegetable consumption among a multi-ethnic sample of 10-12 year old children during pilot testing. Key elements of both games were educational mini-games embedded in the overall game that promoted k...

Structure and Evolution of Scientific Collaboration Networks in a Modern Research Collaboratory

ERIC Educational Resources Information Center

Pepe, Alberto

2010-01-01

This dissertation is a study of scientific collaboration at the Center for Embedded Networked Sensing (CENS), a modern, multi-disciplinary, distributed laboratory involved in sensor network research. By use of survey research and network analysis, this dissertation examines the collaborative ecology of CENS in terms of three networks of…

ETHERNET BASED EMBEDDED SYSTEM FOR FEL DIAGNOSTICS AND CONTROLS

DOE Office of Scientific and Technical Information (OSTI.GOV)

Jianxun Yan; Daniel Sexton; Steven Moore

2006-10-24

An Ethernet based embedded system has been developed to upgrade the Beam Viewer and Beam Position Monitor (BPM) systems within the free-electron laser (FEL) project at Jefferson Lab. The embedded microcontroller was mounted on the front-end I/O cards with software packages such as Experimental Physics and Industrial Control System (EPICS) and Real Time Executive for Multiprocessor System (RTEMS) running as an Input/Output Controller (IOC). By cross compiling with the EPICS, the RTEMS kernel, IOC device supports, and databases all of these can be downloaded into the microcontroller. The first version of the BPM electronics based on the embedded controller wasmore » built and is currently running in our FEL system. The new version of BPM that will use a Single Board IOC (SBIOC), which integrates with an Field Programming Gate Array (FPGA) and a ColdFire embedded microcontroller, is presently under development. The new system has the features of a low cost IOC, an open source real-time operating system, plug&play-like ease of installation and flexibility, and provides a much more localized solution.« less

Failure and recovery in dynamical networks.

PubMed

Böttcher, L; Luković, M; Nagler, J; Havlin, S; Herrmann, H J

2017-02-03

Failure, damage spread and recovery crucially underlie many spatially embedded networked systems ranging from transportation structures to the human body. Here we study the interplay between spontaneous damage, induced failure and recovery in both embedded and non-embedded networks. In our model the network's components follow three realistic processes that capture these features: (i) spontaneous failure of a component independent of the neighborhood (internal failure), (ii) failure induced by failed neighboring nodes (external failure) and (iii) spontaneous recovery of a component. We identify a metastable domain in the global network phase diagram spanned by the model's control parameters where dramatic hysteresis effects and random switching between two coexisting states are observed. This dynamics depends on the characteristic link length of the embedded system. For the Euclidean lattice in particular, hysteresis and switching only occur in an extremely narrow region of the parameter space compared to random networks. We develop a unifying theory which links the dynamics of our model to contact processes. Our unifying framework may help to better understand controllability in spatially embedded and random networks where spontaneous recovery of components can mitigate spontaneous failure and damage spread in dynamical networks.

Self-consistent Green's function embedding for advanced electronic structure methods based on a dynamical mean-field concept

NASA Astrophysics Data System (ADS)

Chibani, Wael; Ren, Xinguo; Scheffler, Matthias; Rinke, Patrick

2016-04-01

We present an embedding scheme for periodic systems that facilitates the treatment of the physically important part (here a unit cell or a supercell) with advanced electronic structure methods, that are computationally too expensive for periodic systems. The rest of the periodic system is treated with computationally less demanding approaches, e.g., Kohn-Sham density-functional theory, in a self-consistent manner. Our scheme is based on the concept of dynamical mean-field theory formulated in terms of Green's functions. Our real-space dynamical mean-field embedding scheme features two nested Dyson equations, one for the embedded cluster and another for the periodic surrounding. The total energy is computed from the resulting Green's functions. The performance of our scheme is demonstrated by treating the embedded region with hybrid functionals and many-body perturbation theory in the GW approach for simple bulk systems. The total energy and the density of states converge rapidly with respect to the computational parameters and approach their bulk limit with increasing cluster (i.e., computational supercell) size.

Multi-class computational evolution: development, benchmark evaluation and application to RNA-Seq biomarker discovery.

PubMed

Crabtree, Nathaniel M; Moore, Jason H; Bowyer, John F; George, Nysia I

2017-01-01

A computational evolution system (CES) is a knowledge discovery engine that can identify subtle, synergistic relationships in large datasets. Pareto optimization allows CESs to balance accuracy with model complexity when evolving classifiers. Using Pareto optimization, a CES is able to identify a very small number of features while maintaining high classification accuracy. A CES can be designed for various types of data, and the user can exploit expert knowledge about the classification problem in order to improve discrimination between classes. These characteristics give CES an advantage over other classification and feature selection algorithms, particularly when the goal is to identify a small number of highly relevant, non-redundant biomarkers. Previously, CESs have been developed only for binary class datasets. In this study, we developed a multi-class CES. The multi-class CES was compared to three common feature selection and classification algorithms: support vector machine (SVM), random k-nearest neighbor (RKNN), and random forest (RF). The algorithms were evaluated on three distinct multi-class RNA sequencing datasets. The comparison criteria were run-time, classification accuracy, number of selected features, and stability of selected feature set (as measured by the Tanimoto distance). The performance of each algorithm was data-dependent. CES performed best on the dataset with the smallest sample size, indicating that CES has a unique advantage since the accuracy of most classification methods suffer when sample size is small. The multi-class extension of CES increases the appeal of its application to complex, multi-class datasets in order to identify important biomarkers and features.

More than Words: Fast Acquisition and Generalization of Orthographic Regularities during Novel Word Learning in Adults

ERIC Educational Resources Information Center

Laine, Matti; Polonyi, Tünde; Abari, Kálmán

2014-01-01

In literates, reading is a fundamental channel for acquiring new vocabulary both in the mother tongue and in foreign languages. By using an artificial language learning task, we examined the acquisition of novel written words and their embedded regularities (an orthographic surface feature and a syllabic feature) in three groups of university…

Multi-Feature Classification of Multi-Sensor Satellite Imagery Based on Dual-Polarimetric Sentinel-1A, Landsat-8 OLI, and Hyperion Images for Urban Land-Cover Classification

PubMed Central

Pan, Jianjun

2018-01-01

This paper focuses on evaluating the ability and contribution of using backscatter intensity, texture, coherence, and color features extracted from Sentinel-1A data for urban land cover classification and comparing different multi-sensor land cover mapping methods to improve classification accuracy. Both Landsat-8 OLI and Hyperion images were also acquired, in combination with Sentinel-1A data, to explore the potential of different multi-sensor urban land cover mapping methods to improve classification accuracy. The classification was performed using a random forest (RF) method. The results showed that the optimal window size of the combination of all texture features was 9 × 9, and the optimal window size was different for each individual texture feature. For the four different feature types, the texture features contributed the most to the classification, followed by the coherence and backscatter intensity features; and the color features had the least impact on the urban land cover classification. Satisfactory classification results can be obtained using only the combination of texture and coherence features, with an overall accuracy up to 91.55% and a kappa coefficient up to 0.8935, respectively. Among all combinations of Sentinel-1A-derived features, the combination of the four features had the best classification result. Multi-sensor urban land cover mapping obtained higher classification accuracy. The combination of Sentinel-1A and Hyperion data achieved higher classification accuracy compared to the combination of Sentinel-1A and Landsat-8 OLI images, with an overall accuracy of up to 99.12% and a kappa coefficient up to 0.9889. When Sentinel-1A data was added to Hyperion images, the overall accuracy and kappa coefficient were increased by 4.01% and 0.0519, respectively. PMID:29382073

Data Mining and Machine Learning Models for Predicting Drug Likeness and Their Disease or Organ Category.

PubMed

Yosipof, Abraham; Guedes, Rita C; García-Sosa, Alfonso T

2018-01-01

Data mining approaches can uncover underlying patterns in chemical and pharmacological property space decisive for drug discovery and development. Two of the most common approaches are visualization and machine learning methods. Visualization methods use dimensionality reduction techniques in order to reduce multi-dimension data into 2D or 3D representations with a minimal loss of information. Machine learning attempts to find correlations between specific activities or classifications for a set of compounds and their features by means of recurring mathematical models. Both models take advantage of the different and deep relationships that can exist between features of compounds, and helpfully provide classification of compounds based on such features or in case of visualization methods uncover underlying patterns in the feature space. Drug-likeness has been studied from several viewpoints, but here we provide the first implementation in chemoinformatics of the t-Distributed Stochastic Neighbor Embedding (t-SNE) method for the visualization and the representation of chemical space, and the use of different machine learning methods separately and together to form a new ensemble learning method called AL Boost. The models obtained from AL Boost synergistically combine decision tree, random forests (RF), support vector machine (SVM), artificial neural network (ANN), k nearest neighbors (kNN), and logistic regression models. In this work, we show that together they form a predictive model that not only improves the predictive force but also decreases bias. This resulted in a corrected classification rate of over 0.81, as well as higher sensitivity and specificity rates for the models. In addition, separation and good models were also achieved for disease categories such as antineoplastic compounds and nervous system diseases, among others. Such models can be used to guide decision on the feature landscape of compounds and their likeness to either drugs or other characteristics, such as specific or multiple disease-category(ies) or organ(s) of action of a molecule.

The effect of embedded bonus rounds on slot machine preference.

PubMed

Belisle, Jordan; Owens, Kelti; Dixon, Mark R; Malkin, Albert; Jordan, Sam D

2017-04-01

Twenty-three university students completed a simulated slot machine task involving the concurrent presentation of two slot machines that were varied both in win density and the inclusion of a bonus round feature to evaluate the effect of embedded bonus rounds on participant response allocation. The results suggest that participants allocated a greater percentage of responses to machines with embedded bonus rounds across both dense (Bonus: M = 68.4, SD = 19.2; No Bonus: M = 51.2; 9.6) and lean (Bonus: M = 48.8, SD = 9.6; No Bonus: M = 31.6, SD = 19.2) reinforcement schedules, in which the overall reinforcement rate across all machines was held constant. © 2016 Society for the Experimental Analysis of Behavior.

Polarization-Analyzing CMOS Image Sensor With Monolithically Embedded Polarizer for Microchemistry Systems.

PubMed

Tokuda, T; Yamada, H; Sasagawa, K; Ohta, J

2009-10-01

This paper proposes and demonstrates a polarization-analyzing CMOS sensor based on image sensor architecture. The sensor was designed targeting applications for chiral analysis in a microchemistry system. The sensor features a monolithically embedded polarizer. Embedded polarizers with different angles were implemented to realize a real-time absolute measurement of the incident polarization angle. Although the pixel-level performance was confirmed to be limited, estimation schemes based on the variation of the polarizer angle provided a promising performance for real-time polarization measurements. An estimation scheme using 180 pixels in a 1deg step provided an estimation accuracy of 0.04deg. Polarimetric measurements of chiral solutions were also successfully performed to demonstrate the applicability of the sensor to optical chiral analysis.

Preparation and application of a carbon paste electrode modified with multi-walled carbon nanotubes and boron-embedded molecularly imprinted composite membranes.

PubMed

Wang, Hongjuan; Qian, Duo; Xiao, Xilin; Deng, Chunyan; Liao, Lifu; Deng, Jian; Lin, Ying-Wu

2018-06-01

An innovative electrochemical sensor was fabricated for the sensitive and selective determination of tinidazole (TNZ), based on a carbon paste electrode (CPE) modified with multi-walled carbon nanotubes (MWCNTs) and boron-embedded molecularly imprinted composite membranes (B-MICMs). Density functional theory (DFT) calculations were carried out to investigate the utility of template-monomer interactions to screen appropriate monomers for the rational design of B-MICMs. The distinct synergic effect of MWCNTs and B-MICMs was evidenced by the positive shift of the reduction peak potential of TNZ at B-MICMs/MWCNTs modified CPE (B-MICMs/MWCNTs/CPE) by about 200 mV, and the 12-fold amplification of the peak current, compared with a bare carbon paste electrode (CPE). Moreover, the coordinate interactions between trisubstituted boron atoms embedded in B-MICMs matrix and nitrogen atoms of TNZ endow the sensor with advanced affinity and specific directionality. Thereafter, a highly sensitive electrochemical analytical method for TNZ was established by different pulse voltammetry (DPV) at B-MICMs/MWCNTs/CPE with a lower detection limit (1.25 × 10 -12  mol L -1 ) (S/N = 3). The practical application of the sensor was demonstrated by determining TNZ in pharmaceutical and biological samples with good precision (RSD 1.36% to 3.85%) and acceptable recoveries (82.40%-104.0%). Copyright © 2018 Elsevier B.V. All rights reserved.

Design and fabrication of an IPMC-embedded tube for minimally invasive surgery applications

NASA Astrophysics Data System (ADS)

Liu, Jiayu; Wang, Yanjie; Zhao, Dongxu; Zhang, Chi; Chen, Hualing; Li, Dichen

2014-03-01

Minimally Invasive Surgery (MIS) is receiving much attention for a number of reasons, including less trauma, faster recovery and enhanced precision. The traditional robotic actuators do not have the capabilities required to fulfill the demand for new applications in MIS. Ionic Polymer-Metal Composite (IPMC), one of the most promising smart materials, has extensive desirable characteristics such as low actuation voltage, large bending deformation and high functionality. Compared with traditional actuators, IPMCs can mimic biological muscle and are highly promising for actuation in robotic surgery. In this paper, a new approach which involves molding and integrating IPMC actuators into a soft silicone tube to create an active actuating tube capable of multi-degree-of-freedom motion is presented. First, according to the structure and performance requirements of the actuating tube, the biaxial bending IPMC actuators fabricated by using solution casting method have been implemented. The silicone was cured at a suitable temperature to form a flexible tube using molds fabricated by 3D Printing technology. Then an assembly based fabrication process was used to mold or integrate biaxial bending IPMC actuators into the soft silicone material to create an active control tube. The IPMC-embedded tube can generate multi-degree-of-freedom motions by controlling each IPMC actuator. Furthermore, the basic performance of the actuators was analyzed, including the displacement and the response speed. Experimental results indicate that IPMC-embedded tubes are promising for applications in MIS.

Time reversal optical tomography locates fluorescent targets in a turbid medium

NASA Astrophysics Data System (ADS)

Wu, Binlin; Cai, W.; Gayen, S. K.

2013-03-01

A fluorescence optical tomography approach that extends time reversal optical tomography (TROT) to locate fluorescent targets embedded in a turbid medium is introduced. It uses a multi-source illumination and multi-detector signal acquisition scheme, along with TR matrix formalism, and multiple signal classification (MUSIC) to construct pseudo-image of the targets. The samples consisted of a single or two small tubes filled with water solution of Indocyanine Green (ICG) dye as targets embedded in a 250 mm × 250 mm × 60 mm rectangular cell filled with Intralipid-20% suspension as the scattering medium. The ICG concentration was 1μM, and the Intralipid-20% concentration was adjusted to provide ~ 1-mm transport length for both excitation wavelength of 790 nm and fluorescence wavelength around 825 nm. The data matrix was constructed using the diffusely transmitted fluorescence signals for all scan positions, and the TR matrix was constructed by multiplying data matrix with its transpose. A pseudo spectrum was calculated using the signal subspace of the TR matrix. Tomographic images were generated using the pseudo spectrum. The peaks in the pseudo images provided locations of the target(s) with sub-millimeter accuracy. Concurrent transmission TROT measurements corroborated fluorescence-TROT findings. The results demonstrate that TROT is a fast approach that can be used to obtain accurate three-dimensional position information of fluorescence targets embedded deep inside a highly scattering medium, such as, a contrast-enhanced tumor in a human breast.

Software defined multi-spectral imaging for Arctic sensor networks

NASA Astrophysics Data System (ADS)

Siewert, Sam; Angoth, Vivek; Krishnamurthy, Ramnarayan; Mani, Karthikeyan; Mock, Kenrick; Singh, Surjith B.; Srivistava, Saurav; Wagner, Chris; Claus, Ryan; Vis, Matthew Demi

2016-05-01

Availability of off-the-shelf infrared sensors combined with high definition visible cameras has made possible the construction of a Software Defined Multi-Spectral Imager (SDMSI) combining long-wave, near-infrared and visible imaging. The SDMSI requires a real-time embedded processor to fuse images and to create real-time depth maps for opportunistic uplink in sensor networks. Researchers at Embry Riddle Aeronautical University working with University of Alaska Anchorage at the Arctic Domain Awareness Center and the University of Colorado Boulder have built several versions of a low-cost drop-in-place SDMSI to test alternatives for power efficient image fusion. The SDMSI is intended for use in field applications including marine security, search and rescue operations and environmental surveys in the Arctic region. Based on Arctic marine sensor network mission goals, the team has designed the SDMSI to include features to rank images based on saliency and to provide on camera fusion and depth mapping. A major challenge has been the design of the camera computing system to operate within a 10 to 20 Watt power budget. This paper presents a power analysis of three options: 1) multi-core, 2) field programmable gate array with multi-core, and 3) graphics processing units with multi-core. For each test, power consumed for common fusion workloads has been measured at a range of frame rates and resolutions. Detailed analyses from our power efficiency comparison for workloads specific to stereo depth mapping and sensor fusion are summarized. Preliminary mission feasibility results from testing with off-the-shelf long-wave infrared and visible cameras in Alaska and Arizona are also summarized to demonstrate the value of the SDMSI for applications such as ice tracking, ocean color, soil moisture, animal and marine vessel detection and tracking. The goal is to select the most power efficient solution for the SDMSI for use on UAVs (Unoccupied Aerial Vehicles) and other drop-in-place installations in the Arctic. The prototype selected will be field tested in Alaska in the summer of 2016.

Man-machine cooperation in advanced teleoperation

NASA Technical Reports Server (NTRS)

Fiorini, Paolo; Das, Hari; Lee, Sukhan

1993-01-01

Teleoperation experiments at JPL have shown that advanced features in a telerobotic system are a necessary condition for good results, but that they are not sufficient to assure consistently good performance by the operators. Two or three operators are normally used during training and experiments to maintain the desired performance. An alternative to this multi-operator control station is a man-machine interface embedding computer programs that can perform some of the operator's functions. In this paper we present our first experiments with these concepts, in which we focused on the areas of real-time task monitoring and interactive path planning. In the first case, when performing a known task, the operator has an automatic aid for setting control parameters and camera views. In the second case, an interactive path planner will rank different path alternatives so that the operator will make the correct control decision. The monitoring function has been implemented with a neural network doing the real-time task segmentation. The interactive path planner was implemented for redundant manipulators to specify arm configurations across the desired path and satisfy geometric, task, and performance constraints.

TWIST and p-Akt immunoexpression in normal oral epithelium oral dysplasia and in oral squamous cell carcinoma

PubMed Central

Yamamoto, Fernanda-Paula; Corrêa Pontes, Flávia-Sirotheau; Cury, Sérgio-Elias; Fonseca, Felipe-Paiva; Rebelo-Pontes, Hélder; Pinto-Júnior, Décio-dos Santos

2012-01-01

Objectives: The aim of this study was to evaluate the immunoexpression of TWIST and p-Akt proteins in oral leukoplakia (OL) and oral squamous cell carcinoma (OSCC), correlating their expressions with the histological features of the lesions. Study design: Immunohistochemical studies were carried out on 10 normal oral epithelium, 30 OL and 20 OSCC formalin-fixed, paraffin-embedded tissue samples. Immunoperoxidase reactions for TWIST and p-Akt proteins were applied on the specimens and the positivity of the reactions was calculated for 1000 epithelial cells. Results: Kruskal-Wallis and Dunn’s post tests revealed a significant difference in TWIST and p-Akt immunoexpression among normal oral mucosa, OL and OSCC. In addition, a significant positive correlation was found between TWIST and p-Akt expressions according to the Pearson’s correlation test. Conclusions: The results obtained in the current study suggest that TWIST and p-Akt may participate of the multi-step process of oral carcinogenesis since its early stages. Key words: Oral cancer, oral leukoplakia, dysplasia, immunohistochemistry. PMID:21743395

Arbitrating Control of Control and Display Units

NASA Technical Reports Server (NTRS)

Sugden, Paul C.

2007-01-01

The ARINC 739 Switch is a computer program that arbitrates control of two multi-function control and display units (MCDUs) between (1) a commercial flight-management computer (FMC) and (2) NASA software used in research on transport aircraft. (MCDUs are the primary interfaces between pilots and FMCs on many commercial aircraft.) This program was recently redesigned into a software library that can be embedded in research application programs. As part of the redesign, this software was combined with software for creating custom pages of information to be displayed on a CDU. This software commands independent switching of the left (pilot s) and right (copilot s) MCDUs. For example, a custom CDU page can control the left CDU while the FMC controls the right CDU. The software uses menu keys to switch control of the CDU between the FMC or a custom CDU page. The software provides an interface that enables custom CDU pages to insert keystrokes into the FMC s CDU input interface. This feature allows the custom CDU pages to manipulate the FMC as if it were a pilot.

KISS for STRAP: user extensions for a protein alignment editor.

PubMed

Gille, Christoph; Lorenzen, Stephan; Michalsky, Elke; Frömmel, Cornelius

2003-12-12

The Structural Alignment Program STRAP is a comfortable comprehensive editor and analyzing tool for protein alignments. A wide range of functions related to protein sequences and protein structures are accessible with an intuitive graphical interface. Recent features include mapping of mutations and polymorphisms onto structures and production of high quality figures for publication. Here we address the general problem of multi-purpose program packages to keep up with the rapid development of bioinformatical methods and the demand for specific program functions. STRAP was remade implementing a novel design which aims at Keeping Interfaces in STRAP Simple (KISS). KISS renders STRAP extendable to bio-scientists as well as to bio-informaticians. Scientists with basic computer skills are capable of implementing statistical methods or embedding existing bioinformatical tools in STRAP themselves. For bio-informaticians STRAP may serve as an environment for rapid prototyping and testing of complex algorithms such as automatic alignment algorithms or phylogenetic methods. Further, STRAP can be applied as an interactive web applet to present data related to a particular protein family and as a teaching tool. JAVA-1.4 or higher. http://www.charite.de/bioinf/strap/

Service Management Database for DSN Equipment

NASA Technical Reports Server (NTRS)

Zendejas, Silvino; Bui, Tung; Bui, Bach; Malhotra, Shantanu; Chen, Fannie; Wolgast, Paul; Allen, Christopher; Luong, Ivy; Chang, George; Sadaqathulla, Syed

2009-01-01

This data- and event-driven persistent storage system leverages the use of commercial software provided by Oracle for portability, ease of maintenance, scalability, and ease of integration with embedded, client-server, and multi-tiered applications. In this role, the Service Management Database (SMDB) is a key component of the overall end-to-end process involved in the scheduling, preparation, and configuration of the Deep Space Network (DSN) equipment needed to perform the various telecommunication services the DSN provides to its customers worldwide. SMDB makes efficient use of triggers, stored procedures, queuing functions, e-mail capabilities, data management, and Java integration features provided by the Oracle relational database management system. SMDB uses a third normal form schema design that allows for simple data maintenance procedures and thin layers of integration with client applications. The software provides an integrated event logging system with ability to publish events to a JMS messaging system for synchronous and asynchronous delivery to subscribed applications. It provides a structured classification of events and application-level messages stored in database tables that are accessible by monitoring applications for real-time monitoring or for troubleshooting and analysis over historical archives.

The CWB2 Cell Wall-Anchoring Module Is Revealed by the Crystal Structures of the Clostridium difficile Cell Wall Proteins Cwp8 and Cwp6.

PubMed

Usenik, Aleksandra; Renko, Miha; Mihelič, Marko; Lindič, Nataša; Borišek, Jure; Perdih, Andrej; Pretnar, Gregor; Müller, Uwe; Turk, Dušan

2017-03-07

Bacterial cell wall proteins play crucial roles in cell survival, growth, and environmental interactions. In Gram-positive bacteria, cell wall proteins include several types that are non-covalently attached via cell wall binding domains. Of the two conserved surface-layer (S-layer)-anchoring modules composed of three tandem SLH or CWB2 domains, the latter have so far eluded structural insight. The crystal structures of Cwp8 and Cwp6 reveal multi-domain proteins, each containing an embedded CWB2 module. It consists of a triangular trimer of Rossmann-fold CWB2 domains, a feature common to 29 cell wall proteins in Clostridium difficile 630. The structural basis of the intact module fold necessary for its binding to the cell wall is revealed. A comparison with previously reported atomic force microscopy data of S-layers suggests that C. difficile S-layers are complex oligomeric structures, likely composed of several different proteins. Copyright © 2017 Elsevier Ltd. All rights reserved.

Transparent self-cleaning dust shield

DOEpatents

Mazumder, Malay K.; Sims, Robert A.; Wilson, James D.

2005-06-28

A transparent electromagnetic shield to protect solar panels and the like from dust deposition. The shield is a panel of clear non-conducting (dielectric) material with embedded parallel electrodes. The panel is coated with a semiconducting film. Desirably the electrodes are transparent. The electrodes are connected to a single-phase AC signal or to a multi-phase AC signal that produces a travelling electromagnetic wave. The electromagnetic field produced by the electrodes lifts dust particles away from the shield and repels charged particles. Deposited dust particles are removed when the electrodes are activated, regardless of the resistivity of the dust. Electrostatic charges on the panel are discharged by the semiconducting film. When used in conjunction with photovoltaic cells, the power for the device may be obtained from the cells themselves. For other surfaces, such as windshields, optical windows and the like, the power must be derived from an external source. One embodiment of the invention employs monitoring and detection devices to determine when the level of obscuration of the screen by dust has reached a threshold level requiring activation of the dust removal feature.

Thermally stratified squeezed flow between two vertical Riga plates with no slip conditions

NASA Astrophysics Data System (ADS)

Farooq, M.; Mansoor, Zahira; Ijaz Khan, M.; Hayat, T.; Anjum, A.; Mir, N. A.

2018-04-01

This paper demonstrates the mixed convective squeezing nanomaterials flow between two vertical plates, one of which is a Riga plate embedded in a thermally stratified medium subject to convective boundary conditions. Heat transfer features are elaborated with viscous dissipation. Single-wall and multi-wall carbon nanotubes are taken as nanoparticles to form a homogeneous solution in the water. A non-linear system of differential equations is obtained for the considered flow by using suitable transformations. Convergence analysis for velocity and temperature is computed and discussed explicitly through BVPh 2.0. Residual errors are also computed by BVPh 2.0 for the dimensionless governing equations. We introduce two undetermined convergence control parameters, i.e. \\hslash_{θ} and \\hslashf , to compute the lowest entire error. The average residual error for the k -th-order approximation is given in a table. The effects of different flow variables on temperature and velocity distributions are sketched graphically and discussed comprehensively. Furthermore the coefficient of skin friction and the Nusselt number are also analyzed through graphical data.

Discriminative Multi-View Interactive Image Re-Ranking.

PubMed

Li, Jun; Xu, Chang; Yang, Wankou; Sun, Changyin; Tao, Dacheng

2017-07-01

Given an unreliable visual patterns and insufficient query information, content-based image retrieval is often suboptimal and requires image re-ranking using auxiliary information. In this paper, we propose a discriminative multi-view interactive image re-ranking (DMINTIR), which integrates user relevance feedback capturing users' intentions and multiple features that sufficiently describe the images. In DMINTIR, heterogeneous property features are incorporated in the multi-view learning scheme to exploit their complementarities. In addition, a discriminatively learned weight vector is obtained to reassign updated scores and target images for re-ranking. Compared with other multi-view learning techniques, our scheme not only generates a compact representation in the latent space from the redundant multi-view features but also maximally preserves the discriminative information in feature encoding by the large-margin principle. Furthermore, the generalization error bound of the proposed algorithm is theoretically analyzed and shown to be improved by the interactions between the latent space and discriminant function learning. Experimental results on two benchmark data sets demonstrate that our approach boosts baseline retrieval quality and is competitive with the other state-of-the-art re-ranking strategies.

MultiMiTar: a novel multi objective optimization based miRNA-target prediction method.

PubMed

Mitra, Ramkrishna; Bandyopadhyay, Sanghamitra

2011-01-01

Machine learning based miRNA-target prediction algorithms often fail to obtain a balanced prediction accuracy in terms of both sensitivity and specificity due to lack of the gold standard of negative examples, miRNA-targeting site context specific relevant features and efficient feature selection process. Moreover, all the sequence, structure and machine learning based algorithms are unable to distribute the true positive predictions preferentially at the top of the ranked list; hence the algorithms become unreliable to the biologists. In addition, these algorithms fail to obtain considerable combination of precision and recall for the target transcripts that are translationally repressed at protein level. In the proposed article, we introduce an efficient miRNA-target prediction system MultiMiTar, a Support Vector Machine (SVM) based classifier integrated with a multiobjective metaheuristic based feature selection technique. The robust performance of the proposed method is mainly the result of using high quality negative examples and selection of biologically relevant miRNA-targeting site context specific features. The features are selected by using a novel feature selection technique AMOSA-SVM, that integrates the multi objective optimization technique Archived Multi-Objective Simulated Annealing (AMOSA) and SVM. MultiMiTar is found to achieve much higher Matthew's correlation coefficient (MCC) of 0.583 and average class-wise accuracy (ACA) of 0.8 compared to the others target prediction methods for a completely independent test data set. The obtained MCC and ACA values of these algorithms range from -0.269 to 0.155 and 0.321 to 0.582, respectively. Moreover, it shows a more balanced result in terms of precision and sensitivity (recall) for the translationally repressed data set as compared to all the other existing methods. An important aspect is that the true positive predictions are distributed preferentially at the top of the ranked list that makes MultiMiTar reliable for the biologists. MultiMiTar is now available as an online tool at www.isical.ac.in/~bioinfo_miu/multimitar.htm. MultiMiTar software can be downloaded from www.isical.ac.in/~bioinfo_miu/multimitar-download.htm.

Salient object detection based on multi-scale contrast.

PubMed

Wang, Hai; Dai, Lei; Cai, Yingfeng; Sun, Xiaoqiang; Chen, Long

2018-05-01

Due to the development of deep learning networks, a salient object detection based on deep learning networks, which are used to extract the features, has made a great breakthrough compared to the traditional methods. At present, the salient object detection mainly relies on very deep convolutional network, which is used to extract the features. In deep learning networks, an dramatic increase of network depth may cause more training errors instead. In this paper, we use the residual network to increase network depth and to mitigate the errors caused by depth increase simultaneously. Inspired by image simplification, we use color and texture features to obtain simplified image with multiple scales by means of region assimilation on the basis of super-pixels in order to reduce the complexity of images and to improve the accuracy of salient target detection. We refine the feature on pixel level by the multi-scale feature correction method to avoid the feature error when the image is simplified at the above-mentioned region level. The final full connection layer not only integrates features of multi-scale and multi-level but also works as classifier of salient targets. The experimental results show that proposed model achieves better results than other salient object detection models based on original deep learning networks. Copyright © 2018 Elsevier Ltd. All rights reserved.

Multi-element germanium detectors for synchrotron applications

DOE PAGES

Rumaiz, A. K.; Kuczewski, A. J.; Mead, J.; ...

2018-04-27

In this paper, we have developed a series of monolithic multi-element germanium detectors, based on sensor arrays produced by the Forschungzentrum Julich, and on Application-specific integrated circuits (ASICs) developed at Brookhaven. Devices have been made with element counts ranging from 64 to 384. These detectors are being used at NSLS-II and APS for a range of diffraction experiments, both monochromatic and energy-dispersive. Compact and powerful readout systems have been developed, based on the new generation of FPGA system-on-chip devices, which provide closely coupled multi-core processors embedded in large gate arrays. Finally, we will discuss the technical details of the systems,more » and present some of the results from them.« less

Multi-element germanium detectors for synchrotron applications

DOE Office of Scientific and Technical Information (OSTI.GOV)

Rumaiz, A. K.; Kuczewski, A. J.; Mead, J.

In this paper, we have developed a series of monolithic multi-element germanium detectors, based on sensor arrays produced by the Forschungzentrum Julich, and on Application-specific integrated circuits (ASICs) developed at Brookhaven. Devices have been made with element counts ranging from 64 to 384. These detectors are being used at NSLS-II and APS for a range of diffraction experiments, both monochromatic and energy-dispersive. Compact and powerful readout systems have been developed, based on the new generation of FPGA system-on-chip devices, which provide closely coupled multi-core processors embedded in large gate arrays. Finally, we will discuss the technical details of the systems,more » and present some of the results from them.« less

A variable-gain output feedback control design approach

NASA Technical Reports Server (NTRS)

Haylo, Nesim

1989-01-01

A multi-model design technique to find a variable-gain control law defined over the whole operating range is proposed. The design is formulated as an optimal control problem which minimizes a cost function weighing the performance at many operating points. The solution is obtained by embedding into the Multi-Configuration Control (MCC) problem, a multi-model robust control design technique. In contrast to conventional gain scheduling which uses a curve fit of single model designs, the optimal variable-gain control law stabilizes the plant at every operating point included in the design. An iterative algorithm to compute the optimal control gains is presented. The methodology has been successfully applied to reconfigurable aircraft flight control and to nonlinear flight control systems.

Multi-physics optimization of three-dimensional microvascular polymeric components

NASA Astrophysics Data System (ADS)

Aragón, Alejandro M.; Saksena, Rajat; Kozola, Brian D.; Geubelle, Philippe H.; Christensen, Kenneth T.; White, Scott R.

2013-01-01

This work discusses the computational design of microvascular polymeric materials, which aim at mimicking the behavior found in some living organisms that contain a vascular system. The optimization of the topology of the embedded three-dimensional microvascular network is carried out by coupling a multi-objective constrained genetic algorithm with a finite-element based physics solver, the latter validated through experiments. The optimization is carried out on multiple conflicting objective functions, namely the void volume fraction left by the network, the energy required to drive the fluid through the network and the maximum temperature when the material is subjected to thermal loads. The methodology presented in this work results in a viable alternative for the multi-physics optimization of these materials for active-cooling applications.

A sea-land segmentation algorithm based on multi-feature fusion for a large-field remote sensing image

NASA Astrophysics Data System (ADS)

Li, Jing; Xie, Weixin; Pei, Jihong

2018-03-01

Sea-land segmentation is one of the key technologies of sea target detection in remote sensing images. At present, the existing algorithms have the problems of low accuracy, low universality and poor automatic performance. This paper puts forward a sea-land segmentation algorithm based on multi-feature fusion for a large-field remote sensing image removing island. Firstly, the coastline data is extracted and all of land area is labeled by using the geographic information in large-field remote sensing image. Secondly, three features (local entropy, local texture and local gradient mean) is extracted in the sea-land border area, and the three features combine a 3D feature vector. And then the MultiGaussian model is adopted to describe 3D feature vectors of sea background in the edge of the coastline. Based on this multi-gaussian sea background model, the sea pixels and land pixels near coastline are classified more precise. Finally, the coarse segmentation result and the fine segmentation result are fused to obtain the accurate sea-land segmentation. Comparing and analyzing the experimental results by subjective vision, it shows that the proposed method has high segmentation accuracy, wide applicability and strong anti-disturbance ability.

A Multi-Modal Face Recognition Method Using Complete Local Derivative Patterns and Depth Maps

PubMed Central

Yin, Shouyi; Dai, Xu; Ouyang, Peng; Liu, Leibo; Wei, Shaojun

2014-01-01

In this paper, we propose a multi-modal 2D + 3D face recognition method for a smart city application based on a Wireless Sensor Network (WSN) and various kinds of sensors. Depth maps are exploited for the 3D face representation. As for feature extraction, we propose a new feature called Complete Local Derivative Pattern (CLDP). It adopts the idea of layering and has four layers. In the whole system, we apply CLDP separately on Gabor features extracted from a 2D image and depth map. Then, we obtain two features: CLDP-Gabor and CLDP-Depth. The two features weighted by the corresponding coefficients are combined together in the decision level to compute the total classification distance. At last, the probe face is assigned the identity with the smallest classification distance. Extensive experiments are conducted on three different databases. The results demonstrate the robustness and superiority of the new approach. The experimental results also prove that the proposed multi-modal 2D + 3D method is superior to other multi-modal ones and CLDP performs better than other Local Binary Pattern (LBP) based features. PMID:25333290

A wireless modular multi-modal multi-node patch platform for robust biosignal monitoring.

PubMed

Pantelopoulos, Alexandros; Saldivar, Enrique; Roham, Masoud

2011-01-01

In this paper a wireless modular, multi-modal, multi-node patch platform is described. The platform comprises low-cost semi-disposable patch design aiming at unobtrusive ambulatory monitoring of multiple physiological parameters. Owing to its modular design it can be interfaced with various low-power RF communication and data storage technologies, while the data fusion of multi-modal and multi-node features facilitates measurement of several biosignals from multiple on-body locations for robust feature extraction. Preliminary results of the patch platform are presented which illustrate the capability to extract respiration rate from three different independent metrics, which combined together can give a more robust estimate of the actual respiratory rate.

Real-time, resource-constrained object classification on a micro-air vehicle

NASA Astrophysics Data System (ADS)

Buck, Louis; Ray, Laura

2013-12-01

A real-time embedded object classification algorithm is developed through the novel combination of binary feature descriptors, a bag-of-visual-words object model and the cortico-striatal loop (CSL) learning algorithm. The BRIEF, ORB and FREAK binary descriptors are tested and compared to SIFT descriptors with regard to their respective classification accuracies, execution times, and memory requirements when used with CSL on a 12.6 g ARM Cortex embedded processor running at 800 MHz. Additionally, the effect of x2 feature mapping and opponent-color representations used with these descriptors is examined. These tests are performed on four data sets of varying sizes and difficulty, and the BRIEF descriptor is found to yield the best combination of speed and classification accuracy. Its use with CSL achieves accuracies between 67% and 95% of those achieved with SIFT descriptors and allows for the embedded classification of a 128x192 pixel image in 0.15 seconds, 60 times faster than classification with SIFT. X2 mapping is found to provide substantial improvements in classification accuracy for all of the descriptors at little cost, while opponent-color descriptors are offer accuracy improvements only on colorful datasets.

The evaluation of multi-structure, multi-atlas pelvic anatomy features in a prostate MR lymphography CAD system

NASA Astrophysics Data System (ADS)

Meijs, M.; Debats, O.; Huisman, H.

2015-03-01

In prostate cancer, the detection of metastatic lymph nodes indicates progression from localized disease to metastasized cancer. The detection of positive lymph nodes is, however, a complex and time consuming task for experienced radiologists. Assistance of a two-stage Computer-Aided Detection (CAD) system in MR Lymphography (MRL) is not yet feasible due to the large number of false positives in the first stage of the system. By introducing a multi-structure, multi-atlas segmentation, using an affine transformation followed by a B-spline transformation for registration, the organ location is given by a mean density probability map. The atlas segmentation is semi-automatically drawn with ITK-SNAP, using Active Contour Segmentation. Each anatomic structure is identified by a label number. Registration is performed using Elastix, using Mutual Information and an Adaptive Stochastic Gradient optimization. The dataset consists of the MRL scans of ten patients, with lymph nodes manually annotated in consensus by two expert readers. The feature map of the CAD system consists of the Multi-Atlas and various other features (e.g. Normalized Intensity and multi-scale Blobness). The voxel-based Gentleboost classifier is evaluated using ROC analysis with cross validation. We show in a set of 10 studies that adding multi-structure, multi-atlas anatomical structure likelihood features improves the quality of the lymph node voxel likelihood map. Multiple structure anatomy maps may thus make MRL CAD more feasible.

An effective convolutional neural network model for Chinese sentiment analysis

NASA Astrophysics Data System (ADS)

Zhang, Yu; Chen, Mengdong; Liu, Lianzhong; Wang, Yadong

2017-06-01

Nowadays microblog is getting more and more popular. People are increasingly accustomed to expressing their opinions on Twitter, Facebook and Sina Weibo. Sentiment analysis of microblog has received significant attention, both in academia and in industry. So far, Chinese microblog exploration still needs lots of further work. In recent years CNN has also been used to deal with NLP tasks, and already achieved good results. However, these methods ignore the effective use of a large number of existing sentimental resources. For this purpose, we propose a Lexicon-based Sentiment Convolutional Neural Networks (LSCNN) model focus on Weibo's sentiment analysis, which combines two CNNs, trained individually base on sentiment features and word embedding, at the fully connected hidden layer. The experimental results show that our model outperforms the CNN model only with word embedding features on microblog sentiment analysis task.

Toward Optimal Manifold Hashing via Discrete Locally Linear Embedding.

PubMed

Rongrong Ji; Hong Liu; Liujuan Cao; Di Liu; Yongjian Wu; Feiyue Huang

2017-11-01

Binary code learning, also known as hashing, has received increasing attention in large-scale visual search. By transforming high-dimensional features to binary codes, the original Euclidean distance is approximated via Hamming distance. More recently, it is advocated that it is the manifold distance, rather than the Euclidean distance, that should be preserved in the Hamming space. However, it retains as an open problem to directly preserve the manifold structure by hashing. In particular, it first needs to build the local linear embedding in the original feature space, and then quantize such embedding to binary codes. Such a two-step coding is problematic and less optimized. Besides, the off-line learning is extremely time and memory consuming, which needs to calculate the similarity matrix of the original data. In this paper, we propose a novel hashing algorithm, termed discrete locality linear embedding hashing (DLLH), which well addresses the above challenges. The DLLH directly reconstructs the manifold structure in the Hamming space, which learns optimal hash codes to maintain the local linear relationship of data points. To learn discrete locally linear embeddingcodes, we further propose a discrete optimization algorithm with an iterative parameters updating scheme. Moreover, an anchor-based acceleration scheme, termed Anchor-DLLH, is further introduced, which approximates the large similarity matrix by the product of two low-rank matrices. Experimental results on three widely used benchmark data sets, i.e., CIFAR10, NUS-WIDE, and YouTube Face, have shown superior performance of the proposed DLLH over the state-of-the-art approaches.

Spectral embedding-based registration (SERg) for multimodal fusion of prostate histology and MRI

NASA Astrophysics Data System (ADS)

Hwuang, Eileen; Rusu, Mirabela; Karthigeyan, Sudha; Agner, Shannon C.; Sparks, Rachel; Shih, Natalie; Tomaszewski, John E.; Rosen, Mark; Feldman, Michael; Madabhushi, Anant

2014-03-01

Multi-modal image registration is needed to align medical images collected from different protocols or imaging sources, thereby allowing the mapping of complementary information between images. One challenge of multimodal image registration is that typical similarity measures rely on statistical correlations between image intensities to determine anatomical alignment. The use of alternate image representations could allow for mapping of intensities into a space or representation such that the multimodal images appear more similar, thus facilitating their co-registration. In this work, we present a spectral embedding based registration (SERg) method that uses non-linearly embedded representations obtained from independent components of statistical texture maps of the original images to facilitate multimodal image registration. Our methodology comprises the following main steps: 1) image-derived textural representation of the original images, 2) dimensionality reduction using independent component analysis (ICA), 3) spectral embedding to generate the alternate representations, and 4) image registration. The rationale behind our approach is that SERg yields embedded representations that can allow for very different looking images to appear more similar, thereby facilitating improved co-registration. Statistical texture features are derived from the image intensities and then reduced to a smaller set by using independent component analysis to remove redundant information. Spectral embedding generates a new representation by eigendecomposition from which only the most important eigenvectors are selected. This helps to accentuate areas of salience based on modality-invariant structural information and therefore better identifies corresponding regions in both the template and target images. The spirit behind SERg is that image registration driven by these areas of salience and correspondence should improve alignment accuracy. In this work, SERg is implemented using Demons to allow the algorithm to more effectively register multimodal images. SERg is also tested within the free-form deformation framework driven by mutual information. Nine pairs of synthetic T1-weighted to T2-weighted brain MRI were registered under the following conditions: five levels of noise (0%, 1%, 3%, 5%, and 7%) and two levels of bias field (20% and 40%) each with and without noise. We demonstrate that across all of these conditions, SERg yields a mean squared error that is 81.51% lower than that of Demons driven by MRI intensity alone. We also spatially align twenty-six ex vivo histology sections and in vivo prostate MRI in order to map the spatial extent of prostate cancer onto corresponding radiologic imaging. SERg performs better than intensity registration by decreasing the root mean squared distance of annotated landmarks in the prostate gland via both Demons algorithm and mutual information-driven free-form deformation. In both synthetic and clinical experiments, the observed improvement in alignment of the template and target images suggest the utility of parametric eigenvector representations and hence SERg for multimodal image registration.

Compressed multi-block local binary pattern for object tracking

NASA Astrophysics Data System (ADS)

Li, Tianwen; Gao, Yun; Zhao, Lei; Zhou, Hao

2018-04-01

Both robustness and real-time are very important for the application of object tracking under a real environment. The focused trackers based on deep learning are difficult to satisfy with the real-time of tracking. Compressive sensing provided a technical support for real-time tracking. In this paper, an object can be tracked via a multi-block local binary pattern feature. The feature vector was extracted based on the multi-block local binary pattern feature, which was compressed via a sparse random Gaussian matrix as the measurement matrix. The experiments showed that the proposed tracker ran in real-time and outperformed the existed compressive trackers based on Haar-like feature on many challenging video sequences in terms of accuracy and robustness.

3D fully convolutional networks for subcortical segmentation in MRI: A large-scale study.

PubMed

Dolz, Jose; Desrosiers, Christian; Ben Ayed, Ismail

2018-04-15

This study investigates a 3D and fully convolutional neural network (CNN) for subcortical brain structure segmentation in MRI. 3D CNN architectures have been generally avoided due to their computational and memory requirements during inference. We address the problem via small kernels, allowing deeper architectures. We further model both local and global context by embedding intermediate-layer outputs in the final prediction, which encourages consistency between features extracted at different scales and embeds fine-grained information directly in the segmentation process. Our model is efficiently trained end-to-end on a graphics processing unit (GPU), in a single stage, exploiting the dense inference capabilities of fully CNNs. We performed comprehensive experiments over two publicly available datasets. First, we demonstrate a state-of-the-art performance on the ISBR dataset. Then, we report a large-scale multi-site evaluation over 1112 unregistered subject datasets acquired from 17 different sites (ABIDE dataset), with ages ranging from 7 to 64 years, showing that our method is robust to various acquisition protocols, demographics and clinical factors. Our method yielded segmentations that are highly consistent with a standard atlas-based approach, while running in a fraction of the time needed by atlas-based methods and avoiding registration/normalization steps. This makes it convenient for massive multi-site neuroanatomical imaging studies. To the best of our knowledge, our work is the first to study subcortical structure segmentation on such large-scale and heterogeneous data. Copyright © 2017 Elsevier Inc. All rights reserved.

Many-body quantum dynamics in the decay of bent dark solitons of Bose-Einstein condensates

NASA Astrophysics Data System (ADS)

Katsimiga, G. C.; Mistakidis, S. I.; Koutentakis, G. M.; Kevrekidis, P. G.; Schmelcher, P.

2017-12-01

The beyond mean-field (MF) dynamics of a bent dark soliton (BDS) embedded in a two-dimensional repulsively interacting Bose-Einstein condensate is explored. We examine the case of a single BDS comparing the MF dynamics to a correlated approach, the multi-configuration time-dependent Hartree method for bosons. Dynamical snaking of this bent structure is observed, signaling the onset of fragmentation which becomes significant during the vortex nucleation. In contrast to the MF approximation ‘filling’ of the vortex core is observed, leading in turn to the formation of filled-core vortices, instead of the MF vortex-antivortex pairs. The resulting smearing effect in the density is a rather generic feature, occurring when solitonic structures are exposed to quantum fluctuations. Here, we show that this filling owes its existence to the dynamical building of an antidark structure developed in the next-to-leading order orbital. We further demonstrate that the aforementioned beyond MF dynamics can be experimentally detected using the variance of single shot measurements. Additionally, a variety of excitations including vortices, oblique dark solitons, and open ring dark soliton-like structures building upon higher-lying orbitals is observed. We demonstrate that signatures of the higher-lying orbital excitations emerge in the total density, and can be clearly captured by inspecting the one-body coherence. In the latter context, the localization of one-body correlations exposes the existence of the multi-orbital vortex-antidark structure.

Waveguide-integrated single- and multi-photon detection at telecom wavelengths using superconducting nanowires

DOE Office of Scientific and Technical Information (OSTI.GOV)

Ferrari, Simone; Kahl, Oliver; Kovalyuk, Vadim

We investigate single- and multi-photon detection regimes of superconducting nanowire detectors embedded in silicon nitride nanophotonic circuits. At near-infrared wavelengths, simultaneous detection of up to three photons is observed for 120 nm wide nanowires biased far from the critical current, while narrow nanowires below 100 nm provide efficient single photon detection. A theoretical model is proposed to determine the different detection regimes and to calculate the corresponding internal quantum efficiency. The predicted saturation of the internal quantum efficiency in the single photon regime agrees well with plateau behavior observed at high bias currents.

One-Water Hydrologic Flow Model (MODFLOW-OWHM)

USGS Publications Warehouse

Hanson, Randall T.; Boyce, Scott E.; Schmid, Wolfgang; Hughes, Joseph D.; Mehl, Steffen W.; Leake, Stanley A.; Maddock, Thomas; Niswonger, Richard G.

2014-01-01

The One-Water Hydrologic Flow Model (MF-OWHM) is a MODFLOW-based integrated hydrologic flow model (IHM) that is the most complete version, to date, of the MODFLOW family of hydrologic simulators needed for the analysis of a broad range of conjunctive-use issues. Conjunctive use is the combined use of groundwater and surface water. MF-OWHM allows the simulation, analysis, and management of nearly all components of human and natural water movement and use in a physically-based supply-and-demand framework. MF-OWHM is based on the Farm Process for MODFLOW-2005 (MF-FMP2) combined with Local Grid Refinement (LGR) for embedded models to allow use of the Farm Process (FMP) and Streamflow Routing (SFR) within embedded grids. MF-OWHM also includes new features such as the Surface-water Routing Process (SWR), Seawater Intrusion (SWI), and Riparian Evapotrasnpiration (RIP-ET), and new solvers such as Newton-Raphson (NWT) and nonlinear preconditioned conjugate gradient (PCGN). This IHM also includes new connectivities to expand the linkages for deformation-, flow-, and head-dependent flows. Deformation-dependent flows are simulated through the optional linkage to simulated land subsidence with a vertically deforming mesh. Flow-dependent flows now include linkages between the new SWR with SFR and FMP, as well as connectivity with embedded models for SFR and FMP through LGR. Head-dependent flows now include a modified Hydrologic Flow Barrier Package (HFB) that allows optional transient HFB capabilities, and the flow between any two layers that are adjacent along a depositional or erosional boundary or displaced along a fault. MF-OWHM represents a complete operational hydrologic model that fully links the movement and use of groundwater, surface water, and imported water for consumption by irrigated agriculture, but also of water used in urban areas and by natural vegetation. Supply and demand components of water use are analyzed under demand-driven and supply-constrained conditions. From large- to small-scale settings, MF-OWHM has the unique set of capabilities to simulate and analyze historical, present, and future conjunctive-use conditions. MF-OWHM is especially useful for the analysis of agricultural water use where few data are available for pumpage, land use, or agricultural information. The features presented in this IHM include additional linkages with SFR, SWR, Drain-Return (DRT), Multi-Node Wells (MNW1 and MNW2), and Unsaturated-Zone Flow (UZF). Thus, MF-OWHM helps to reduce the loss of water during simulation of the hydrosphere and helps to account for “all of the water everywhere and all of the time.” In addition to groundwater, surface-water, and landscape budgets, MF-OWHM provides more options for observations of land subsidence, hydraulic properties, and evapotranspiration (ET) than previous models. Detailed landscape budgets combined with output of estimates of actual evapotranspiration facilitates linkage to remotely sensed observations as input or as additional observations for parameter estimation or water-use analysis. The features of FMP have been extended to allow for temporally variable water-accounting units (farms) that can be linked to land-use models and the specification of both surface-water and groundwater allotments to facilitate sustainability analysis and connectivity to the Groundwater Management Process (GWM). An example model described in this report demonstrates the application of MF-OWHM with the addition of land subsidence and a vertically deforming mesh, delayed recharge through an unsaturated zone, rejected infiltration in a riparian area, changes in demand caused by deficiency in supply, and changes in multi-aquifer pumpage caused by constraints imposed through the Farm Process and the MNW2 Package, and changes in surface water such as runoff, streamflow, and canal flows through SFR and SWR linkages.

Recurrence plot statistics and the effect of embedding

NASA Astrophysics Data System (ADS)

March, T. K.; Chapman, S. C.; Dendy, R. O.

2005-01-01

Recurrence plots provide a graphical representation of the recurrent patterns in a timeseries, the quantification of which is a relatively new field. Here we derive analytical expressions which relate the values of key statistics, notably determinism and entropy of line length distribution, to the correlation sum as a function of embedding dimension. These expressions are obtained by deriving the transformation which generates an embedded recurrence plot from an unembedded plot. A single unembedded recurrence plot thus provides the statistics of all possible embedded recurrence plots. If the correlation sum scales exponentially with embedding dimension, we show that these statistics are determined entirely by the exponent of the exponential. This explains the results of Iwanski and Bradley [J.S. Iwanski, E. Bradley, Recurrence plots of experimental data: to embed or not to embed? Chaos 8 (1998) 861-871] who found that certain recurrence plot statistics are apparently invariant to embedding dimension for certain low-dimensional systems. We also examine the relationship between the mutual information content of two timeseries and the common recurrent structure seen in their recurrence plots. This allows time-localized contributions to mutual information to be visualized. This technique is demonstrated using geomagnetic index data; we show that the AU and AL geomagnetic indices share half their information, and find the timescale on which mutual features appear.

Propellers in Saturn's rings

NASA Astrophysics Data System (ADS)

Sremcevic, M.; Stewart, G. R.; Albers, N.; Esposito, L. W.

2013-12-01

Theoretical studies and simulations have demonstrated the effects caused by objects embedded in planetary rings. Even if the objects are too small to be directly observed, each creates a much larger gravitational imprint on the surrounding ring material. These strongly depend on the mass of the object and range from "S" like propeller-shaped structures for about 100m-sized icy bodies to the opening of circumferential gaps as in the case of the embedded moons Pan and Daphnis and their corresponding Encke and Keeler Gaps. Since the beginning of the Cassini mission many of these smaller objects (~<500m in size) have been indirectly identified in Saturn's A ring through their propeller signature in the images. Furthermore, recent Cassini observations indicate the possible existence of objects embedded even in Saturn's B and C ring. In this paper we present evidence for the existence of propellers in Saturn's B ring by combining data from Cassini Ultraviolet Imaging Spectrograph (UVIS) and Imaging Science Subsystem (ISS) experiments. We show evidence that B ring seems to harbor two distinct populations of propellers: "big" propellers covering tens of degrees in azimuth situated in the densest part of B ring, and "small" propellers in less dense inner B ring that are similar in size and shape to known A ring propellers. The population of "big" propellers is exemplified with a single object which is observed for 5 years of Cassini data. The object is seen as a very elongated bright stripe (40 degrees wide) in unlit Cassini images, and dark stripe in lit geometries. In total we report observing the feature in images at 18 different epochs between 2005 and 2010. In UVIS occultations we observe this feature as an optical depth depletion in 14 out of 93 occultation cuts at corrotating longitudes compatible with imaging data. Combining the available Cassini data we infer that the object is a partial gap located at r=112,921km embedded in the high optical depth region of the B ring. The gap moves at Kepler speed appropriate for its radial location. Radial offsets of the gap locations in UVIS occultations are consistent with an asymmetric propeller shape. The asymmetry of the observed shape is most likely a consequence of the strong surface mass density gradient, as the feature is located at an edge between high and relatively low optical depth. From the radial separation of the propeller wings we estimate that the embedded body is about 1.5km in size. In addition to the population of "big" propellers we found evidence for a population of much smaller propellers which are more similar to known A ring propellers (size <500m). We have found one significant feature in beta Centauri Rev96 UVIS occultation at r=94,958km. The feature represents a gap with a width of 300m. This gap is statistically significant and consists of 6 consequent high counts. All other UVIS occultations show a flat and boring profile at this location. The r=94,958km feature is very similar in shape and size to a known detection of A ring propeller Bleriot from zeta Persei Rev42 occultation. This feature is also found as a dark spot moving at Kepler speed across several ISS images. Additionally we found 5 more small propeller candidates in ISS images of the inner B ring.

Exploring EFL Students' Visual Literacy Skills and Global Understanding through Their Analysis of Louis Vuitton's Advertisement Featuring Mikhail Gorbachev

ERIC Educational Resources Information Center

Takaya, Kentei

2016-01-01

Visual literacy is an important skill for students to have in order to interpret embedded messages on signs and in advertisements successfully. As advertisements today tend to feature iconic people or events that shaped the modern world, it is crucial to develop students' visual literacy skills so they can comprehend the intended messages. This…

Influence of Embedded Fibers and an Epithelium Layer on the Glottal Closure Pattern in a Physical Vocal Fold Model

ERIC Educational Resources Information Center

Xuan, Yue; Zhang, Zhaoyan

2014-01-01

Purpose: The purpose of this study was to explore the possible structural and material property features that may facilitate complete glottal closure in an otherwise isotropic physical vocal fold model. Method: Seven vocal fold models with different structural features were used in this study. An isotropic model was used as the baseline model, and…

Two-Photon Polymerization of Defects in Photonic Crystals

DTIC Science & Technology

2006-01-01

technique employs two-photon polymerization (TPP) (for description, see Section 2.2) to fabricate high-resolution 3D embedded polymer features within... polymer , and therefore does not influence the polymerization . The image contrast is from the different reflectivities of the interfaces in the system due...Spectroscopy also confirmed for the first time the successful polymerization of a uniform, dense polymer feature throughout the thickness of the

On computing stress in polymer systems involving multi-body potentials from molecular dynamics simulation

DOE Office of Scientific and Technical Information (OSTI.GOV)

Fu, Yao, E-mail: fu5@mailbox.sc.edu, E-mail: jhsong@cec.sc.edu; Song, Jeong-Hoon, E-mail: fu5@mailbox.sc.edu, E-mail: jhsong@cec.sc.edu

2014-08-07

Hardy stress definition has been restricted to pair potentials and embedded-atom method potentials due to the basic assumptions in the derivation of a symmetric microscopic stress tensor. Force decomposition required in the Hardy stress expression becomes obscure for multi-body potentials. In this work, we demonstrate the invariance of the Hardy stress expression for a polymer system modeled with multi-body interatomic potentials including up to four atoms interaction, by applying central force decomposition of the atomic force. The balance of momentum has been demonstrated to be valid theoretically and tested under various numerical simulation conditions. The validity of momentum conservation justifiesmore » the extension of Hardy stress expression to multi-body potential systems. Computed Hardy stress has been observed to converge to the virial stress of the system with increasing spatial averaging volume. This work provides a feasible and reliable linkage between the atomistic and continuum scales for multi-body potential systems.« less

Method and system for mesh network embedded devices

NASA Technical Reports Server (NTRS)

Wang, Ray (Inventor)

2009-01-01

A method and system for managing mesh network devices. A mesh network device with integrated features creates an N-way mesh network with a full mesh network topology or a partial mesh network topology.

A diagram retrieval method with multi-label learning

NASA Astrophysics Data System (ADS)

Fu, Songping; Lu, Xiaoqing; Liu, Lu; Qu, Jingwei; Tang, Zhi

2015-01-01

In recent years, the retrieval of plane geometry figures (PGFs) has attracted increasing attention in the fields of mathematics education and computer science. However, the high cost of matching complex PGF features leads to the low efficiency of most retrieval systems. This paper proposes an indirect classification method based on multi-label learning, which improves retrieval efficiency by reducing the scope of compare operation from the whole database to small candidate groups. Label correlations among PGFs are taken into account for the multi-label classification task. The primitive feature selection for multi-label learning and the feature description of visual geometric elements are conducted individually to match similar PGFs. The experiment results show the competitive performance of the proposed method compared with existing PGF retrieval methods in terms of both time consumption and retrieval quality.

A novel chemotherapeutic sensitivity-testing system based on collagen gel droplet embedded 3D-culture methods for hepatocellular carcinoma.

PubMed

Hou, Jun; Hong, Zhixian; Feng, Fan; Chai, Yantao; Zhang, Yunkai; Jiang, Qiyu; Hu, Yan; Wu, Shunquan; Wu, Yingsong; Gao, Xunian; Chen, Qiong; Wan, Yong; Bi, Jingfeng; Zhang, Zheng

2017-11-08

Patients suffering from advanced stage hepatocellular carcinoma (HCC) often exhibit a poor prognosis or dismal clinical outcomes due to ineffective chemotherapy or a multi-drug resistance (MDR) process. Thus, it is urgent to develop a new chemotherapeutic sensitivity testing system for HCC treatment. The presence study investigated the potential application of a novel chemotherapeutic sensitivity-testing system based on a collagen gel droplet embedded 3D-culture system (CD-DST). Primary cells were separating from surgical resection specimens and then tested by CD-DST. To identify whether HCC cell lines or cells separating from clinical specimens contain MDR features, the cells were treated with an IC 50 (half maximal inhibitory concentration) or IC max (maximal inhibitory concentration) concentration of antitumor agents, e.g., 5-furuolouracil (5-FU), paclitaxel (PAC), cisplatin (CDDP), epirubicin (EPI), or oxaliplatin (L-OHP), and the inhibitory rates (IRs) were calculated. HepG2 cells were sensitive to 5-FU, PAC, CDDP, EPI, or L-OHP; the IC 50 value is 0.83 ± 0.45 μg/ml, 0.03 ± 0.02 μg/ml, 1.15 ± 0.75 μg/ml, 0.09 ± 0.03 μg/ml, or 1.76 ± 0.44 μg/ml, respectively. Only eight (8/26), nine (9/26), or five (5/26) patients were sensitive to the IC max concentration of CDDP, EPI, or L-OHP; whereas only three (3/26), four (4/26), or two (2/26) patients were sensitive to the IC 50 concentration of CDDP, EPI, or L-OHP. No patients were sensitive to 5-FU or PAC. The in vitro drug sensitivity exanimation revealed the MDR features of HCC and examined the sensitivity of HCC cells from clinical specimens to anti-tumor agents. CD-DST may be a useful method to predict the potential clinical benefits of anticancer agents for HCC patients.

A School Wide Approach to Leading Pedagogical Enhancement: An Australian Perspective

ERIC Educational Resources Information Center

Conway, Joan M.; Andrews, Dorothy

2016-01-01

This paper presents how some Australian schools are changing their approaches to leading the teaching and learning in their diverse and multi-characteristic contexts. Experiences of these schools shows that the development of a school wide approach to pedagogy and its implementation needs to be firmly embedded in the leadership of learning.…

The "Where" of Doctoral Research: The Role of "Place" in Creating Meaning

ERIC Educational Resources Information Center

Greenwood, Janinka

2018-01-01

This article explores the importance of place within doctoral research. It considers place as localised, experiential, interactional, embedded in history and discourses, and often multi-faceted and fluid. With a focus on the field of education, it argues that doctoral students need to navigate between the university, the place of study and the…

Proceedings from the Workshop on Nanoscience for the Soldier

DTIC Science & Technology

2001-02-09

Affordable, Durable, Flexible Enabled by Active Devices Miniature Ventilation, Cooling & Heating Multi-Functional, Hybrid Power Embedded Micro-Sensors...functional element • Rifle protection, back support & comfort, load bearing stability & interfaces with family of back packs & cooling/ heating system...Integrated physiological & medical sensors – Conductive or Fiber Optic fibers for Data & Power Distribution – Carbon Fiber Heating at wrists

An ancient Roman bowl embedded in a soil sample: surface shaded three dimensional display using data from a multi-detector CT.

PubMed

De Maeseneer, M; Buls, N; Cleeren, N; Lenchik, L; De Mey, J

2006-01-01

We present an unusual application of multidetector CT and shaded surface rendering in the investigation of a soil sample, containing an ancient Roman bronze bowl. The CT findings were of fundamental importance in helping the archaeologists study the bronze bowl from the soil sample.

Using DEWIS and R for Multi-Staged Statistics e-Assessments

ERIC Educational Resources Information Center

Gwynllyw, D. Rhys; Weir, Iain S.; Henderson, Karen L.

2016-01-01

We demonstrate how the DEWIS e-Assessment system may use embedded R code to facilitate the assessment of students' ability to perform involved statistical analyses. The R code has been written to emulate SPSS output and thus the statistical results for each bespoke data set can be generated efficiently and accurately using standard R routines.…

A scale space feature based registration technique for fusion of satellite imagery

NASA Technical Reports Server (NTRS)

Raghavan, Srini; Cromp, Robert F.; Campbell, William C.

1997-01-01

Feature based registration is one of the most reliable methods to register multi-sensor images (both active and passive imagery) since features are often more reliable than intensity or radiometric values. The only situation where a feature based approach will fail is when the scene is completely homogenous or densely textural in which case a combination of feature and intensity based methods may yield better results. In this paper, we present some preliminary results of testing our scale space feature based registration technique, a modified version of feature based method developed earlier for classification of multi-sensor imagery. The proposed approach removes the sensitivity in parameter selection experienced in the earlier version as explained later.

Fabrication of embedded microball lens in PMMA with high repetition rate femtosecond fiber laser.

PubMed

Zheng, Chong; Hu, Anming; Li, Ruozhou; Bridges, Denzel; Chen, Tao

2015-06-29

Embedded microball lenses with superior optical properties function as convex microball lens (VMBL) and concave microball lens (CMBL) were fabricated inside a PMMA substrate with a high repetition rate femtosecond fiber laser. The VMBL was created by femtosecond laser-induced refractive index change, while the CMBL was fabricated due to the heat accumulation effect of the successive laser pulses irradiation at a high repetition rate. The processing window for both types of the lenses was studied and optimized, and the optical properties were also tested by imaging a remote object with an inverted microscope. In order to obtain the microball lenses with adjustable focal lengths and suppressed optical aberration, a shape control method was thus proposed and examined with experiments and ZEMAX® simulations. Applying the optimized fabrication conditions, two types of the embedded microball lenses arrays were fabricated and then tested with imaging experiments. This technology allows the direct fabrication of microlens inside transparent bulk polymer material which has great application potential in multi-function integrated microfluidic devices.

'No one to trust': the cultural embedding of atomism in financial markets.

PubMed

Ailon, Galit

2018-05-13

The paper ethnographically explores the cultural embedding of atomistic indifference in online, global financial markets: arenas that have been digitally designed according to economic ideals and that demand an extreme form of relational and social dissociation from the partners to exchange and from those affected by the transactions. Its case-study is lay financial-trading in Israel, a country undergoing extensive neoliberalization. The study shows that dissociation is embedded in an economic culture marked by constant, multi-sited declarations that economic-Others are cold, uncaring and manipulative. It takes shape as traders convert the distrust towards Others into distrust towards portions of the Self that represent links to these Others, namely their own social-psychology and social concern. Acting atomistically and selfishly in the market thus entails considerable reflexive work. The paper contributes to an ongoing debate on the moral and cultural embeddedness of markets in general and of the expanding financial markets in particular. © London School of Economics and Political Science 2018.

Computation of multi-dimensional viscous supersonic jet flow

NASA Technical Reports Server (NTRS)

Kim, Y. N.; Buggeln, R. C.; Mcdonald, H.

1986-01-01

A new method has been developed for two- and three-dimensional computations of viscous supersonic flows with embedded subsonic regions adjacent to solid boundaries. The approach employs a reduced form of the Navier-Stokes equations which allows solution as an initial-boundary value problem in space, using an efficient noniterative forward marching algorithm. Numerical instability associated with forward marching algorithms for flows with embedded subsonic regions is avoided by approximation of the reduced form of the Navier-Stokes equations in the subsonic regions of the boundary layers. Supersonic and subsonic portions of the flow field are simultaneously calculated by a consistently split linearized block implicit computational algorithm. The results of computations for a series of test cases relevant to internal supersonic flow is presented and compared with data. Comparison between data and computation are in general excellent thus indicating that the computational technique has great promise as a tool for calculating supersonic flow with embedded subsonic regions. Finally, a User's Manual is presented for the computer code used to perform the calculations.

LDFT-based watermarking resilient to local desynchronization attacks.

PubMed

Tian, Huawei; Zhao, Yao; Ni, Rongrong; Qin, Lunming; Li, Xuelong

2013-12-01

Up to now, a watermarking scheme that is robust against desynchronization attacks (DAs) is still a grand challenge. Most image watermarking resynchronization schemes in literature can survive individual global DAs (e.g., rotation, scaling, translation, and other affine transforms), but few are resilient to challenging cropping and local DAs. The main reason is that robust features for watermark synchronization are only globally invariable rather than locally invariable. In this paper, we present a blind image watermarking resynchronization scheme against local transform attacks. First, we propose a new feature transform named local daisy feature transform (LDFT), which is not only globally but also locally invariable. Then, the binary space partitioning (BSP) tree is used to partition the geometrically invariant LDFT space. In the BSP tree, the location of each pixel is fixed under global transform, local transform, and cropping. Lastly, the watermarking sequence is embedded bit by bit into each leaf node of the BSP tree by using the logarithmic quantization index modulation watermarking embedding method. Simulation results show that the proposed watermarking scheme can survive numerous kinds of distortions, including common image-processing attacks, local and global DAs, and noninvertible cropping.

DOE Office of Scientific and Technical Information (OSTI.GOV)

Potash, Peter J.; Bell, Eric B.; Harrison, Joshua J.

Predictive models for tweet deletion have been a relatively unexplored area of Twitter-related computational research. We first approach the deletion of tweets as a spam detection problem, applying a small set of handcrafted features to improve upon the current state-of-the- art in predicting deleted tweets. Next, we apply our approach to a dataset of deleted tweets that better reflects the current deletion rate. Since tweets are deleted for reasons beyond just the presence of spam, we apply topic modeling and text embeddings in order to capture the semantic content of tweets that can lead to tweet deletion. Our goal ismore » to create an effective model that has a low-dimensional feature space and is also language-independent. A lean model would be computationally advantageous processing high-volumes of Twitter data, which can reach 9,885 tweets per second. Our results show that a small set of spam-related features combined with word topics and character-level text embeddings provide the best f1 when trained with a random forest model. The highest precision of the deleted tweet class is achieved by a modification of paragraph2vec to capture author identity.« less

Learning Behavior Characterization with Multi-Feature, Hierarchical Activity Sequences

ERIC Educational Resources Information Center

Ye, Cheng; Segedy, James R.; Kinnebrew, John S.; Biswas, Gautam

2015-01-01

This paper discusses Multi-Feature Hierarchical Sequential Pattern Mining, MFH-SPAM, a novel algorithm that efficiently extracts patterns from students' learning activity sequences. This algorithm extends an existing sequential pattern mining algorithm by dynamically selecting the level of specificity for hierarchically-defined features…

Raft cultivation area extraction from high resolution remote sensing imagery by fusing multi-scale region-line primitive association features

NASA Astrophysics Data System (ADS)

Wang, Min; Cui, Qi; Wang, Jie; Ming, Dongping; Lv, Guonian

2017-01-01

In this paper, we first propose several novel concepts for object-based image analysis, which include line-based shape regularity, line density, and scale-based best feature value (SBV), based on the region-line primitive association framework (RLPAF). We then propose a raft cultivation area (RCA) extraction method for high spatial resolution (HSR) remote sensing imagery based on multi-scale feature fusion and spatial rule induction. The proposed method includes the following steps: (1) Multi-scale region primitives (segments) are obtained by image segmentation method HBC-SEG, and line primitives (straight lines) are obtained by phase-based line detection method. (2) Association relationships between regions and lines are built based on RLPAF, and then multi-scale RLPAF features are extracted and SBVs are selected. (3) Several spatial rules are designed to extract RCAs within sea waters after land and water separation. Experiments show that the proposed method can successfully extract different-shaped RCAs from HR images with good performance.

Knee Kinematics Estimation Using Multi-Body Optimisation Embedding a Knee Joint Stiffness Matrix: A Feasibility Study.

PubMed

Richard, Vincent; Lamberto, Giuliano; Lu, Tung-Wu; Cappozzo, Aurelio; Dumas, Raphaël

2016-01-01

The use of multi-body optimisation (MBO) to estimate joint kinematics from stereophotogrammetric data while compensating for soft tissue artefact is still open to debate. Presently used joint models embedded in MBO, such as mechanical linkages, constitute a considerable simplification of joint function, preventing a detailed understanding of it. The present study proposes a knee joint model where femur and tibia are represented as rigid bodies connected through an elastic element the behaviour of which is described by a single stiffness matrix. The deformation energy, computed from the stiffness matrix and joint angles and displacements, is minimised within the MBO. Implemented as a "soft" constraint using a penalty-based method, this elastic joint description challenges the strictness of "hard" constraints. In this study, estimates of knee kinematics obtained using MBO embedding four different knee joint models (i.e., no constraints, spherical joint, parallel mechanism, and elastic joint) were compared against reference kinematics measured using bi-planar fluoroscopy on two healthy subjects ascending stairs. Bland-Altman analysis and sensitivity analysis investigating the influence of variations in the stiffness matrix terms on the estimated kinematics substantiate the conclusions. The difference between the reference knee joint angles and displacements and the corresponding estimates obtained using MBO embedding the stiffness matrix showed an average bias and standard deviation for kinematics of 0.9±3.2° and 1.6±2.3 mm. These values were lower than when no joint constraints (1.1±3.8°, 2.4±4.1 mm) or a parallel mechanism (7.7±3.6°, 1.6±1.7 mm) were used and were comparable to the values obtained with a spherical joint (1.0±3.2°, 1.3±1.9 mm). The study demonstrated the feasibility of substituting an elastic joint for more classic joint constraints in MBO.

Knee Kinematics Estimation Using Multi-Body Optimisation Embedding a Knee Joint Stiffness Matrix: A Feasibility Study

PubMed Central

Richard, Vincent; Lamberto, Giuliano; Lu, Tung-Wu; Cappozzo, Aurelio; Dumas, Raphaël

2016-01-01

The use of multi-body optimisation (MBO) to estimate joint kinematics from stereophotogrammetric data while compensating for soft tissue artefact is still open to debate. Presently used joint models embedded in MBO, such as mechanical linkages, constitute a considerable simplification of joint function, preventing a detailed understanding of it. The present study proposes a knee joint model where femur and tibia are represented as rigid bodies connected through an elastic element the behaviour of which is described by a single stiffness matrix. The deformation energy, computed from the stiffness matrix and joint angles and displacements, is minimised within the MBO. Implemented as a “soft” constraint using a penalty-based method, this elastic joint description challenges the strictness of “hard” constraints. In this study, estimates of knee kinematics obtained using MBO embedding four different knee joint models (i.e., no constraints, spherical joint, parallel mechanism, and elastic joint) were compared against reference kinematics measured using bi-planar fluoroscopy on two healthy subjects ascending stairs. Bland-Altman analysis and sensitivity analysis investigating the influence of variations in the stiffness matrix terms on the estimated kinematics substantiate the conclusions. The difference between the reference knee joint angles and displacements and the corresponding estimates obtained using MBO embedding the stiffness matrix showed an average bias and standard deviation for kinematics of 0.9±3.2° and 1.6±2.3 mm. These values were lower than when no joint constraints (1.1±3.8°, 2.4±4.1 mm) or a parallel mechanism (7.7±3.6°, 1.6±1.7 mm) were used and were comparable to the values obtained with a spherical joint (1.0±3.2°, 1.3±1.9 mm). The study demonstrated the feasibility of substituting an elastic joint for more classic joint constraints in MBO. PMID:27314586

Sealed substrate carrier for electroplating

DOEpatents

Ganti, Kalyana Bhargava [Fremont, CA

2012-07-17

One embodiment relates to a substrate carrier for use in electroplating a plurality of substrates. The substrate carrier includes a non-conductive carrier body on which the substrates are held, and conductive lines are embedded within the carrier body. A conductive bus bar is embedded into a top side of the carrier body and is conductively coupled to the conductive lines. A thermoplastic overmold covers a portion of the bus bar, and there is a plastic-to-plastic bond between the thermoplastic overmold and the non-conductive carrier body. Other embodiments, aspects and features are also disclosed.

Effect of X-ray irradiation on the optical absorption of СdSe1-xTex nanocrystals embedded in borosilicate glass

NASA Astrophysics Data System (ADS)

Prymak, M. V.; Azhniuk, Yu. M.; Solomon, A. M.; Krasilinets, V. M.; Lopushansky, V. V.; Bodnar, I. V.; Gomonnai, A. V.; Zahn, D. R. T.

2012-07-01

The effect of X-ray irradiation on the optical absorption spectra of CdSe1-xTex nanocrystals embedded in a borosilicate matrix is studied. The observed blue shift of the absorption edge and bleaching of the confinement-related features in the spectra are related to X-ray induced negative ionization of the nanocrystals with charge transfer across the nanocrystal/matrix interface. The radiation-induced changes are observed to recover after longer post-irradiation storage at room temperature.

Ab initio simulations of scanning-tunneling-microscope images with embedding techniques and application to C58-dimers on Au(111).

PubMed

Wilhelm, Jan; Walz, Michael; Stendel, Melanie; Bagrets, Alexei; Evers, Ferdinand

2013-05-14

We present a modification of the standard electron transport methodology based on the (non-equilibrium) Green's function formalism to efficiently simulate STM-images. The novel feature of this method is that it employs an effective embedding technique that allows us to extrapolate properties of metal substrates with adsorbed molecules from quantum-chemical cluster calculations. To illustrate the potential of this approach, we present an application to STM-images of C58-dimers immobilized on Au(111)-surfaces that is motivated by recent experiments.

Highly birefringent polymer microstructured optical fibers embedded in composite materials

NASA Astrophysics Data System (ADS)

Lesiak, P.; SzelÄ g, M.; Kuczkowski, M.; Domański, A. W.; Woliński, T. R.

2013-05-01

Composite structures are made from two or more constituent materials with significantly different physical or chemical properties and they remain separate and distinct in a macroscopic level within the finished structure. This feature allows for introducing highly birefringent polymer microstructured optical fibers into the composite material. These new fibers can consist of only two polymer materials (PMMA and PC) with similar value of the Young modulus as the composite material so any stresses induced in the composite material can be easily measured by the proposed embedded fiber optic sensors.

Positron accumulation effect in particles embedded in a low-density matrix

NASA Astrophysics Data System (ADS)

Dryzek, Jerzy; Siemek, Krzysztof

2015-02-01

Systematic studies of the so-called positron accumulation effect for samples with particles embedded in a matrix are reported. This effect is related to energetic positrons which penetrate inhomogeneous medium. Due to differences in the linear absorption coefficient, different amounts of positrons are accumulated and annihilate in the identical volume of both materials. Positron lifetime spectroscopy and Doppler broadening of the annihilation line using Na-22 positrons were applied to the studies of the epoxy resin samples with embedded micro-sized particles of transition metals, i.e., Ni, Sn, Mo, W, and nonmetal particles, i.e., Si and NaF. The significant difference between the determined fraction of positrons annihilating in the particles and the particle volume fraction indicates the positron accumulation effect. The simple phenomenological model and Monte Carlo simulations are able to describe the main features of the obtained dependencies. The aluminum alloy with embedded Sn nanoparticles is also considered for demonstration differences between the accumulation and another related effect, i.e., the positron affinity.

Visual processing in adolescents with autism spectrum disorder: evidence from embedded figures and configural superiority tests.

PubMed

Dillen, Claudia; Steyaert, Jean; Op de Beeck, Hans P; Boets, Bart

2015-05-01

The embedded figures test has often been used to reveal weak central coherence in individuals with autism spectrum disorder (ASD). Here, we administered a more standardized automated version of the embedded figures test in combination with the configural superiority task, to investigate the effect of contextual modulation on local feature detection in 23 adolescents with ASD and 26 matched typically developing controls. On both tasks both groups performed largely similarly in terms of accuracy and reaction time, and both displayed the contextual modulation effect. This indicates that individuals with ASD are equally sensitive compared to typically developing individuals to the contextual effects of the task and that there is no evidence for a local processing bias in adolescents with ASD.

A feature dictionary supporting a multi-domain medical knowledge base.

PubMed

Naeymi-Rad, F

1989-01-01

Because different terminology is used by physicians of different specialties in different locations to refer to the same feature (signs, symptoms, test results), it is essential that our knowledge development tools provide a means to access a common pool of terms. This paper discusses the design of an online medical dictionary that provides a solution to this problem for developers of multi-domain knowledge bases for MEDAS (Medical Emergency Decision Assistance System). Our Feature Dictionary supports phrase equivalents for features, feature interactions, feature classifications, and translations to the binary features generated by the expert during knowledge creation. It is also used in the conversion of a domain knowledge to the database used by the MEDAS inference diagnostic sessions. The Feature Dictionary also provides capabilities for complex queries across multiple domains using the supported relations. The Feature Dictionary supports three methods for feature representation: (1) for binary features, (2) for continuous valued features, and (3) for derived features.

Diagnosis of multiple sclerosis from EEG signals using nonlinear methods.

PubMed

Torabi, Ali; Daliri, Mohammad Reza; Sabzposhan, Seyyed Hojjat

2017-12-01

EEG signals have essential and important information about the brain and neural diseases. The main purpose of this study is classifying two groups of healthy volunteers and Multiple Sclerosis (MS) patients using nonlinear features of EEG signals while performing cognitive tasks. EEG signals were recorded when users were doing two different attentional tasks. One of the tasks was based on detecting a desired change in color luminance and the other task was based on detecting a desired change in direction of motion. EEG signals were analyzed in two ways: EEG signals analysis without rhythms decomposition and EEG sub-bands analysis. After recording and preprocessing, time delay embedding method was used for state space reconstruction; embedding parameters were determined for original signals and their sub-bands. Afterwards nonlinear methods were used in feature extraction phase. To reduce the feature dimension, scalar feature selections were done by using T-test and Bhattacharyya criteria. Then, the data were classified using linear support vector machines (SVM) and k-nearest neighbor (KNN) method. The best combination of the criteria and classifiers was determined for each task by comparing performances. For both tasks, the best results were achieved by using T-test criterion and SVM classifier. For the direction-based and the color-luminance-based tasks, maximum classification performances were 93.08 and 79.79% respectively which were reached by using optimal set of features. Our results show that the nonlinear dynamic features of EEG signals seem to be useful and effective in MS diseases diagnosis.

Efficiency Vermont - Embedding energy efficiency into low-income programs and services

EPA Pesticide Factsheets

Discover the key features, approaches, partners, funding sources, and achievements of the Efficiency Vermont program and how it has been able to reach nearly one-half of the state’s low-income population.

Enhancing fuzzy robot navigation systems by mimicking human visual perception of natural terrain traversibility

NASA Technical Reports Server (NTRS)

Tunstel, E.; Howard, A.; Edwards, D.; Carlson, A.

2001-01-01

This paper presents a technique for learning to assess terrain traversability for outdoor mobile robot navigation using human-embedded logic and real-time perception of terrain features extracted from image data.

Rapid Prototyping of Robotic Systems

DTIC Science & Technology

2007-06-01

Nowak, S. Peterson, “Feature Oriented Domain Analysis ( FODA ) Feasibility Study,” Technical Report, CMU/SEI-90-TR-21, Software Engineering Institute...32 3. Embedded System Control Language..............................................33 viii 4. Architecture Analysis and Design Language...41 5. Analysis

Morphological studies of Hyphoderma cremeoalbum and Radulomyces roseolus

Treesearch

Karen K. Nakasone

2010-01-01

Type studies reveal that Radulomyces roseolus is conspecific with Hyphoderma cremeoalbum (Basidiomycota, Polyporales). Embedded, fusoid cystidia and haplohyphidia are critical diagnostic features of H. cremeoalbum. Known from Europe, United States, Argentina, and New Zealand, its preferred...

Oriented modulation for watermarking in direct binary search halftone images.

PubMed

Guo, Jing-Ming; Su, Chang-Cheng; Liu, Yun-Fu; Lee, Hua; Lee, Jiann-Der

2012-09-01

In this paper, a halftoning-based watermarking method is presented. This method enables high pixel-depth watermark embedding, while maintaining high image quality. This technique is capable of embedding watermarks with pixel depths up to 3 bits without causing prominent degradation to the image quality. To achieve high image quality, the parallel oriented high-efficient direct binary search (DBS) halftoning is selected to be integrated with the proposed orientation modulation (OM) method. The OM method utilizes different halftone texture orientations to carry different watermark data. In the decoder, the least-mean-square-trained filters are applied for feature extraction from watermarked images in the frequency domain, and the naïve Bayes classifier is used to analyze the extracted features and ultimately to decode the watermark data. Experimental results show that the DBS-based OM encoding method maintains a high degree of image quality and realizes the processing efficiency and robustness to be adapted in printing applications.

Position-aware deep multi-task learning for drug-drug interaction extraction.

PubMed

Zhou, Deyu; Miao, Lei; He, Yulan

2018-05-01

A drug-drug interaction (DDI) is a situation in which a drug affects the activity of another drug synergistically or antagonistically when being administered together. The information of DDIs is crucial for healthcare professionals to prevent adverse drug events. Although some known DDIs can be found in purposely-built databases such as DrugBank, most information is still buried in scientific publications. Therefore, automatically extracting DDIs from biomedical texts is sorely needed. In this paper, we propose a novel position-aware deep multi-task learning approach for extracting DDIs from biomedical texts. In particular, sentences are represented as a sequence of word embeddings and position embeddings. An attention-based bidirectional long short-term memory (BiLSTM) network is used to encode each sentence. The relative position information of words with the target drugs in text is combined with the hidden states of BiLSTM to generate the position-aware attention weights. Moreover, the tasks of predicting whether or not two drugs interact with each other and further distinguishing the types of interactions are learned jointly in multi-task learning framework. The proposed approach has been evaluated on the DDIExtraction challenge 2013 corpus and the results show that with the position-aware attention only, our proposed approach outperforms the state-of-the-art method by 0.99% for binary DDI classification, and with both position-aware attention and multi-task learning, our approach achieves a micro F-score of 72.99% on interaction type identification, outperforming the state-of-the-art approach by 1.51%, which demonstrates the effectiveness of the proposed approach. Copyright © 2018 Elsevier B.V. All rights reserved.

Multi-element fiber technology for space-division multiplexing applications.

PubMed

Jain, S; Rancaño, V J F; May-Smith, T C; Petropoulos, P; Sahu, J K; Richardson, D J

2014-02-24

A novel technological approach to space division multiplexing (SDM) based on the use of multiple individual fibers embedded in a common polymer coating material is presented, which is referred to as Multi-Element Fiber (MEF). The approach ensures ultralow crosstalk between spatial channels and allows for cost-effective ways of realizing multi-spatial channel amplification and signal multiplexing/demultiplexing. Both the fabrication and characterization of a passive 3-element MEF for data transmission, and an active 5-element erbium/ytterbium doped MEF for cladding-pumped optical amplification that uses one of the elements as an integrated pump delivery fiber is reported. Finally, both components were combined to emulate an optical fiber network comprising SDM transmission lines and amplifiers, and illustrate the compatibility of the approach with existing installed single-mode WDM fiber systems.

Combined multi-modal photoacoustic tomography, optical coherence tomography (OCT) and OCT angiography system with an articulated probe for in vivo human skin structure and vasculature imaging

PubMed Central

Liu, Mengyang; Chen, Zhe; Zabihian, Behrooz; Sinz, Christoph; Zhang, Edward; Beard, Paul C.; Ginner, Laurin; Hoover, Erich; Minneman, Micheal P.; Leitgeb, Rainer A.; Kittler, Harald; Drexler, Wolfgang

2016-01-01

Cutaneous blood flow accounts for approximately 5% of cardiac output in human and plays a key role in a number of a physiological and pathological processes. We show for the first time a multi-modal photoacoustic tomography (PAT), optical coherence tomography (OCT) and OCT angiography system with an articulated probe to extract human cutaneous vasculature in vivo in various skin regions. OCT angiography supplements the microvasculature which PAT alone is unable to provide. Co-registered volumes for vessel network is further embedded in the morphologic image provided by OCT. This multi-modal system is therefore demonstrated as a valuable tool for comprehensive non-invasive human skin vasculature and morphology imaging in vivo. PMID:27699106

[The specialty clinical centers within the structure of the regional multi-specialty hospital].

PubMed

Fadeev, M G

2008-01-01

The analysis of the functioning of the regional referral clinical center of hand surgery, the eye injury center, the pediatric burns center and the neurosurgical center situated on the basis of large multi-field hospitals of the City of Ekaterinburg is presented. Such common conditions of their activity as experienced manpower availability and medical Academy chairs maintenance are revealed. The special referral clinical centers organized prior to the perstroyka and reformation, continue to function successfully providing high-tech medical care to the patients of the megapolis and to the inhabitants of the Sverdlovskaya Oblast. The effectiveness and perspectiveness of further functioning of the special referral clinical centers embedded into the structure of the municipal multi-field hospitals in the conditions of health reforms is demonstrated.

Applying Jlint to Space Exploration Software

NASA Technical Reports Server (NTRS)

Artho, Cyrille; Havelund, Klaus

2004-01-01

Java is a very successful programming language which is also becoming widespread in embedded systems, where software correctness is critical. Jlint is a simple but highly efficient static analyzer that checks a Java program for several common errors, such as null pointer exceptions, and overflow errors. It also includes checks for multi-threading problems, such as deadlocks and data races. The case study described here shows the effectiveness of Jlint in find-false positives in the multi-threading warnings gives an insight into design patterns commonly used in multi-threaded code. The results show that a few analysis techniques are sufficient to avoid almost all false positives. These techniques include investigating all possible callers and a few code idioms. Verifying the correct application of these patterns is still crucial, because their correct usage is not trivial.

Novel Wireless-Communicating Textiles Made from Multi-Material and Minimally-Invasive Fibers

PubMed Central

Gorgutsa, Stepan; Bélanger-Garnier, Victor; Ung, Bora; Viens, Jeff; Gosselin, Benoit; LaRochelle, Sophie; Messaddeq, Younes

2014-01-01

The ability to integrate multiple materials into miniaturized fiber structures enables the realization of novel biomedical textile devices with higher-level functionalities and minimally-invasive attributes. In this work, we present novel textile fabrics integrating unobtrusive multi-material fibers that communicate through 2.4 GHz wireless networks with excellent signal quality. The conductor elements of the textiles are embedded within the fibers themselves, providing electrical and chemical shielding against the environment, while preserving the mechanical and cosmetic properties of the garments. These multi-material fibers combine insulating and conducting materials into a well-defined geometry, and represent a cost-effective and minimally-invasive approach to sensor fabrics and bio-sensing textiles connected in real time to mobile communications infrastructures, suitable for a variety of health and life science applications. PMID:25325335

A dynamic multi-level optimal design method with embedded finite-element modeling for power transformers

NASA Astrophysics Data System (ADS)

Zhang, Yunpeng; Ho, Siu-lau; Fu, Weinong

2018-05-01

This paper proposes a dynamic multi-level optimal design method for power transformer design optimization (TDO) problems. A response surface generated by second-order polynomial regression analysis is updated dynamically by adding more design points, which are selected by Shifted Hammersley Method (SHM) and calculated by finite-element method (FEM). The updating stops when the accuracy requirement is satisfied, and optimized solutions of the preliminary design are derived simultaneously. The optimal design level is modulated through changing the level of error tolerance. Based on the response surface of the preliminary design, a refined optimal design is added using multi-objective genetic algorithm (MOGA). The effectiveness of the proposed optimal design method is validated through a classic three-phase power TDO problem.

Novel wireless-communicating textiles made from multi-material and minimally-invasive fibers.

PubMed

Bélanger-Garnier, Victor; Gorgutsa, Stephan; Ung, Bora; Viens, Jeff; Gosselin, Benoit; LaRochelle, Sophie; Messaddeq, Younes

2014-01-01

The ability to integrate multiple materials into miniaturized fiber structures enables the realization of novel biomedical textile devices with higher-level functionalities and minimally-invasive attributes. In this work, we present novel textile fabrics integrating unobtrusive multi-material fibers that communicate through 2.4 GHz wireless networks with excellent signal quality. The conductor elements of the textiles are embedded within the fibers themselves, providing electrical and chemical shielding against the environment, while preserving the mechanical and cosmetic properties of the garments. These multi-material fibers combine insulating and conducting materials into a well-defined geometry, and represent a cost-effective and minimally-invasive approach to sensor fabrics and bio-sensing textiles connected in real time to mobile communications infrastructures, suitable for a variety of health and life science applications.

Novel wireless-communicating textiles made from multi-material and minimally-invasive fibers.

PubMed

Gorgutsa, Stepan; Bélanger-Garnier, Victor; Ung, Bora; Viens, Jeff; Gosselin, Benoit; LaRochelle, Sophie; Messaddeq, Younes

2014-10-16

The ability to integrate multiple materials into miniaturized fiber structures enables the realization of novel biomedical textile devices with higher-level functionalities and minimally-invasive attributes. In this work, we present novel textile fabrics integrating unobtrusive multi-material fibers that communicate through 2.4 GHz wireless networks with excellent signal quality. The conductor elements of the textiles are embedded within the fibers themselves, providing electrical and chemical shielding against the environment, while preserving the mechanical and cosmetic properties of the garments. These multi-material fibers combine insulating and conducting materials into a well-defined geometry, and represent a cost-effective and minimally-invasive approach to sensor fabrics and bio-sensing textiles connected in real time to mobile communications infrastructures, suitable for a variety of health and life science applications.

Multi-Quadrant Biopsy Technique Improves Diagnostic Ability in Large Heterogeneous Renal Masses.

PubMed

Abel, E Jason; Heckman, Jennifer E; Hinshaw, Louis; Best, Sara; Lubner, Meghan; Jarrard, David F; Downs, Tracy M; Nakada, Stephen Y; Lee, Fred T; Huang, Wei; Ziemlewicz, Timothy

2015-10-01

Percutaneous biopsy obtained from a single location is prone to sampling error in large heterogeneous renal masses, leading to nondiagnostic results or failure to detect poor prognostic features. We evaluated the accuracy of percutaneous biopsy for large renal masses using a modified multi-quadrant technique vs a standard biopsy technique. Clinical and pathological data for all patients with cT2 or greater renal masses who underwent percutaneous biopsy from 2009 to 2014 were reviewed. The multi-quadrant technique was defined as multiple core biopsies from at least 4 separate solid enhancing areas in the tumor. The incidence of nondiagnostic findings, sarcomatoid features and procedural complications was recorded, and concordance between biopsy specimens and nephrectomy pathology was compared. A total of 122 biopsies were performed for 117 tumors in 116 patients (46 using the standard biopsy technique and 76 using the multi-quadrant technique). Median tumor size was 10 cm (IQR 8-12). Biopsy was nondiagnostic in 5 of 46 (10.9%) standard and 0 of 76 (0%) multi-quadrant biopsies (p=0.007). Renal cell carcinoma was identified in 96 of 115 (82.0%) tumors and nonrenal cell carcinoma tumors were identified in 21 (18.0%). One complication occurred using the standard biopsy technique and no complications were reported using the multi-quadrant technique. Sarcomatoid features were present in 23 of 96 (23.9%) large renal cell carcinomas studied. Sensitivity for identifying sarcomatoid features was higher using the multi-quadrant technique compared to the standard biopsy technique at 13 of 15 (86.7%) vs 2 of 8 (25.0%) (p=0.0062). The multi-quadrant percutaneous biopsy technique increases the ability to identify aggressive pathological features in large renal tumors and decreases nondiagnostic biopsy rates. Copyright © 2015 American Urological Association Education and Research, Inc. Published by Elsevier Inc. All rights reserved.

The BBaRTS Healthy Teeth Behaviour Change Programme for preventing dental caries in primary school children: study protocol for a cluster randomised controlled trial.

PubMed

Pine, Cynthia; Adair, Pauline; Robinson, Louise; Burnside, Girvan; Moynihan, Paula; Wade, William; Kistler, James; Curnow, Morag; Henderson, Mary

2016-02-20

Oral health behaviours such as establishing twice-daily toothbrushing and sugar control intake need parental self-efficacy (PSE) to prevent the development of childhood dental caries. A previous study has shown that behaviour change techniques (BCTs) delivered via a storybook can improve parental self-efficacy to undertake twice-daily toothbrushing. to determine whether an intervention (BBaRTS, Bedtime Brush and Read Together to Sleep), designed to increase PSE; delivered through storybooks with embedded BCTs, parenting skills and oral health messages, can improve child oral health compared to (1) an exactly similar intervention containing no behaviour change techniques, and (2) the BBaRTS intervention supplemented with home supply of fluoride toothpaste and supervised toothbrushing on schooldays. A 2-year, three-arm, multicentre, cluster randomised controlled trial. children (estimated 2000-2600) aged 5-7 years and their families from 60 UK primary schools. Test group 1: a series of eight children's storybooks developed by a psychologist, public health dentist, science educator, children's author and illustrators, with guidance from the Department for Education (England). The books feature animal characters and contain embedded dental health messages, parenting skills and BCTs to promote good oral health routines focused on controlling sugar intake and toothbrushing, as well as reading at bedtime. Books are given out over 2 years. Test group 2: as Test group 1 plus home supplies of fluoride toothpaste (1000 ppmF), and daily supervised toothbrushing in school on schooldays. Active Control group: series of eight books with exactly the same stories, characters and illustrations, but without BCTs, dental health messages or parenting skills. Annual child dental examinations and parental questionnaires will be undertaken. A sub-set of participants will be invited to join an embedded study of the child's diet and salivary microbiota composition. dental caries experience in permanent teeth at age 7-8 years. A multi-disciplinary team was established to develop the BBaRTS Children's Healthy Teeth Programme. The books were developed in partnership with the Department for Education (England), informed by a series of focus groups with children, teachers and parents. ISRCTN21461006 (date of registration 23 September 2015).

Embedded biofilm, a new biofilm model based on the embedded growth of bacteria.

PubMed

Jung, Yong-Gyun; Choi, Jungil; Kim, Soo-Kyoung; Lee, Joon-Hee; Kwon, Sunghoon

2015-01-01

A variety of systems have been developed to study biofilm formation. However, most systems are based on the surface-attached growth of microbes under shear stress. In this study, we designed a microfluidic channel device, called a microfluidic agarose channel (MAC), and found that microbial cells in the MAC system formed an embedded cell aggregative structure (ECAS). ECASs were generated from the embedded growth of bacterial cells in an agarose matrix and better mimicked the clinical environment of biofilms formed within mucus or host tissue under shear-free conditions. ECASs were developed with the production of extracellular polymeric substances (EPS), the most important feature of biofilms, and eventually burst to release planktonic cells, which resembles the full developmental cycle of biofilms. Chemical and genetic effects have also confirmed that ECASs are a type of biofilm. Unlike the conventional biofilms formed in the flow cell model system, this embedded-type biofilm completes the developmental cycle in only 9 to 12 h and can easily be observed with ordinary microscopes. We suggest that ECASs are a type of biofilm and that the MAC is a system for observing biofilm formation. Copyright © 2015, American Society for Microbiology. All Rights Reserved.

Java Source Code Analysis for API Migration to Embedded Systems

DOE Office of Scientific and Technical Information (OSTI.GOV)

Winter, Victor; McCoy, James A.; Guerrero, Jonathan

Embedded systems form an integral part of our technological infrastructure and oftentimes play a complex and critical role within larger systems. From the perspective of reliability, security, and safety, strong arguments can be made favoring the use of Java over C in such systems. In part, this argument is based on the assumption that suitable subsets of Java’s APIs and extension libraries are available to embedded software developers. In practice, a number of Java-based embedded processors do not support the full features of the JVM. For such processors, source code migration is a mechanism by which key abstractions offered bymore » APIs and extension libraries can made available to embedded software developers. The analysis required for Java source code-level library migration is based on the ability to correctly resolve element references to their corresponding element declarations. A key challenge in this setting is how to perform analysis for incomplete source-code bases (e.g., subsets of libraries) from which types and packages have been omitted. This article formalizes an approach that can be used to extend code bases targeted for migration in such a manner that the threats associated the analysis of incomplete code bases are eliminated.« less

Multi-image CAD employing features derived from ipsilateral mammographic views

NASA Astrophysics Data System (ADS)

Good, Walter F.; Zheng, Bin; Chang, Yuan-Hsiang; Wang, Xiao Hui; Maitz, Glenn S.; Gur, David

1999-05-01

On mammograms, certain kinds of features related to masses (e.g., location, texture, degree of spiculation, and integrated density difference) tend to be relatively invariant, or at last predictable, with respect to breast compression. Thus, ipsilateral pairs of mammograms may contain information not available from analyzing single views separately. To demonstrate the feasibility of incorporating multi-view features into CAD algorithm, `single-image' CAD was applied to each individual image in a set of 60 ipsilateral studies, after which all possible pairs of suspicious regions, consisting of one from each view, were formed. For these 402 pairs we defined and evaluated `multi-view' features such as: (1) relative position of centers of regions; (2) ratio of lengths of region projections parallel to nipple axis lines; (3) ratio of integrated contrast difference; (4) ratio of the sizes of the suspicious regions; and (5) measure of relative complexity of region boundaries. Each pair was identified as either a `true positive/true positive' (T) pair (i.e., two regions which are projections of the same actual mass), or as a falsely associated pair (F). Distributions for each feature were calculated. A Bayesian network was trained and tested to classify pairs of suspicious regions based exclusively on the multi-view features described above. Distributions for all features were significantly difference for T versus F pairs as indicated by likelihood ratios. Performance of the Bayesian network, which was measured by ROC analysis, indicates a significant ability to distinguish between T pairs and F pairs (Az equals 0.82 +/- 0.03), using information that is attributed to the multi-view content. This study is the first demonstration that there is a significant amount of spatial information that can be derived from ipsilateral pairs of mammograms.

Age and gender estimation using Region-SIFT and multi-layered SVM

NASA Astrophysics Data System (ADS)

Kim, Hyunduk; Lee, Sang-Heon; Sohn, Myoung-Kyu; Hwang, Byunghun

2018-04-01

In this paper, we propose an age and gender estimation framework using the region-SIFT feature and multi-layered SVM classifier. The suggested framework entails three processes. The first step is landmark based face alignment. The second step is the feature extraction step. In this step, we introduce the region-SIFT feature extraction method based on facial landmarks. First, we define sub-regions of the face. We then extract SIFT features from each sub-region. In order to reduce the dimensions of features we employ a Principal Component Analysis (PCA) and a Linear Discriminant Analysis (LDA). Finally, we classify age and gender using a multi-layered Support Vector Machines (SVM) for efficient classification. Rather than performing gender estimation and age estimation independently, the use of the multi-layered SVM can improve the classification rate by constructing a classifier that estimate the age according to gender. Moreover, we collect a dataset of face images, called by DGIST_C, from the internet. A performance evaluation of proposed method was performed with the FERET database, CACD database, and DGIST_C database. The experimental results demonstrate that the proposed approach classifies age and performs gender estimation very efficiently and accurately.

Addressing Hydro-economic Modeling Limitations - A Limited Foresight Sacramento Valley Model and an Open-source Modeling Platform

NASA Astrophysics Data System (ADS)

Harou, J. J.; Hansen, K. M.

2008-12-01

Increased scarcity of world water resources is inevitable given the limited supply and increased human pressures. The idea that "some scarcity is optimal" must be accepted for rational resource use and infrastructure management decisions to be made. Hydro-economic systems models are unique at representing the overlap of economic drivers, socio-political forces and distributed water resource systems. They demonstrate the tangible benefits of cooperation and integrated flexible system management. Further improvement of models, quality control practices and software will be needed for these academic policy tools to become accepted into mainstream water resource practice. Promising features include: calibration methods, limited foresight optimization formulations, linked simulation-optimization approaches (e.g. embedding pre-existing calibrated simulation models), spatial groundwater models, stream-aquifer interactions and stream routing, etc.. Conventional user-friendly decision support systems helped spread simulation models on a massive scale. Hydro-economic models must also find a means to facilitate construction, distribution and use. Some of these issues and model features are illustrated with a hydro-economic optimization model of the Sacramento Valley. Carry-over storage value functions are used to limit hydrologic foresight of the multi- period optimization model. Pumping costs are included in the formulation by tracking regional piezometric head of groundwater sub-basins. To help build and maintain this type of network model, an open-source water management modeling software platform is described and initial project work is discussed. The objective is to generically facilitate the connection of models, such as those developed in a modeling environment (GAMS, MatLab, Octave, "), to a geographic user interface (drag and drop node-link network) and a database (topology, parameters and time series). These features aim to incrementally move hydro- economic models in the direction of more practical implementation.

Multiscale Embedded Gene Co-expression Network Analysis

PubMed Central

Song, Won-Min; Zhang, Bin

2015-01-01

Gene co-expression network analysis has been shown effective in identifying functional co-expressed gene modules associated with complex human diseases. However, existing techniques to construct co-expression networks require some critical prior information such as predefined number of clusters, numerical thresholds for defining co-expression/interaction, or do not naturally reproduce the hallmarks of complex systems such as the scale-free degree distribution of small-worldness. Previously, a graph filtering technique called Planar Maximally Filtered Graph (PMFG) has been applied to many real-world data sets such as financial stock prices and gene expression to extract meaningful and relevant interactions. However, PMFG is not suitable for large-scale genomic data due to several drawbacks, such as the high computation complexity O(|V|3), the presence of false-positives due to the maximal planarity constraint, and the inadequacy of the clustering framework. Here, we developed a new co-expression network analysis framework called Multiscale Embedded Gene Co-expression Network Analysis (MEGENA) by: i) introducing quality control of co-expression similarities, ii) parallelizing embedded network construction, and iii) developing a novel clustering technique to identify multi-scale clustering structures in Planar Filtered Networks (PFNs). We applied MEGENA to a series of simulated data and the gene expression data in breast carcinoma and lung adenocarcinoma from The Cancer Genome Atlas (TCGA). MEGENA showed improved performance over well-established clustering methods and co-expression network construction approaches. MEGENA revealed not only meaningful multi-scale organizations of co-expressed gene clusters but also novel targets in breast carcinoma and lung adenocarcinoma. PMID:26618778

Effective Beginning Handwriting Instruction: Multi-modal, Consistent Format for 2 Years, and Linked to Spelling and Composing.

PubMed

Wolf, Beverly; Abbott, Robert D; Berninger, Virginia W

2017-02-01

In Study 1, the treatment group ( N = 33 first graders, M = 6 years 10 months, 16 girls) received Slingerland multi-modal (auditory, visual, tactile, motor through hand, and motor through mouth) manuscript (unjoined) handwriting instruction embedded in systematic spelling, reading, and composing lessons; and the control group ( N =16 first graders, M = 7 years 1 month, 7 girls) received manuscript handwriting instruction not systematically related to the other literacy activities. ANOVA showed both groups improved on automatic alphabet writing from memory; but ANCOVA with the automatic alphabet writing task as covariate showed that the treatment group improved significantly more than control group from the second to ninth month of first grade on dictated spelling and recognition of word-specific spellings among phonological foils. In Study 2 new groups received either a second year of manuscript ( N = 29, M = 7 years 8 months, 16 girls) or introduction to cursive (joined) instruction in second grade ( N = 24, M = 8 years 0 months, 11 girls) embedded in the Slingerland literacy program. ANCOVA with automatic alphabet writing as covariate showed that those who received a second year of manuscript handwriting instruction improved more on sustained handwriting over 30, 60, and 90 seconds than those who had had only one year of manuscript instruction; both groups improved in spelling and composing from the second to ninth month of second grade. Results are discussed in reference to mastering one handwriting format before introducing another format at a higher grade level and always embedding handwriting instruction in writing and reading instruction aimed at all levels of language.

Effective Beginning Handwriting Instruction: Multi-modal, Consistent Format for 2 Years, and Linked to Spelling and Composing

PubMed Central

Wolf, Beverly; Abbott, Robert D.; Berninger, Virginia W.

2016-01-01

In Study 1, the treatment group (N = 33 first graders, M = 6 years 10 months, 16 girls) received Slingerland multi-modal (auditory, visual, tactile, motor through hand, and motor through mouth) manuscript (unjoined) handwriting instruction embedded in systematic spelling, reading, and composing lessons; and the control group (N =16 first graders, M = 7 years 1 month, 7 girls) received manuscript handwriting instruction not systematically related to the other literacy activities. ANOVA showed both groups improved on automatic alphabet writing from memory; but ANCOVA with the automatic alphabet writing task as covariate showed that the treatment group improved significantly more than control group from the second to ninth month of first grade on dictated spelling and recognition of word-specific spellings among phonological foils. In Study 2 new groups received either a second year of manuscript (N = 29, M = 7 years 8 months, 16 girls) or introduction to cursive (joined) instruction in second grade (N = 24, M = 8 years 0 months, 11 girls) embedded in the Slingerland literacy program. ANCOVA with automatic alphabet writing as covariate showed that those who received a second year of manuscript handwriting instruction improved more on sustained handwriting over 30, 60, and 90 seconds than those who had had only one year of manuscript instruction; both groups improved in spelling and composing from the second to ninth month of second grade. Results are discussed in reference to mastering one handwriting format before introducing another format at a higher grade level and always embedding handwriting instruction in writing and reading instruction aimed at all levels of language. PMID:28190930

Multiscale Embedded Gene Co-expression Network Analysis.

PubMed

Song, Won-Min; Zhang, Bin

2015-11-01

Gene co-expression network analysis has been shown effective in identifying functional co-expressed gene modules associated with complex human diseases. However, existing techniques to construct co-expression networks require some critical prior information such as predefined number of clusters, numerical thresholds for defining co-expression/interaction, or do not naturally reproduce the hallmarks of complex systems such as the scale-free degree distribution of small-worldness. Previously, a graph filtering technique called Planar Maximally Filtered Graph (PMFG) has been applied to many real-world data sets such as financial stock prices and gene expression to extract meaningful and relevant interactions. However, PMFG is not suitable for large-scale genomic data due to several drawbacks, such as the high computation complexity O(|V|3), the presence of false-positives due to the maximal planarity constraint, and the inadequacy of the clustering framework. Here, we developed a new co-expression network analysis framework called Multiscale Embedded Gene Co-expression Network Analysis (MEGENA) by: i) introducing quality control of co-expression similarities, ii) parallelizing embedded network construction, and iii) developing a novel clustering technique to identify multi-scale clustering structures in Planar Filtered Networks (PFNs). We applied MEGENA to a series of simulated data and the gene expression data in breast carcinoma and lung adenocarcinoma from The Cancer Genome Atlas (TCGA). MEGENA showed improved performance over well-established clustering methods and co-expression network construction approaches. MEGENA revealed not only meaningful multi-scale organizations of co-expressed gene clusters but also novel targets in breast carcinoma and lung adenocarcinoma.

Residual Shuffling Convolutional Neural Networks for Deep Semantic Image Segmentation Using Multi-Modal Data

NASA Astrophysics Data System (ADS)

Chen, K.; Weinmann, M.; Gao, X.; Yan, M.; Hinz, S.; Jutzi, B.; Weinmann, M.

2018-05-01

In this paper, we address the deep semantic segmentation of aerial imagery based on multi-modal data. Given multi-modal data composed of true orthophotos and the corresponding Digital Surface Models (DSMs), we extract a variety of hand-crafted radiometric and geometric features which are provided separately and in different combinations as input to a modern deep learning framework. The latter is represented by a Residual Shuffling Convolutional Neural Network (RSCNN) combining the characteristics of a Residual Network with the advantages of atrous convolution and a shuffling operator to achieve a dense semantic labeling. Via performance evaluation on a benchmark dataset, we analyze the value of different feature sets for the semantic segmentation task. The derived results reveal that the use of radiometric features yields better classification results than the use of geometric features for the considered dataset. Furthermore, the consideration of data on both modalities leads to an improvement of the classification results. However, the derived results also indicate that the use of all defined features is less favorable than the use of selected features. Consequently, data representations derived via feature extraction and feature selection techniques still provide a gain if used as the basis for deep semantic segmentation.

Rapid prototyping of SoC-based real-time vision system: application to image preprocessing and face detection

NASA Astrophysics Data System (ADS)

Jridi, Maher; Alfalou, Ayman

2017-05-01

By this paper, the major goal is to investigate the Multi-CPU/FPGA SoC (System on Chip) design flow and to transfer a know-how and skills to rapidly design embedded real-time vision system. Our aim is to show how the use of these devices can be benefit for system level integration since they make possible simultaneous hardware and software development. We take the facial detection and pretreatments as case study since they have a great potential to be used in several applications such as video surveillance, building access control and criminal identification. The designed system use the Xilinx Zedboard platform. The last is the central element of the developed vision system. The video acquisition is performed using either standard webcam connected to the Zedboard via USB interface or several camera IP devices. The visualization of video content and intermediate results are possible with HDMI interface connected to HD display. The treatments embedded in the system are as follow: (i) pre-processing such as edge detection implemented in the ARM and in the reconfigurable logic, (ii) software implementation of motion detection and face detection using either ViolaJones or LBP (Local Binary Pattern), and (iii) application layer to select processing application and to display results in a web page. One uniquely interesting feature of the proposed system is that two functions have been developed to transmit data from and to the VDMA port. With the proposed optimization, the hardware implementation of the Sobel filter takes 27 ms and 76 ms for 640x480, and 720p resolutions, respectively. Hence, with the FPGA implementation, an acceleration of 5 times is obtained which allow the processing of 37 fps and 13 fps for 640x480, and 720p resolutions, respectively.

Image-adaptive and robust digital wavelet-domain watermarking for images

NASA Astrophysics Data System (ADS)

Zhao, Yi; Zhang, Liping

2018-03-01

We propose a new frequency domain wavelet based watermarking technique. The key idea of our scheme is twofold: multi-tier solution representation of image and odd-even quantization embedding/extracting watermark. Because many complementary watermarks need to be hidden, the watermark image designed is image-adaptive. The meaningful and complementary watermark images was embedded into the original image (host image) by odd-even quantization modifying coefficients, which was selected from the detail wavelet coefficients of the original image, if their magnitudes are larger than their corresponding Just Noticeable Difference thresholds. The tests show good robustness against best-known attacks such as noise addition, image compression, median filtering, clipping as well as geometric transforms. Further research may improve the performance by refining JND thresholds.

Self adaptive multi-scale morphology AVG-Hat filter and its application to fault feature extraction for wheel bearing

NASA Astrophysics Data System (ADS)

Deng, Feiyue; Yang, Shaopu; Tang, Guiji; Hao, Rujiang; Zhang, Mingliang

2017-04-01

Wheel bearings are essential mechanical components of trains, and fault detection of the wheel bearing is of great significant to avoid economic loss and casualty effectively. However, considering the operating conditions, detection and extraction of the fault features hidden in the heavy noise of the vibration signal have become a challenging task. Therefore, a novel method called adaptive multi-scale AVG-Hat morphology filter (MF) is proposed to solve it. The morphology AVG-Hat operator not only can suppress the interference of the strong background noise greatly, but also enhance the ability of extracting fault features. The improved envelope spectrum sparsity (IESS), as a new evaluation index, is proposed to select the optimal filtering signal processed by the multi-scale AVG-Hat MF. It can present a comprehensive evaluation about the intensity of fault impulse to the background noise. The weighted coefficients of the different scale structural elements (SEs) in the multi-scale MF are adaptively determined by the particle swarm optimization (PSO) algorithm. The effectiveness of the method is validated by analyzing the real wheel bearing fault vibration signal (e.g. outer race fault, inner race fault and rolling element fault). The results show that the proposed method could improve the performance in the extraction of fault features effectively compared with the multi-scale combined morphological filter (CMF) and multi-scale morphology gradient filter (MGF) methods.

Consensus embedding: theory, algorithms and application to segmentation and classification of biomedical data

PubMed Central

2012-01-01

Background Dimensionality reduction (DR) enables the construction of a lower dimensional space (embedding) from a higher dimensional feature space while preserving object-class discriminability. However several popular DR approaches suffer from sensitivity to choice of parameters and/or presence of noise in the data. In this paper, we present a novel DR technique known as consensus embedding that aims to overcome these problems by generating and combining multiple low-dimensional embeddings, hence exploiting the variance among them in a manner similar to ensemble classifier schemes such as Bagging. We demonstrate theoretical properties of consensus embedding which show that it will result in a single stable embedding solution that preserves information more accurately as compared to any individual embedding (generated via DR schemes such as Principal Component Analysis, Graph Embedding, or Locally Linear Embedding). Intelligent sub-sampling (via mean-shift) and code parallelization are utilized to provide for an efficient implementation of the scheme. Results Applications of consensus embedding are shown in the context of classification and clustering as applied to: (1) image partitioning of white matter and gray matter on 10 different synthetic brain MRI images corrupted with 18 different combinations of noise and bias field inhomogeneity, (2) classification of 4 high-dimensional gene-expression datasets, (3) cancer detection (at a pixel-level) on 16 image slices obtained from 2 different high-resolution prostate MRI datasets. In over 200 different experiments concerning classification and segmentation of biomedical data, consensus embedding was found to consistently outperform both linear and non-linear DR methods within all applications considered. Conclusions We have presented a novel framework termed consensus embedding which leverages ensemble classification theory within dimensionality reduction, allowing for application to a wide range of high-dimensional biomedical data classification and segmentation problems. Our generalizable framework allows for improved representation and classification in the context of both imaging and non-imaging data. The algorithm offers a promising solution to problems that currently plague DR methods, and may allow for extension to other areas of biomedical data analysis. PMID:22316103

Controlled nerve growth factor release from multi-ply alginate/chitosan-based nerve conduits.

PubMed

Pfister, Lukas A; Alther, Eva; Papaloïzos, Michaël; Merkle, Hans P; Gander, Bruno

2008-06-01

The delivery kinetics of growth factors has been suggested to play an important role in the regeneration of peripheral nerves following axotomy. In this context, we designed a nerve conduit (NC) with adjustable release kinetics of nerve growth factor (NGF). A multi-ply system was designed where NC consisting of a polyelectrolyte alginate/chitosan complex was coated with layers of poly(lactide-co-glycolide) (PLGA) to control the release of embedded NGF. Prior to assessing the in vitro NGF release from NC, various release test media, with and without stabilizers for NGF, were evaluated to ensure adequate quantification of NGF by ELISA. Citrate (pH 5.0) and acetate (pH 5.5) buffered saline solutions containing 0.05% Tween 20 yielded the most reliable results for ELISA active NGF. The in vitro release experiments revealed that the best results in terms of reproducibility and release control were achieved when the NGF was embedded between two PLGA layers and the ends of the NC tightly sealed by the PLGA coatings. The release kinetics could be efficiently adjusted by accommodating NGF at different radial locations within the NC. A sustained release of bioactive NGF in the low nanogram per day range was obtained for at least 15days. In conclusion, the developed multi-ply NGF loaded NC is considered a suitable candidate for future implantation studies to gain insight into the relationship between local growth factor availability and nerve regeneration.

Neptune

NASA Image and Video Library

1999-07-25

This image of Neptune was taken through the clear filter of the narrow-angle camera on July 16, 1989 by NASA Voyager 2 spacecraft. The image was processed by computer to show the newly resolved dark oval feature embedded in the middle of the dusky south

Bonded polyimide fuel cell package

DOEpatents

Morse, Jeffrey D.; Jankowski, Alan; Graff, Robert T.; Bettencourt, Kerry

2010-06-08

Described herein are processes for fabricating microfluidic fuel cell systems with embedded components in which micron-scale features are formed by bonding layers of DuPont Kapton.TM. polyimide laminate. A microfluidic fuel cell system fabricated using this process is also described.

Influence of the geometric configuration of accretion flow on the black hole spin dependence of relativistic acoustic geometry

NASA Astrophysics Data System (ADS)

Tarafdar, Pratik; Das, Tapas K.

Linear perturbation of general relativistic accretion of low angular momentum hydrodynamic fluid onto a Kerr black hole leads to the formation of curved acoustic geometry embedded within the background flow. Characteristic features of such sonic geometry depend on the black hole spin. Such dependence can be probed by studying the correlation of the acoustic surface gravity κ with the Kerr parameter a. The κ-a relationship further gets influenced by the geometric configuration of the accretion flow structure. In this work, such influence has been studied for multitransonic shocked accretion where linear perturbation of general relativistic flow profile leads to the formation of two analogue black hole-type horizons formed at the sonic points and one analogue white hole-type horizon which is formed at the shock location producing divergent acoustic surface gravity. Dependence of the κ-a relationship on the geometric configuration has also been studied for monotransonic accretion, over the entire span of the Kerr parameter including retrograde flow. For accreting astrophysical black holes, the present work thus investigates how the salient features of the embedded relativistic sonic geometry may be determined not only by the background spacetime, but also by the flow configuration of the embedding matter.

DOE Office of Scientific and Technical Information (OSTI.GOV)

Learn, Mark Walter

Sandia National Laboratories is currently developing new processing and data communication architectures for use in future satellite payloads. These architectures will leverage the flexibility and performance of state-of-the-art static-random-access-memory-based Field Programmable Gate Arrays (FPGAs). One such FPGA is the radiation-hardened version of the Virtex-5 being developed by Xilinx. However, not all features of this FPGA are being radiation-hardened by design and could still be susceptible to on-orbit upsets. One such feature is the embedded hard-core PPC440 processor. Since this processor is implemented in the FPGA as a hard-core, traditional mitigation approaches such as Triple Modular Redundancy (TMR) are not availablemore » to improve the processor's on-orbit reliability. The goal of this work is to investigate techniques that can help mitigate the embedded hard-core PPC440 processor within the Virtex-5 FPGA other than TMR. Implementing various mitigation schemes reliably within the PPC440 offers a powerful reconfigurable computing resource to these node-based processing architectures. This document summarizes the work done on the cache mitigation scheme for the embedded hard-core PPC440 processor within the Virtex-5 FPGAs, and describes in detail the design of the cache mitigation scheme and the testing conducted at the radiation effects facility on the Texas A&M campus.« less

Tensor manifold-based extreme learning machine for 2.5-D face recognition

NASA Astrophysics Data System (ADS)

Chong, Lee Ying; Ong, Thian Song; Teoh, Andrew Beng Jin

2018-01-01

We explore the use of the Gabor regional covariance matrix (GRCM), a flexible matrix-based descriptor that embeds the Gabor features in the covariance matrix, as a 2.5-D facial descriptor and an effective means of feature fusion for 2.5-D face recognition problems. Despite its promise, matching is not a trivial problem for GRCM since it is a special instance of a symmetric positive definite (SPD) matrix that resides in non-Euclidean space as a tensor manifold. This implies that GRCM is incompatible with the existing vector-based classifiers and distance matchers. Therefore, we bridge the gap of the GRCM and extreme learning machine (ELM), a vector-based classifier for the 2.5-D face recognition problem. We put forward a tensor manifold-compliant ELM and its two variants by embedding the SPD matrix randomly into reproducing kernel Hilbert space (RKHS) via tensor kernel functions. To preserve the pair-wise distance of the embedded data, we orthogonalize the random-embedded SPD matrix. Hence, classification can be done using a simple ridge regressor, an integrated component of ELM, on the random orthogonal RKHS. Experimental results show that our proposed method is able to improve the recognition performance and further enhance the computational efficiency.

Reduced Financial Resources and the Strategic Position of Community Colleges: How an "Embedded Community College" Can Neutralize External Pressures

ERIC Educational Resources Information Center

Namuo, Clyne G. H.

2013-01-01

This multi-site case study is really the story of three same-state community colleges (Bridge and Buffer Community College, Grants and Reserves Community College, and Crystal Ball Community College) two years after they suffered a potentially catastrophic 50% reduction in state allocations. This study examined their responses to those reductions…

26th International Symposium on Ballistics

DTIC Science & Technology

2011-09-16

judicious use of analytical predictions correlated with ballistic testing and post - test failure morphology investigations. •Our approach...ballistic predictions. The numerical predictions correlate well with the damage pattern. Post - Test Morphology Simulation Imbedded Steel Plate Removed Post ... Test •Numerical simulation of damage to embedded steel plate compares well with the post - test plate morphology •Multi-strike modeling in work

Words Spoken with Insistence: "Wak'as" and the Limits of the Bolivian Multi-Institutional Democracy

ERIC Educational Resources Information Center

Cuelenaere, Laurence Janine

2009-01-01

Building on 18 months of fieldwork in the Bolivian highlands, this dissertation examines how traversing landscapes, through the mediation of spatial practices and spoken words, are embedded in systems of belief. By focusing on "wak'as" (i.e. sacred objects) and on how the inhabitants of the Altiplano relate to the Andean deities known as…

Superior Disembedding Performance of High-Functioning Individuals with Autism Spectrum Disorders and Their Parents: The Need for Subtle Measures

ERIC Educational Resources Information Center

de Jonge, Maretha V.; Kemner, Chantal; van Engeland, Herman

2006-01-01

We assessed the disembedding performance on the Embedded Figures Test (EFT) of high-functioning subjects with autism or autism spectrum disorders from multi-incidence families and the performance of their parents. The individuals with autism spectrum disorders were significantly faster than matched controls in locating the shape, but their parents…

Adaptive Multi-Sensor Interrogation of Targets Embedded in Complex Environments

DTIC Science & Technology

2010-06-09

to efficient refinement of data from distributed networked sensor systems for interpretation by both machines and humans in a low latency and...of a DP draw: Tk^HIltiU-^). Vk*& Beta{l,a), d’k ~ d" H. (19) where 5g - is a point measure concentrated at 9*k (each 9*k is termed an atom

Participatory Concepts of Multidisciplinary/Professional Working on an Early Childhood Studies Degree Course in the UK

ERIC Educational Resources Information Center

Bath, Caroline

2011-01-01

This paper aims to explore democratic values in higher education pedagogies, as related to an Early Childhood Studies (ECS) degree course in an English university. It seeks to find out what constitutes a multi-disciplinary course from both student and tutor perspectives. It is contextualised by the concepts of participation embedded in the idea of…

Meta II: Multi-Model Language Suite for Cyber Physical Systems

DTIC Science & Technology

2013-03-01

AVM META) projects have developed tools for designing cyber physical (or Mechatronic ) Systems . These systems are increasingly complex, take much...projects have developed tools for designing cyber physical (CPS) (or Mechatronic ) systems . Exemplified by modern amphibious and ground military...and parametric interface of Simulink models and defines associations with CyPhy components and component interfaces. 2. Embedded Systems Modeling

Sequencing Embedded Multimodal Representations in a Writing to Learn Approach to the Teaching of Electricity

ERIC Educational Resources Information Center

Hand, Brian; Gunel, Murat; Ulu, Cuneyt

2009-01-01

In the study of science topics especially in physics students are expected to move between different modes of representation when dealing with a particular concept as any science concept can be represented in several different modes. The difficulty for students is that they are often unable to move between these multi-modal representations and…

Design a software real-time operation platform for wave piercing catamarans motion control using linear quadratic regulator based genetic algorithm.

PubMed

Liang, Lihua; Yuan, Jia; Zhang, Songtao; Zhao, Peng

2018-01-01

This work presents optimal linear quadratic regulator (LQR) based on genetic algorithm (GA) to solve the two degrees of freedom (2 DoF) motion control problem in head seas for wave piercing catamarans (WPC). The proposed LQR based GA control strategy is to select optimal weighting matrices (Q and R). The seakeeping performance of WPC based on proposed algorithm is challenged because of multi-input multi-output (MIMO) system of uncertain coefficient problems. Besides the kinematical constraint problems of WPC, the external conditions must be considered, like the sea disturbance and the actuators (a T-foil and two flaps) control. Moreover, this paper describes the MATLAB and LabVIEW software plats to simulate the reduction effects of WPC. Finally, the real-time (RT) NI CompactRIO embedded controller is selected to test the effectiveness of the actuators based on proposed techniques. In conclusion, simulation and experimental results prove the correctness of the proposed algorithm. The percentage of heave and pitch reductions are more than 18% in different high speeds and bad sea conditions. And the results also verify the feasibility of NI CompactRIO embedded controller.

Exploring the Use of Individualized, Reflective Guidance In an Educational Multi-User Virtual Environment

NASA Astrophysics Data System (ADS)

Nelson, Brian C.

2007-02-01

This study examines the patterns of use and potential impact of individualized, reflective guidance in an educational Multi-User Virtual Environment (MUVE). A guidance system embedded within a MUVE-based scientific inquiry curriculum was implemented with a sample of middle school students in an exploratory study investigating (a) whether access to the guidance system was associated with improved learning, (b) whether students viewing more guidance messages saw greater improvement on content tests than those viewing less, and (c) whether there were any differences in guidance use among boys and girls. Initial experimental findings showed that basic access to individualized guidance used with a MUVE had no measurable impact on learning. However, post-hoc exploratory analyses indicated that increased use of the system among those with access to it was positively associated with content test score gains. In addition, differences were found in overall learning outcomes by gender and in patterns of guidance use by boys and girls, with girls outperforming boys across a spectrum of guidance system use. Based on these exploratory findings, the paper suggests design guidelines for the development of guidance systems embedded in MUVEs and outlines directions for further research.

Design a software real-time operation platform for wave piercing catamarans motion control using linear quadratic regulator based genetic algorithm

PubMed Central

Liang, Lihua; Zhang, Songtao; Zhao, Peng

2018-01-01

This work presents optimal linear quadratic regulator (LQR) based on genetic algorithm (GA) to solve the two degrees of freedom (2 DoF) motion control problem in head seas for wave piercing catamarans (WPC). The proposed LQR based GA control strategy is to select optimal weighting matrices (Q and R). The seakeeping performance of WPC based on proposed algorithm is challenged because of multi-input multi-output (MIMO) system of uncertain coefficient problems. Besides the kinematical constraint problems of WPC, the external conditions must be considered, like the sea disturbance and the actuators (a T-foil and two flaps) control. Moreover, this paper describes the MATLAB and LabVIEW software plats to simulate the reduction effects of WPC. Finally, the real-time (RT) NI CompactRIO embedded controller is selected to test the effectiveness of the actuators based on proposed techniques. In conclusion, simulation and experimental results prove the correctness of the proposed algorithm. The percentage of heave and pitch reductions are more than 18% in different high speeds and bad sea conditions. And the results also verify the feasibility of NI CompactRIO embedded controller. PMID:29709008

Electro-optical backplane demonstrator with integrated multimode gradient-index thin glass waveguide panel

NASA Astrophysics Data System (ADS)

Schröder, Henning; Brusberg, Lars; Pitwon, Richard; Whalley, Simon; Wang, Kai; Miller, Allen; Herbst, Christian; Weber, Daniel; Lang, Klaus-Dieter

2015-03-01

Optical interconnects for data transmission at board level offer increased energy efficiency, system density, and bandwidth scalability compared to purely copper driven systems. We present recent results on manufacturing of electrooptical printed circuit board (PCB) with integrated planar glass waveguides. The graded index multi-mode waveguides are patterned inside commercially available thin-glass panels by performing a specific ion-exchange process. The glass waveguide panel is embedded within the layer stack-up of a PCB using proven industrial processes. This paper describes the design, manufacture, assembly and characterization of the first electro-optical backplane demonstrator based on integrated planar glass waveguides. The electro-optical backplane in question is created by laminating the glass waveguide panel into a conventional multi-layer electronic printed circuit board stack-up. High precision ferrule mounts are automatically assembled, which will enable MT compliant connectors to be plugged accurately to the embedded waveguide interfaces on the glass panel edges. The demonstration platform comprises a standardized sub-rack chassis and five pluggable test cards each housing optical engines and pluggable optical connectors. The test cards support a variety of different data interfaces and can support data rates of up to 32 Gb/s per channel.

Multi-stream portrait of the Cosmic web

NASA Astrophysics Data System (ADS)

Ramachandra, Nesar; Shandarin, Sergei

2016-03-01

We report the results of the first study of the multi-stream environment of dark matter haloes in cosmological N-body simulations in the ΛCDM cosmology. The full dynamical state of dark matter can be described as a three-dimensional sub-manifold in six-dimensional phase space - the dark matter sheet. In our study we use a Lagrangian sub-manifold x = x (q , t) (where x and q are co-moving Eulerian and Lagrangian coordinates respectively), which is dynamically equivalent to the dark matter sheet but is more convenient for numerical analysis. Our major results can be summarized as follows. At the resolution of the simulation, the cosmic web represents a hierarchical structure: each halo is embedded in the filamentary framework of the web predominantly at the filament crossings, and each filament is embedded in the wall like fabric of the web at the wall crossings. Locally, each halo or sub-halo is a peak in the number of streams field. The number of streams in the neighbouring filaments is higher than in the neighbouring walls. The walls are regions where number of streams is equal to three or a few. Voids are uniquely defined by the local condition requiring to be a single-stream flow region.

Analysis of the Harrier forebody/inlet design using computational techniques

NASA Technical Reports Server (NTRS)

Chow, Chuen-Yen

1993-01-01

Under the support of this Cooperative Agreement, computations of transonic flow past the complex forebody/inlet configuration of the AV-8B Harrier II have been performed. The actual aircraft configuration was measured and its surface and surrounding domain were defined using computational structured grids. The thin-layer Navier-Stokes equations were used to model the flow along with the Chimera embedded multi-grid technique. A fully conservative, alternating direction implicit (ADI), approximately-factored, partially flux-split algorithm was employed to perform the computation. An existing code was altered to conform with the needs of the study, and some special engine face boundary conditions were developed. The algorithm incorporated the Chimera technique and an algebraic turbulence model in order to deal with the embedded multi-grids and viscous governing equations. Comparison with experimental data has yielded good agreement for the simplifications incorporated into the analysis. The aim of the present research was to provide a methodology for the numerical solution of complex, combined external/internal flows. This is the first time-dependent Navier-Stokes solution for a geometry in which the fuselage and inlet share a wall. The results indicate the methodology used here is a viable tool for transonic aircraft modeling.

Particle on a torus knot: Constrained dynamics and semi-classical quantization in a magnetic field

DOE Office of Scientific and Technical Information (OSTI.GOV)

Das, Praloy, E-mail: praloydasdurgapur@gmail.com; Pramanik, Souvik, E-mail: souvick.in@gmail.com; Ghosh, Subir, E-mail: subirghosh20@gmail.com

2016-11-15

Kinematics and dynamics of a particle moving on a torus knot poses an interesting problem as a constrained system. In the first part of the paper we have derived the modified symplectic structure or Dirac brackets of the above model in Dirac’s Hamiltonian framework, both in toroidal and Cartesian coordinate systems. This algebra has been used to study the dynamics, in particular small fluctuations in motion around a specific torus. The spatial symmetries of the system have also been studied. In the second part of the paper we have considered the quantum theory of a charge moving in a torusmore » knot in the presence of a uniform magnetic field along the axis of the torus in a semiclassical quantization framework. We exploit the Einstein–Brillouin–Keller (EBK) scheme of quantization that is appropriate for multidimensional systems. Embedding of the knot on a specific torus is inherently two dimensional that gives rise to two quantization conditions. This shows that although the system, after imposing the knot condition reduces to a one dimensional system, even then it has manifested non-planar features which shows up again in the study of fractional angular momentum. Finally we compare the results obtained from EBK (multi-dimensional) and Bohr–Sommerfeld (single dimensional) schemes. The energy levels and fractional spin depend on the torus knot parameters that specifies its non-planar features. Interestingly, we show that there can be non-planar corrections to the planar anyon-like fractional spin.« less

Evaluating and Improving Cloud Processes in the Multi-Scale Modeling Framework

DOE Office of Scientific and Technical Information (OSTI.GOV)

Ackerman, Thomas P.

2015-03-01

The research performed under this grant was intended to improve the embedded cloud model in the Multi-scale Modeling Framework (MMF) for convective clouds by using a 2-moment microphysics scheme rather than the single moment scheme used in all the MMF runs to date. The technical report and associated documents describe the results of testing the cloud resolving model with fixed boundary conditions and evaluation of model results with data. The overarching conclusion is that such model evaluations are problematic because errors in the forcing fields control the results so strongly that variations in parameterization values cannot be usefully constrained

Use of multi-functional flexible micro-sensors for in situ measurement of temperature, voltage and fuel flow in a proton exchange membrane fuel cell.

PubMed

Lee, Chi-Yuan; Chan, Pin-Cheng; Lee, Chung-Ju

2010-01-01

Temperature, voltage and fuel flow distribution all contribute considerably to fuel cell performance. Conventional methods cannot accurately determine parameter changes inside a fuel cell. This investigation developed flexible and multi-functional micro sensors on a 40 μm-thick stainless steel foil substrate by using micro-electro-mechanical systems (MEMS) and embedded them in a proton exchange membrane fuel cell (PEMFC) to measure the temperature, voltage and flow. Users can monitor and control in situ the temperature, voltage and fuel flow distribution in the cell. Thereby, both fuel cell performance and lifetime can be increased.

Cellerator: extending a computer algebra system to include biochemical arrows for signal transduction simulations

NASA Technical Reports Server (NTRS)

Shapiro, Bruce E.; Levchenko, Andre; Meyerowitz, Elliot M.; Wold, Barbara J.; Mjolsness, Eric D.

2003-01-01

Cellerator describes single and multi-cellular signal transduction networks (STN) with a compact, optionally palette-driven, arrow-based notation to represent biochemical reactions and transcriptional activation. Multi-compartment systems are represented as graphs with STNs embedded in each node. Interactions include mass-action, enzymatic, allosteric and connectionist models. Reactions are translated into differential equations and can be solved numerically to generate predictive time courses or output as systems of equations that can be read by other programs. Cellerator simulations are fully extensible and portable to any operating system that supports Mathematica, and can be indefinitely nested within larger data structures to produce highly scaleable models.

MUSIC-type imaging of a thin penetrable inclusion from its multi-static response matrix

NASA Astrophysics Data System (ADS)

Park, Won-Kwang; Lesselier, Dominique

2009-07-01

The imaging of a thin inclusion, with dielectric and/or magnetic contrasts with respect to the embedding homogeneous medium, is investigated. A MUSIC-type algorithm operating at a single time-harmonic frequency is developed in order to map the inclusion (that is, to retrieve its supporting curve) from scattered field data collected within the multi-static response matrix. Numerical experiments carried out for several types of inclusions (dielectric and/or magnetic ones, straight or curved ones), mostly single inclusions and also two of them close by as a straightforward extension, illustrate the pros and cons of the proposed imaging method.

TargetCrys: protein crystallization prediction by fusing multi-view features with two-layered SVM.

PubMed

Hu, Jun; Han, Ke; Li, Yang; Yang, Jing-Yu; Shen, Hong-Bin; Yu, Dong-Jun

2016-11-01

The accurate prediction of whether a protein will crystallize plays a crucial role in improving the success rate of protein crystallization projects. A common critical problem in the development of machine-learning-based protein crystallization predictors is how to effectively utilize protein features extracted from different views. In this study, we aimed to improve the efficiency of fusing multi-view protein features by proposing a new two-layered SVM (2L-SVM) which switches the feature-level fusion problem to a decision-level fusion problem: the SVMs in the 1st layer of the 2L-SVM are trained on each of the multi-view feature sets; then, the outputs of the 1st layer SVMs, which are the "intermediate" decisions made based on the respective feature sets, are further ensembled by a 2nd layer SVM. Based on the proposed 2L-SVM, we implemented a sequence-based protein crystallization predictor called TargetCrys. Experimental results on several benchmark datasets demonstrated the efficacy of the proposed 2L-SVM for fusing multi-view features. We also compared TargetCrys with existing sequence-based protein crystallization predictors and demonstrated that the proposed TargetCrys outperformed most of the existing predictors and is competitive with the state-of-the-art predictors. The TargetCrys webserver and datasets used in this study are freely available for academic use at: http://csbio.njust.edu.cn/bioinf/TargetCrys .

Realization of Chinese word segmentation based on deep learning method

NASA Astrophysics Data System (ADS)

Wang, Xuefei; Wang, Mingjiang; Zhang, Qiquan

2017-08-01

In recent years, with the rapid development of deep learning, it has been widely used in the field of natural language processing. In this paper, I use the method of deep learning to achieve Chinese word segmentation, with large-scale corpus, eliminating the need to construct additional manual characteristics. In the process of Chinese word segmentation, the first step is to deal with the corpus, use word2vec to get word embedding of the corpus, each character is 50. After the word is embedded, the word embedding feature is fed to the bidirectional LSTM, add a linear layer to the hidden layer of the output, and then add a CRF to get the model implemented in this paper. Experimental results show that the method used in the 2014 People's Daily corpus to achieve a satisfactory accuracy.

Baryon spectrum from superconformal quantum mechanics and its light-front holographic embedding

DOE PAGES

de Teramond, Guy F.; Dosch, Hans Gunter; Brodsky, Stanley J.

2015-02-27

We describe the observed light-baryon spectrum by extending superconformal quantum mechanics to the light front and its embedding in AdS space. This procedure uniquely determines the confinement potential for arbitrary half-integer spin. To this end, we show that fermionic wave equations in AdS space are dual to light-front supersymmetric quantum-mechanical bound-state equations in physical space-time. The specific breaking of conformal invariance explains hadronic properties common to light mesons and baryons, such as the observed mass pattern in the radial and orbital excitations, from the spectrum generating algebra. Lastly, the holographic embedding in AdS also explains distinctive and systematic features, suchmore » as the spin-J degeneracy for states with the same orbital angular momentum, observed in the light-baryon spectrum.« less

DOE Office of Scientific and Technical Information (OSTI.GOV)

Wickstrom, Gregory Lloyd; Gale, Jason Carl; Ma, Kwok Kee

The Sandia Secure Processor (SSP) is a new native Java processor that has been specifically designed for embedded applications. The SSP's design is a system composed of a core Java processor that directly executes Java bytecodes, on-chip intelligent IO modules, and a suite of software tools for simulation and compiling executable binary files. The SSP is unique in that it provides a way to control real-time IO modules for embedded applications. The system software for the SSP is a 'class loader' that takes Java .class files (created with your favorite Java compiler), links them together, and compiles a binary. Themore » complete SSP system provides very powerful functionality with very light hardware requirements with the potential to be used in a wide variety of small-system embedded applications. This paper gives a detail description of the Sandia Secure Processor and its unique features.« less

Enhanced performance of microfluidic soft pressure sensors with embedded solid microspheres

NASA Astrophysics Data System (ADS)

Shin, Hee-Sup; Ryu, Jaiyoung; Majidi, Carmel; Park, Yong-Lae

2016-02-01

The cross-sectional geometry of an embedded microchannel influences the electromechanical response of a soft microfluidic sensor to applied surface pressure. When a pressure is exerted on the surface of the sensor deforming the soft structure, the cross-sectional area of the embedded channel filled with a conductive fluid decreases, increasing the channel’s electrical resistance. This electromechanical coupling can be tuned by adding solid microspheres into the channel. In order to determine the influence of microspheres, we use both analytic and computational methods to predict the pressure responses of soft microfluidic sensors with two different channel cross-sections: a square and an equilateral triangular. The analytical models were derived from contact mechanics in which microspheres were regarded as spherical indenters, and finite element analysis (FEA) was used for simulation. For experimental validation, sensor samples with the two different channel cross-sections were prepared and tested. For comparison, the sensor samples were tested both with and without microspheres. All three results from the analytical models, the FEA simulations, and the experiments showed reasonable agreement confirming that the multi-material soft structure significantly improved its pressure response in terms of both linearity and sensitivity. The embedded solid particles enhanced the performance of soft sensors while maintaining their flexible and stretchable mechanical characteristic. We also provide analytical and experimental analyses of hysteresis of microfluidic soft sensors considering a resistive force to the shape recovery of the polymer structure by the embedded viscous fluid.

Evidence of tampering in watermark identification

NASA Astrophysics Data System (ADS)

McLauchlan, Lifford; Mehrübeoglu, Mehrübe

2009-08-01

In this work, watermarks are embedded in digital images in the discrete wavelet transform (DWT) domain. Principal component analysis (PCA) is performed on the DWT coefficients. Next higher order statistics based on the principal components and the eigenvalues are determined for different sets of images. Feature sets are analyzed for different types of attacks in m dimensional space. The results demonstrate the separability of the features for the tampered digital copies. Different feature sets are studied to determine more effective tamper evident feature sets. The digital forensics, the probable manipulation(s) or modification(s) performed on the digital information can be identified using the described technique.

Integrable scalar cosmologies. II. Can they fit into Gauged Extended Supergravity or be encoded in N=1 superpotentials?

NASA Astrophysics Data System (ADS)

Fré, P.; Sorin, A. S.; Trigiante, M.

2014-04-01

The question whether the integrable one-field cosmologies classified in a previous paper by Fré, Sagnotti and Sorin can be embedded as consistent one-field truncations into Extended Gauged Supergravity or in N=1 supergravity gauged by a superpotential without the use of D-terms is addressed in this paper. The answer is that such an embedding is very difficult and rare but not impossible. Indeed, we were able to find two examples of integrable models embedded in supergravity in this way. Both examples are fitted into N=1 supergravity by means of a very specific and interesting choice of the superpotential W(z). The question whether there are examples of such an embedding in Extended Gauged Supergravity remains open. In the present paper, relying on the embedding tensor formalism we classified all gaugings of the N=2 STU model, confirming, in the absence on hypermultiplets, the uniqueness of the stable de Sitter vacuum found several years ago by Fré, Trigiante and Van Proeyen and excluding the embedding of any integrable cosmological model. A detailed analysis of the space of exact solutions of the first supergravity-embedded integrable cosmological model revealed several new features worth an in-depth consideration. When the scalar potential has an extremum at a negative value, the Universe necessarily collapses into a Big Crunch notwithstanding its spatial flatness. The causal structure of these Universes is quite different from that of the closed, positive curved, Universe: indeed, in this case the particle and event horizons do not coincide and develop complicated patterns. The cosmological consequences of this unexpected mechanism deserve careful consideration. The Cartan fieldshi associated with the Cartan generators of the Lie algebra G, whose number equals the rank r of G/H. For instance, in models associated with toroidal or orbifold compactifications, fields of this type are generically interpreted as radii of the underlying multi-tori. The axion fieldsbI associated with the roots of the Lie algebra G. The kinetic terms of Cartan scalars have the canonical form ∑ir α/i22 ∂μhi∂μ hi, up to constant coefficients, while for the axion scalars entering solvable coset representatives, the αi2 factors leave way to exponential functions exp[βihi] of Cartan fields. The scalar potentials of Gauged Supergravity are polynomial functions of the coset representatives, so that after the truncation to Cartan sectors, setting the axions to constant values, one is led naturally to combinations of exponentials of the type encountered in [1]. Yet the devil lies in the details, since the integrable potentials do result from exponential functions exp[βh], but with rigidly fixed ratios between the βi entering the exponents and the αi entering the kinetic terms. The candidate potentials are displayed in Tables 1 and 2 following the notations and the nomenclature of [1]. As a result, the possible role of integrable potentials in Gauged Supergravity theories is not evident a priori, and actually, the required ratios are quite difficult to be obtained. Notwithstanding these difficulties we were able to identify a pair of examples, showing that although rare, supergravity integrable cosmological models based on G/H scalar manifolds

Experimental investigation of multi-scale non-equilibrium plasma dynamics

NASA Astrophysics Data System (ADS)

Bellan, Paul

2013-10-01

Lab experiments at Caltech resolve complex, detailed MHD dynamics spatially and temporally. Unbalanced forces drive fast plasma flows which tend to self-collimate via self-pinching. Collimation results from flow stagnation compressing embedded magnetic flux and so amplifying the magnetic field responsible for pinching. Measurements show that the collimated flow is essentially a dense plasma jet with embedded axial and azimuthal magnetic fields, i.e., a magnetic flux tube (flux rope). The measured jet velocity is in good agreement with an MHD acceleration model. Depending on how flux tube radius varies with axial position, jets flow into a flux tube from both ends or from just one end. Jets kink when the flux tube in which they are embedded breaches the Kruskal-Shafranov stability limit. The lateral acceleration of a sufficiently strong kink can produce an enormous effective gravity which provides the environment for an observed fine-scale, extremely fast Rayleigh-Taylor (RT) instability. The RT can erode the jet current channel to be smaller than the ion skin depth so there is a cascade from the ideal MHD scale of the kink to the non-MHD ion skin depth scale. This process can result in a magnetic reconnection whereby the jet and its embedded flux tube break. Supported by USDOE.

EFFECTS OF EPISODIC SUBLUXATION EVENTS ON THIRD BODY INGRESS AND EMBEDMENT IN THE THA BEARING SURFACE

PubMed Central

Heiner, Anneliese D.; Lundberg, Hannah J.; Baer, Thomas E.; Pedersen, Douglas R.; Callaghan, John J.; Brown, Thomas D.

2008-01-01

In total joint arthroplasty, third body particle access to the articulating surfaces results in accelerated wear. Hip joint subluxation is an under-recognized means by which third body particles could potentially enter the otherwise closely conforming articular bearing space. The present study was designed to test the hypothesis that, other factors being equal, even occasional events of femoral head subluxation greatly increase the number of third body particles that enter the bearing space and become embedded in the acetabular liner, as compared to level walking cycles alone. Ten metal-on-polyethylene hip joint head-liner pairs were tested in a multi-axis joint motion simulator, with CoCrMo third body particles added to the synovial fluid analog. All component pairs were tested for two hours of level walking; half also were subjected to twenty intermittent subluxation events. The number and location of embedded particles on the acetabular liners were then determined. Subluxation dramatically increased the number of third body particles embedded in the acetabular liners, and it considerably increased the amount of scratch damage on the femoral heads. Since both third body particles and subluxation frequently occur in contemporary total hip arthroplasty, their potent synergy needs to be factored prominently into strategies to minimize wear. PMID:18561936

Sea-land segmentation for infrared remote sensing images based on superpixels and multi-scale features

NASA Astrophysics Data System (ADS)

Lei, Sen; Zou, Zhengxia; Liu, Dunge; Xia, Zhenghuan; Shi, Zhenwei

2018-06-01

Sea-land segmentation is a key step for the information processing of ocean remote sensing images. Traditional sea-land segmentation algorithms ignore the local similarity prior of sea and land, and thus fail in complex scenarios. In this paper, we propose a new sea-land segmentation method for infrared remote sensing images to tackle the problem based on superpixels and multi-scale features. Considering the connectivity and local similarity of sea or land, we interpret the sea-land segmentation task in view of superpixels rather than pixels, where similar pixels are clustered and the local similarity are explored. Moreover, the multi-scale features are elaborately designed, comprising of gray histogram and multi-scale total variation. Experimental results on infrared bands of Landsat-8 satellite images demonstrate that the proposed method can obtain more accurate and more robust sea-land segmentation results than the traditional algorithms.

Data Mining and Machine Learning Models for Predicting Drug Likeness and their Disease or Organ Category

NASA Astrophysics Data System (ADS)

Yosipof, Abraham; Guedes, Rita C.; García-Sosa, Alfonso T.

2018-05-01

Data mining approaches can uncover underlying patterns in chemical and pharmacological property space decisive for drug discovery and development. Two of the most common approaches are visualization and machine learning methods. Visualization methods use dimensionality reduction techniques in order to reduce multi-dimension data into 2D or 3D representations with a minimal loss of information. Machine learning attempts to find correlations between specific activities or classifications for a set of compounds and their features by means of recurring mathematical models. Both models take advantage of the different and deep relationships that can exist between features of compounds, and helpfully provide classification of compounds based on such features. Drug-likeness has been studied from several viewpoints, but here we provide the first implementation in chemoinformatics of the t-Distributed Stochastic Neighbor Embedding (t-SNE) method for the visualization and the representation of chemical space, and the use of different machine learning methods separately and together to form a new ensemble learning method called AL Boost. The models obtained from AL Boost synergistically combine decision tree, random forests (RF), support vector machine (SVM), artificial neuronal network (ANN), k nearest neighbors (kNN), and logistic regression models. In this work, we show that together they form a predictive model that not only improves the predictive force but also decreases bias. This resulted in a corrected classification rate of over 0.81, as well as higher sensitivity and specificity rates for the models. In addition, separation and good models were also achieved for disease categories such as antineoplastic compounds and nervous system diseases, among others. Such models can be used to guide decision on the feature landscape of compounds and their likeness to either drugs or other characteristics, such as specific or multiple disease-category(ies) or organ(s) of action of a molecule.

Uniform competency-based local feature extraction for remote sensing images

NASA Astrophysics Data System (ADS)

Sedaghat, Amin; Mohammadi, Nazila

2018-01-01

Local feature detectors are widely used in many photogrammetry and remote sensing applications. The quantity and distribution of the local features play a critical role in the quality of the image matching process, particularly for multi-sensor high resolution remote sensing image registration. However, conventional local feature detectors cannot extract desirable matched features either in terms of the number of correct matches or the spatial and scale distribution in multi-sensor remote sensing images. To address this problem, this paper proposes a novel method for uniform and robust local feature extraction for remote sensing images, which is based on a novel competency criterion and scale and location distribution constraints. The proposed method, called uniform competency (UC) local feature extraction, can be easily applied to any local feature detector for various kinds of applications. The proposed competency criterion is based on a weighted ranking process using three quality measures, including robustness, spatial saliency and scale parameters, which is performed in a multi-layer gridding schema. For evaluation, five state-of-the-art local feature detector approaches, namely, scale-invariant feature transform (SIFT), speeded up robust features (SURF), scale-invariant feature operator (SFOP), maximally stable extremal region (MSER) and hessian-affine, are used. The proposed UC-based feature extraction algorithms were successfully applied to match various synthetic and real satellite image pairs, and the results demonstrate its capability to increase matching performance and to improve the spatial distribution. The code to carry out the UC feature extraction is available from href="https://www.researchgate.net/publication/317956777_UC-Feature_Extraction.

Far-Infrared Based Pedestrian Detection for Driver-Assistance Systems Based on Candidate Filters, Gradient-Based Feature and Multi-Frame Approval Matching

PubMed Central

Wang, Guohua; Liu, Qiong

2015-01-01

Far-infrared pedestrian detection approaches for advanced driver-assistance systems based on high-dimensional features fail to simultaneously achieve robust and real-time detection. We propose a robust and real-time pedestrian detection system characterized by novel candidate filters, novel pedestrian features and multi-frame approval matching in a coarse-to-fine fashion. Firstly, we design two filters based on the pedestrians’ head and the road to select the candidates after applying a pedestrian segmentation algorithm to reduce false alarms. Secondly, we propose a novel feature encapsulating both the relationship of oriented gradient distribution and the code of oriented gradient to deal with the enormous variance in pedestrians’ size and appearance. Thirdly, we introduce a multi-frame approval matching approach utilizing the spatiotemporal continuity of pedestrians to increase the detection rate. Large-scale experiments indicate that the system works in real time and the accuracy has improved about 9% compared with approaches based on high-dimensional features only. PMID:26703611

Far-Infrared Based Pedestrian Detection for Driver-Assistance Systems Based on Candidate Filters, Gradient-Based Feature and Multi-Frame Approval Matching.

PubMed

Wang, Guohua; Liu, Qiong

2015-12-21

Far-infrared pedestrian detection approaches for advanced driver-assistance systems based on high-dimensional features fail to simultaneously achieve robust and real-time detection. We propose a robust and real-time pedestrian detection system characterized by novel candidate filters, novel pedestrian features and multi-frame approval matching in a coarse-to-fine fashion. Firstly, we design two filters based on the pedestrians' head and the road to select the candidates after applying a pedestrian segmentation algorithm to reduce false alarms. Secondly, we propose a novel feature encapsulating both the relationship of oriented gradient distribution and the code of oriented gradient to deal with the enormous variance in pedestrians' size and appearance. Thirdly, we introduce a multi-frame approval matching approach utilizing the spatiotemporal continuity of pedestrians to increase the detection rate. Large-scale experiments indicate that the system works in real time and the accuracy has improved about 9% compared with approaches based on high-dimensional features only.

The design and application of a multi-band IR imager

NASA Astrophysics Data System (ADS)

Li, Lijuan

2018-02-01

Multi-band IR imaging system has many applications in security, national defense, petroleum and gas industry, etc. So the relevant technologies are getting more and more attention in rent years. As we know, when used in missile warning and missile seeker systems, multi-band IR imaging technology has the advantage of high target recognition capability and low false alarm rate if suitable spectral bands are selected. Compared with traditional single band IR imager, multi-band IR imager can make use of spectral features in addition to space and time domain features to discriminate target from background clutters and decoys. So, one of the key work is to select the right spectral bands in which the feature difference between target and false target is evident and is well utilized. Multi-band IR imager is a useful instrument to collect multi-band IR images of target, backgrounds and decoys for spectral band selection study at low cost and with adjustable parameters and property compared with commercial imaging spectrometer. In this paper, a multi-band IR imaging system is developed which is suitable to collect 4 spectral band images of various scenes at every turn and can be expanded to other short-wave and mid-wave IR spectral bands combination by changing filter groups. The multi-band IR imaging system consists of a broad band optical system, a cryogenic InSb large array detector, a spinning filter wheel and electronic processing system. The multi-band IR imaging system's performance is tested in real data collection experiments.

Multifractal modeling, segmentation, prediction, and statistical validation of posterior fossa tumors

NASA Astrophysics Data System (ADS)

Islam, Atiq; Iftekharuddin, Khan M.; Ogg, Robert J.; Laningham, Fred H.; Sivakumar, Bhuvaneswari

2008-03-01

In this paper, we characterize the tumor texture in pediatric brain magnetic resonance images (MRIs) and exploit these features for automatic segmentation of posterior fossa (PF) tumors. We focus on PF tumor because of the prevalence of such tumor in pediatric patients. Due to varying appearance in MRI, we propose to model the tumor texture with a multi-fractal process, such as a multi-fractional Brownian motion (mBm). In mBm, the time-varying Holder exponent provides flexibility in modeling irregular tumor texture. We develop a detailed mathematical framework for mBm in two-dimension and propose a novel algorithm to estimate the multi-fractal structure of tissue texture in brain MRI based on wavelet coefficients. This wavelet based multi-fractal feature along with MR image intensity and a regular fractal feature obtained using our existing piecewise-triangular-prism-surface-area (PTPSA) method, are fused in segmenting PF tumor and non-tumor regions in brain T1, T2, and FLAIR MR images respectively. We also demonstrate a non-patient-specific automated tumor prediction scheme based on these image features. We experimentally show the tumor discriminating power of our novel multi-fractal texture along with intensity and fractal features in automated tumor segmentation and statistical prediction. To evaluate the performance of our tumor prediction scheme, we obtain ROCs and demonstrate how sharply the curves reach the specificity of 1.0 sacrificing minimal sensitivity. Experimental results show the effectiveness of our proposed techniques in automatic detection of PF tumors in pediatric MRIs.

Maintainable substrate carrier for electroplating

DOEpatents

Chen, Chen-An [Milpitas, CA; Abas, Emmanuel Chua [Laguna, PH; Divino, Edmundo Anida [Cavite, PH; Ermita, Jake Randal G [Laguna, PH; Capulong, Jose Francisco S [Laguna, PH; Castillo, Arnold Villamor [Batangas, PH; Ma,; Xiaobing, Diana [Saratoga, CA

2012-07-17

One embodiment relates to a substrate carrier for use in electroplating a plurality of substrates. The carrier includes a non-conductive carrier body on which the substrates are placed and conductive lines embedded within the carrier body. A plurality of conductive clip attachment parts are attached in a permanent manner to the conductive lines embedded within the carrier body. A plurality of contact clips are attached in a removable manner to the clip attachment parts. The contact clips hold the substrates in place and conductively connecting the substrates with the conductive lines. Other embodiments, aspects and features are also disclosed.

Fuzzy Logic-Based Audio Pattern Recognition

NASA Astrophysics Data System (ADS)

Malcangi, M.

2008-11-01

Audio and audio-pattern recognition is becoming one of the most important technologies to automatically control embedded systems. Fuzzy logic may be the most important enabling methodology due to its ability to rapidly and economically model such application. An audio and audio-pattern recognition engine based on fuzzy logic has been developed for use in very low-cost and deeply embedded systems to automate human-to-machine and machine-to-machine interaction. This engine consists of simple digital signal-processing algorithms for feature extraction and normalization, and a set of pattern-recognition rules manually tuned or automatically tuned by a self-learning process.

Maintainable substrate carrier for electroplating

DOEpatents

Chen, Chen-An; Abas, Emmanuel Chua; Divino, Edmundo Anida; Ermita, Jake Randal G.; Capulong, Jose Francisco S.; Castillo, Arnold Villamor; Ma, Diana Xiaobing

2016-08-02

One embodiment relates to a substrate carrier for use in electroplating a plurality of substrates. The carrier includes a non-conductive carrier body on which the substrates are placed and conductive lines embedded within the carrier body. A plurality of conductive clip attachment parts are attached in a permanent manner to the conductive lines embedded within the carrier body. A plurality of contact clips are attached in a removable manner to the clip attachment parts. The contact clips hold the substrates in place and conductively connecting the substrates with the conductive lines. Other embodiments, aspects and features are also disclosed.

Scalar-Tensor Black Holes Embedded in an Expanding Universe

NASA Astrophysics Data System (ADS)

Tretyakova, Daria; Latosh, Boris

2018-02-01

In this review we focus our attention on scalar-tensor gravity models and their empirical verification in terms of black hole and wormhole physics. We focus on a black hole, embedded in an expanding universe, describing both cosmological and astrophysical scales. We show that in scalar-tensor gravity it is quite common that the local geometry is isolated from the cosmological expansion, so that it does not backreact on the black hole metric. We try to extract common features of scalar-tensor black holes in an expanding universe and point out the gaps that must be filled.

Static strain tuning of quantum dots embedded in a photonic wire

NASA Astrophysics Data System (ADS)

Tumanov, D.; Vaish, N.; Nguyen, H. A.; Curé, Y.; Gérard, J.-M.; Claudon, J.; Donatini, F.; Poizat, J.-Ph.

2018-03-01

We use strain to statically tune the semiconductor band gap of individual InAs quantum dots (QDs) embedded in a GaAs photonic wire featuring very efficient single photon collection. Thanks to the geometry of the structure, we are able to shift the QD excitonic transition by more than 25 meV by using nano-manipulators to apply the stress. Moreover, owing to the strong transverse strain gradient generated in the structure, we can relatively tune two QDs located in the wire waveguide and bring them in resonance, opening the way to the observation of collective effects such as superradiance.

An annotation system for 3D fluid flow visualization

NASA Technical Reports Server (NTRS)

Loughlin, Maria M.; Hughes, John F.

1995-01-01

Annotation is a key activity of data analysis. However, current systems for data analysis focus almost exclusively on visualization. We propose a system which integrates annotations into a visualization system. Annotations are embedded in 3D data space, using the Post-it metaphor. This embedding allows contextual-based information storage and retrieval, and facilitates information sharing in collaborative environments. We provide a traditional database filter and a Magic Lens filter to create specialized views of the data. The system has been customized for fluid flow applications, with features which allow users to store parameters of visualization tools and sketch 3D volumes.

Scale Issues in Air Quality Modeling Policy Support

EPA Science Inventory

This study examines the issues relating to the use of regional photochemical air quality models for evaluating their performance in reproducing the spatio-temporal features embedded in the observations and for designing emission control strategies needed to achieve compliance wit...

A Framework for Evaluating Regional-Scale Numerical Photochemical Modeling Systems

EPA Science Inventory

This paper discusses the need for critically evaluating regional-scale (~ 200-2000 km) three dimensional numerical photochemical air quality modeling systems to establish a model's credibility in simulating the spatio-temporal features embedded in the observations. Because of li...