An Embedded Multi-Agent Systems Based Industrial Wireless Sensor Network

Science.gov (United States)

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

Multi-dimension feature fusion for action recognition

Science.gov (United States)

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.

Real time polarization sensor image processing on an embedded FPGA/multi-core DSP system

Science.gov (United States)

Bednara, Marcus; Chuchacz-Kowalczyk, Katarzyna

2015-05-01

Most embedded image processing SoCs available on the market are highly optimized for typical consumer applications like video encoding/decoding, motion estimation or several image enhancement processes as used in DSLR or digital video cameras. For non-consumer applications, on the other hand, optimized embedded hardware is rarely available, so often PC based image processing systems are used. We show how a real time capable image processing system for a non-consumer application - namely polarization image data processing - can be efficiently implemented on an FPGA and multi-core DSP based embedded hardware platform.

Design of massively parallel hardware multi-processors for highly-demanding embedded applications

NARCIS (Netherlands)

Jozwiak, L.; Jan, Y.

2013-01-01

Many new embedded applications require complex computations to be performed to tight schedules, while at the same time demanding low energy consumption and low cost. For implementation of these highly-demanding applications, highly-optimized application-specific multi-processor system-on-a-chip

Design Optimization of Multi-Cluster Embedded Systems for Real-Time Applications

DEFF Research Database (Denmark)

Pop, Paul; Eles, Petru; Peng, Zebo

2004-01-01

We present an approach to design optimization of multi-cluster embedded systems consisting of time-triggered and event-triggered clusters, interconnected via gateways. In this paper, we address design problems which are characteristic to multi-clusters: partitioning of the system functionality...... into time-triggered and event-triggered domains, process mapping, and the optimization of parameters corresponding to the communication protocol. We present several heuristics for solving these problems. Our heuristics are able to find schedulable implementations under limited resources, achieving...... an efficient utilization of the system. The developed algorithms are evaluated using extensive experiments and a real-life example....

Design Optimization of Multi-Cluster Embedded Systems for Real-Time Applications

DEFF Research Database (Denmark)

Pop, Paul; Eles, Petru; Peng, Zebo

2006-01-01

We present an approach to design optimization of multi-cluster embedded systems consisting of time-triggered and event-triggered clusters, interconnected via gateways. In this paper, we address design problems which are characteristic to multi-clusters: partitioning of the system functionality...... into time-triggered and event-triggered domains, process mapping, and the optimization of parameters corresponding to the communication protocol. We present several heuristics for solving these problems. Our heuristics are able to find schedulable implementations under limited resources, achieving...... an efficient utilization of the system. The developed algorithms are evaluated using extensive experiments and a real-life example....

Self-Organization in Embedded Real-Time Systems

CERN Document Server

Brinkschulte, Uwe; Rettberg, Achim

2013-01-01

This book describes the emerging field of self-organizing, multicore, distributed and real-time embedded systems.  Self-organization of both hardware and software can be a key technique to handle the growing complexity of modern computing systems. Distributed systems running hundreds of tasks on dozens of processors, each equipped with multiple cores, requires self-organization principles to ensure efficient and reliable operation. This book addresses various, so-called Self-X features such as self-configuration, self-optimization, self-adaptation, self-healing and self-protection. Presents open components for embedded real-time adaptive and self-organizing applications; Describes innovative techniques in: scheduling, memory management, quality of service, communications supporting organic real-time applications; Covers multi-/many-core embedded systems supporting real-time adaptive systems and power-aware, adaptive hardware and software systems; Includes case studies of open embedded real-time self-organizi...

Design concepts for a virtualizable embedded MPSoC architecture enabling virtualization in embedded multi-processor systems

CERN Document Server

Biedermann, Alexander

2014-01-01

Alexander Biedermann presents a generic hardware-based virtualization approach, which may transform an array of any off-the-shelf embedded processors into a multi-processor system with high execution dynamism. Based on this approach, he highlights concepts for the design of energy aware systems, self-healing systems as well as parallelized systems. For the latter, the novel so-called Agile Processing scheme is introduced by the author, which enables a seamless transition between sequential and parallel execution schemes. The design of such virtualizable systems is further aided by introduction

Feature selection and multi-kernel learning for adaptive graph regularized nonnegative matrix factorization

KAUST Repository

Wang, Jim Jing-Yan

2014-09-20

Nonnegative matrix factorization (NMF), a popular part-based representation technique, does not capture the intrinsic local geometric structure of the data space. Graph regularized NMF (GNMF) was recently proposed to avoid this limitation by regularizing NMF with a nearest neighbor graph constructed from the input data set. However, GNMF has two main bottlenecks. First, using the original feature space directly to construct the graph is not necessarily optimal because of the noisy and irrelevant features and nonlinear distributions of data samples. Second, one possible way to handle the nonlinear distribution of data samples is by kernel embedding. However, it is often difficult to choose the most suitable kernel. To solve these bottlenecks, we propose two novel graph-regularized NMF methods, AGNMFFS and AGNMFMK, by introducing feature selection and multiple-kernel learning to the graph regularized NMF, respectively. Instead of using a fixed graph as in GNMF, the two proposed methods learn the nearest neighbor graph that is adaptive to the selected features and learned multiple kernels, respectively. For each method, we propose a unified objective function to conduct feature selection/multi-kernel learning, NMF and adaptive graph regularization simultaneously. We further develop two iterative algorithms to solve the two optimization problems. Experimental results on two challenging pattern classification tasks demonstrate that the proposed methods significantly outperform state-of-the-art data representation methods.

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

Science.gov (United States)

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.

Feature-based Alignment of Volumetric Multi-modal Images

Science.gov (United States)

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

Safe and Efficient Support for Embeded Multi-Processors in ADA

Science.gov (United States)

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.

Multi-Stage Recognition of Speech Emotion Using Sequential Forward Feature Selection

Directory of Open Access Journals (Sweden)

Liogienė Tatjana

2016-07-01

Full Text Available The intensive research of speech emotion recognition introduced a huge collection of speech emotion features. Large feature sets complicate the speech emotion recognition task. Among various feature selection and transformation techniques for one-stage classification, multiple classifier systems were proposed. The main idea of multiple classifiers is to arrange the emotion classification process in stages. Besides parallel and serial cases, the hierarchical arrangement of multi-stage classification is most widely used for speech emotion recognition. In this paper, we present a sequential-forward-feature-selection-based multi-stage classification scheme. The Sequential Forward Selection (SFS and Sequential Floating Forward Selection (SFFS techniques were employed for every stage of the multi-stage classification scheme. Experimental testing of the proposed scheme was performed using the German and Lithuanian emotional speech datasets. Sequential-feature-selection-based multi-stage classification outperformed the single-stage scheme by 12–42 % for different emotion sets. The multi-stage scheme has shown higher robustness to the growth of emotion set. The decrease in recognition rate with the increase in emotion set for multi-stage scheme was lower by 10–20 % in comparison with the single-stage case. Differences in SFS and SFFS employment for feature selection were negligible.

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

Science.gov (United States)

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…

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

Science.gov (United States)

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.

Manifold regularized multi-task feature selection for multi-modality classification in Alzheimer's disease.

Science.gov (United States)

Jie, Biao; Zhang, Daoqiang; Cheng, Bo; Shen, Dinggang

2013-01-01

Accurate diagnosis of Alzheimer's disease (AD), as well as its prodromal stage (i.e., mild cognitive impairment, MCI), is very important for possible delay and early treatment of the disease. Recently, multi-modality methods have been used for fusing information from multiple different and complementary imaging and non-imaging modalities. Although there are a number of existing multi-modality methods, few of them have addressed the problem of joint identification of disease-related brain regions from multi-modality data for classification. In this paper, we proposed a manifold regularized multi-task learning framework to jointly select features from multi-modality data. Specifically, we formulate the multi-modality classification as a multi-task learning framework, where each task focuses on the classification based on each modality. In order to capture the intrinsic relatedness among multiple tasks (i.e., modalities), we adopted a group sparsity regularizer, which ensures only a small number of features to be selected jointly. In addition, we introduced a new manifold based Laplacian regularization term to preserve the geometric distribution of original data from each task, which can lead to the selection of more discriminative features. Furthermore, we extend our method to the semi-supervised setting, which is very important since the acquisition of a large set of labeled data (i.e., diagnosis of disease) is usually expensive and time-consuming, while the collection of unlabeled data is relatively much easier. To validate our method, we have performed extensive evaluations on the baseline Magnetic resonance imaging (MRI) and fluorodeoxyglucose positron emission tomography (FDG-PET) data of Alzheimer's Disease Neuroimaging Initiative (ADNI) database. Our experimental results demonstrate the effectiveness of the proposed method.

Design of multi-channel analyzer's monitoring system based on embedded system

International Nuclear Information System (INIS)

Yang Tao; Wei Yixiang

2007-01-01

A new Multi-Channel Analyzer's Monitoring system based on ARM9 Embedded system is introduced in this paper. Some solutions to problem are also discussed during the procedure of design, installation and debugging on Linux system. The Monitoring system is developed by using MiniGUI and Linux software system API, with the functions of collecting, displaying and I/O data controlling 1024 channels datum. They are all realized in real time, with the merits of low cost, small size and portability. All these lay the foundation of developing homemade Digital and Portable nuclear spectrometers. (authors)

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

Science.gov (United States)

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.

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

Science.gov (United States)

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.

Schedulability-Driven Frame Packing for Multi-Cluster Distributed Embedded Systems

DEFF Research Database (Denmark)

Pop, Paul; Eles, Petru; Peng, Zebo

2003-01-01

We present an approach to frame packing for multi-cluster distributed embedded systems consisting of time-triggered and event-triggered clusters, interconnected via gateways. In our approach, the application messages are packed into frames such that the application is schedulable. Thus, we have...... also proposed a schedulability analysis for applications consisting of mixed event-triggered and time-triggered processes and messages, and a worst case queuing delay analysis for the gateways, responsible for routing inter-cluster traffic. Optimization heuristics for frame packing aiming at producing...... a schedulable system have been proposed. Extensive experiments and a real-life example show the efficiency of our frame-packing approach....

Deep PDF parsing to extract features for detecting embedded malware.

Energy Technology Data Exchange (ETDEWEB)

Munson, Miles Arthur; Cross, Jesse S. (Missouri University of Science and Technology, Rolla, MO)

2011-09-01

The number of PDF files with embedded malicious code has risen significantly in the past few years. This is due to the portability of the file format, the ways Adobe Reader recovers from corrupt PDF files, the addition of many multimedia and scripting extensions to the file format, and many format properties the malware author may use to disguise the presence of malware. Current research focuses on executable, MS Office, and HTML formats. In this paper, several features and properties of PDF Files are identified. Features are extracted using an instrumented open source PDF viewer. The feature descriptions of benign and malicious PDFs can be used to construct a machine learning model for detecting possible malware in future PDF files. The detection rate of PDF malware by current antivirus software is very low. A PDF file is easy to edit and manipulate because it is a text format, providing a low barrier to malware authors. Analyzing PDF files for malware is nonetheless difficult because of (a) the complexity of the formatting language, (b) the parsing idiosyncrasies in Adobe Reader, and (c) undocumented correction techniques employed in Adobe Reader. In May 2011, Esparza demonstrated that PDF malware could be hidden from 42 of 43 antivirus packages by combining multiple obfuscation techniques [4]. One reason current antivirus software fails is the ease of varying byte sequences in PDF malware, thereby rendering conventional signature-based virus detection useless. The compression and encryption functions produce sequences of bytes that are each functions of multiple input bytes. As a result, padding the malware payload with some whitespace before compression/encryption can change many of the bytes in the final payload. In this study we analyzed a corpus of 2591 benign and 87 malicious PDF files. While this corpus is admittedly small, it allowed us to test a system for collecting indicators of embedded PDF malware. We will call these indicators features throughout

Multi-axial strain transfer from laminated CFRP composites to embedded Bragg sensor: I. Parametric study

International Nuclear Information System (INIS)

Luyckx, G; Voet, E; De Waele, W; Degrieck, J

2010-01-01

Embedded optical fibre sensors are considered in numerous applications for structural health monitoring purposes. However, since the optical fibre and the host material in which it is embedded, will have different material properties, strain in both materials will not be equal when load is applied. Therefore, the multi-axial strain transfer from the host material to the embedded sensor (optical fibre) has to be considered in detail. In the first part of this paper the strain transfer will be determined using finite element modelling of a circular isotropic glass fibre embedded first in an isotropic host and second in an anisotropic composite material. The strain transfer or relation depends on the mechanical properties of the host material and the sensor (Young's modulus and Poisson's ratio), on the lay-up of the composite material (uni-directional lay-up/cross-ply lay-up) and the position of the sensor in a certain layer. In the second part of the paper the developed strain transfer model will be evaluated for one specific lay-up and sensor type

Manifold Regularized Multi-Task Feature Selection for Multi-Modality Classification in Alzheimer’s Disease

Science.gov (United States)

Jie, Biao; Cheng, Bo

2014-01-01

Accurate diagnosis of Alzheimer’s disease (AD), as well as its pro-dromal stage (i.e., mild cognitive impairment, MCI), is very important for possible delay and early treatment of the disease. Recently, multi-modality methods have been used for fusing information from multiple different and complementary imaging and non-imaging modalities. Although there are a number of existing multi-modality methods, few of them have addressed the problem of joint identification of disease-related brain regions from multi-modality data for classification. In this paper, we proposed a manifold regularized multi-task learning framework to jointly select features from multi-modality data. Specifically, we formulate the multi-modality classification as a multi-task learning framework, where each task focuses on the classification based on each modality. In order to capture the intrinsic relatedness among multiple tasks (i.e., modalities), we adopted a group sparsity regularizer, which ensures only a small number of features to be selected jointly. In addition, we introduced a new manifold based Laplacian regularization term to preserve the geometric distribution of original data from each task, which can lead to the selection of more discriminative features. Furthermore, we extend our method to the semi-supervised setting, which is very important since the acquisition of a large set of labeled data (i.e., diagnosis of disease) is usually expensive and time-consuming, while the collection of unlabeled data is relatively much easier. To validate our method, we have performed extensive evaluations on the baseline Magnetic resonance imaging (MRI) and fluorodeoxyglucose positron emission tomography (FDG-PET) data of Alzheimer’s Disease Neuroimaging Initiative (ADNI) database. Our experimental results demonstrate the effectiveness of the proposed method. PMID:24505676

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

Science.gov (United States)

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.…

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

Science.gov (United States)

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.

SAR Target Recognition Based on Multi-feature Multiple Representation Classifier Fusion

Directory of Open Access Journals (Sweden)

Zhang Xinzheng

2017-10-01

Full Text Available In this paper, we present a Synthetic Aperture Radar (SAR image target recognition algorithm based on multi-feature multiple representation learning classifier fusion. First, it extracts three features from the SAR images, namely principal component analysis, wavelet transform, and Two-Dimensional Slice Zernike Moments (2DSZM features. Second, we harness the sparse representation classifier and the cooperative representation classifier with the above-mentioned features to get six predictive labels. Finally, we adopt classifier fusion to obtain the final recognition decision. We researched three different classifier fusion algorithms in our experiments, and the results demonstrate thatusing Bayesian decision fusion gives thebest recognition performance. The method based on multi-feature multiple representation learning classifier fusion integrates the discrimination of multi-features and combines the sparse and cooperative representation classification performance to gain complementary advantages and to improve recognition accuracy. The experiments are based on the Moving and Stationary Target Acquisition and Recognition (MSTAR database,and they demonstrate the effectiveness of the proposed approach.

Design of signal reception and processing system of embedded ultrasonic endoscope

Science.gov (United States)

Li, Ming; Yu, Feng; Zhang, Ruiqiang; Li, Yan; Chen, Xiaodong; Yu, Daoyin

2009-11-01

Embedded Ultrasonic Endoscope, based on embedded microprocessor and embedded real-time operating system, sends a micro ultrasonic probe into coelom through the biopsy channel of the Electronic Endoscope to get the fault histology features of digestive organs by rotary scanning, and acquires the pictures of the alimentary canal mucosal surface. At the same time, ultrasonic signals are processed by signal reception and processing system, forming images of the full histology of the digestive organs. Signal Reception and Processing System is an important component of Embedded Ultrasonic Endoscope. However, the traditional design, using multi-level amplifiers and special digital processing circuits to implement signal reception and processing, is no longer satisfying the standards of high-performance, miniaturization and low power requirements that embedded system requires, and as a result of the high noise that multi-level amplifier brought, the extraction of small signal becomes hard. Therefore, this paper presents a method of signal reception and processing based on double variable gain amplifier and FPGA, increasing the flexibility and dynamic range of the Signal Reception and Processing System, improving system noise level, and reducing power consumption. Finally, we set up the embedded experiment system, using a transducer with the center frequency of 8MHz to scan membrane samples, and display the image of ultrasonic echo reflected by each layer of membrane, with a frame rate of 5Hz, verifying the correctness of the system.

Cloud Detection by Fusing Multi-Scale Convolutional Features

Science.gov (United States)

Li, Zhiwei; Shen, Huanfeng; Wei, Yancong; Cheng, Qing; Yuan, Qiangqiang

2018-04-01

Clouds detection is an important pre-processing step for accurate application of optical satellite imagery. Recent studies indicate that deep learning achieves best performance in image segmentation tasks. Aiming at boosting the accuracy of cloud detection for multispectral imagery, especially for those that contain only visible and near infrared bands, in this paper, we proposed a deep learning based cloud detection method termed MSCN (multi-scale cloud net), which segments cloud by fusing multi-scale convolutional features. MSCN was trained on a global cloud cover validation collection, and was tested in more than ten types of optical images with different resolution. Experiment results show that MSCN has obvious advantages over the traditional multi-feature combined cloud detection method in accuracy, especially when in snow and other areas covered by bright non-cloud objects. Besides, MSCN produced more detailed cloud masks than the compared deep cloud detection convolution network. The effectiveness of MSCN make it promising for practical application in multiple kinds of optical imagery.

Oriented Edge-Based Feature Descriptor for Multi-Sensor Image Alignment and Enhancement

Directory of Open Access Journals (Sweden)

Myung-Ho Ju

2013-10-01

Full Text Available In this paper, we present an efficient image alignment and enhancement method for multi-sensor images. The shape of the object captured in a multi-sensor images can be determined by comparing variability of contrast using corresponding edges across multi-sensor image. Using this cue, we construct a robust feature descriptor based on the magnitudes of the oriented edges. Our proposed method enables fast image alignment by identifying matching features in multi-sensor images. We enhance the aligned multi-sensor images through the fusion of the salient regions from each image. The results of stitching the multi-sensor images and their enhancement demonstrate that our proposed method can align and enhance multi-sensor images more efficiently than previous methods.

Quality-Driven Model-Based Design of MultiProcessor Embedded Systems for Highlydemanding Applications

DEFF Research Database (Denmark)

Jozwiak, Lech; Madsen, Jan

2013-01-01

The recent spectacular progress in modern nano-dimension semiconductor technology enabled implementation of a complete complex multi-processor system on a single chip (MPSoC), global networking and mobile wire-less communication, and facilitated a fast progress in these areas. New important...... accessible or distant) objects, installations, machines or devices, or even implanted in human or animal body can serve as examples. However, many of the modern embedded application impose very stringent functional and parametric demands. Moreover, the spectacular advances in microelectronics introduced...

Schedulability Analysis and Optimization for the Synthesis of Multi-Cluster Distributed Embedded Systems

DEFF Research Database (Denmark)

Pop, Paul; Eles, Petru; Peng, Zebo

2003-01-01

We present an approach to schedulability analysis for the synthesis of multi-cluster distributed embedded systems consisting of time-triggered and event-triggered clusters, interconnected via gateways. We have also proposed a buffer size and worst case queuing delay analysis for the gateways......, responsible for routing inter-cluster traffic. Optimization heuristics for the priority assignment and synthesis of bus access parameters aimed at producing a schedulable system with minimal buffer needs have been proposed. Extensive experiments and a real-life example show the efficiency of our approaches....

Schedulability Analysis and Optimization for the Synthesis of Multi-Cluster Distributed Embedded Systems

DEFF Research Database (Denmark)

Pop, Paul; Eles, Petru; Peng, Zebo

2003-01-01

An approach to schedulability analysis for the synthesis of multi-cluster distributed embedded systems consisting of time-triggered and event-triggered clusters, interconnected via gateways, is presented. A buffer size and worst case queuing delay analysis for the gateways, responsible for routing...... inter-cluster traffic, is also proposed. Optimisation heuristics for the priority assignment and synthesis of bus access parameters aimed at producing a schedulable system with minimal buffer needs have been proposed. Extensive experiments and a real-life example show the efficiency of the approaches....

Contextual Multi-armed Bandits under Feature Uncertainty

Energy Technology Data Exchange (ETDEWEB)

Yun, Seyoung [Los Alamos National Lab. (LANL), Los Alamos, NM (United States); Nam, Jun Hyun [Korea Advanced Inst. Science and Technology (KAIST), Daejeon (Korea, Republic of); Mo, Sangwoo [Korea Advanced Inst. Science and Technology (KAIST), Daejeon (Korea, Republic of); Shin, Jinwoo [Korea Advanced Inst. Science and Technology (KAIST), Daejeon (Korea, Republic of)

2017-03-03

We study contextual multi-armed bandit problems under linear realizability on rewards and uncertainty (or noise) on features. For the case of identical noise on features across actions, we propose an algorithm, coined NLinRel, having O(T⁷/₈(log(dT)+K√d)) regret bound for T rounds, K actions, and d-dimensional feature vectors. Next, for the case of non-identical noise, we observe that popular linear hypotheses including NLinRel are impossible to achieve such sub-linear regret. Instead, under assumption of Gaussian feature vectors, we prove that a greedy algorithm has O(T²/₃√log d)regret bound with respect to the optimal linear hypothesis. Utilizing our theoretical understanding on the Gaussian case, we also design a practical variant of NLinRel, coined Universal-NLinRel, for arbitrary feature distributions. It first runs NLinRel for finding the ‘true’ coefficient vector using feature uncertainties and then adjust it to minimize its regret using the statistical feature information. We justify the performance of Universal-NLinRel on both synthetic and real-world datasets.

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

Science.gov (United States)

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.

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

Science.gov (United States)

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.

Tensor-based Multi-view Feature Selection with Applications to Brain Diseases

Science.gov (United States)

Cao, Bokai; He, Lifang; Kong, Xiangnan; Yu, Philip S.; Hao, Zhifeng; Ragin, Ann B.

2015-01-01

In the era of big data, we can easily access information from multiple views which may be obtained from different sources or feature subsets. Generally, different views provide complementary information for learning tasks. Thus, multi-view learning can facilitate the learning process and is prevalent in a wide range of application domains. For example, in medical science, measurements from a series of medical examinations are documented for each subject, including clinical, imaging, immunologic, serologic and cognitive measures which are obtained from multiple sources. Specifically, for brain diagnosis, we can have different quantitative analysis which can be seen as different feature subsets of a subject. It is desirable to combine all these features in an effective way for disease diagnosis. However, some measurements from less relevant medical examinations can introduce irrelevant information which can even be exaggerated after view combinations. Feature selection should therefore be incorporated in the process of multi-view learning. In this paper, we explore tensor product to bring different views together in a joint space, and present a dual method of tensor-based multi-view feature selection (dual-Tmfs) based on the idea of support vector machine recursive feature elimination. Experiments conducted on datasets derived from neurological disorder demonstrate the features selected by our proposed method yield better classification performance and are relevant to disease diagnosis. PMID:25937823

An Improved Rotation Forest for Multi-Feature Remote-Sensing Imagery Classification

Directory of Open Access Journals (Sweden)

Yingchang Xiu

2017-11-01

Full Text Available Multi-feature, especially multi-temporal, remote-sensing data have the potential to improve land cover classification accuracy. However, sometimes it is difficult to utilize all the features efficiently. To enhance classification performance based on multi-feature imagery, an improved rotation forest, combining Principal Component Analysis (PCA and a boosting naïve Bayesian tree (NBTree, is proposed. First, feature extraction was carried out with PCA. The feature set was randomly split into several disjoint subsets; then, PCA was applied to each subset, and new training data for linear extracted features based on original training data were obtained. These steps were repeated several times. Second, based on the new training data, a boosting naïve Bayesian tree was constructed as the base classifier, which aims to achieve lower prediction error than a decision tree in the original rotation forest. At the classification phase, the improved rotation forest has two-layer voting. It first obtains several predictions through weighted voting in a boosting naïve Bayesian tree; then, the first-layer vote predicts by majority to obtain the final result. To examine the classification performance, the improved rotation forest was applied to multi-feature remote-sensing images, including MODIS Enhanced Vegetation Index (EVI imagery time series, MODIS Surface Reflectance products and ancillary data in Shandong Province for 2013. The EVI imagery time series was preprocessed using harmonic analysis of time series (HANTS to reduce the noise effects. The overall accuracy of the final classification result was 89.17%, and the Kappa coefficient was 0.71, which outperforms the original rotation forest and other classifier ensemble results, as well as the NASA land cover product. However, this new algorithm requires more computational time, meaning the efficiency needs to be further improved. Generally, the improved rotation forest has a potential advantage in

The evaluation of multi-structure, multi-atlas pelvic anatomy features in a prostate MR lymphography CAD system

Science.gov (United States)

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.

Middle latency response correlates of single and double deviant stimuli in a multi-feature paradigm.

Science.gov (United States)

Althen, H; Huotilainen, M; Grimm, S; Escera, C

2016-01-01

This study aimed to test single and double deviance-related modulations of the middle latency response (MLR) and the applicability of the optimum-2 multi-feature paradigm. The MLR and the MMN to frequency, intensity and double-feature deviants of an optimum-2 multi-feature paradigm and the MMN to double-feature deviants of an oddball paradigm were recorded in young adults. Double deviants elicited significant enhancements of the Nb and Pb MLR waves compared with the waves elicited by standard stimuli. These enhancements equalled approximately the sum of the numerical amplitude differences elicited by the single deviants. In contrast, the MMN to double deviants did not show such additivity. MMNs elicited by double deviants of the multi-feature and the oddball paradigm showed no significant difference in amplitude or latency. The optimum-2 multi-feature paradigm is suitable for recording double deviance-related modulations of the MLR. Interspersed intensity and frequency deviants in the standard trace of the optimum-2 condition multi-feature paradigm did not weaken the double MMN. The optimum-2 multi-feature paradigm could be especially beneficial for clinical studies on early deviance-related modulations in the MLR, due to its optimized utilization of the recording time. Copyright © 2015 International Federation of Clinical Neurophysiology. Published by Elsevier Ireland Ltd. All rights reserved.

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

Science.gov (United States)

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.

Multi-Objective Particle Swarm Optimization Approach for Cost-Based Feature Selection in Classification.

Science.gov (United States)

Zhang, Yong; Gong, Dun-Wei; Cheng, Jian

2017-01-01

Feature selection is an important data-preprocessing technique in classification problems such as bioinformatics and signal processing. Generally, there are some situations where a user is interested in not only maximizing the classification performance but also minimizing the cost that may be associated with features. This kind of problem is called cost-based feature selection. However, most existing feature selection approaches treat this task as a single-objective optimization problem. This paper presents the first study of multi-objective particle swarm optimization (PSO) for cost-based feature selection problems. The task of this paper is to generate a Pareto front of nondominated solutions, that is, feature subsets, to meet different requirements of decision-makers in real-world applications. In order to enhance the search capability of the proposed algorithm, a probability-based encoding technology and an effective hybrid operator, together with the ideas of the crowding distance, the external archive, and the Pareto domination relationship, are applied to PSO. The proposed PSO-based multi-objective feature selection algorithm is compared with several multi-objective feature selection algorithms on five benchmark datasets. Experimental results show that the proposed algorithm can automatically evolve a set of nondominated solutions, and it is a highly competitive feature selection method for solving cost-based feature selection problems.

Extended feature-fusion guidelines to improve image-based multi-modal biometrics

CSIR Research Space (South Africa)

Brown, Dane

2016-09-01

Full Text Available The feature-level, unlike the match score-level, lacks multi-modal fusion guidelines. This work demonstrates a practical approach for improved image-based biometric feature-fusion. The approach extracts and combines the face, fingerprint...

Tensor Train Neighborhood Preserving Embedding

Science.gov (United States)

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.

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

Science.gov (United States)

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.

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

Science.gov (United States)

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.

Web Server Embedded System

Directory of Open Access Journals (Sweden)

Adharul Muttaqin

2014-07-01

Full Text Available Abstrak Embedded sistem saat ini menjadi perhatian khusus pada teknologi komputer, beberapa sistem operasi linux dan web server yang beraneka ragam juga sudah dipersiapkan untuk mendukung sistem embedded, salah satu aplikasi yang dapat digunakan dalam operasi pada sistem embedded adalah web server. Pemilihan web server pada lingkungan embedded saat ini masih jarang dilakukan, oleh karena itu penelitian ini dilakukan dengan menitik beratkan pada dua buah aplikasi web server yang tergolong memiliki fitur utama yang menawarkan “keringanan” pada konsumsi CPU maupun memori seperti Light HTTPD dan Tiny HTTPD. Dengan menggunakan parameter thread (users, ramp-up periods, dan loop count pada stress test embedded system, penelitian ini menawarkan solusi web server manakah diantara Light HTTPD dan Tiny HTTPD yang memiliki kecocokan fitur dalam penggunaan embedded sistem menggunakan beagleboard ditinjau dari konsumsi CPU dan memori. Hasil penelitian menunjukkan bahwa dalam hal konsumsi CPU pada beagleboard embedded system lebih disarankan penggunaan Light HTTPD dibandingkan dengan tiny HTTPD dikarenakan terdapat perbedaan CPU load yang sangat signifikan antar kedua layanan web tersebut Kata kunci: embedded system, web server Abstract Embedded systems are currently of particular concern in computer technology, some of the linux operating system and web server variegated also prepared to support the embedded system, one of the applications that can be used in embedded systems are operating on the web server. Selection of embedded web server on the environment is still rarely done, therefore this study was conducted with a focus on two web application servers belonging to the main features that offer a "lightness" to the CPU and memory consumption as Light HTTPD and Tiny HTTPD. By using the parameters of the thread (users, ramp-up periods, and loop count on a stress test embedded systems, this study offers a solution of web server which between the Light

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

Science.gov (United States)

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.

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

International Nuclear Information System (INIS)

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

2013-01-01

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. (paper)

Embedded multi-channel data acquisition system on FPGA for Aditya Tokamak

Energy Technology Data Exchange (ETDEWEB)

Rajpal, Rachana, E-mail: rachana@ipr.res.in [Institute for Plasma Research, Gandhinagar, Gujarat (India); Mandaliya, Hitesh, E-mail: hitesh@ipr.res.in [ITER, Cadarache (France); Patel, Jignesh, E-mail: jjp@ipr.res.in [ITER, Cadarache (France); Kumari, Praveena, E-mail: praveena@ipr.res.in [Institute for Plasma Research, Gandhinagar, Gujarat (India); Gautam, Pramila, E-mail: pramila@ipr.res.in [Institute for Plasma Research, Gandhinagar, Gujarat (India); Raulji, Vismaysinh, E-mail: vismay@ipr.res.in [Institute for Plasma Research, Gandhinagar, Gujarat (India); Edappala, Praveenlal, E-mail: praveen@ipr.res.in [Institute for Plasma Research, Gandhinagar, Gujarat (India); Pujara, H.D, E-mail: pujara@ipr.res [Institute for Plasma Research, Gandhinagar, Gujarat (India); Jha, R., E-mail: jha@ipr.res.in [Institute for Plasma Research, Gandhinagar, Gujarat (India)

2016-11-15

Highlights: • 64 channel data acquisition, interface to PC/104 bus, using single board computer. • Integration of all components in single hardware to make it standalone and portable. • Development of application software in Qt on Linux platform for better performance and low cost compared to Windows. • Explored and utilized FPGA resources for hardware interfacing. - Abstract: The 64 channel data acquisition board is designed to meet the future demand of acquisition channels for plasma diagnostics. The inherent features of the board are 16 bit resolution, programmable sampling rate upto 200 kS/s/ch and simultaneous acquisition. To make system embedded and compact, 8 Analog Inputs ADC chip, 4M × 16 bit RAM memory, Field Programmable Gate Arrays, PC/104 platform and single board computer are used. High speed timing control signals for all ADCs and RAMs are generated by FPGA. The system is standalone, portable and interface through Ethernet. The acquisition application is developed in Qt. on Linux platform, in SBC. Due to ethernet connectivity and onboard processing, system can be integrated into Aditya and SST-1 data acquisition system. The performance of hardware is tested on Linux and Windows Embedded OS. The paper describes design, hardware and software architecture, implementation and results of 64 channel DAQ system.

Embedded multi-channel data acquisition system on FPGA for Aditya Tokamak

International Nuclear Information System (INIS)

Rajpal, Rachana; Mandaliya, Hitesh; Patel, Jignesh; Kumari, Praveena; Gautam, Pramila; Raulji, Vismaysinh; Edappala, Praveenlal; Pujara, H.D; Jha, R.

2016-01-01

Highlights: • 64 channel data acquisition, interface to PC/104 bus, using single board computer. • Integration of all components in single hardware to make it standalone and portable. • Development of application software in Qt on Linux platform for better performance and low cost compared to Windows. • Explored and utilized FPGA resources for hardware interfacing. - Abstract: The 64 channel data acquisition board is designed to meet the future demand of acquisition channels for plasma diagnostics. The inherent features of the board are 16 bit resolution, programmable sampling rate upto 200 kS/s/ch and simultaneous acquisition. To make system embedded and compact, 8 Analog Inputs ADC chip, 4M × 16 bit RAM memory, Field Programmable Gate Arrays, PC/104 platform and single board computer are used. High speed timing control signals for all ADCs and RAMs are generated by FPGA. The system is standalone, portable and interface through Ethernet. The acquisition application is developed in Qt. on Linux platform, in SBC. Due to ethernet connectivity and onboard processing, system can be integrated into Aditya and SST-1 data acquisition system. The performance of hardware is tested on Linux and Windows Embedded OS. The paper describes design, hardware and software architecture, implementation and results of 64 channel DAQ system.

A feature dictionary supporting a multi-domain medical knowledge base.

Science.gov (United States)

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.

Feature Selection using Multi-objective Genetic Algorith m: A Hybrid Approach

OpenAIRE

Ahuja, Jyoti; GJUST - Guru Jambheshwar University of Sciecne and Technology; Ratnoo, Saroj Dahiya; GJUST - Guru Jambheshwar University of Sciecne and Technology

2015-01-01

Feature selection is an important pre-processing task for building accurate and comprehensible classification models. Several researchers have applied filter, wrapper or hybrid approaches using genetic algorithms which are good candidates for optimization problems that involve large search spaces like in the case of feature selection. Moreover, feature selection is an inherently multi-objective problem with many competing objectives involving size, predictive power and redundancy of the featu...

Generating description with multi-feature fusion and saliency maps of image

Science.gov (United States)

Liu, Lisha; Ding, Yuxuan; Tian, Chunna; Yuan, Bo

2018-04-01

Generating description for an image can be regard as visual understanding. It is across artificial intelligence, machine learning, natural language processing and many other areas. In this paper, we present a model that generates description for images based on RNN (recurrent neural network) with object attention and multi-feature of images. The deep recurrent neural networks have excellent performance in machine translation, so we use it to generate natural sentence description for images. The proposed method uses single CNN (convolution neural network) that is trained on ImageNet to extract image features. But we think it can not adequately contain the content in images, it may only focus on the object area of image. So we add scene information to image feature using CNN which is trained on Places205. Experiments show that model with multi-feature extracted by two CNNs perform better than which with a single feature. In addition, we make saliency weights on images to emphasize the salient objects in images. We evaluate our model on MSCOCO based on public metrics, and the results show that our model performs better than several state-of-the-art methods.

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

Science.gov (United States)

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.

Multi-task feature learning by using trace norm regularization

Directory of Open Access Journals (Sweden)

Jiangmei Zhang

2017-11-01

Full Text Available Multi-task learning can extract the correlation of multiple related machine learning problems to improve performance. This paper considers applying the multi-task learning method to learn a single task. We propose a new learning approach, which employs the mixture of expert model to divide a learning task into several related sub-tasks, and then uses the trace norm regularization to extract common feature representation of these sub-tasks. A nonlinear extension of this approach by using kernel is also provided. Experiments conducted on both simulated and real data sets demonstrate the advantage of the proposed approach.

Alexnet Feature Extraction and Multi-Kernel Learning for Objectoriented Classification

Science.gov (United States)

Ding, L.; Li, H.; Hu, C.; Zhang, W.; Wang, S.

2018-04-01

In view of the fact that the deep convolutional neural network has stronger ability of feature learning and feature expression, an exploratory research is done on feature extraction and classification for high resolution remote sensing images. Taking the Google image with 0.3 meter spatial resolution in Ludian area of Yunnan Province as an example, the image segmentation object was taken as the basic unit, and the pre-trained AlexNet deep convolution neural network model was used for feature extraction. And the spectral features, AlexNet features and GLCM texture features are combined with multi-kernel learning and SVM classifier, finally the classification results were compared and analyzed. The results show that the deep convolution neural network can extract more accurate remote sensing image features, and significantly improve the overall accuracy of classification, and provide a reference value for earthquake disaster investigation and remote sensing disaster evaluation.

ALEXNET FEATURE EXTRACTION AND MULTI-KERNEL LEARNING FOR OBJECTORIENTED CLASSIFICATION

Directory of Open Access Journals (Sweden)

L. Ding

2018-04-01

Full Text Available In view of the fact that the deep convolutional neural network has stronger ability of feature learning and feature expression, an exploratory research is done on feature extraction and classification for high resolution remote sensing images. Taking the Google image with 0.3 meter spatial resolution in Ludian area of Yunnan Province as an example, the image segmentation object was taken as the basic unit, and the pre-trained AlexNet deep convolution neural network model was used for feature extraction. And the spectral features, AlexNet features and GLCM texture features are combined with multi-kernel learning and SVM classifier, finally the classification results were compared and analyzed. The results show that the deep convolution neural network can extract more accurate remote sensing image features, and significantly improve the overall accuracy of classification, and provide a reference value for earthquake disaster investigation and remote sensing disaster evaluation.

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

Science.gov (United States)

2017-03-20

Kaiserslautern Kaiserslautern, Germany Sandeep Shukla FERMAT Lab Electrical and Computer Engineering Department Virginia Tech 900 North Glebe Road...Software Engineering , Software Producibility, Component-based software design, behavioral types, behavioral type inference, Polychronous model of...near future, many embedded applications including safety critical ones as used in avionics, automotive , mission control systems will run on

A Heterogeneous Multi-core Architecture with a Hardware Kernel for Control Systems

DEFF Research Database (Denmark)

Li, Gang; Guan, Wei; Sierszecki, Krzysztof

2012-01-01

Rapid industrialisation has resulted in a demand for improved embedded control systems with features such as predictability, high processing performance and low power consumption. Software kernel implementation on a single processor is becoming more difficult to satisfy those constraints. This pa......Rapid industrialisation has resulted in a demand for improved embedded control systems with features such as predictability, high processing performance and low power consumption. Software kernel implementation on a single processor is becoming more difficult to satisfy those constraints......). Second, a heterogeneous multi-core architecture is investigated, focusing on its performance in relation to hard real-time constraints and predictable behavior. Third, the hardware implementation of HARTEX is designated to support the heterogeneous multi-core architecture. This hardware kernel has...... several advantages over a similar kernel implemented in software: higher-speed processing capability, parallel computation, and separation between the kernel itself and the applications being run. A microbenchmark has been used to compare the hardware kernel with the software kernel, and compare...

Composable Virtual Platforms for Mixed-Criticality Embedded Systems

NARCIS (Netherlands)

Beyranvand Nejad, A.

2014-01-01

Recent trends show a steady increase towards concurrently executing more and more applications on a single embedded system. Multi-Processor System-on-Chip (MPSoC) architectures are proposed to allow complex design of embedded systems. This is achieved by integrating as many processing resources as

Composable virtual platforms for mixed-criticality embedded systems

NARCIS (Netherlands)

Nejad, A.B.

2014-01-01

Recent trends show a steady increase towards concurrently executing more and more applications on a single embedded system. Multi-Processor System-on-Chip (MPSoC) architectures are proposed to allow complex design of embedded systems. This is achieved by integrating as many processing resources as

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

Directory of Open Access Journals (Sweden)

Huile Xu

2016-12-01

Full Text Available 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.

Effect of foundation embedment on the response of a multi-story building to earthquake excitation

Directory of Open Access Journals (Sweden)

Hamood Mohammed

2018-01-01

Full Text Available During an earthquake, the behaviour of any structure is affected not only by the superstructure response, but also by the response of the soil beneath. Recent structural failure patterns have indicated the significance of soil-structure interaction (SSI effects. The present study focuses on SSI analysis considering the embedment depth of the foundation of a symmetric six stories reinforced concrete (RCspace bare frame building resting on stiff soil and subjected to seismic loading. The finite element analysis software ANSYS v17.2 is used. Time history (TH analysis has been adopted. The Response in terms of lateral displacements, base shear forces, base moments and variation in natural time periods are calculated from the analysis of the soil foundation structure interaction (SFSI model. Results are compared with that obtained from conventional method assuming rigid support at the base (fixed base of the structure. The results show that the SFSI considering different embedment depths are significant in altering the seismic response of the multi-story building (MSB.

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

Science.gov (United States)

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.

Embedded control system for high power RF amplifiers

International Nuclear Information System (INIS)

Sharma, Deepak Kumar; Gupta, Alok Kumar; Jain, Akhilesh; Hannurkar, P.R.

2011-01-01

RF power devices are usually very sensitive to overheat and reflected RF power; hence a protective interlock system is required to be embedded with high power solid state RF amplifiers. The solid state RF amplifiers have salient features of graceful degradation and very low mean time to repair (MTTR). In order to exploit these features in favour of lowest system downtime, a real-time control system is embedded with high power RF amplifiers. The control system is developed with the features of monitoring, measurement and network publishing of various parameters, historical data logging, alarm generation, displaying data to the operator and tripping the system in case of any interlock failure. This paper discusses the design philosophy, features, functions and implementation details of the embedded control system. (author)

Integral Images: Efficient Algorithms for Their Computation and Storage in Resource-Constrained Embedded Vision Systems.

Science.gov (United States)

Ehsan, Shoaib; Clark, Adrian F; Naveed ur Rehman; McDonald-Maier, Klaus D

2015-07-10

The integral image, an intermediate image representation, has found extensive use in multi-scale local feature detection algorithms, such as Speeded-Up Robust Features (SURF), allowing fast computation of rectangular features at constant speed, independent of filter size. For resource-constrained real-time embedded vision systems, computation and storage of integral image presents several design challenges due to strict timing and hardware limitations. Although calculation of the integral image only consists of simple addition operations, the total number of operations is large owing to the generally large size of image data. Recursive equations allow substantial decrease in the number of operations but require calculation in a serial fashion. This paper presents two new hardware algorithms that are based on the decomposition of these recursive equations, allowing calculation of up to four integral image values in a row-parallel way without significantly increasing the number of operations. An efficient design strategy is also proposed for a parallel integral image computation unit to reduce the size of the required internal memory (nearly 35% for common HD video). Addressing the storage problem of integral image in embedded vision systems, the paper presents two algorithms which allow substantial decrease (at least 44.44%) in the memory requirements. Finally, the paper provides a case study that highlights the utility of the proposed architectures in embedded vision systems.

Integral Images: Efficient Algorithms for Their Computation and Storage in Resource-Constrained Embedded Vision Systems

Directory of Open Access Journals (Sweden)

Shoaib Ehsan

2015-07-01

Full Text Available The integral image, an intermediate image representation, has found extensive use in multi-scale local feature detection algorithms, such as Speeded-Up Robust Features (SURF, allowing fast computation of rectangular features at constant speed, independent of filter size. For resource-constrained real-time embedded vision systems, computation and storage of integral image presents several design challenges due to strict timing and hardware limitations. Although calculation of the integral image only consists of simple addition operations, the total number of operations is large owing to the generally large size of image data. Recursive equations allow substantial decrease in the number of operations but require calculation in a serial fashion. This paper presents two new hardware algorithms that are based on the decomposition of these recursive equations, allowing calculation of up to four integral image values in a row-parallel way without significantly increasing the number of operations. An efficient design strategy is also proposed for a parallel integral image computation unit to reduce the size of the required internal memory (nearly 35% for common HD video. Addressing the storage problem of integral image in embedded vision systems, the paper presents two algorithms which allow substantial decrease (at least 44.44% in the memory requirements. Finally, the paper provides a case study that highlights the utility of the proposed architectures in embedded vision systems.

Rolling Bearing Fault Diagnosis Using Modified Neighborhood Preserving Embedding and Maximal Overlap Discrete Wavelet Packet Transform with Sensitive Features Selection

Directory of Open Access Journals (Sweden)

Fei Dong

2018-01-01

Full Text Available In order to enhance the performance of bearing fault diagnosis and classification, features extraction and features dimensionality reduction have become more important. The original statistical feature set was calculated from single branch reconstruction vibration signals obtained by using maximal overlap discrete wavelet packet transform (MODWPT. In order to reduce redundancy information of original statistical feature set, features selection by adjusted rand index and sum of within-class mean deviations (FSASD was proposed to select fault sensitive features. Furthermore, a modified features dimensionality reduction method, supervised neighborhood preserving embedding with label information (SNPEL, was proposed to realize low-dimensional representations for high-dimensional feature space. Finally, vibration signals collected from two experimental test rigs were employed to evaluate the performance of the proposed procedure. The results show that the effectiveness, adaptability, and superiority of the proposed procedure can serve as an intelligent bearing fault diagnosis system.

Development of an Erlang System Adaopted to Embedded Devices

OpenAIRE

Andersson, Fredrik; Bergström, Fabian

2011-01-01

Erlang is a powerful and robust language for writing massively parallel and distributed applications. With the introduction of multi-core ARM processors, the embedded market will be looking for ways of taking advantage of the newfound opportunities for parallelism. To support the development of embedded applications using Erlang we want to provide Erlang and Embedded developers with a run-time system suited for embedded devices. We have managed to shrink the disk size of the Erlang runtime sy...

Feature-Fusion Guidelines for Image-Based Multi-Modal Biometric Fusion

Directory of Open Access Journals (Sweden)

Dane Brown

2017-07-01

Full Text Available The feature level, unlike the match score level, lacks multi-modal fusion guidelines. This work demonstrates a new approach for improved image-based biometric feature-fusion. The approach extracts and combines the face, fingerprint and palmprint at the feature level for improved human identification accuracy. Feature-fusion guidelines, proposed in our recent work, are extended by adding a new face segmentation method and the support vector machine classifier. The new face segmentation method improves the face identification equal error rate (EER by 10%. The support vector machine classifier combined with the new feature selection approach, proposed in our recent work, outperforms other classifiers when using a single training sample. Feature-fusion guidelines take the form of strengths and weaknesses as observed in the applied feature processing modules during preliminary experiments. The guidelines are used to implement an effective biometric fusion system at the feature level, using a novel feature-fusion methodology, reducing the EER of two groups of three datasets namely: SDUMLA face, SDUMLA fingerprint and IITD palmprint; MUCT Face, MCYT Fingerprint and CASIA Palmprint.

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

Science.gov (United States)

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

Study on the construction of multi-dimensional Remote Sensing feature space for hydrological drought

International Nuclear Information System (INIS)

Xiang, Daxiang; Tan, Debao; Wen, Xiongfei; Shen, Shaohong; Li, Zhe; Cui, Yuanlai

2014-01-01

Hydrological drought refers to an abnormal water shortage caused by precipitation and surface water shortages or a groundwater imbalance. Hydrological drought is reflected in a drop of surface water, decrease of vegetation productivity, increase of temperature difference between day and night and so on. Remote sensing permits the observation of surface water, vegetation, temperature and other information from a macro perspective. This paper analyzes the correlation relationship and differentiation of both remote sensing and surface measured indicators, after the selection and extraction a series of representative remote sensing characteristic parameters according to the spectral characterization of surface features in remote sensing imagery, such as vegetation index, surface temperature and surface water from HJ-1A/B CCD/IRS data. Finally, multi-dimensional remote sensing features such as hydrological drought are built on a intelligent collaborative model. Further, for the Dong-ting lake area, two drought events are analyzed for verification of multi-dimensional features using remote sensing data with different phases and field observation data. The experiments results proved that multi-dimensional features are a good method for hydrological drought

Electronic nose with a new feature reduction method and a multi-linear classifier for Chinese liquor classification

Energy Technology Data Exchange (ETDEWEB)

Jing, Yaqi; Meng, Qinghao, E-mail: qh-meng@tju.edu.cn; Qi, Peifeng; Zeng, Ming; Li, Wei; Ma, Shugen [Tianjin Key Laboratory of Process Measurement and Control, Institute of Robotics and Autonomous Systems, School of Electrical Engineering and Automation, Tianjin University, Tianjin 300072 (China)

2014-05-15

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 classification 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.

Electronic nose with a new feature reduction method and a multi-linear classifier for Chinese liquor classification

International Nuclear Information System (INIS)

Jing, Yaqi; Meng, Qinghao; Qi, Peifeng; Zeng, Ming; Li, Wei; Ma, Shugen

2014-01-01

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 classification 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

A novel framework for feature extraction in multi-sensor action potential sorting.

Science.gov (United States)

Wu, Shun-Chi; Swindlehurst, A Lee; Nenadic, Zoran

2015-09-30

Extracellular recordings of multi-unit neural activity have become indispensable in neuroscience research. The analysis of the recordings begins with the detection of the action potentials (APs), followed by a classification step where each AP is associated with a given neural source. A feature extraction step is required prior to classification in order to reduce the dimensionality of the data and the impact of noise, allowing source clustering algorithms to work more efficiently. In this paper, we propose a novel framework for multi-sensor AP feature extraction based on the so-called Matched Subspace Detector (MSD), which is shown to be a natural generalization of standard single-sensor algorithms. Clustering using both simulated data and real AP recordings taken in the locust antennal lobe demonstrates that the proposed approach yields features that are discriminatory and lead to promising results. Unlike existing methods, the proposed algorithm finds joint spatio-temporal feature vectors that match the dominant subspace observed in the two-dimensional data without needs for a forward propagation model and AP templates. The proposed MSD approach provides more discriminatory features for unsupervised AP sorting applications. Copyright © 2015 Elsevier B.V. All rights reserved.

Vehicle license plate recognition based on geometry restraints and multi-feature decision

Science.gov (United States)

Wu, Jianwei; Wang, Zongyue

2005-10-01

Vehicle license plate (VLP) recognition is of great importance to many traffic applications. Though researchers have paid much attention to VLP recognition there has not been a fully operational VLP recognition system yet for many reasons. This paper discusses a valid and practical method for vehicle license plate recognition based on geometry restraints and multi-feature decision including statistical and structural features. In general, the VLP recognition includes the following steps: the location of VLP, character segmentation, and character recognition. This paper discusses the three steps in detail. The characters of VLP are always declining caused by many factors, which makes it more difficult to recognize the characters of VLP, therefore geometry restraints such as the general ratio of length and width, the adjacent edges being perpendicular are used for incline correction. Image Moment has been proved to be invariant to translation, rotation and scaling therefore image moment is used as one feature for character recognition. Stroke is the basic element for writing and hence taking it as a feature is helpful to character recognition. Finally we take the image moment, the strokes and the numbers of each stroke for each character image and some other structural features and statistical features as the multi-feature to match each character image with sample character images so that each character image can be recognized by BP neural net. The proposed method combines statistical and structural features for VLP recognition, and the result shows its validity and efficiency.

Operating system concepts for embedded multicores

OpenAIRE

Horst, Oliver; Schmidt, Adriaan

2014-01-01

Currently we can see an increasing adoption of multi-core platforms in the area of embedded systems. While these new hardware platforms offer the potential to satisfy the ever increasing demand for computational power, they pose considerable challenges with regard to software development. This affects the application software itself, but also the system design and architecture. Here, we address the consequences for operating system architecture in embedded systems. After dis-cussing current a...

Model-based design of adaptive embedded systems

CERN Document Server

Hamberg, Roelof; Reckers, Frans; Verriet, Jacques

2013-01-01

Today’s embedded systems have to operate in a wide variety of dynamically changing environmental circumstances. Adaptivity, the ability of a system to autonomously adapt itself, is a means to optimise a system’s behaviour to accommodate changes in its environment. It involves making in-product trade-offs between system qualities at system level. The main challenge in the development of adaptive systems is keeping control of the intrinsic complexity of such systems while working with multi-disciplinary teams to create different parts of the system. Model-Based Development of Adaptive Embedded Systems focuses on the development of adaptive embedded systems both from an architectural and methodological point of view. It describes architectural solution patterns for adaptive systems and state-of-the-art model-based methods and techniques to support adaptive system development. In particular, the book describes the outcome of the Octopus project, a cooperation of a multi-disciplinary team of academic and indus...

Cancer microarray data feature selection using multi-objective binary particle swarm optimization algorithm

Science.gov (United States)

Annavarapu, Chandra Sekhara Rao; Dara, Suresh; Banka, Haider

2016-01-01

Cancer investigations in microarray data play a major role in cancer analysis and the treatment. Cancer microarray data consists of complex gene expressed patterns of cancer. In this article, a Multi-Objective Binary Particle Swarm Optimization (MOBPSO) algorithm is proposed for analyzing cancer gene expression data. Due to its high dimensionality, a fast heuristic based pre-processing technique is employed to reduce some of the crude domain features from the initial feature set. Since these pre-processed and reduced features are still high dimensional, the proposed MOBPSO algorithm is used for finding further feature subsets. The objective functions are suitably modeled by optimizing two conflicting objectives i.e., cardinality of feature subsets and distinctive capability of those selected subsets. As these two objective functions are conflicting in nature, they are more suitable for multi-objective modeling. The experiments are carried out on benchmark gene expression datasets, i.e., Colon, Lymphoma and Leukaemia available in literature. The performance of the selected feature subsets with their classification accuracy and validated using 10 fold cross validation techniques. A detailed comparative study is also made to show the betterment or competitiveness of the proposed algorithm. PMID:27822174

Prediction of breast cancer risk using a machine learning approach embedded with a locality preserving projection algorithm

Science.gov (United States)

Heidari, Morteza; Zargari Khuzani, Abolfazl; Hollingsworth, Alan B.; Danala, Gopichandh; Mirniaharikandehei, Seyedehnafiseh; Qiu, Yuchen; Liu, Hong; Zheng, Bin

2018-02-01

In order to automatically identify a set of effective mammographic image features and build an optimal breast cancer risk stratification model, this study aims to investigate advantages of applying a machine learning approach embedded with a locally preserving projection (LPP) based feature combination and regeneration algorithm to predict short-term breast cancer risk. A dataset involving negative mammograms acquired from 500 women was assembled. This dataset was divided into two age-matched classes of 250 high risk cases in which cancer was detected in the next subsequent mammography screening and 250 low risk cases, which remained negative. First, a computer-aided image processing scheme was applied to segment fibro-glandular tissue depicted on mammograms and initially compute 44 features related to the bilateral asymmetry of mammographic tissue density distribution between left and right breasts. Next, a multi-feature fusion based machine learning classifier was built to predict the risk of cancer detection in the next mammography screening. A leave-one-case-out (LOCO) cross-validation method was applied to train and test the machine learning classifier embedded with a LLP algorithm, which generated a new operational vector with 4 features using a maximal variance approach in each LOCO process. Results showed a 9.7% increase in risk prediction accuracy when using this LPP-embedded machine learning approach. An increased trend of adjusted odds ratios was also detected in which odds ratios increased from 1.0 to 11.2. This study demonstrated that applying the LPP algorithm effectively reduced feature dimensionality, and yielded higher and potentially more robust performance in predicting short-term breast cancer risk.

Aura: A Multi-Featured Programming Framework in Python

Directory of Open Access Journals (Sweden)

2010-09-01

Full Text Available This paper puts forward the design, programming and application of innovative educational software, ‘Aura’ made using Python and PyQt Python bindings. The research paper presents a new concept of using a single tool to relate between syntaxes of various programming languages and algorithms. It radically increases their understanding and retaining capacity, since they can correlate between many programming languages. The software is a totally unorthodox attempt towards helping students who have their first tryst with programming languages. The application is designed to help students understand how algorithms work and thus, help them in learning multiple programming languages on a single platform using an interactive graphical user interface. This paper elucidates how using Python and PyQt bindings, a comprehensive feature rich application, that implements an interactive algorithm building technique, a web browser, multiple programming language framework, a code generator and a real time code sharing hub be embedded into a single interface. And also explains, that using Python as building tool, it requires much less coding than conventional feature rich applications coded in other programming languages, and at the same time does not compromise on stability, inter-operability and robustness of the application.

TargetCrys: protein crystallization prediction by fusing multi-view features with two-layered SVM.

Science.gov (United States)

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 .

An enhanced data visualization method for diesel engine malfunction classification using multi-sensor signals.

Science.gov (United States)

Li, Yiqing; Wang, Yu; Zi, Yanyang; Zhang, Mingquan

2015-10-21

The various multi-sensor signal features from a diesel engine constitute a complex high-dimensional dataset. The non-linear dimensionality reduction method, t-distributed stochastic neighbor embedding (t-SNE), provides an effective way to implement data visualization for complex high-dimensional data. However, irrelevant features can deteriorate the performance of data visualization, and thus, should be eliminated a priori. This paper proposes a feature subset score based t-SNE (FSS-t-SNE) data visualization method to deal with the high-dimensional data that are collected from multi-sensor signals. In this method, the optimal feature subset is constructed by a feature subset score criterion. Then the high-dimensional data are visualized in 2-dimension space. According to the UCI dataset test, FSS-t-SNE can effectively improve the classification accuracy. An experiment was performed with a large power marine diesel engine to validate the proposed method for diesel engine malfunction classification. Multi-sensor signals were collected by a cylinder vibration sensor and a cylinder pressure sensor. Compared with other conventional data visualization methods, the proposed method shows good visualization performance and high classification accuracy in multi-malfunction classification of a diesel engine.

Design of hand held RID's monitoring system based on embedded system

International Nuclear Information System (INIS)

Wang Hongwei; Wei Yixiang

2008-01-01

In this paper we introduce the design of monitoring system for the hand held radionuclide identification device (RID), constructed under the embedded operating system of WinCE. At first, we introduce the design of hardware and software platform, and following is the major part of technical view of the software system, including the driver development, P/Invoke mechanism to call the C/C++ subroutines, multi-thread technology. In the experimental hardware platform, we have developed a front-end monitoring system for portable device targeted nuclide identification and orientation. It's a full-featured and flexible system, with the functions of data acquisition, radioactivity locating, data import and export, etc. (authors)

Aging linear viscoelasticity of matrix-inclusion composite materials featuring ellipsoidal inclusions

OpenAIRE

LAVERGNE, Francis; SAB, Karam; SANAHUJA, Julien; BORNERT, Michel; TOULEMONDE, Charles

2016-01-01

A multi-scale homogenization scheme is proposed to estimate the time-dependent strains of fiber-reinforced concrete. This material is modeled as an aging linear viscoelastic composite material featuring ellipsoidal inclusions embedded in a viscoelastic cementitious matrix characterized by a time-dependent Poisson's ratio. To this end, the homogenization scheme proposed in Lavergne et al. [1] is adapted to the case of a time-dependent Poisson's ratio and it is successfully validated on a non-a...

Theoretical analysis of enhanced light output from a GaN light emitting diode with an embedded photonic crystal

International Nuclear Information System (INIS)

Wen Feng; Liu Deming; Huang Lirong

2010-01-01

The enhancement of the light output of an embedded photonic crystal light emitting diode is investigated based on the finite-difference time-domain modeling. The embedded photonic crystal (PC) lattice type, multi-layer embedded PC, distance between the multiple quantum well and the embedded PC are studied. It is found that the embedded one dimensional PC can act as well as embedded two dimensional PCs. The emitted light flux in the up direction can be increased by a new kind of multi-layer embedded PC. Also, we show that the light output in the up direction for the LED with both surfaces and embedded PC could be as high as five times that of a conventional LED. (semiconductor devices)

Theoretical analysis of enhanced light output from a GaN light emitting diode with an embedded photonic crystal

Energy Technology Data Exchange (ETDEWEB)

Wen Feng; Liu Deming; Huang Lirong, E-mail: hlr5649@163.co [Wuhan National Laboratory for Optoelectronics, College of Opto-Electronics Science and Engineering, Huazhong University of Science and Technology, Wuhan 430074 (China)

2010-10-15

The enhancement of the light output of an embedded photonic crystal light emitting diode is investigated based on the finite-difference time-domain modeling. The embedded photonic crystal (PC) lattice type, multi-layer embedded PC, distance between the multiple quantum well and the embedded PC are studied. It is found that the embedded one dimensional PC can act as well as embedded two dimensional PCs. The emitted light flux in the up direction can be increased by a new kind of multi-layer embedded PC. Also, we show that the light output in the up direction for the LED with both surfaces and embedded PC could be as high as five times that of a conventional LED. (semiconductor devices)

Research on Face Recognition Based on Embedded System

Directory of Open Access Journals (Sweden)

Hong Zhao

2013-01-01

Full Text Available Because a number of image feature data to store, complex calculation to execute during the face recognition, therefore the face recognition process was realized only by PCs with high performance. In this paper, the OpenCV facial Haar-like features were used to identify face region; the Principal Component Analysis (PCA was employed in quick extraction of face features and the Euclidean Distance was also adopted in face recognition; as thus, data amount and computational complexity would be reduced effectively in face recognition, and the face recognition could be carried out on embedded platform. Finally, based on Tiny6410 embedded platform, a set of embedded face recognition systems was constructed. The test results showed that the system has stable operation and high recognition rate can be used in portable and mobile identification and authentication.

Prostate cancer multi-feature analysis using trans-rectal ultrasound images

International Nuclear Information System (INIS)

Mohamed, S S; Salama, M M A; Kamel, M; El-Saadany, E F; Rizkalla, K; Chin, J

2005-01-01

This note focuses on extracting and analysing prostate texture features from trans-rectal ultrasound (TRUS) images for tissue characterization. One of the principal contributions of this investigation is the use of the information of the images' frequency domain features and spatial domain features to attain a more accurate diagnosis. Each image is divided into regions of interest (ROIs) by the Gabor multi-resolution analysis, a crucial stage, in which segmentation is achieved according to the frequency response of the image pixels. The pixels with a similar response to the same filter are grouped to form one ROI. Next, from each ROI two different statistical feature sets are constructed; the first set includes four grey level dependence matrix (GLDM) features and the second set consists of five grey level difference vector (GLDV) features. These constructed feature sets are then ranked by the mutual information feature selection (MIFS) algorithm. Here, the features that provide the maximum mutual information of each feature and class (cancerous and non-cancerous) and the minimum mutual information of the selected features are chosen, yeilding a reduced feature subset. The two constructed feature sets, GLDM and GLDV, as well as the reduced feature subset, are examined in terms of three different classifiers: the condensed k-nearest neighbour (CNN), the decision tree (DT) and the support vector machine (SVM). The accuracy classification results range from 87.5% to 93.75%, where the performance of the SVM and that of the DT are significantly better than the performance of the CNN. (note)

Multi-domain transformational design flow for embedded systems

NARCIS (Netherlands)

Rovers, K.C.; van de Burgwal, M.D.; Kuper, Jan; Kokkeler, Andre B.J.; Smit, Gerardus Johannes Maria

2011-01-01

Current tools for embedded system design have limited support for modelling the interaction of the system with its physical environment. Furthermore, the natural representation of (streaming, real-time) applications with dataflow models is not supported by most tools. However, integrating multiple

[The relationship of attachment features and multi-impulsive symptoms in eating disorders].

Science.gov (United States)

Szalai, Tamás Dömötör

2017-07-01

Attachment dysfunctions determine borderline personality disorder, which is a frequent background factor of multi-impulsivity; however, the relationship between attachment and multi-impulsive eating disorders is almost unexplored. To compare attachment features of multi-impulsive and classical eating disorder patients with individuals without eating disorders, and to test attachment as a predictor of multi-impulsivity. A cross-sectional survey (148 females, mean age: 30.9 years) investigated maternal, paternal and adult attachment, depression, anxiety, eating disorder and multi-impulsive symptoms in these groups. Altogether 41.3% of the individuals without eating disorders, 17.6% of classical and 11.8% of multi-impulsive eating disorder patients had secure attachment. Multi-impulsive patients had the most severe eating disorder symptoms (F (2) = 17.733) and the lowest paternal care (F (2) = 3.443). Preoccupied and fearful attachment explained 14.5% of multi-impulsive symptoms; however, with adjustment for depression only latter one remained the predictor of multi-impulsivity (t = 5.166, peating disorder patients from the aspects of both symptoms and attachment. Handling their negative moods may hold therapeutic potentials. Longitudinal studies are required to investigate the therapeutic value of paternal care, attachment preoccupation and fearfulness. Orv Hetil. 2017; 158(27): 1058-1066.

An effective method for cirrhosis recognition based on multi-feature fusion

Science.gov (United States)

Chen, Yameng; Sun, Gengxin; Lei, Yiming; Zhang, Jinpeng

2018-04-01

Liver disease is one of the main causes of human healthy problem. Cirrhosis, of course, is the critical phase during the development of liver lesion, especially the hepatoma. Many clinical cases are still influenced by the subjectivity of physicians in some degree, and some objective factors such as illumination, scale, edge blurring will affect the judgment of clinicians. Then the subjectivity will affect the accuracy of diagnosis and the treatment of patients. In order to solve the difficulty above and improve the recognition rate of liver cirrhosis, we propose a method of multi-feature fusion to obtain more robust representations of texture in ultrasound liver images, the texture features we extract include local binary pattern(LBP), gray level co-occurrence matrix(GLCM) and histogram of oriented gradient(HOG). In this paper, we firstly make a fusion of multi-feature to recognize cirrhosis and normal liver based on parallel combination concept, and the experimental results shows that the classifier is effective for cirrhosis recognition which is evaluated by the satisfying classification rate, sensitivity and specificity of receiver operating characteristic(ROC), and cost time. Through the method we proposed, it will be helpful to improve the accuracy of diagnosis of cirrhosis and prevent the development of liver lesion towards hepatoma.

Coupled binary embedding for large-scale image retrieval.

Science.gov (United States)

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.

Chinese Medical Question Answer Matching Using End-to-End Character-Level Multi-Scale CNNs

Directory of Open Access Journals (Sweden)

Sheng Zhang

2017-07-01

Full Text Available This paper focuses mainly on the problem of Chinese medical question answer matching, which is arguably more challenging than open-domain question answer matching in English due to the combination of its domain-restricted nature and the language-specific features of Chinese. We present an end-to-end character-level multi-scale convolutional neural framework in which character embeddings instead of word embeddings are used to avoid Chinese word segmentation in text preprocessing, and multi-scale convolutional neural networks (CNNs are then introduced to extract contextual information from either question or answer sentences over different scales. The proposed framework can be trained with minimal human supervision and does not require any handcrafted features, rule-based patterns, or external resources. To validate our framework, we create a new text corpus, named cMedQA, by harvesting questions and answers from an online Chinese health and wellness community. The experimental results on the cMedQA dataset show that our framework significantly outperforms several strong baselines, and achieves an improvement of top-1 accuracy by up to 19%.

Trusted computing for embedded systems

CERN Document Server

Soudris, Dimitrios; Anagnostopoulos, Iraklis

2015-01-01

This book describes the state-of-the-art in trusted computing for embedded systems. It shows how a variety of security and trusted computing problems are addressed currently and what solutions are expected to emerge in the coming years. The discussion focuses on attacks aimed at hardware and software for embedded systems, and the authors describe specific solutions to create security features. Case studies are used to present new techniques designed as industrial security solutions. Coverage includes development of tamper resistant hardware and firmware mechanisms for lightweight embedded devices, as well as those serving as security anchors for embedded platforms required by applications such as smart power grids, smart networked and home appliances, environmental and infrastructure sensor networks, etc. ·         Enables readers to address a variety of security threats to embedded hardware and software; ·         Describes design of secure wireless sensor networks, to address secure authen...

Embedded data acquisition system with MDSPlus

International Nuclear Information System (INIS)

Rajpal, Rachana; Patel, Jigneshkumar; Kumari, Praveena; Panchal, Vipul; Chattopadhyay, P.K.; Pujara, Harshad; Saxena, Y.C.

2012-01-01

This data acquisition system (DAS) is designed and developed to cater the increasing demand of Plasma Diagnostics for Aditya Tokamak as well as to support the basic physics research going on at Institute for Plasma Research. The main design criteria were to design a system with minimum resources and flexible to cater the needs of slow and fast diagnostic channels and can be easily integrated with the existing data acquisition system of Aditya Tokamak. The DAS is designed on embedded PC/104 platform. This is a multi channel system which supports standard features of commercially available DAS. The control and bus interface logic are implemented using Very High Speed Hardware Description Language (VHDL) on Complex Programmable Logic Device (CPLD). For Aditya Tokamak pulse experiment, the software application is designed such that the data is directly integrated to the MDSplus tree of Aditya DAS. The detailed hardware and software design, development and testing results will be discussed in the paper.

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

Science.gov (United States)

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.

Arrhythmia Classification Based on Multi-Domain Feature Extraction for an ECG Recognition System

Directory of Open Access Journals (Sweden)

Hongqiang Li

2016-10-01

Full Text Available Automatic recognition of arrhythmias is particularly important in the diagnosis of heart diseases. This study presents an electrocardiogram (ECG recognition system based on multi-domain feature extraction to classify ECG beats. An improved wavelet threshold method for ECG signal pre-processing is applied to remove noise interference. A novel multi-domain feature extraction method is proposed; this method employs kernel-independent component analysis in nonlinear feature extraction and uses discrete wavelet transform to extract frequency domain features. The proposed system utilises a support vector machine classifier optimized with a genetic algorithm to recognize different types of heartbeats. An ECG acquisition experimental platform, in which ECG beats are collected as ECG data for classification, is constructed to demonstrate the effectiveness of the system in ECG beat classification. The presented system, when applied to the MIT-BIH arrhythmia database, achieves a high classification accuracy of 98.8%. Experimental results based on the ECG acquisition experimental platform show that the system obtains a satisfactory classification accuracy of 97.3% and is able to classify ECG beats efficiently for the automatic identification of cardiac arrhythmias.

Views on Evolvability of Embedded Systems

NARCIS (Netherlands)

Laar, P. van de; Punter, T.

2011-01-01

Evolvability, the ability to respond effectively to change, represents a major challenge to today's high-end embedded systems, such as those developed in the medical domain by Philips Healthcare. These systems are typically developed by multi-disciplinary teams, located around the world, and are in

Views on evolvability of embedded systems

NARCIS (Netherlands)

Laar, van de P.J.L.J.; Punter, H.T.

2011-01-01

Evolvability, the ability to respond effectively to change, represents a major challenge to today's high-end embedded systems, such as those developed in the medical domain by Philips Healthcare. These systems are typically developed by multi-disciplinary teams, located around the world, and are in

MULTI-SCALE SEGMENTATION OF HIGH RESOLUTION REMOTE SENSING IMAGES BY INTEGRATING MULTIPLE FEATURES

Directory of Open Access Journals (Sweden)

Y. Di

2017-05-01

Full Text Available Most of multi-scale segmentation algorithms are not aiming at high resolution remote sensing images and have difficulty to communicate and use layers’ information. In view of them, we proposes a method of multi-scale segmentation of high resolution remote sensing images by integrating multiple features. First, Canny operator is used to extract edge information, and then band weighted distance function is built to obtain the edge weight. According to the criterion, the initial segmentation objects of color images can be gained by Kruskal minimum spanning tree algorithm. Finally segmentation images are got by the adaptive rule of Mumford–Shah region merging combination with spectral and texture information. The proposed method is evaluated precisely using analog images and ZY-3 satellite images through quantitative and qualitative analysis. The experimental results show that the multi-scale segmentation of high resolution remote sensing images by integrating multiple features outperformed the software eCognition fractal network evolution algorithm (highest-resolution network evolution that FNEA on the accuracy and slightly inferior to FNEA on the efficiency.

Multi-label learning with fuzzy hypergraph regularization for protein subcellular location prediction.

Science.gov (United States)

Chen, Jing; Tang, Yuan Yan; Chen, C L Philip; Fang, Bin; Lin, Yuewei; Shang, Zhaowei

2014-12-01

Protein subcellular location prediction aims to predict the location where a protein resides within a cell using computational methods. Considering the main limitations of the existing methods, we propose a hierarchical multi-label learning model FHML for both single-location proteins and multi-location proteins. The latent concepts are extracted through feature space decomposition and label space decomposition under the nonnegative data factorization framework. The extracted latent concepts are used as the codebook to indirectly connect the protein features to their annotations. We construct dual fuzzy hypergraphs to capture the intrinsic high-order relations embedded in not only feature space, but also label space. Finally, the subcellular location annotation information is propagated from the labeled proteins to the unlabeled proteins by performing dual fuzzy hypergraph Laplacian regularization. The experimental results on the six protein benchmark datasets demonstrate the superiority of our proposed method by comparing it with the state-of-the-art methods, and illustrate the benefit of exploiting both feature correlations and label correlations.

A Hierarchical Feature Extraction Model for Multi-Label Mechanical Patent Classification

Directory of Open Access Journals (Sweden)

Jie Hu

2018-01-01

Full Text Available Various studies have focused on feature extraction methods for automatic patent classification in recent years. However, most of these approaches are based on the knowledge from experts in related domains. Here we propose a hierarchical feature extraction model (HFEM for multi-label mechanical patent classification, which is able to capture both local features of phrases as well as global and temporal semantics. First, a n-gram feature extractor based on convolutional neural networks (CNNs is designed to extract salient local lexical-level features. Next, a long dependency feature extraction model based on the bidirectional long–short-term memory (BiLSTM neural network model is proposed to capture sequential correlations from higher-level sequence representations. Then the HFEM algorithm and its hierarchical feature extraction architecture are detailed. We establish the training, validation and test datasets, containing 72,532, 18,133, and 2679 mechanical patent documents, respectively, and then check the performance of HFEMs. Finally, we compared the results of the proposed HFEM and three other single neural network models, namely CNN, long–short-term memory (LSTM, and BiLSTM. The experimental results indicate that our proposed HFEM outperforms the other compared models in both precision and recall.

The Multi-Feature Hypothesis: Connectionist Guidelines for L2 Task Design

Science.gov (United States)

Moonen, Machteld; de Graaff, Rick; Westhoff, Gerard; Brekelmans, Mieke

2014-01-01

This study focuses on the effects of task type on the retention and ease of activation of second language (L2) vocabulary, based on the multi-feature hypothesis (Moonen, De Graaff, & Westhoff, 2006). Two tasks were compared: a writing task and a list-learning task. It was hypothesized that performing the writing task would yield higher…

3D Embedded Reconfigurable Riometer for Heliospheric Space Missions

Science.gov (United States)

Dekoulis, George

2016-07-01

This paper describes the development of a new three-dimensional embedded reconfigurable Riometer for performing remote sensing of planetary magnetospheres. The system couples the in situ measurements of probe or orbiter magnetospheric space missions. The new prototype features a multi-frequency mode that allows measurements at frequencies, where heliospheric physics events' signatures are distinct on the ionized planetary plasma. For our planet similar measurements are meaningful for frequencies below 55 MHz. Observation frequencies above 55 MHz yield to direct measurements of the Cosmic Microwave Background intensity. The system acts as a prototyping platform for subsequent space exploration phased-array imaging experiments, due to its high-intensity scientific processing capabilities. The performance improvement over existing systems in operation is in the range of 80%, due to the state-of-the-art hardware and scientific processing used.

EMBEDDED CONTROL SYSTEM FOR MOBILE ROBOTS WITH DIFFERENTIAL DRIVE

Directory of Open Access Journals (Sweden)

Michal KOPČÍK

2017-09-01

Full Text Available This article deals with design and implementation of control system for mobile robots with differential drive using embedded system. This designed embedded system consists of single control board featuring ARM based microcontroller which control the peripherals in real time and perform all low-level motion control. Designed embedded system can be easily expanded with additional sensors, actuators or control units to enhance applicability of mobile robot. Designed embedded system also features build-in communication module, which can be used for data for data acquisition and control of the mobile robot. Control board was implemented on two different types of mobile robots with differential drive, one of which was wheeled and other was tracked. These mobile robots serve as testing platform for Fault Detection and Isolation using hardware and analytical redundancy using Multisensor Data Fusion based on Kalman filters.

Computer vision camera with embedded FPGA processing

Science.gov (United States)

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.

Reconfiguration of Computation and Communication Resources in Multi-Core Real-Time Embedded Systems

DEFF Research Database (Denmark)

Pezzarossa, Luca

-core platform. Our approach is to associate reconfiguration with operational mode changes where the system, during normal operation, changes a subset of the executing tasks to adapt its behaviour to new conditions. Reconfiguration is therefore used during a mode change to modify the real-time guaranteed services...... of the communication channels between the tasks that are affected by the reconfiguration. This thesis investigates the use of reconfiguration in the context of multicore realtime systems targeting embedded applications. We address the reconfiguration of both the computation and the communication resources of a multi...... by the communication fabric between the cores of the platform. To support this, we present a new network on chip architecture, named Argo 2, that allows instantaneous and time-predictable reconfiguration of the communication channels. Our reconfiguration-capable architecture is prototyped using the existing time...

A sea-land segmentation algorithm based on multi-feature fusion for a large-field remote sensing image

Science.gov (United States)

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.

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

Science.gov (United States)

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.

A embedded Linux system based on PowerPC

International Nuclear Information System (INIS)

Ye Mei; Zhao Jingwei; Chu Yuanping

2006-01-01

The authors will introduce a Embedded Linux System based on PowerPC as well as the base method on how to establish the system. The goal of the system is to build a test system of VMEbus device. It also can be used to setup the small data acquisition and control system. Two types of compiler are provided by the developer system according to the features of the system and the Power PC. At the top of the article some typical embedded Operation system will be introduced and the features of different system will be provided. And then the method on how to build a embedded Linux system as well as the key technique will be discussed in detail. Finally a successful read-write example will be given based on the test system. (authors)

Graph embedding with rich information through heterogeneous graph

KAUST Repository

Sun, Guolei

2017-11-12

Graph embedding, aiming to learn low-dimensional representations for nodes in graphs, has attracted increasing attention due to its critical application including node classification, link prediction and clustering in social network analysis. Most existing algorithms for graph embedding only rely on the topology information and fail to use the copious information in nodes as well as edges. As a result, their performance for many tasks may not be satisfactory. In this thesis, we proposed a novel and general framework for graph embedding with rich text information (GERI) through constructing a heterogeneous network, in which we integrate node and edge content information with graph topology. Specially, we designed a novel biased random walk to explore the constructed heterogeneous network with the notion of flexible neighborhood. Our sampling strategy can compromise between BFS and DFS local search on heterogeneous graph. To further improve our algorithm, we proposed semi-supervised GERI (SGERI), which learns graph embedding in an discriminative manner through heterogeneous network with label information. The efficacy of our method is demonstrated by extensive comparison experiments with 9 baselines over multi-label and multi-class classification on various datasets including Citeseer, Cora, DBLP and Wiki. It shows that GERI improves the Micro-F1 and Macro-F1 of node classification up to 10%, and SGERI improves GERI by 5% in Wiki.

Knee Kinematics Estimation Using Multi-Body Optimisation Embedding a Knee Joint Stiffness Matrix: A Feasibility Study.

Science.gov (United States)

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.

Sea-land segmentation for infrared remote sensing images based on superpixels and multi-scale features

Science.gov (United States)

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.

Utilizing Multi-Field Text Features for Efficient Email Spam Filtering

Directory of Open Access Journals (Sweden)

Wuying Liu

2012-06-01

Full Text Available Large-scale spam emails cause a serious waste of time and resources. This paper investigates the text features of email documents and the feature noises among multi-field texts, resulting in an observation of a power law distribution of feature strings within each text field. According to the observation, we propose an efficient filtering approach including a compound weight method and a lightweight field text classification algorithm. The compound weight method considers both the historical classifying ability of each field classifier and the classifying contribution of each text field in the current classified email. The lightweight field text classification algorithm straightforwardly calculates the arithmetical average of multiple conditional probabilities predicted from feature strings according to a string-frequency index for labeled emails storing. The string-frequency index structure has a random-sampling-based compressible property owing to the power law distribution and can largely reduce the storage space. The experimental results in the TREC spam track show that the proposed approach can complete the filtering task in low space cost and high speed, whose overall performance 1-ROCA exceeds the best one among the participators at the trec07p evaluation.

Embedded Incremental Feature Selection for Reinforcement Learning

Science.gov (United States)

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

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

Directory of Open Access Journals (Sweden)

Kun-Ching Wang

2015-01-01

Full Text Available 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.

Intrinsic Embedded Sensors for Polymeric Mechatronics: Flexure and Force Sensing

Directory of Open Access Journals (Sweden)

Leif P. Jentoft

2014-02-01

Full Text Available 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.

Frozen density embedding with non-integer subsystems' particle numbers.

Science.gov (United States)

Fabiano, Eduardo; Laricchia, Savio; Della Sala, Fabio

2014-03-21

We extend the frozen density embedding theory to non-integer subsystems' particles numbers. Different features of this formulation are discussed, with special concern for approximate embedding calculations. In particular, we highlight the relation between the non-integer particle-number partition scheme and the resulting embedding errors. Finally, we provide a discussion of the implications of the present theory for the derivative discontinuity issue and the calculation of chemical reactivity descriptors.

Comparison of Pilot Symbol Embedded Channel Estimation Algorithms

Directory of Open Access Journals (Sweden)

P. Kadlec

2009-12-01

Full Text Available In the paper, algorithms of the pilot symbol embedded channel estimation are compared. Attention is turned to the Least Square (LS channel estimation and the Sliding Correlator (SC algorithm. Both algorithms are implemented in Matlab to estimate the Channel Impulse Response (CIR of a channel exhibiting multi-path propagation. Algorithms are compared from the viewpoint of computational demands, influence of the Additive White Gaussian Noise (AWGN, an embedded pilot symbol and a computed CIR over the estimation error.

Smart multicore embedded systems

CERN Document Server

Bertels, Koen; Karlsson, Sven; Pacull, François

2014-01-01

This book provides a single-source reference to the state-of-the-art of high-level programming models and compilation tool-chains for embedded system platforms. The authors address challenges faced by programmers developing software to implement parallel applications in embedded systems, where very often they are forced to rewrite sequential programs into parallel software, taking into account all the low level features and peculiarities of the underlying platforms. Readers will benefit from these authors’ approach, which takes into account both the application requirements and the platform specificities of various embedded systems from different industries. Parallel programming tool-chains are described that take as input parameters both the application and the platform model, then determine relevant transformations and mapping decisions on the concrete platform, minimizing user intervention and hiding the difficulties related to the correct and efficient use of memory hierarchy and low level code generati...

Six transformer based asymmetrical embedded Z-source inverters

DEFF Research Database (Denmark)

Wei, Mo; Poh Chiang, Loh; Chi, Jin

2013-01-01

Embedded/Asymmetrical embedded Z-source inverters were proposed to maintain smooth input current/voltage across the dc source and within the impedance network, remain the shoot-through feature used to boost up the dc-link voltage without adding bulky filter at input side. This paper introduces a ...... a class of transformer based asymmetrical embedded Z-source inverters which keep the smooth input current and voltage while achieving enhanced voltage boost capability. The presented inverters are verified by laboratory prototypes experimentally....

Exploiting Higher Order and Multi-modal Features for 3D Object Detection

DEFF Research Database (Denmark)

Kiforenko, Lilita

that describe object visual appearance such as shape, colour, texture etc. This thesis focuses on robust object detection and pose estimation of rigid objects using 3D information. The thesis main contributions are novel feature descriptors together with object detection and pose estimation algorithms....... The initial work introduces a feature descriptor that uses edge categorisation in combination with a local multi-modal histogram descriptor in order to detect objects with little or no texture or surface variation. The comparison is performed with a state-of-the-art method, which is outperformed...... of the methods work well for one type of objects in a specific scenario, in another scenario or with different objects they might fail, therefore more robust solutions are required. The typical problem solution is the design of robust feature descriptors, where feature descriptors contain information...

The Speech multi features fusion perceptual hash algorithm based on tensor decomposition

Science.gov (United States)

Huang, Y. B.; Fan, M. H.; Zhang, Q. Y.

2018-03-01

With constant progress in modern speech communication technologies, the speech data is prone to be attacked by the noise or maliciously tampered. In order to make the speech perception hash algorithm has strong robustness and high efficiency, this paper put forward a speech perception hash algorithm based on the tensor decomposition and multi features is proposed. This algorithm analyses the speech perception feature acquires each speech component wavelet packet decomposition. LPCC, LSP and ISP feature of each speech component are extracted to constitute the speech feature tensor. Speech authentication is done by generating the hash values through feature matrix quantification which use mid-value. Experimental results showing that the proposed algorithm is robust for content to maintain operations compared with similar algorithms. It is able to resist the attack of the common background noise. Also, the algorithm is highly efficiency in terms of arithmetic, and is able to meet the real-time requirements of speech communication and complete the speech authentication quickly.

Embedded design based virtual instrument program for positron beam automation

International Nuclear Information System (INIS)

Jayapandian, J.; Gururaj, K.; Abhaya, S.; Parimala, J.; Amarendra, G.

2008-01-01

Automation of positron beam experiment with a single chip embedded design using a programmable system on chip (PSoC) which provides easy interfacing of the high-voltage DC power supply is reported. Virtual Instrument (VI) control program written in Visual Basic 6.0 ensures the following functions (i) adjusting of sample high voltage by interacting with the programmed PSoC hardware, (ii) control of personal computer (PC) based multi channel analyzer (MCA) card for energy spectroscopy, (iii) analysis of the obtained spectrum to extract the relevant line shape parameters, (iv) plotting of relevant parameters and (v) saving the file in the appropriate format. The present study highlights the hardware features of the PSoC hardware module as well as the control of MCA and other units through programming in Visual Basic

Bubbling and on-off intermittency in bailout embeddings.

Science.gov (United States)

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 multi-sensor fusion-based deep feature learning approach for rotating machinery diagnosis

Science.gov (United States)

Liu, Jie; Hu, Youmin; Wang, Yan; Wu, Bo; Fan, Jikai; Hu, Zhongxu

2018-05-01

The diagnosis of complicated fault severity problems in rotating machinery systems is an important issue that affects the productivity and quality of manufacturing processes and industrial applications. However, it usually suffers from several deficiencies. (1) A considerable degree of prior knowledge and expertise is required to not only extract and select specific features from raw sensor signals, and but also choose a suitable fusion for sensor information. (2) Traditional artificial neural networks with shallow architectures are usually adopted and they have a limited ability to learn the complex and variable operating conditions. In multi-sensor-based diagnosis applications in particular, massive high-dimensional and high-volume raw sensor signals need to be processed. In this paper, an integrated multi-sensor fusion-based deep feature learning (IMSFDFL) approach is developed to identify the fault severity in rotating machinery processes. First, traditional statistics and energy spectrum features are extracted from multiple sensors with multiple channels and combined. Then, a fused feature vector is constructed from all of the acquisition channels. Further, deep feature learning with stacked auto-encoders is used to obtain the deep features. Finally, the traditional softmax model is applied to identify the fault severity. The effectiveness of the proposed IMSFDFL approach is primarily verified by a one-stage gearbox experimental platform that uses several accelerometers under different operating conditions. This approach can identify fault severity more effectively than the traditional approaches.

Dynamic memory management for embedded systems

CERN Document Server

Atienza Alonso, David; Poucet, Christophe; Peón-Quirós, Miguel; Bartzas, Alexandros; Catthoor, Francky; Soudris, Dimitrios

2015-01-01

This book provides a systematic and unified methodology, including basic principles and reusable processes, for dynamic memory management (DMM) in embedded systems.  The authors describe in detail how to design and optimize the use of dynamic memory in modern, multimedia and network applications, targeting the latest generation of portable embedded systems, such as smartphones. Coverage includes a variety of design and optimization topics in electronic design automation of DMM, from high-level software optimization to microarchitecture-level hardware support. The authors describe the design of multi-layer dynamic data structures for the final memory hierarchy layers of the target portable embedded systems and how to create a low-fragmentation, cost-efficient, dynamic memory management subsystem out of configurable components for the particular memory allocation and de-allocation patterns for each type of application.  The design methodology described in this book is based on propagating constraints among de...

Multi(scale)gravity: a telescope for the micro-world

International Nuclear Information System (INIS)

Kogan, I.I.

2001-01-01

A short review of modern status of multi-gravity, i.e. modification of gravity at both short and large distances is given. Usually embedding of standard model and general relativity into any multidimensional construction gives rise to all possible sorts of new effects in a micro-world but we can also get a very drastic modification of these laws of gravity at ultra-large scale. One of the reason why multi-gravity can modify CMB (cosmic microwave background) is that it leads to a large distance modification of the curvature. One of very striking features of multi-gravity is that it gives us a some sort of a dark matter whose origin is that it is just matter from other branes. The author shows that on a 5-dimensional case and at large distances, multi-gravity opens a window in extra dimensions and gravitationally matter which is localized on other branes can be felt. (A.C.)

Computers as components principles of embedded computing system design

CERN Document Server

Wolf, Marilyn

2012-01-01

Computers as Components: Principles of Embedded Computing System Design, 3e, presents essential knowledge on embedded systems technology and techniques. Updated for today's embedded systems design methods, this edition features new examples including digital signal processing, multimedia, and cyber-physical systems. Author Marilyn Wolf covers the latest processors from Texas Instruments, ARM, and Microchip Technology plus software, operating systems, networks, consumer devices, and more. Like the previous editions, this textbook: Uses real processors to demonstrate both technology and tec

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

Science.gov (United States)

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.

Geomorphological change detection using object-based feature extraction from multi-temporal LIDAR data

NARCIS (Netherlands)

Seijmonsbergen, A.C.; Anders, N.S.; Bouten, W.; Feitosa, R.Q.; da Costa, G.A.O.P.; de Almeida, C.M.; Fonseca, L.M.G.; Kux, H.J.H.

2012-01-01

Multi-temporal LiDAR DTMs are used for the development and testing of a method for geomorphological change analysis in western Austria. Our test area is located on a mountain slope in the Gargellen Valley in western Austria. Six geomorphological features were mapped by using stratified Object-Based

Classification of Focal and Non Focal Epileptic Seizures Using Multi-Features and SVM Classifier.

Science.gov (United States)

Sriraam, N; Raghu, S

2017-09-02

Identifying epileptogenic zones prior to surgery is an essential and crucial step in treating patients having pharmacoresistant focal epilepsy. Electroencephalogram (EEG) is a significant measurement benchmark to assess patients suffering from epilepsy. This paper investigates the application of multi-features derived from different domains to recognize the focal and non focal epileptic seizures obtained from pharmacoresistant focal epilepsy patients from Bern Barcelona database. From the dataset, five different classification tasks were formed. Total 26 features were extracted from focal and non focal EEG. Significant features were selected using Wilcoxon rank sum test by setting p-value (p z > 1.96) at 95% significance interval. Hypothesis was made that the effect of removing outliers improves the classification accuracy. Turkey's range test was adopted for pruning outliers from feature set. Finally, 21 features were classified using optimized support vector machine (SVM) classifier with 10-fold cross validation. Bayesian optimization technique was adopted to minimize the cross-validation loss. From the simulation results, it was inferred that the highest sensitivity, specificity, and classification accuracy of 94.56%, 89.74%, and 92.15% achieved respectively and found to be better than the state-of-the-art approaches. Further, it was observed that the classification accuracy improved from 80.2% with outliers to 92.15% without outliers. The classifier performance metrics ensures the suitability of the proposed multi-features with optimized SVM classifier. It can be concluded that the proposed approach can be applied for recognition of focal EEG signals to localize epileptogenic zones.

The art of programming embedded systems

CERN Document Server

Ganssle, Jack

1992-01-01

Embedded systems are products such as microwave ovens, cars, and toys that rely on an internal microprocessor. This book is oriented toward the design engineer or programmer who writes the computer code for such a system. There are a number of problems specific to the embedded systems designer, and this book addresses them and offers practical solutions.Key Features* Offers cookbook routines, algorithms, and design techniques* Includes tips for handling debugging management and testing* Explores the philosophy of tightly coupling software and hardware in programming and dev

A Generic multi-dimensional feature extraction method using multiobjective genetic programming.

Science.gov (United States)

Zhang, Yang; Rockett, Peter I

2009-01-01

In this paper, we present a generic feature extraction method for pattern classification using multiobjective genetic programming. This not only evolves the (near-)optimal set of mappings from a pattern space to a multi-dimensional decision space, but also simultaneously optimizes the dimensionality of that decision space. The presented framework evolves vector-to-vector feature extractors that maximize class separability. We demonstrate the efficacy of our approach by making statistically-founded comparisons with a wide variety of established classifier paradigms over a range of datasets and find that for most of the pairwise comparisons, our evolutionary method delivers statistically smaller misclassification errors. At very worst, our method displays no statistical difference in a few pairwise comparisons with established classifier/dataset combinations; crucially, none of the misclassification results produced by our method is worse than any comparator classifier. Although principally focused on feature extraction, feature selection is also performed as an implicit side effect; we show that both feature extraction and selection are important to the success of our technique. The presented method has the practical consequence of obviating the need to exhaustively evaluate a large family of conventional classifiers when faced with a new pattern recognition problem in order to attain a good classification accuracy.

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

Science.gov (United States)

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.

Detection of relationships among multi-modal brain imaging meta-features via information flow.

Science.gov (United States)

Miller, Robyn L; Vergara, Victor M; Calhoun, Vince D

2018-01-15

Neuroscientists and clinical researchers are awash in data from an ever-growing number of imaging and other bio-behavioral modalities. This flow of brain imaging data, taken under resting and various task conditions, combines with available cognitive measures, behavioral information, genetic data plus other potentially salient biomedical and environmental information to create a rich but diffuse data landscape. The conditions being studied with brain imaging data are often extremely complex and it is common for researchers to employ more than one imaging, behavioral or biological data modality (e.g., genetics) in their investigations. While the field has advanced significantly in its approach to multimodal data, the vast majority of studies still ignore joint information among two or more features or modalities. We propose an intuitive framework based on conditional probabilities for understanding information exchange between features in what we are calling a feature meta-space; that is, a space consisting of many individual featurae spaces. Features can have any dimension and can be drawn from any data source or modality. No a priori assumptions are made about the functional form (e.g., linear, polynomial, exponential) of captured inter-feature relationships. We demonstrate the framework's ability to identify relationships between disparate features of varying dimensionality by applying it to a large multi-site, multi-modal clinical dataset, balance between schizophrenia patients and controls. In our application it exposes both expected (previously observed) relationships, and novel relationships rarely considered investigated by clinical researchers. To the best of our knowledge there is not presently a comparably efficient way to capture relationships of indeterminate functional form between features of arbitrary dimension and type. We are introducing this method as an initial foray into a space that remains relatively underpopulated. The framework we propose is

Pharmacovigilance from social media: mining adverse drug reaction mentions using sequence labeling with word embedding cluster features.

Science.gov (United States)

Nikfarjam, Azadeh; Sarker, Abeed; O'Connor, Karen; Ginn, Rachel; Gonzalez, Graciela

2015-05-01

Social media is becoming increasingly popular as a platform for sharing personal health-related information. This information can be utilized for public health monitoring tasks, particularly for pharmacovigilance, via the use of natural language processing (NLP) techniques. However, the language in social media is highly informal, and user-expressed medical concepts are often nontechnical, descriptive, and challenging to extract. There has been limited progress in addressing these challenges, and thus far, advanced machine learning-based NLP techniques have been underutilized. Our objective is to design a machine learning-based approach to extract mentions of adverse drug reactions (ADRs) from highly informal text in social media. We introduce ADRMine, a machine learning-based concept extraction system that uses conditional random fields (CRFs). ADRMine utilizes a variety of features, including a novel feature for modeling words' semantic similarities. The similarities are modeled by clustering words based on unsupervised, pretrained word representation vectors (embeddings) generated from unlabeled user posts in social media using a deep learning technique. ADRMine outperforms several strong baseline systems in the ADR extraction task by achieving an F-measure of 0.82. Feature analysis demonstrates that the proposed word cluster features significantly improve extraction performance. It is possible to extract complex medical concepts, with relatively high performance, from informal, user-generated content. Our approach is particularly scalable, suitable for social media mining, as it relies on large volumes of unlabeled data, thus diminishing the need for large, annotated training data sets. © The Author 2015. Published by Oxford University Press on behalf of the American Medical Informatics Association.

Knee Kinematics Estimation Using Multi-Body Optimisation Embedding a Knee Joint Stiffness Matrix: A Feasibility Study.

Directory of Open Access Journals (Sweden)

Vincent Richard

Full Text Available 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.

A scale-entropy diffusion equation to describe the multi-scale features of turbulent flames near a wall

Science.gov (United States)

Queiros-Conde, D.; Foucher, F.; Mounaïm-Rousselle, C.; Kassem, H.; Feidt, M.

2008-12-01

Multi-scale features of turbulent flames near a wall display two kinds of scale-dependent fractal features. In scale-space, an unique fractal dimension cannot be defined and the fractal dimension of the front is scale-dependent. Moreover, when the front approaches the wall, this dependency changes: fractal dimension also depends on the wall-distance. Our aim here is to propose a general geometrical framework that provides the possibility to integrate these two cases, in order to describe the multi-scale structure of turbulent flames interacting with a wall. Based on the scale-entropy quantity, which is simply linked to the roughness of the front, we thus introduce a general scale-entropy diffusion equation. We define the notion of “scale-evolutivity” which characterises the deviation of a multi-scale system from the pure fractal behaviour. The specific case of a constant “scale-evolutivity” over the scale-range is studied. In this case, called “parabolic scaling”, the fractal dimension is a linear function of the logarithm of scale. The case of a constant scale-evolutivity in the wall-distance space implies that the fractal dimension depends linearly on the logarithm of the wall-distance. We then verified experimentally, that parabolic scaling represents a good approximation of the real multi-scale features of turbulent flames near a wall.

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

Science.gov (United States)

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.

Physical Activity Recognition from Smartphone Embedded Sensors

DEFF Research Database (Denmark)

Prudêncio, João; Aguiar, Ana; Roetter, Daniel Enrique Lucani

2013-01-01

The ubiquity of smartphones has motivated efforts to use the embedded sensors to detect various aspects of user context to transparently provide personalized and contextualized services to the user. One relevant piece of context is the physical activity of the smartphone user. In this paper, we...... propose a novel set of features for distinguishing five physical activities using only sensors embedded in the smartphone. Specifically, we introduce features that are normalized using the orientation sensor such that horizontal and vertical movements are explicitly computed. We evaluate a neural network...... classifier in experiments in the wild with multiple users and hardware, we achieve accuracies above 90% for a single user and phone, and above 65% for multiple users, which is higher that similar works on the same set of activities, demonstrating the potential of our approach....

Carrier dynamics in InAs quantum dots embedded in InGaAs/GaAs multi quantum well structures

International Nuclear Information System (INIS)

Espinola, J L Casas; Dybic, M; Ostapenko, S; Torchynska, T V; Polupan, G

2007-01-01

Ground and multi excited state photoluminescence, as well as its temperature dependence, in InAs quantum dots embedded in symmetric In x Ga 1-x As/GaAs (x = 0.15) quantum wells (DWELL) have been investigated. The solution of the set of rate equations for exciton dynamics (relaxation into QWs or QDs and thermal escape) solved by us earlier is used for analysis the variety of thermal activation energies of photoluminescence thermal quenching for ground and multi excited states of InAs QDs. The obtained solutions were used at the discussion of the variety of activation energies of PL thermal quenching in InAs QDs. It is revealed three different regimes of thermally activated quenching of the QD PL intensity. These three regimes were attributed to thermal escape of excitons: i) from the high energy excited states of InAs QDs into the WL with follows exciton re-localization; ii) from the In x Ga 1-x As QWs into the GaAs barrier and iii) from the WL into the GaAs barrier with their subsequent nonradiative recombination in GaAs barrier

Diverse Power Iteration Embeddings and Its Applications

Energy Technology Data Exchange (ETDEWEB)

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

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 detection 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.

Bearing Fault Diagnosis Based on Statistical Locally Linear Embedding.

Science.gov (United States)

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

2015-07-06

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.

Raft cultivation area extraction from high resolution remote sensing imagery by fusing multi-scale region-line primitive association features

Science.gov (United States)

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.

Video2vec Embeddings Recognize Events When Examples Are Scarce.

Science.gov (United States)

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.

Quadrilateral mesh fitting that preserves sharp features based on multi-normals for Laplacian energy

Directory of Open Access Journals (Sweden)

Yusuke Imai

2014-04-01

Full Text Available Because the cost of performance testing using actual products is expensive, manufacturers use lower-cost computer-aided design simulations for this function. In this paper, we propose using hexahedral meshes, which are more accurate than tetrahedral meshes, for finite element analysis. We propose automatic hexahedral mesh generation with sharp features to precisely represent the corresponding features of a target shape. Our hexahedral mesh is generated using a voxel-based algorithm. In our previous works, we fit the surface of the voxels to the target surface using Laplacian energy minimization. We used normal vectors in the fitting to preserve sharp features. However, this method could not represent concave sharp features precisely. In this proposal, we improve our previous Laplacian energy minimization by adding a term that depends on multi-normal vectors instead of using normal vectors. Furthermore, we accentuate a convex/concave surface subset to represent concave sharp features.

Integrating Multi-omic features exploiting Chromosome Conformation Capture data

Directory of Open Access Journals (Sweden)

Ivan eMerelli

2015-02-01

Full Text Available The representation, integration and interpretation of omic data is a complex task, in particular considering the huge amount of information that is daily produced in molecular biology laboratories all around the world. The reason is that sequencing data regarding expression profiles, methylation patterns, and chromatin domains is difficult to harmonize in a systems biology view, since genome browsers only allow coordinate-based representations, discarding functional clusters created by the spatial conformation of the DNA in the nucleus. In this context, recent progresses in high throughput molecular biology techniques and bioinformatics have provided insights into chromatin interactions on a larger scale and offer a formidable support for the interpretation of multi-omic data. In particular, a novel sequencing technique called Chromosome Conformation Capture (3C allows the analysis of the chromosome organization in the cell’s natural state. While performed genome wide, this technique is usually called Hi-C. Inspired by service applications such as Google Maps, we developed NuChart, an R package that integrates Hi-C data to describe the chromosomal neighbourhood starting from the information about gene positions, with the possibility of mapping on the achieved graphs genomic features such as methylation patterns and histone modifications, along with expression profiles. In this paper we show the importance of the NuChart application for the integration of multi-omic data in a systems biology fashion, with particular interest in cytogenetic applications of these techniques. Moreover, we demonstrate how the integration of multi-omic data can provide useful information in understanding why genes are in certain specific positions inside the nucleus and how epigenetic patterns correlate with their expression.

Design of Embedded Wireless Sensor and its Soft Encapsulation for Embedded Monitoring of Helicopter Planetary Gear Set

International Nuclear Information System (INIS)

Qin Guojun; Hu Niaoqing

2012-01-01

Planetary gear set, as an important part of helicopter, is with the characteristics of multi-point and time-varying position engagement. For the revolution of planetary gears round sun gear, directions of vibration and pulse created by tooth damage change continuously. If an accelerometer fixed on the surface of gearbox, the angle between the directions of pulse force and accelerometer sensitivity will change continuously, which will causes that the components of pulse force on the sensitivity direction vary with time and the features of damage are very difficult to extract from the signals. Aiming at this problem, a type of embedded wireless sensor node was designed firstly, which can be fixed on the carrier of planetary gear, and acquires the damage-related vibration signals in a fixed direction of pulse force. Then, to avoid the corrosion of electronic components by the lubrication oil in gearbox, the protect restrictions of the sensor node was investigated and a kind of soft encapsulation method is applied. Finally, real vibration signal is measured and transmitted by the designed and/or encapsulated sensor node. The experiments show that the sensor can measure vibration effectively.

A low-cost high-performance embedded platform for accelerator controls

International Nuclear Information System (INIS)

Cleva, Stefano; Bogani, Alessio Igor; Pivetta, Lorenzo

2012-01-01

Over the last years the mobile and hand-held device market has seen a dramatic performance improvement of the microprocessors employed for these systems. As an interesting side effect, this brings the opportunity of adopting these microprocessors to build small low-cost embedded boards, featuring lots of processing power and input/output capabilities. Moreover, being capable of running a full featured operating system such as Gnu/Linux, and even a control system toolkit such as Tango, these boards can also be used in control systems as front-end or embedded computers. In order to evaluate the feasibility of this idea, an activity has started at Elettra to select, evaluate and validate a commercial embedded device able to guarantee production grade reliability, competitive costs and an open source platform. The preliminary results of this work are presented. (author)

Embedded multiprocessors scheduling and synchronization

CERN Document Server

Sriram, Sundararajan

2009-01-01

Techniques for Optimizing Multiprocessor Implementations of Signal Processing ApplicationsAn indispensable component of the information age, signal processing is embedded in a variety of consumer devices, including cell phones and digital television, as well as in communication infrastructure, such as media servers and cellular base stations. Multiple programmable processors, along with custom hardware running in parallel, are needed to achieve the computation throughput required of such applications. Reviews important research in key areas related to the multiprocessor implementation of multi

A data-driven multi-model methodology with deep feature selection for short-term wind forecasting

International Nuclear Information System (INIS)

Feng, Cong; Cui, Mingjian; Hodge, Bri-Mathias; Zhang, Jie

2017-01-01

Highlights: • An ensemble model is developed to produce both deterministic and probabilistic wind forecasts. • A deep feature selection framework is developed to optimally determine the inputs to the forecasting methodology. • The developed ensemble methodology has improved the forecasting accuracy by up to 30%. - Abstract: With the growing wind penetration into the power system worldwide, improving wind power forecasting accuracy is becoming increasingly important to ensure continued economic and reliable power system operations. In this paper, a data-driven multi-model wind forecasting methodology is developed with a two-layer ensemble machine learning technique. The first layer is composed of multiple machine learning models that generate individual forecasts. A deep feature selection framework is developed to determine the most suitable inputs to the first layer machine learning models. Then, a blending algorithm is applied in the second layer to create an ensemble of the forecasts produced by first layer models and generate both deterministic and probabilistic forecasts. This two-layer model seeks to utilize the statistically different characteristics of each machine learning algorithm. A number of machine learning algorithms are selected and compared in both layers. This developed multi-model wind forecasting methodology is compared to several benchmarks. The effectiveness of the proposed methodology is evaluated to provide 1-hour-ahead wind speed forecasting at seven locations of the Surface Radiation network. Numerical results show that comparing to the single-algorithm models, the developed multi-model framework with deep feature selection procedure has improved the forecasting accuracy by up to 30%.

A Heterogeneous Multi-core Architecture with a Hardware Kernel for Control Systems

DEFF Research Database (Denmark)

Li, Gang; Guan, Wei; Sierszecki, Krzysztof

2012-01-01

Rapid industrialisation has resulted in a demand for improved embedded control systems with features such as predictability, high processing performance and low power consumption. Software kernel implementation on a single processor is becoming more difficult to satisfy those constraints....... This paper presents a multi-core architecture incorporating a hardware kernel on FPGAs, intended for high performance applications in control engineering domain. First, the hardware kernel is investigated on the basis of a component-based real-time kernel HARTEX (Hard Real-Time Executive for Control Systems...

Feasibility study on embedded transport core calculations

International Nuclear Information System (INIS)

Ivanov, B.; Zikatanov, L.; Ivanov, K.

2007-01-01

The main objective of this study is to develop an advanced core calculation methodology based on embedded diffusion and transport calculations. The scheme proposed in this work is based on embedded diffusion or SP 3 pin-by-pin local fuel assembly calculation within the framework of the Nodal Expansion Method (NEM) diffusion core calculation. The SP 3 method has gained popularity in the last 10 years as an advanced method for neutronics calculation. NEM is a multi-group nodal diffusion code developed, maintained and continuously improved at the Pennsylvania State University. The developed calculation scheme is a non-linear iteration process, which involves cross-section homogenization, on-line discontinuity factors generation, and boundary conditions evaluation by the global solution passed to the local calculation. In order to accomplish the local calculation, a new code has been developed based on the Finite Elements Method (FEM), which is capable of performing both diffusion and SP 3 calculations. The new code will be used in the framework of the NEM code in order to perform embedded pin-by-pin diffusion and SP 3 calculations on fuel assembly basis. The development of the diffusion and SP 3 FEM code is presented first following by its application to several problems. Description of the proposed embedded scheme is provided next as well as the obtained preliminary results of the C3 MOX benchmark. The results from the embedded calculations are compared with direct pin-by-pin whole core calculations in terms of accuracy and efficiency followed by conclusions made about the feasibility of the proposed embedded approach. (authors)

Flexible feature-space-construction architecture and its VLSI implementation for multi-scale object detection

Science.gov (United States)

Luo, Aiwen; An, Fengwei; Zhang, Xiangyu; Chen, Lei; Huang, Zunkai; Jürgen Mattausch, Hans

2018-04-01

Feature extraction techniques are a cornerstone of object detection in computer-vision-based applications. The detection performance of vison-based detection systems is often degraded by, e.g., changes in the illumination intensity of the light source, foreground-background contrast variations or automatic gain control from the camera. In order to avoid such degradation effects, we present a block-based L1-norm-circuit architecture which is configurable for different image-cell sizes, cell-based feature descriptors and image resolutions according to customization parameters from the circuit input. The incorporated flexibility in both the image resolution and the cell size for multi-scale image pyramids leads to lower computational complexity and power consumption. Additionally, an object-detection prototype for performance evaluation in 65 nm CMOS implements the proposed L1-norm circuit together with a histogram of oriented gradients (HOG) descriptor and a support vector machine (SVM) classifier. The proposed parallel architecture with high hardware efficiency enables real-time processing, high detection robustness, small chip-core area as well as low power consumption for multi-scale object detection.

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

Science.gov (United States)

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.

An integrated compact airborne multispectral imaging system using embedded computer

Science.gov (United States)

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.

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

Science.gov (United States)

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.

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

Science.gov (United States)

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.

VideoStory Embeddings Recognize Events when Examples are Scarce

OpenAIRE

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

2015-01-01

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 VideoStory, the correlati...

Processing of word stress related acoustic information: A multi-feature MMN study.

Science.gov (United States)

Honbolygó, Ferenc; Kolozsvári, Orsolya; Csépe, Valéria

2017-08-01

In the present study, we investigated the processing of word stress related acoustic features in a word context. In a passive oddball multi-feature MMN experiment, we presented a disyllabic pseudo-word with two acoustically similar syllables as standard stimulus, and five contrasting deviants that differed from the standard in that they were either stressed on the first syllable or contained a vowel change. Stress was realized by an increase of f0, intensity, vowel duration or consonant duration. The vowel change was used to investigate if phonemic and prosodic changes elicit different MMN components. As a control condition, we presented non-speech counterparts of the speech stimuli. Results showed all but one feature (non-speech intensity deviant) eliciting the MMN component, which was larger for speech compared to non-speech stimuli. Two other components showed stimulus related effects: the N350 and the LDN (Late Discriminative Negativity). The N350 appeared to the vowel duration and consonant duration deviants, specifically to features related to the temporal characteristics of stimuli, while the LDN was present for all features, and it was larger for speech than for non-speech stimuli. We also found that the f0 and consonant duration features elicited a larger MMN than other features. These results suggest that stress as a phonological feature is processed based on long-term representations, and listeners show a specific sensitivity to segmental and suprasegmental cues signaling the prosodic boundaries of words. These findings support a two-stage model in the perception of stress and phoneme related acoustical information. Copyright © 2017 Elsevier B.V. All rights reserved.

A comparative study of different feature sets for recognition of handwritten Arabic numerals using a Multi Layer Perceptron

OpenAIRE

Das, Nibaran; Mollah, Ayatullah Faruk; Sarkar, Ram; Basu, Subhadip

2010-01-01

The work presents a comparative assessment of seven different feature sets for recognition of handwritten Arabic numerals using a Multi Layer Perceptron (MLP) based classifier. The seven feature sets employed here consist of shadow features, octant centroids, longest runs, angular distances, effective spans, dynamic centers of gravity, and some of their combinations. On experimentation with a database of 3000 samples, the maximum recognition rate of 95.80% is observed with both of two separat...

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

Science.gov (United States)

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).

Silver-embedded screens in the intensive care unit. A new tool to control multi-drug resistant bacterial cross-transmission.

Science.gov (United States)

Ruiz, J; Ramirez, P; Villarreal, E; Gordon, M; Cuesta, S; Piñol, M; Frasquet, J; Castellanos, Á

2017-08-01

The purpose of this study was to assess the effectiveness of silver-embedded surfaces (BactiBlock®) to prevent surface colonization by multi-resistant bacteria (MRB) and to reduce the incidence of MRB colonization and infection in patients admitted to an intensive care unit (ICU). A 6-month prospective observational study in a 24-bed mixed ICU divided into two identical subunits (12 beds each) was designed. Seven solid mobile screens were placed in one of the subunits while in the other cloth screens remained. Solid screens were constructed with high-density polyethylene embedded in Bactiblock®. To evaluate the effectiveness of screens coated with Bactiblock®, number of MRB isolates on screens were compared for 6 months. Likewise, numbers of new patients and ICU-stays with MRB colonization in the two subunits were compared. One hundred forty screen samples were collected in 10-point prevalent days. MRB were detected on 28 (20.0%) samples. Over the 70 samples taken on cloth folding screens, MRB were detected in 25 (35.7%), while only 3 (4.3%) of the 70 samples taken on Bactiblock® screens were positive for MRB (p unit with Bactiblock® screens presented fewer number of ICU stays with MRB colonization (27.8% vs 47.1%; p units, proving to be an useful tool in the control of MRB.

Sensor-Based Auto-Focusing System Using Multi-Scale Feature Extraction and Phase Correlation Matching

Directory of Open Access Journals (Sweden)

Jinbeum Jang

2015-03-01

Full Text Available This paper presents a novel auto-focusing system based on a CMOS sensor containing pixels with different phases. Robust extraction of features in a severely defocused image is the fundamental problem of a phase-difference auto-focusing system. In order to solve this problem, a multi-resolution feature extraction algorithm is proposed. Given the extracted features, the proposed auto-focusing system can provide the ideal focusing position using phase correlation matching. The proposed auto-focusing (AF algorithm consists of four steps: (i acquisition of left and right images using AF points in the region-of-interest; (ii feature extraction in the left image under low illumination and out-of-focus blur; (iii the generation of two feature images using the phase difference between the left and right images; and (iv estimation of the phase shifting vector using phase correlation matching. Since the proposed system accurately estimates the phase difference in the out-of-focus blurred image under low illumination, it can provide faster, more robust auto focusing than existing systems.

Representation of Block-Based Image Features in a Multi-Scale Framework for Built-Up Area Detection

Directory of Open Access Journals (Sweden)

Zhongwen Hu

2016-02-01

Full Text Available The accurate extraction and mapping of built-up areas play an important role in many social, economic, and environmental studies. In this paper, we propose a novel approach for built-up area detection from high spatial resolution remote sensing images, using a block-based multi-scale feature representation framework. First, an image is divided into small blocks, in which the spectral, textural, and structural features are extracted and represented using a multi-scale framework; a set of refined Harris corner points is then used to select blocks as training samples; finally, a built-up index image is obtained by minimizing the normalized spectral, textural, and structural distances to the training samples, and a built-up area map is obtained by thresholding the index image. Experiments confirm that the proposed approach is effective for high-resolution optical and synthetic aperture radar images, with different scenes and different spatial resolutions.

Solving multi-objective job shop problem using nature-based algorithms: new Pareto approximation features

Directory of Open Access Journals (Sweden)

Jarosław Rudy

2015-01-01

Full Text Available In this paper the job shop scheduling problem (JSP with minimizing two criteria simultaneously is considered. JSP is frequently used model in real world applications of combinatorial optimization. Multi-objective job shop problems (MOJSP were rarely studied. We implement and compare two multi-agent nature-based methods, namely ant colony optimization (ACO and genetic algorithm (GA for MOJSP. Both of those methods employ certain technique, taken from the multi-criteria decision analysis in order to establish ranking of solutions. ACO and GA differ in a method of keeping information about previously found solutions and their quality, which affects the course of the search. In result, new features of Pareto approximations provided by said algorithms are observed: aside from the slight superiority of the ACO method the Pareto frontier approximations provided by both methods are disjoint sets. Thus, both methods can be used to search mutually exclusive areas of the Pareto frontier.

Java for Cost Effective Embedded Real-Time Software

DEFF Research Database (Denmark)

Korsholm, Stephan Erbs

2012-01-01

This thesis presents the analysis, design and implementation of the Hardware near Virtual Machine (HVM) - a Java virtual machine for embedded devices. The HVM supports the execution of Java programs on low-end embedded hard- ware environments with as little as a few kB of RAM and 32 kB of ROM....... The HVM is based on a Java-to-C translation mechanism and it produces selfcontained, strict ANSI-C code that has been specially crafted to allow it to be embedded into existing C based build and execution environments; environ- ments which may be based on non standard C compilers and libraries. The HVM...... does not require a POSIX-like OS, nor does it require a C runtime library to be present for the target. The main distinguishing feature of the HVM is to support the stepwise addition of Java into an existing C based build and execution environment for low-end embedded systems. This will allow...

Java for Cost Effective Embedded Real-Time Software

DEFF Research Database (Denmark)

Korsholm, Stephan

This thesis presents the analysis, design and implementation of the Hardware near Virtual Machine (HVM) - a Java virtual machine for embedded devices. The HVM supports the execution of Java programs on low-end embedded hard- ware environments with as little as a few kB of RAM and 32 kB of ROM....... The HVM is based on a Java-to-C translation mechanism and it produces self- contained, strict ANSI-C code that has been specially crafted to allow it to be embedded into existing C based build and execution environments; environ- ments which may be based on non standard C compilers and libraries. The HVM...... does not require a POSIX-like OS, nor does it require a C runtime library to be present for the target. The main distinguishing feature of the HVM is to support the stepwise addition of Java into an existing C based build and execution environment for low-end embedded systems. This will allow...

Modelling and Analyses of Embedded Systems Design

DEFF Research Database (Denmark)

Brekling, Aske Wiid

We present the MoVES languages: a language with which embedded systems can be specified at a stage in the development process where an application is identified and should be mapped to an execution platform (potentially multi- core). We give a formal model for MoVES that captures and gives......-based verification is a promising approach for assisting developers of embedded systems. We provide examples of system verifications that, in size and complexity, point in the direction of industrially-interesting systems....... semantics to the elements of specifications in the MoVES language. We show that even for seem- ingly simple systems, the complexity of verifying real-time constraints can be overwhelming - but we give an upper limit to the size of the search-space that needs examining. Furthermore, the formal model exposes...

Healthy full-term infants' brain responses to emotionally and linguistically relevant sounds using a multi-feature mismatch negativity (MMN) paradigm.

Science.gov (United States)

Kostilainen, Kaisamari; Wikström, Valtteri; Pakarinen, Satu; Videman, Mari; Karlsson, Linnea; Keskinen, Maria; Scheinin, Noora M; Karlsson, Hasse; Huotilainen, Minna

2018-03-23

We evaluated the feasibility of a multi-feature mismatch negativity (MMN) paradigm in studying auditory processing of healthy newborns. The aim was to examine the automatic change-detection and processing of semantic and emotional information in speech in newborns. Brain responses of 202 healthy newborns were recorded with a multi-feature paradigm including a Finnish bi-syllabic pseudo-word/ta-ta/as a standard stimulus, six linguistically relevant deviant stimuli and three emotionally relevant stimuli (happy, sad, angry). Clear responses to emotional sounds were found already at the early latency window 100-200 ms, whereas responses to linguistically relevant minor changes and emotional stimuli at the later latency window 300-500 ms did not reach significance. Moreover, significant interaction between gender and emotional stimuli was found in the early latency window. Further studies on using multi-feature paradigms with linguistic and emotional stimuli in newborns are needed, especially those containing of follow-ups, enabling the assessment of the predictive value of early variations between subjects. Copyright © 2018 The Authors. Published by Elsevier B.V. All rights reserved.

The comparison of MSCT multi-phase scan features between benign prostatic hyperplasia and prostate cancer

International Nuclear Information System (INIS)

Liu Jingang; Wang Xizhen; Niu Qingliang; Lu Hongkai; Wang Bin

2009-01-01

Objective: To investigate the multi-phase contrast-enhanced features of multi-slice computed tomography (MSCT) of benign prostatic hyperplasia (BPH) and prostate cancer (PCa). Methods: Thirty-five BPH and twenty- seven PCa were examined with multi-phase contrast-enhanced MSCT scan. The peak time, maximum attenuation value (MAV) and time density curve (TDC) were recorded, and the slope of the contrast media uptake curve was calculated. Result: Significant differences between BPH and PCa in the type of the curves and the peak time were observed (P<0.01). The slopes of BPH and PCa were 0.45+0.25 and 0.7 6+0.34 respectively, the slope of PCa was higher than that of BPH (P<0.05). MAVs of BPH and PCa were (44.057±10.261) HU and (46.778±11.140) HU respectively, and there was no significant difference between them (P>0.05). Conclusion: The multi-phase MSCT scan can reflect the blood supply and enhancement characters of BPH and PCa, which are important in detection and differential diagnosis of the prostate diseases. (authors)

Embedded Leverage

DEFF Research Database (Denmark)

Frazzini, Andrea; Heje Pedersen, Lasse

find that asset classes with embedded leverage offer low risk-adjusted returns and, in the cross-section, higher embedded leverage is associated with lower returns. A portfolio which is long low-embedded-leverage securities and short high-embedded-leverage securities earns large abnormal returns...

Embedding potentials for excited states of embedded species

International Nuclear Information System (INIS)

Wesolowski, Tomasz A.

2014-01-01

Frozen-Density-Embedding Theory (FDET) is a formalism to obtain the upper bound of the ground-state energy of the total system and the corresponding embedded wavefunction by means of Euler-Lagrange equations [T. A. Wesolowski, Phys. Rev. A 77(1), 012504 (2008)]. FDET provides the expression for the embedding potential as a functional of the electron density of the embedded species, electron density of the environment, and the field generated by other charges in the environment. Under certain conditions, FDET leads to the exact ground-state energy and density of the whole system. Following Perdew-Levy theorem on stationary states of the ground-state energy functional, the other-than-ground-state stationary states of the FDET energy functional correspond to excited states. In the present work, we analyze such use of other-than-ground-state embedded wavefunctions obtained in practical calculations, i.e., when the FDET embedding potential is approximated. Three computational approaches based on FDET, that assure self-consistent excitation energy and embedded wavefunction dealing with the issue of orthogonality of embedded wavefunctions for different states in a different manner, are proposed and discussed

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

KAUST Repository

Dai, Hanjun

2017-07-26

Motivation: 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. Results: 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 (HMM) 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 HMMs 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 data sets 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.

Video2vec Embeddings Recognize Events when Examples are Scarce

OpenAIRE

Habibian, A.; Mensink, T.; Snoek, C.G.M.

2017-01-01

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 correlatio...

French processes for waste embedding. The use of epoxy resin for waste containment

International Nuclear Information System (INIS)

Augustin, X.; Gauthey, J.C.

1993-01-01

The low- and medium-level wastes generated by nuclear facilities when operating as well as during their decommissioning (dismantling, decontamination, etc.) are embedded for the purpose of obtaining a product suitable for disposal. Due to the varieties of waste produced, it was necessary to resort to multi-purpose techniques to solve problems relating to their embedding. The process for waste embedding in thermosetting polymer (polyester, epoxy) developed by the French Atomic Energy Commission (CEA) and its subsidiary TECHNICATOME is easy to operate and yields excellent results having regard to volume reduction and containment of radioisotopes (particularly caesium). The industrial development of this process has led to the design of small, flexible, fixed or mobile, embedding stations. Examples illustrating the increasing use of this process during facility dismantling are described

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

Science.gov (United States)

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.

Composable local memory organisation for streaming applications on embedded MPSoCs

NARCIS (Netherlands)

Ambrose, J.; Molnos, A.; Nelson, A.; Cotofana, S.; Goossens, K.G.W.; Juurlink, B.

2011-01-01

Multi-Processor Systems on a Chip (MPSoCs) are suitable platforms for the implementation of complex embedded applications. An MPSoC is composable if the functional and temporal behaviour of each application is independent of the absence or presence of other applications. Composability is required

Multi-example feature-constrained back-projection method for image super-resolution

Institute of Scientific and Technical Information of China (English)

Junlei Zhang; Dianguang Gai; Xin Zhang; Xuemei Li

2017-01-01

Example-based super-resolution algorithms,which predict unknown high-resolution image information using a relationship model learnt from known high- and low-resolution image pairs, have attracted considerable interest in the field of image processing. In this paper, we propose a multi-example feature-constrained back-projection method for image super-resolution. Firstly, we take advantage of a feature-constrained polynomial interpolation method to enlarge the low-resolution image. Next, we consider low-frequency images of different resolutions to provide an example pair. Then, we use adaptive k NN search to find similar patches in the low-resolution image for every image patch in the high-resolution low-frequency image, leading to a regression model between similar patches to be learnt. The learnt model is applied to the low-resolution high-frequency image to produce high-resolution high-frequency information. An iterative back-projection algorithm is used as the final step to determine the final high-resolution image.Experimental results demonstrate that our method improves the visual quality of the high-resolution image.

Ablation mass features in multi-pulses femtosecond laser ablate molybdenum target

Science.gov (United States)

Zhao, Dongye; Gierse, Niels; Wegner, Julian; Pretzler, Georg; Oelmann, Jannis; Brezinsek, Sebastijan; Liang, Yunfeng; Neubauer, Olaf; Rasinski, Marcin; Linsmeier, Christian; Ding, Hongbin

2018-03-01

In this study, the ablation mass features related to reflectivity of bulk Molybdenum (Mo) were investigated by a Ti: Sa 6 fs laser pulse at central wavelength 790 nm. The ablated mass removal was determined using Confocal Microscopy (CM) technique. The surface reflectivity was calibrated and measured by a Lambda 950 spectrophotometer as well as a CCD camera during laser ablation. The ablation mass loss per pulse increase with the increasing of laser shots, meanwhile the surface reflectivity decrease. The multi-pulses (100 shots) ablation threshold of Mo was determined to be 0.15 J/cm2. The incubation coefficient was estimated as 0.835. The reflectivity change of the Mo target surface following multi-pulses laser ablation were studied as a function of laser ablation shots at various laser fluences from 1.07 J/cm2 to 36.23 J/cm2. The results of measured reflectivity indicate that surface reflectivity of Mo target has a significant decline in the first 3-laser pulses at the various fluences. These results are important for developing a quantitative analysis model for laser induced ablation and laser induced breakdown spectroscopy for the first wall diagnosis of EAST tokamak.

FASTBUS Standard Routines implementation for Fermilab embedded processor boards

International Nuclear Information System (INIS)

Pangburn, J.; Patrick, J.; Kent, S.; Oleynik, G.; Pordes, R.; Votava, M.; Heyes, G.; Watson, W.A. III

1992-10-01

In collaboration with CEBAF, Fermilab's Online Support Department and the CDF experiment have produced a new implementation of the IEEE FASTBUS Standard Routines for two embedded processor FASTBUS boards: the Fermilab Smart Crate Controller (FSCC) and the FASTBUS Readout Controller (FRC). Features of this implementation include: portability (to other embedded processor boards), remote source-level debugging, high speed, optional generation of very high-speed code for readout applications, and built-in Sun RPC support for execution of FASTBUS transactions and lists over the network

Safety-critical Java for low-end embedded platforms

DEFF Research Database (Denmark)

Søndergaard, Hans; Korsholm, Stephan E.; Ravn, Anders Peter

2012-01-01

We present an implementation of the Safety-Critical Java profile (SCJ), targeted for low-end embedded platforms with as little as 16 kB RAM and 256 kB flash. The distinctive features of the implementation are a combination of a lean Java virtual machine (HVM), with a bare metal kernel implementing...... hardware objects, first level interrupt handlers, and native variables, and an infrastructure written in Java which is minimized through program specialization. The HVM allows the implementation to be easily ported to embedded platforms which have a C compiler as part of the development environment...

Identification of Subtype-Specific Prognostic Genes for Early-Stage Lung Adenocarcinoma and Squamous Cell Carcinoma Patients Using an Embedded Feature Selection Algorithm.

Directory of Open Access Journals (Sweden)

Suyan Tian

Full Text Available The existence of fundamental differences between lung adenocarcinoma (AC and squamous cell carcinoma (SCC in their underlying mechanisms motivated us to postulate that specific genes might exist relevant to prognosis of each histology subtype. To test on this research hypothesis, we previously proposed a simple Cox-regression model based feature selection algorithm and identified successfully some subtype-specific prognostic genes when applying this method to real-world data. In this article, we continue our effort on identification of subtype-specific prognostic genes for AC and SCC, and propose a novel embedded feature selection method by extending Threshold Gradient Descent Regularization (TGDR algorithm and minimizing on a corresponding negative partial likelihood function. Using real-world datasets and simulated ones, we show these two proposed methods have comparable performance whereas the new proposal is superior in terms of model parsimony. Our analysis provides some evidence on the existence of such subtype-specific prognostic genes, more investigation is warranted.

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

Science.gov (United States)

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.

Predicting human splicing branchpoints by combining sequence-derived features and multi-label learning methods.

Science.gov (United States)

Zhang, Wen; Zhu, Xiaopeng; Fu, Yu; Tsuji, Junko; Weng, Zhiping

2017-12-01

Alternative splicing is the critical process in a single gene coding, which removes introns and joins exons, and splicing branchpoints are indicators for the alternative splicing. Wet experiments have identified a great number of human splicing branchpoints, but many branchpoints are still unknown. In order to guide wet experiments, we develop computational methods to predict human splicing branchpoints. Considering the fact that an intron may have multiple branchpoints, we transform the branchpoint prediction as the multi-label learning problem, and attempt to predict branchpoint sites from intron sequences. First, we investigate a variety of intron sequence-derived features, such as sparse profile, dinucleotide profile, position weight matrix profile, Markov motif profile and polypyrimidine tract profile. Second, we consider several multi-label learning methods: partial least squares regression, canonical correlation analysis and regularized canonical correlation analysis, and use them as the basic classification engines. Third, we propose two ensemble learning schemes which integrate different features and different classifiers to build ensemble learning systems for the branchpoint prediction. One is the genetic algorithm-based weighted average ensemble method; the other is the logistic regression-based ensemble method. In the computational experiments, two ensemble learning methods outperform benchmark branchpoint prediction methods, and can produce high-accuracy results on the benchmark dataset.

Introduction to Embedded Systems Using ANSI C and the Arduino Development Environment

CERN Document Server

Russell, David

2010-01-01

Many electrical and computer engineering projects involve some kind of embedded system in which a microcontroller sits at the center as the primary source of control. The recently-developed Arduino development platform includes an inexpensive hardware development board hosting an eight-bit ATMEL ATmega-family processor and a Java-based software-development environment. These features allow an embedded systems beginner the ability to focus their attention on learning how to write embedded software instead of wasting time overcoming the engineering CAD tools learning curve. The goal of this text

Embedded Systems

Indian Academy of Sciences (India)

Embedded system, micro-con- troller ... Embedded systems differ from general purpose computers in many ... Low cost: As embedded systems are extensively used in con- .... operating systems for the desktop computers where scheduling.

Using a multi-feature paradigm to measure mismatch responses to minimal sound contrasts in children with cochlear implants and hearing aids.

Science.gov (United States)

Uhlén, Inger; Engström, Elisabet; Kallioinen, Petter; Nakeva von Mentzer, Cecilia; Lyxell, Björn; Sahlén, Birgitta; Lindgren, Magnus; Ors, Marianne

2017-10-01

Our aim was to explore whether a multi-feature paradigm (Optimum-1) for eliciting mismatch negativity (MMN) would objectively capture difficulties in perceiving small sound contrasts in children with hearing impairment (HI) listening through their hearing aids (HAs) and/or cochlear implants (CIs). Children aged 5-7 years with HAs, CIs and children with normal hearing (NH) were tested in a free-field setting using a multi-feature paradigm with deviations in pitch, intensity, gap, duration, and location. There were significant mismatch responses across all subjects that were positive (p-MMR) for the gap and pitch deviants (F(1,43) = 5.17, p = 0.028 and F(1,43) = 6.56, p = 0.014, respectively) and negative (MMN) for the duration deviant (F(1,43) = 4.74, p = 0.035). Only the intensity deviant showed a significant group interaction with MMN in the HA group and p-MMR in the CI group (F(2,43) = 3.40, p = 0.043). The p-MMR correlated negatively with age, with the strongest correlation in the NH subjects. In the CI group, the late discriminative negativity (LDN) was replaced by a late positivity with a significant group interaction for the location deviant. Children with severe HI can be assessed through their hearing device with a fast multi-feature paradigm. For further studies a multi-feature paradigm including more complex speech sounds may better capture variation in auditory processing in these children. © 2017 Scandinavian Psychological Associations and John Wiley & Sons Ltd.

An Indoor Slam Method Based on Kinect and Multi-Feature Extended Information Filter

Science.gov (United States)

Chang, M.; Kang, Z.

2017-09-01

Based on the frame of ORB-SLAM in this paper the transformation parameters between adjacent Kinect image frames are computed using ORB keypoints, from which priori information matrix and information vector are calculated. The motion update of multi-feature extended information filter is then realized. According to the point cloud data formed by depth image, ICP algorithm was used to extract the point features of the point cloud data in the scene and built an observation model while calculating a-posteriori information matrix and information vector, and weakening the influences caused by the error accumulation in the positioning process. Furthermore, this paper applied ORB-SLAM frame to realize autonomous positioning in real time in interior unknown environment. In the end, Lidar was used to get data in the scene in order to estimate positioning accuracy put forward in this paper.

AN INDOOR SLAM METHOD BASED ON KINECT AND MULTI-FEATURE EXTENDED INFORMATION FILTER

Directory of Open Access Journals (Sweden)

M. Chang

2017-09-01

Full Text Available Based on the frame of ORB-SLAM in this paper the transformation parameters between adjacent Kinect image frames are computed using ORB keypoints, from which priori information matrix and information vector are calculated. The motion update of multi-feature extended information filter is then realized. According to the point cloud data formed by depth image, ICP algorithm was used to extract the point features of the point cloud data in the scene and built an observation model while calculating a-posteriori information matrix and information vector, and weakening the influences caused by the error accumulation in the positioning process. Furthermore, this paper applied ORB-SLAM frame to realize autonomous positioning in real time in interior unknown environment. In the end, Lidar was used to get data in the scene in order to estimate positioning accuracy put forward in this paper.

An object-oriented feature-based design system face-based detection of feature interactions

International Nuclear Information System (INIS)

Ariffin Abdul Razak

1999-01-01

This paper presents an object-oriented, feature-based design system which supports the integration of design and manufacture by ensuring that part descriptions fully account for any feature interactions. Manufacturing information is extracted from the feature descriptions in the form of volumes and Tool Access Directions, TADs. When features interact, both volumes and TADs are updated. This methodology has been demonstrated by developing a prototype system in which ACIS attributes are used to record feature information within the data structure of the solid model. The system implemented in the C++ programming language and embedded in a menu-driven X-windows user interface to the ACIS 3D Toolkit. (author)

Conceptualizing Embedded Configuration

DEFF Research Database (Denmark)

Oddsson, Gudmundur Valur; Hvam, Lars; Lysgaard, Ole

2006-01-01

and services. The general idea can be named embedded configuration. In this article we intend to conceptualize embedded configuration, what it is and is not. The difference between embedded configuration, sales configuration and embedded software is explained. We will look at what is needed to make embedded...... configuration systems. That will include requirements to product modelling techniques. An example with consumer electronics will illuminate the elements of embedded configuration in settings that most can relate to. The question of where embedded configuration would be relevant is discussed, and the current...

Multi-level gene/MiRNA feature selection using deep belief nets and active learning.

Science.gov (United States)

Ibrahim, Rania; Yousri, Noha A; Ismail, Mohamed A; El-Makky, Nagwa M

2014-01-01

Selecting the most discriminative genes/miRNAs has been raised as an important task in bioinformatics to enhance disease classifiers and to mitigate the dimensionality curse problem. Original feature selection methods choose genes/miRNAs based on their individual features regardless of how they perform together. Considering group features instead of individual ones provides a better view for selecting the most informative genes/miRNAs. Recently, deep learning has proven its ability in representing the data in multiple levels of abstraction, allowing for better discrimination between different classes. However, the idea of using deep learning for feature selection is not widely used in the bioinformatics field yet. In this paper, a novel multi-level feature selection approach named MLFS is proposed for selecting genes/miRNAs based on expression profiles. The approach is based on both deep and active learning. Moreover, an extension to use the technique for miRNAs is presented by considering the biological relation between miRNAs and genes. Experimental results show that the approach was able to outperform classical feature selection methods in hepatocellular carcinoma (HCC) by 9%, lung cancer by 6% and breast cancer by around 10% in F1-measure. Results also show the enhancement in F1-measure of our approach over recently related work in [1] and [2].

RapidIO as a multi-purpose interconnect

Science.gov (United States)

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.

SAFCM: A Security-Aware Feedback Control Mechanism for Distributed Real-Time Embedded Systems

DEFF Research Database (Denmark)

Ma, Yue; Jiang, Wei; Sang, Nan

2012-01-01

Distributed Real-time Embedded (DRE) systems are facing great challenges in networked, unpredictable and especially unsecured environments. In such systems, there is a strong need to enforce security on distributed computing nodes in order to guard against potential threats, while satisfying......-time systems, a multi-input multi-output feedback loop is designed and a model predictive controller is deployed based on an equation model that describes the dynamic behavior of the DRE systems. This control loop uses security level scaling to globally control the CPU utilization and security performance...... for the whole system. We propose a "security level" metric based on an evolution of cryptography algorithms used in embedded systems. Experimental results demonstrate that SAFCM not only has the excellent adaptivity compared to open-loop mechanism, but also has a better overall performance than PID control...

Multi-sparse dictionary colorization algorithm based on the feature classification and detail enhancement

Science.gov (United States)

Yan, Dan; Bai, Lianfa; Zhang, Yi; Han, Jing

2018-02-01

For the problems of missing details and performance of the colorization based on sparse representation, we propose a conceptual model framework for colorizing gray-scale images, and then a multi-sparse dictionary colorization algorithm based on the feature classification and detail enhancement (CEMDC) is proposed based on this framework. The algorithm can achieve a natural colorized effect for a gray-scale image, and it is consistent with the human vision. First, the algorithm establishes a multi-sparse dictionary classification colorization model. Then, to improve the accuracy rate of the classification, the corresponding local constraint algorithm is proposed. Finally, we propose a detail enhancement based on Laplacian Pyramid, which is effective in solving the problem of missing details and improving the speed of image colorization. In addition, the algorithm not only realizes the colorization of the visual gray-scale image, but also can be applied to the other areas, such as color transfer between color images, colorizing gray fusion images, and infrared images.

Far-Infrared Based Pedestrian Detection for Driver-Assistance Systems Based on Candidate Filters, Gradient-Based Feature and Multi-Frame Approval Matching.

Science.gov (United States)

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.

Far-Infrared Based Pedestrian Detection for Driver-Assistance Systems Based on Candidate Filters, Gradient-Based Feature and Multi-Frame Approval Matching

Directory of Open Access Journals (Sweden)

Guohua Wang

2015-12-01

Full Text Available 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.

Mapping embedded applications on MPSoCs : the MNEMEE approach

NARCIS (Netherlands)

Baloukas, C.; Papadopoulos, L.; Soudris, D.; Stuijk, S.; Jovanovic, O.; Schmoll, F.; Cordes, D.; Pyka, A.; Mallik, A.; Mamagkakis, S.; Capman, F.; Collet, S.; Mitas, N.; Kritharidis, D.

2010-01-01

As embedded systems are becoming the center of our digital life, system design becomes progressively harder. The integration of multiple features on devices with limited resources requires careful and exhaustive exploration of the design search space in order to efficiently map modern applications

Gait recognition using kinect and locally linear embedding ...

African Journals Online (AJOL)

This paper presents the use of locally linear embedding (LLE) as feature extraction technique for classifying a person's identity based on their walking gait patterns. Skeleton data acquired from Microsoft Kinect camera were used as an input for (1). Multilayer Perceptron (MLP) and (2). LLE with MLP. The MLP classification ...

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

Science.gov (United States)

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.

HybridGO-Loc: mining hybrid features on gene ontology for predicting subcellular localization of multi-location proteins.

Science.gov (United States)

Wan, Shibiao; Mak, Man-Wai; Kung, Sun-Yuan

2014-01-01

Protein subcellular localization prediction, as an essential step to elucidate the functions in vivo of proteins and identify drugs targets, has been extensively studied in previous decades. Instead of only determining subcellular localization of single-label proteins, recent studies have focused on predicting both single- and multi-location proteins. Computational methods based on Gene Ontology (GO) have been demonstrated to be superior to methods based on other features. However, existing GO-based methods focus on the occurrences of GO terms and disregard their relationships. This paper proposes a multi-label subcellular-localization predictor, namely HybridGO-Loc, that leverages not only the GO term occurrences but also the inter-term relationships. This is achieved by hybridizing the GO frequencies of occurrences and the semantic similarity between GO terms. Given a protein, a set of GO terms are retrieved by searching against the gene ontology database, using the accession numbers of homologous proteins obtained via BLAST search as the keys. The frequency of GO occurrences and semantic similarity (SS) between GO terms are used to formulate frequency vectors and semantic similarity vectors, respectively, which are subsequently hybridized to construct fusion vectors. An adaptive-decision based multi-label support vector machine (SVM) classifier is proposed to classify the fusion vectors. Experimental results based on recent benchmark datasets and a new dataset containing novel proteins show that the proposed hybrid-feature predictor significantly outperforms predictors based on individual GO features as well as other state-of-the-art predictors. For readers' convenience, the HybridGO-Loc server, which is for predicting virus or plant proteins, is available online at http://bioinfo.eie.polyu.edu.hk/HybridGoServer/.

Noncommutativity and Duality through the Symplectic Embedding Formalism

Directory of Open Access Journals (Sweden)

Everton M.C. Abreu

2010-07-01

Full Text Available This work is devoted to review the gauge embedding of either commutative and noncommutative (NC theories using the symplectic formalism framework. To sum up the main features of the method, during the process of embedding, the infinitesimal gauge generators of the gauge embedded theory are easily and directly chosen. Among other advantages, this enables a greater control over the final Lagrangian and brings some light on the so-called ''arbitrariness problem''. This alternative embedding formalism also presents a way to obtain a set of dynamically dual equivalent embedded Lagrangian densities which is obtained after a finite number of steps in the iterative symplectic process, oppositely to the result proposed using the BFFT formalism. On the other hand, we will see precisely that the symplectic embedding formalism can be seen as an alternative and an efficient procedure to the standard introduction of the Moyal product in order to produce in a natural way a NC theory. In order to construct a pedagogical explanation of the method to the nonspecialist we exemplify the formalism showing that the massive NC U(1 theory is embedded in a gauge theory using this alternative systematic path based on the symplectic framework. Further, as other applications of the method, we describe exactly how to obtain a Lagrangian description for the NC version of some systems reproducing well known theories. Naming some of them, we use the procedure in the Proca model, the irrotational fluid model and the noncommutative self-dual model in order to obtain dual equivalent actions for these theories. To illustrate the process of noncommutativity introduction we use the chiral oscillator and the nondegenerate mechanics.

Design of remote weather monitor system based on embedded web database

International Nuclear Information System (INIS)

Gao Jiugang; Zhuang Along

2010-01-01

The remote weather monitoring system is designed by employing the embedded Web database technology and the S3C2410 microprocessor as the core. The monitoring system can simultaneously monitor the multi-channel sensor signals, and can give a dynamic Web pages display of various types of meteorological information on the remote computer. It gives a elaborated introduction of the construction and application of the Web database under the embedded Linux. Test results show that the client access the Web page via the GPRS or the Internet, acquires data and uses an intuitive graphical way to display the value of various types of meteorological information. (authors)

Automatic Sleep Staging using Multi-dimensional Feature Extraction and Multi-kernel Fuzzy Support Vector Machine

Directory of Open Access Journals (Sweden)

Yanjun Zhang

2014-01-01

Full Text Available This paper employed the clinical Polysomnographic (PSG data, mainly including all-night Electroencephalogram (EEG, Electrooculogram (EOG and Electromyogram (EMG signals of subjects, and adopted the American Academy of Sleep Medicine (AASM clinical staging manual as standards to realize automatic sleep staging. Authors extracted eighteen different features of EEG, EOG and EMG in time domains and frequency domains to construct the vectors according to the existing literatures as well as clinical experience. By adopting sleep samples self-learning, the linear combination of weights and parameters of multiple kernels of the fuzzy support vector machine (FSVM were learned and the multi-kernel FSVM (MK-FSVM was constructed. The overall agreement between the experts' scores and the results presented was 82.53%. Compared with previous results, the accuracy of N1 was improved to some extent while the accuracies of other stages were approximate, which well reflected the sleep structure. The staging algorithm proposed in this paper is transparent, and worth further investigation.

Joint Facial Action Unit Detection and Feature Fusion: A Multi-conditional Learning Approach.

Science.gov (United States)

Eleftheriadis, Stefanos; Rudovic, Ognjen; Pantic, Maja

2016-10-05

Automated analysis of facial expressions can benefit many domains, from marketing to clinical diagnosis of neurodevelopmental disorders. Facial expressions are typically encoded as a combination of facial muscle activations, i.e., action units. Depending on context, these action units co-occur in specific patterns, and rarely in isolation. Yet, most existing methods for automatic action unit detection fail to exploit dependencies among them, and the corresponding facial features. To address this, we propose a novel multi-conditional latent variable model for simultaneous fusion of facial features and joint action unit detection. Specifically, the proposed model performs feature fusion in a generative fashion via a low-dimensional shared subspace, while simultaneously performing action unit detection using a discriminative classification approach. We show that by combining the merits of both approaches, the proposed methodology outperforms existing purely discriminative/generative methods for the target task. To reduce the number of parameters, and avoid overfitting, a novel Bayesian learning approach based on Monte Carlo sampling is proposed, to integrate out the shared subspace. We validate the proposed method on posed and spontaneous data from three publicly available datasets (CK+, DISFA and Shoulder-pain), and show that both feature fusion and joint learning of action units leads to improved performance compared to the state-of-the-art methods for the task.

ETHERNET BASED EMBEDDED SYSTEM FOR FEL DIAGNOSTICS AND CONTROLS

International Nuclear Information System (INIS)

Jianxun Yan; Daniel Sexton; Steven Moore; Albert Grippo; Kevin Jordan

2006-01-01

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 was 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 and play-like ease of installation and flexibility, and provides a much more localized solution

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

Science.gov (United States)

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.

Feature extraction based on extended multi-attribute profiles and sparse autoencoder for remote sensing image classification

Science.gov (United States)

Teffahi, Hanane; Yao, Hongxun; Belabid, Nasreddine; Chaib, Souleyman

2018-02-01

The satellite images with very high spatial resolution have been recently widely used in image classification topic as it has become challenging task in remote sensing field. Due to a number of limitations such as the redundancy of features and the high dimensionality of the data, different classification methods have been proposed for remote sensing images classification particularly the methods using feature extraction techniques. This paper propose a simple efficient method exploiting the capability of extended multi-attribute profiles (EMAP) with sparse autoencoder (SAE) for remote sensing image classification. The proposed method is used to classify various remote sensing datasets including hyperspectral and multispectral images by extracting spatial and spectral features based on the combination of EMAP and SAE by linking them to kernel support vector machine (SVM) for classification. Experiments on new hyperspectral image "Huston data" and multispectral image "Washington DC data" shows that this new scheme can achieve better performance of feature learning than the primitive features, traditional classifiers and ordinary autoencoder and has huge potential to achieve higher accuracy for classification in short running time.

Multi-Input Converter with MPPT Feature for Wind-PV Power Generation System

Directory of Open Access Journals (Sweden)

Chih-Lung Shen

2013-01-01

Full Text Available A multi-input converter (MIC to process wind-PV power is proposed, designed, analyzed, simulated, and implemented. The MIC cannot only process solar energy but deal with wind power, of which structure is derived from forward-type DC/DC converter to step-down/up voltage for charger systems, DC distribution applications, or grid connection. The MIC comprises an upper modified double-ended forward, a lower modified double-ended forward, a common output inductor, and a DSP-based system controller. The two modified double-ended forwards can operate individually or simultaneously so as to accommodate the variation of the hybrid renewable energy under different atmospheric conditions. While the MIC operates at interleaving mode, better performance can be achieved and volume also is reduced. The proposed MIC is capable of recycling the energy stored in the leakage inductance and obtaining high step-up output voltage. In order to draw maximum power from wind turbine and PV panel, perturb-and-observe method is adopted to achieve maximum power point tracking (MPPT feature. The MIC is constructed, analyzed, simulated, and tested. Simulations and hardware measurements have demonstrated the feasibility and functionality of the proposed multi-input converter.

Revisiting the Robustness of PET-Based Textural Features in the Context of Multi-Centric Trials.

Science.gov (United States)

Bailly, Clément; Bodet-Milin, Caroline; Couespel, Solène; Necib, Hatem; Kraeber-Bodéré, Françoise; Ansquer, Catherine; Carlier, Thomas

2016-01-01

This study aimed to investigate the variability of textural features (TF) as a function of acquisition and reconstruction parameters within the context of multi-centric trials. The robustness of 15 selected TFs were studied as a function of the number of iterations, the post-filtering level, input data noise, the reconstruction algorithm and the matrix size. A combination of several reconstruction and acquisition settings was devised to mimic multi-centric conditions. We retrospectively studied data from 26 patients enrolled in a diagnostic study that aimed to evaluate the performance of PET/CT 68Ga-DOTANOC in gastro-entero-pancreatic neuroendocrine tumors. Forty-one tumors were extracted and served as the database. The coefficient of variation (COV) or the absolute deviation (for the noise study) was derived and compared statistically with SUVmax and SUVmean results. The majority of investigated TFs can be used in a multi-centric context when each parameter is considered individually. The impact of voxel size and noise in the input data were predominant as only 4 TFs presented a high/intermediate robustness against SUV-based metrics (Entropy, Homogeneity, RP and ZP). When combining several reconstruction settings to mimic multi-centric conditions, most of the investigated TFs were robust enough against SUVmax except Correlation, Contrast, LGRE, LGZE and LZLGE. Considering previously published results on either reproducibility or sensitivity against delineation approach and our findings, it is feasible to consider Homogeneity, Entropy, Dissimilarity, HGRE, HGZE and ZP as relevant for being used in multi-centric trials.

A New Feature Extraction Method Based on EEMD and Multi-Scale Fuzzy Entropy for Motor Bearing

Directory of Open Access Journals (Sweden)

Huimin Zhao

2016-12-01

Full Text Available Feature extraction is one of the most important, pivotal, and difficult problems in mechanical fault diagnosis, which directly relates to the accuracy of fault diagnosis and the reliability of early fault prediction. Therefore, a new fault feature extraction method, called the EDOMFE method based on integrating ensemble empirical mode decomposition (EEMD, mode selection, and multi-scale fuzzy entropy is proposed to accurately diagnose fault in this paper. The EEMD method is used to decompose the vibration signal into a series of intrinsic mode functions (IMFs with a different physical significance. The correlation coefficient analysis method is used to calculate and determine three improved IMFs, which are close to the original signal. The multi-scale fuzzy entropy with the ability of effective distinguishing the complexity of different signals is used to calculate the entropy values of the selected three IMFs in order to form a feature vector with the complexity measure, which is regarded as the inputs of the support vector machine (SVM model for training and constructing a SVM classifier (EOMSMFD based on EDOMFE and SVM for fulfilling fault pattern recognition. Finally, the effectiveness of the proposed method is validated by real bearing vibration signals of the motor with different loads and fault severities. The experiment results show that the proposed EDOMFE method can effectively extract fault features from the vibration signal and that the proposed EOMSMFD method can accurately diagnose the fault types and fault severities for the inner race fault, the outer race fault, and rolling element fault of the motor bearing. Therefore, the proposed method provides a new fault diagnosis technology for rotating machinery.

Secure wireless embedded systems via component-based design

DEFF Research Database (Denmark)

Hjorth, T.; Torbensen, R.

2010-01-01

This paper introduces the method secure-by-design as a way of constructing wireless embedded systems using component-based modeling frameworks. This facilitates design of secure applications through verified, reusable software. Following this method we propose a security framework with a secure c......, with full support for confidentiality, authentication, and integrity using keypairs. The approach has been demonstrated in a multi-platform home automation prototype that can remotely unlock a door using a PDA over the Internet....

Automated prostate cancer detection via comprehensive multi-parametric magnetic resonance imaging texture feature models

International Nuclear Information System (INIS)

Khalvati, Farzad; Wong, Alexander; Haider, Masoom A.

2015-01-01

Prostate cancer is the most common form of cancer and the second leading cause of cancer death in North America. Auto-detection of prostate cancer can play a major role in early detection of prostate cancer, which has a significant impact on patient survival rates. While multi-parametric magnetic resonance imaging (MP-MRI) has shown promise in diagnosis of prostate cancer, the existing auto-detection algorithms do not take advantage of abundance of data available in MP-MRI to improve detection accuracy. The goal of this research was to design a radiomics-based auto-detection method for prostate cancer via utilizing MP-MRI data. In this work, we present new MP-MRI texture feature models for radiomics-driven detection of prostate cancer. In addition to commonly used non-invasive imaging sequences in conventional MP-MRI, namely T2-weighted MRI (T2w) and diffusion-weighted imaging (DWI), our proposed MP-MRI texture feature models incorporate computed high-b DWI (CHB-DWI) and a new diffusion imaging modality called correlated diffusion imaging (CDI). Moreover, the proposed texture feature models incorporate features from individual b-value images. A comprehensive set of texture features was calculated for both the conventional MP-MRI and new MP-MRI texture feature models. We performed feature selection analysis for each individual modality and then combined best features from each modality to construct the optimized texture feature models. The performance of the proposed MP-MRI texture feature models was evaluated via leave-one-patient-out cross-validation using a support vector machine (SVM) classifier trained on 40,975 cancerous and healthy tissue samples obtained from real clinical MP-MRI datasets. The proposed MP-MRI texture feature models outperformed the conventional model (i.e., T2w+DWI) with regard to cancer detection accuracy. Comprehensive texture feature models were developed for improved radiomics-driven detection of prostate cancer using MP-MRI. Using a

A FRAMEWORK OF CHANGE DETECTION BASED ON COMBINED MORPHOLOGICA FEATURES AND MULTI-INDEX CLASSIFICATION

Directory of Open Access Journals (Sweden)

S. Li

2017-09-01

Full Text Available Remote sensing images are particularly well suited for analysis of land cover change. In this paper, we present a new framework for detection of changing land cover using satellite imagery. Morphological features and a multi-index are used to extract typical objects from the imagery, including vegetation, water, bare land, buildings, and roads. Our method, based on connected domains, is different from traditional methods; it uses image segmentation to extract morphological features, while the enhanced vegetation index (EVI, the differential water index (NDWI are used to extract vegetation and water, and a fragmentation index is used to the correct extraction results of water. HSV transformation and threshold segmentation extract and remove the effects of shadows on extraction results. Change detection is performed on these results. One of the advantages of the proposed framework is that semantic information is extracted automatically using low-level morphological features and indexes. Another advantage is that the proposed method detects specific types of change without any training samples. A test on ZY-3 images demonstrates that our framework has a promising capability to detect change.

a Framework of Change Detection Based on Combined Morphologica Features and Multi-Index Classification

Science.gov (United States)

Li, S.; Zhang, S.; Yang, D.

2017-09-01

Remote sensing images are particularly well suited for analysis of land cover change. In this paper, we present a new framework for detection of changing land cover using satellite imagery. Morphological features and a multi-index are used to extract typical objects from the imagery, including vegetation, water, bare land, buildings, and roads. Our method, based on connected domains, is different from traditional methods; it uses image segmentation to extract morphological features, while the enhanced vegetation index (EVI), the differential water index (NDWI) are used to extract vegetation and water, and a fragmentation index is used to the correct extraction results of water. HSV transformation and threshold segmentation extract and remove the effects of shadows on extraction results. Change detection is performed on these results. One of the advantages of the proposed framework is that semantic information is extracted automatically using low-level morphological features and indexes. Another advantage is that the proposed method detects specific types of change without any training samples. A test on ZY-3 images demonstrates that our framework has a promising capability to detect change.

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

Science.gov (United States)

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

Deep learning-based Diabetic Retinopathy assessment on embedded system.

Science.gov (United States)

Ardiyanto, Igi; Nugroho, Hanung Adi; Buana, Ratna Lestari Budiani

2017-07-01

Diabetic Retinopathy (DR) is a disease which affect the vision ability. The observation by an ophthalmologist usually conducted by analyzing the retinal images of the patient which are marked by some DR features. However some misdiagnosis are usually found due to human error. Here, a deep learning-based low-cost embedded system is established to assist the doctor for grading the severity of the DR from the retinal images. A compact deep learning algorithm named Deep-DR-Net which fits on a small embedded board is afterwards proposed for such purposes. In the heart of Deep-DR-Net, a cascaded encoder-classifier network is arranged using residual style for ensuring the small model size. The usage of different types of convolutional layers subsequently guarantees the features richness of the network for differentiating the grade of the DR. Experimental results show the capability of the proposed system for detecting the existence as well as grading the severity of the DR symptomps.

Automatic Generalizability Method of Urban Drainage Pipe Network Considering Multi-Features

Science.gov (United States)

Zhu, S.; Yang, Q.; Shao, J.

2018-05-01

Urban drainage systems are indispensable dataset for storm-flooding simulation. Given data availability and current computing power, the structure and complexity of urban drainage systems require to be simplify. However, till data, the simplify procedure mainly depend on manual operation that always leads to mistakes and lower work efficiency. This work referenced the classification methodology of road system, and proposed a conception of pipeline stroke. Further, length of pipeline, angle between two pipelines, the pipeline belonged road level and diameter of pipeline were chosen as the similarity criterion to generate the pipeline stroke. Finally, designed the automatic method to generalize drainage systems with the concern of multi-features. This technique can improve the efficiency and accuracy of the generalization of drainage systems. In addition, it is beneficial to the study of urban storm-floods.

Soft Somatosensitive Actuators via Embedded 3D Printing.

Science.gov (United States)

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.

The Interaction Features of the Multi-Level Retaining Walls with Soil Mass

Science.gov (United States)

Boyko, Igor; Skochko, Liudmyla; Zhuk, Veronica

2017-09-01

The interaction features of multi-level retaining walls with soil base were researched by changing their geometric parameters and locality at the plan. During excavation of deep foundation pits it is important to choose the type of constructions which influences on the horizontal displacements. The distance between the levels of retaining walls should be based on the results of numerical modelling. The objective of this paper is to present a comparison between the data of numerical simulations and the results of the in-situ lateral tests of couple piles. The problems have been solved by using the following soil models: Coulomb-Mohr model; model, which is based on the dilatation theory; elastic-plastic model with variable stiffness parameters.

Operational analysis and comparative evaluation of embedded Z-Source inverters

DEFF Research Database (Denmark)

Blaabjerg, Frede; Gao, F.; Loh, P.C.

2008-01-01

) circuitry connected instead of the generic voltage source inverter (VSI) circuitry. Further proceeding on to the topological variation, parallel embedded Z-source inverters are presented with the detailed analysis of topological configuration and operational principles showing that they are the superior......This paper presents various embedded Z-source (EZ-source) inverters broadly classified as shunt or parallel embedded Z-source inverter. Being different from the traditional Z-source inverter, EZ-source inverters are constructed by inserting dc sources into the X-shaped impedance network so...... that the dc input current flows smoothly during the whole switching period unlike the traditional Z-source inverter. This feature is interesting when PV panels or fuel cells are assumed to power load since the continuous input current flow reduces control complexity of dc source and system design burden...

Sensor Selection method for IoT systems – focusing on embedded system requirements

Directory of Open Access Journals (Sweden)

Hirayama Masayuki

2016-01-01

Full Text Available Recently, various types of sensors have been developed. Using these sensors, IoT systems have become hot topics in embedded system domain. However, sensor selections for embedded systems are not well discussed up to now. This paper focuses on embedded system’s features and architecture, and proposes a sensor selection method which is composed seven steps. In addition, we applied the proposed method to a simple example – a sensor selection for computer scored answer sheet reader unit. From this case study, an idea to use FTA in sensor selection is also discussed.

Very wide register : an asymmetric register file organization for low power embedded processors.

NARCIS (Netherlands)

Raghavan, P.; Lambrechts, A.; Jayapala, M.; Catthoor, F.; Verkest, D.T.M.L.; Corporaal, H.

2007-01-01

In current embedded systems processors, multi-ported register files are one of the most power hungry parts of the processor, even when they are clustered. This paper presents a novel register file architecture, which has single ported cells and asymmetric interfaces to the memory and to the

Image segmentation-based robust feature extraction for color image watermarking

Science.gov (United States)

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.

Atomic dynamics of tin nanoparticles embedded into porous glass

Energy Technology Data Exchange (ETDEWEB)

Parshin, P. P.; Zemlyanov, M. G., E-mail: zeml@isssph.kiae.ru; Panova, G. Kh.; Shikov, A. A. [Russian Research Centre Kurchatov Institute (Russian Federation); Kumzerov, Yu. A.; Naberezhnov, A. A. [Russian Academy of Sciences, Ioffe Physicotechnical Institute (Russian Federation); Sergueev, I.; Crichton, W. [European Synchrotron Radiation Facility (France); Chumakov, A. I. [Russian Research Centre Kurchatov Institute (Russian Federation); Rueffer, R. [European Synchrotron Radiation Facility (France)

2012-03-15

The method of resonant nuclear inelastic absorption of synchrotron radiation has been used to study the phonon spectrum for tin nanoparticles (with a natural isotope mixture) embedded into a porous glassy (silica) matrix with an average pore diameter of 7 nm in comparison to the analogous spectrum of bulk tin enriched with {sup 119}Sn isotope. Differences between the spectra have been observed, which are related to both the dimensional effects and specific structural features of the porous glass-tin nanocomposite. Peculiarities in the dynamics of tin atoms embedded into nanopores of glass are interpreted in terms of a qualitative model of the nanocomposite structure.

Atomic dynamics of tin nanoparticles embedded into porous glass

International Nuclear Information System (INIS)

Parshin, P. P.; Zemlyanov, M. G.; Panova, G. Kh.; Shikov, A. A.; Kumzerov, Yu. A.; Naberezhnov, A. A.; Sergueev, I.; Crichton, W.; Chumakov, A. I.; Rüffer, R.

2012-01-01

The method of resonant nuclear inelastic absorption of synchrotron radiation has been used to study the phonon spectrum for tin nanoparticles (with a natural isotope mixture) embedded into a porous glassy (silica) matrix with an average pore diameter of 7 nm in comparison to the analogous spectrum of bulk tin enriched with 119 Sn isotope. Differences between the spectra have been observed, which are related to both the dimensional effects and specific structural features of the porous glass-tin nanocomposite. Peculiarities in the dynamics of tin atoms embedded into nanopores of glass are interpreted in terms of a qualitative model of the nanocomposite structure.

Embedded Web Technology: Applying World Wide Web Standards to Embedded Systems

Science.gov (United States)

Ponyik, Joseph G.; York, David W.

2002-01-01

Embedded Systems have traditionally been developed in a highly customized manner. The user interface hardware and software along with the interface to the embedded system are typically unique to the system for which they are built, resulting in extra cost to the system in terms of development time and maintenance effort. World Wide Web standards have been developed in the passed ten years with the goal of allowing servers and clients to intemperate seamlessly. The client and server systems can consist of differing hardware and software platforms but the World Wide Web standards allow them to interface without knowing about the details of system at the other end of the interface. Embedded Web Technology is the merging of Embedded Systems with the World Wide Web. Embedded Web Technology decreases the cost of developing and maintaining the user interface by allowing the user to interface to the embedded system through a web browser running on a standard personal computer. Embedded Web Technology can also be used to simplify an Embedded System's internal network.

Revisiting the Robustness of PET-Based Textural Features in the Context of Multi-Centric Trials.

Directory of Open Access Journals (Sweden)

Clément Bailly

Full Text Available This study aimed to investigate the variability of textural features (TF as a function of acquisition and reconstruction parameters within the context of multi-centric trials.The robustness of 15 selected TFs were studied as a function of the number of iterations, the post-filtering level, input data noise, the reconstruction algorithm and the matrix size. A combination of several reconstruction and acquisition settings was devised to mimic multi-centric conditions. We retrospectively studied data from 26 patients enrolled in a diagnostic study that aimed to evaluate the performance of PET/CT 68Ga-DOTANOC in gastro-entero-pancreatic neuroendocrine tumors. Forty-one tumors were extracted and served as the database. The coefficient of variation (COV or the absolute deviation (for the noise study was derived and compared statistically with SUVmax and SUVmean results.The majority of investigated TFs can be used in a multi-centric context when each parameter is considered individually. The impact of voxel size and noise in the input data were predominant as only 4 TFs presented a high/intermediate robustness against SUV-based metrics (Entropy, Homogeneity, RP and ZP. When combining several reconstruction settings to mimic multi-centric conditions, most of the investigated TFs were robust enough against SUVmax except Correlation, Contrast, LGRE, LGZE and LZLGE.Considering previously published results on either reproducibility or sensitivity against delineation approach and our findings, it is feasible to consider Homogeneity, Entropy, Dissimilarity, HGRE, HGZE and ZP as relevant for being used in multi-centric trials.

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

Science.gov (United States)

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.

Low Power Multi-Hop Networking Analysis in Intelligent Environments.

Science.gov (United States)

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.

Embedded, everywhere: a research agenda for networked systems of embedded computers

National Research Council Canada - National Science Library

Committee on Networked Systems of Embedded Computers; National Research Council Staff; Division on Engineering and Physical Sciences; Computer Science and Telecommunications Board; National Academy of Sciences

2001-01-01

.... Embedded, Everywhere explores the potential of networked systems of embedded computers and the research challenges arising from embedding computation and communications technology into a wide variety of applicationsâ...

Multi-class parkinsonian disorders classification with quantitative MR markers and graph-based features using support vector machines.

Science.gov (United States)

Morisi, Rita; Manners, David Neil; Gnecco, Giorgio; Lanconelli, Nico; Testa, Claudia; Evangelisti, Stefania; Talozzi, Lia; Gramegna, Laura Ludovica; Bianchini, Claudio; Calandra-Buonaura, Giovanna; Sambati, Luisa; Giannini, Giulia; Cortelli, Pietro; Tonon, Caterina; Lodi, Raffaele

2018-02-01

In this study we attempt to automatically classify individual patients with different parkinsonian disorders, making use of pattern recognition techniques to distinguish among several forms of parkinsonisms (multi-class classification), based on a set of binary classifiers that discriminate each disorder from all others. We combine diffusion tensor imaging, proton spectroscopy and morphometric-volumetric data to obtain MR quantitative markers, which are provided to support vector machines with the aim of recognizing the different parkinsonian disorders. Feature selection is used to find the most important features for classification. We also exploit a graph-based technique on the set of quantitative markers to extract additional features from the dataset, and increase classification accuracy. When graph-based features are not used, the MR markers that are most frequently automatically extracted by the feature selection procedure reflect alterations in brain regions that are also usually considered to discriminate parkinsonisms in routine clinical practice. Graph-derived features typically increase the diagnostic accuracy, and reduce the number of features required. The results obtained in the work demonstrate that support vector machines applied to multimodal brain MR imaging and using graph-based features represent a novel and highly accurate approach to discriminate parkinsonisms, and a useful tool to assist the diagnosis. Copyright © 2017 Elsevier Ltd. All rights reserved.

AUTOMATIC GENERALIZABILITY METHOD OF URBAN DRAINAGE PIPE NETWORK CONSIDERING MULTI-FEATURES

Directory of Open Access Journals (Sweden)

S. Zhu

2018-05-01

Full Text Available Urban drainage systems are indispensable dataset for storm-flooding simulation. Given data availability and current computing power, the structure and complexity of urban drainage systems require to be simplify. However, till data, the simplify procedure mainly depend on manual operation that always leads to mistakes and lower work efficiency. This work referenced the classification methodology of road system, and proposed a conception of pipeline stroke. Further, length of pipeline, angle between two pipelines, the pipeline belonged road level and diameter of pipeline were chosen as the similarity criterion to generate the pipeline stroke. Finally, designed the automatic method to generalize drainage systems with the concern of multi-features. This technique can improve the efficiency and accuracy of the generalization of drainage systems. In addition, it is beneficial to the study of urban storm-floods.

Visual Localization across Seasons Using Sequence Matching Based on Multi-Feature Combination.

Science.gov (United States)

Qiao, Yongliang

2017-10-25

Visual localization is widely used in autonomous navigation system and Advanced Driver Assistance Systems (ADAS). However, visual-based localization in seasonal changing situations is one of the most challenging topics in computer vision and the intelligent vehicle community. The difficulty of this task is related to the strong appearance changes that occur in scenes due to weather or season changes. In this paper, a place recognition based visual localization method is proposed, which realizes the localization by identifying previously visited places using the sequence matching method. It operates by matching query image sequences to an image database acquired previously (video acquired during traveling period). In this method, in order to improve matching accuracy, multi-feature is constructed by combining a global GIST descriptor and local binary feature CSLBP (Center-symmetric local binary patterns) to represent image sequence. Then, similarity measurement according to Chi-square distance is used for effective sequences matching. For experimental evaluation, the relationship between image sequence length and sequences matching performance is studied. To show its effectiveness, the proposed method is tested and evaluated in four seasons outdoor environments. The results have shown improved precision-recall performance against the state-of-the-art SeqSLAM algorithm.

Embedding of radioactive wastes by thermosetting resins

International Nuclear Information System (INIS)

Baer, A.; Traxler, A.; Limongi, A.; Thiery, D.

The process for embedding radioactive wastes in thermosetting resins perfected and applied at the Grenoble Nuclear Research Center and its application to the treatment of radioactive wastes from Light-Water Nuclear Power Plants (PWR and BWR) are presented. The various types of wastes are enumerated and their activities and quantities are estimated: evaporator concentrates, ion exchange resins, filtration sludges, filters, various solid wastes, etc. The authors review the orientations of the research performed and indicate, for each type of waste considered, the cycle of treatment operations from rendering the radioelements insoluble to drying the concentrates to final embedding. The operational safety of the process and the safety of transport and storage of the embedded wastes are investigated. The essential technical features concerning the safety of the installation and of the final product obtained are presented. In particular, results are presented from tests of resistance to fire, irradiation, leaching, etc., these being characteristics which represent safety criteria. The economic aspects of the process are considered by presenting the influences of the reduction of volume and weight of wastes to be stored, simplicity of installations and cost of primary materials

The Interaction Features of the Multi-Level Retaining Walls with Soil Mass

Directory of Open Access Journals (Sweden)

Boyko Igor

2017-09-01

Full Text Available The interaction features of multi-level retaining walls with soil base were researched by changing their geometric parameters and locality at the plan. During excavation of deep foundation pits it is important to choose the type of constructions which influences on the horizontal displacements. The distance between the levels of retaining walls should be based on the results of numerical modelling. The objective of this paper is to present a comparison between the data of numerical simulations and the results of the in-situ lateral tests of couple piles. The problems have been solved by using the following soil models: Coulomb-Mohr model; model, which is based on the dilatation theory; elastic-plastic model with variable stiffness parameters.

Analysis and optimisation of heterogeneous real-time embedded systems

DEFF Research Database (Denmark)

Pop, Paul; Eles, Petru; Peng, Zebo

2005-01-01

. The success of such new design methods depends on the availability of analysis and optimisation techniques. Analysis and optimisation techniques for heterogeneous real-time embedded systems are presented in the paper. The authors address in more detail a particular class of such systems called multi...... of application messages to frames. Optimisation heuristics for frame packing aimed at producing a schedulable system are presented. Extensive experiments and a real-life example show the efficiency of the frame-packing approach....

Analysis and optimisation of heterogeneous real-time embedded systems

DEFF Research Database (Denmark)

Pop, Paul; Eles, Petru; Peng, Zebo

2006-01-01

. The success of such new design methods depends on the availability of analysis and optimisation techniques. Analysis and optimisation techniques for heterogeneous real-time embedded systems are presented in the paper. The authors address in more detail a particular class of such systems called multi...... of application messages to frames. Optimisation heuristics for frame packing aimed at producing a schedulable system are presented. Extensive experiments and a real-life example show the efficiency of the frame-packing approach....

Analysis and Optimization of Heterogeneous Real-Time Embedded Systems

DEFF Research Database (Denmark)

Pop, Paul; Eles, Petru; Peng, Zebo

2005-01-01

. The success of such new design methods depends on the availability of analysis and optimization techniques. In this paper, we present analysis and optimization techniques for heterogeneous real-time embedded systems. We address in more detail a particular class of such systems called multi-clusters, composed...... to frames. Optimization heuristics for frame packing aiming at producing a schedulable system are presented. Extensive experiments and a real-life example show the efficiency of the frame-packing approach....

The design of multi-channel pulse amplitude analyzer based on ARM micro controller

International Nuclear Information System (INIS)

Li Hai; Li Xiang; Liu Caixue

2010-01-01

It introduces the design of multi-channel pulse amplitude analyzer based on embedded ARM micro-controller. The embedded and real-time system μC/OS-II builds up the real-time and stability of the system and advances the integration. (authors)

Multiscale Embedded Gene Co-expression Network Analysis.

Directory of Open Access Journals (Sweden)

Won-Min Song

2015-11-01

Full Text Available 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.

Multiscale Embedded Gene Co-expression Network Analysis.

Science.gov (United States)

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.

Multi-scale Analysis of High Resolution Topography: Feature Extraction and Identification of Landscape Characteristic Scales

Science.gov (United States)

Passalacqua, P.; Sangireddy, H.; Stark, C. P.

2015-12-01

With the advent of digital terrain data, detailed information on terrain characteristics and on scale and location of geomorphic features is available over extended areas. Our ability to observe landscapes and quantify topographic patterns has greatly improved, including the estimation of fluxes of mass and energy across landscapes. Challenges still remain in the analysis of high resolution topography data; the presence of features such as roads, for example, challenges classic methods for feature extraction and large data volumes require computationally efficient extraction and analysis methods. Moreover, opportunities exist to define new robust metrics of landscape characterization for landscape comparison and model validation. In this presentation we cover recent research in multi-scale and objective analysis of high resolution topography data. We show how the analysis of the probability density function of topographic attributes such as slope, curvature, and topographic index contains useful information for feature localization and extraction. The analysis of how the distributions change across scales, quantified by the behavior of modal values and interquartile range, allows the identification of landscape characteristic scales, such as terrain roughness. The methods are introduced on synthetic signals in one and two dimensions and then applied to a variety of landscapes of different characteristics. Validation of the methods includes the analysis of modeled landscapes where the noise distribution is known and features of interest easily measured.

An Approach for Patient-Specific Multi-domain Vascular Mesh Generation Featuring Spatially Varying Wall Thickness Modeling

OpenAIRE

Raut, Samarth S.; Liu, Peng; Finol, Ender A.

2015-01-01

In this work, we present a computationally efficient image-derived volume mesh generation approach for vasculatures that implements spatially varying patient-specific wall thickness with a novel inward extrusion of the wall surface mesh. Multi-domain vascular meshes with arbitrary numbers, locations, and patterns of both iliac bifurcations and thrombi can be obtained without the need to specify features or landmark points as input. In addition, the mesh output is coordinate-frame independent ...

Slice&Dice: Recognizing Food Preparation Activities Using Embedded Accelerometers

Science.gov (United States)

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.

Embedded Ultrasonics for SHM of Space Applications

Science.gov (United States)

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 novel relational regularization feature selection method for joint regression and classification in AD diagnosis.

Science.gov (United States)

Zhu, Xiaofeng; Suk, Heung-Il; Wang, Li; Lee, Seong-Whan; Shen, Dinggang

2017-05-01

In this paper, we focus on joint regression and classification for Alzheimer's disease diagnosis and propose a new feature selection method by embedding the relational information inherent in the observations into a sparse multi-task learning framework. Specifically, the relational information includes three kinds of relationships (such as feature-feature relation, response-response relation, and sample-sample relation), for preserving three kinds of the similarity, such as for the features, the response variables, and the samples, respectively. To conduct feature selection, we first formulate the objective function by imposing these three relational characteristics along with an ℓ 2,1 -norm regularization term, and further propose a computationally efficient algorithm to optimize the proposed objective function. With the dimension-reduced data, we train two support vector regression models to predict the clinical scores of ADAS-Cog and MMSE, respectively, and also a support vector classification model to determine the clinical label. We conducted extensive experiments on the Alzheimer's Disease Neuroimaging Initiative (ADNI) dataset to validate the effectiveness of the proposed method. Our experimental results showed the efficacy of the proposed method in enhancing the performances of both clinical scores prediction and disease status identification, compared to the state-of-the-art methods. Copyright © 2015 Elsevier B.V. All rights reserved.

Learning Rich Features from RGB-D Images for Object Detection and Segmentation

OpenAIRE

Gupta, Saurabh; Girshick, Ross; Arbeláez, Pablo; Malik, Jitendra

2014-01-01

In this paper we study the problem of object detection for RGB-D images using semantically rich image and depth features. We propose a new geocentric embedding for depth images that encodes height above ground and angle with gravity for each pixel in addition to the horizontal disparity. We demonstrate that this geocentric embedding works better than using raw depth images for learning feature representations with convolutional neural networks. Our final object detection system achieves an av...

Urban Area Extent Extraction in Spaceborne HR and VHR Data Using Multi-Resolution Features

Directory of Open Access Journals (Sweden)

Gianni Cristian Iannelli

2014-09-01

Full Text Available Detection of urban area extents by means of remotely sensed data is a difficult task, especially because of the multiple, diverse definitions of what an “urban area” is. The models of urban areas listed in technical literature are based on the combination of spectral information with spatial patterns, possibly at different spatial resolutions. Starting from the same data set, “urban area” extraction may thus lead to multiple outputs. If this is done in a well-structured framework, however, this may be considered as an advantage rather than an issue. This paper proposes a novel framework for urban area extent extraction from multispectral Earth Observation (EO data. The key is to compute and combine spectral and multi-scale spatial features. By selecting the most adequate features, and combining them with proper logical rules, the approach allows matching multiple urban area models. Experimental results for different locations in Brazil and Kenya using High-Resolution (HR data prove the usefulness and flexibility of the framework.

A SIMPLE PARAFFIN EMBEDDED PROTOCOL FOR FISH EGG, EMBRYO, AND LARVAE

Directory of Open Access Journals (Sweden)

Gratiana Eka Wijayanti

2017-06-01

Full Text Available This paper describes a simple protocol of paraffin-embedded histological section for fish eggs, embryo and larvae of the hard-lipped barb and the giant gourami. The specimens were fixed in Bouin solution, washed in 70% ethanol, then were dehydrated in a series of ethanol solution of increasing concentration until absolute ethanol was reached. The specimens were cleared in graded xylene and were infiltrated with liquid paraffin then were embedded in pure paraffin. Upon sectioning, at 4–5 µm thick the specimens were attached to the gelatin-coated glass slide and let to dry at room temperature or 37°C overnight. The specimens were deparaffinized in xylene, rehydrated then were stained with hematoxylin and eosin. After being dehydrated in graded ethanol, the specimens were cleared in xylene and were mounted with an organic mounting agent. Any step in preparing histological section including samples collection, fixation, dehydration, infiltration and embedding might contribute to the quality of histological features. A proper knowledge of the tissues beeing processed, fixative solution and the histological techniques is essential to gain good results. Bouin fixative is preferable to fix fish larvae and produce a good histological feature. Decalcification is necessary to produce a good histological section on the specimens containing bone.

The impact of image reconstruction settings on 18F-FDG PET radiomic features. Multi-scanner phantom and patient studies

International Nuclear Information System (INIS)

Shiri, Isaac; Abdollahi, Hamid; Rahmim, Arman; Ghaffarian, Pardis; Geramifar, Parham; Bitarafan-Rajabi, Ahmad

2017-01-01

The purpose of this study was to investigate the robustness of different PET/CT image radiomic features over a wide range of different reconstruction settings. Phantom and patient studies were conducted, including two PET/CT scanners. Different reconstruction algorithms and parameters including number of sub-iterations, number of subsets, full width at half maximum (FWHM) of Gaussian filter, scan time per bed position and matrix size were studied. Lesions were delineated and one hundred radiomic features were extracted. All radiomics features were categorized based on coefficient of variation (COV). Forty seven percent features showed COV ≤ 5% and 10% of which showed COV > 20%. All geometry based, 44% and 41% of intensity based and texture based features were found as robust respectively. In regard to matrix size, 56% and 6% of all features were found non-robust (COV > 20%) and robust (COV ≤ 5%) respectively. Variability and robustness of PET/CT image radiomics in advanced reconstruction settings is feature-dependent, and different settings have different effects on different features. Radiomic features with low COV can be considered as good candidates for reproducible tumour quantification in multi-center studies. (orig.)

The impact of image reconstruction settings on 18F-FDG PET radiomic features. Multi-scanner phantom and patient studies

Energy Technology Data Exchange (ETDEWEB)

Shiri, Isaac; Abdollahi, Hamid [Iran University of Medical Sciences, Department of Medical Physics, School of Medicine, Tehran (Iran, Islamic Republic of); Rahmim, Arman [Johns Hopkins University, Department of Radiology, Baltimore, MD (United States); Johns Hopkins University, Department of Electrical and Computer Engineering, Baltimore, MD (United States); Ghaffarian, Pardis [Shahid Beheshti University of Medical Sciences, Chronic Respiratory Diseases Research Center, National Research Institute of Tuberculosis and Lung Diseases (NRITLD), Tehran (Iran, Islamic Republic of); Shahid Beheshti University of Medical Sciences, PET/CT and Cyclotron Center, Masih Daneshvari Hospital, Tehran (Iran, Islamic Republic of); Geramifar, Parham [Tehran University of Medical Sciences, Research Center for Nuclear Medicine, Shariati Hospital, Tehran (Iran, Islamic Republic of); Bitarafan-Rajabi, Ahmad [Iran University of Medical Sciences, Department of Medical Physics, School of Medicine, Tehran (Iran, Islamic Republic of); Iran University of Medical Sciences, Department of Nuclear Medicine, Rajaei Cardiovascular, Medical and Research Center, Tehran (Iran, Islamic Republic of)

2017-11-15

The purpose of this study was to investigate the robustness of different PET/CT image radiomic features over a wide range of different reconstruction settings. Phantom and patient studies were conducted, including two PET/CT scanners. Different reconstruction algorithms and parameters including number of sub-iterations, number of subsets, full width at half maximum (FWHM) of Gaussian filter, scan time per bed position and matrix size were studied. Lesions were delineated and one hundred radiomic features were extracted. All radiomics features were categorized based on coefficient of variation (COV). Forty seven percent features showed COV ≤ 5% and 10% of which showed COV > 20%. All geometry based, 44% and 41% of intensity based and texture based features were found as robust respectively. In regard to matrix size, 56% and 6% of all features were found non-robust (COV > 20%) and robust (COV ≤ 5%) respectively. Variability and robustness of PET/CT image radiomics in advanced reconstruction settings is feature-dependent, and different settings have different effects on different features. Radiomic features with low COV can be considered as good candidates for reproducible tumour quantification in multi-center studies. (orig.)

Embedded systems handbook

CERN Document Server

Zurawski, Richard

2005-01-01

Embedded systems are nearly ubiquitous, and books on individual topics or components of embedded systems are equally abundant. Unfortunately, for those designers who thirst for knowledge of the big picture of embedded systems there is not a drop to drink. Until now. The Embedded Systems Handbook is an oasis of information, offering a mix of basic and advanced topics, new solutions and technologies arising from the most recent research efforts, and emerging trends to help you stay current in this ever-changing field.With preeminent contributors from leading industrial and academic institutions

Agent-Oriented Embedded Control System Design and Development of a Vision-Based Automated Guided Vehicle

Directory of Open Access Journals (Sweden)

Wu Xing

2012-07-01

Full Text Available This paper presents a control system design and development approach for a vision-based automated guided vehicle (AGV based on the multi-agent system (MAS methodology and embedded system resources. A three-phase agent-oriented design methodology Prometheus is used to analyse system functions, construct operation scenarios, define agent types and design the MAS coordination mechanism. The control system is then developed in an embedded implementation containing a digital signal processor (DSP and an advanced RISC machine (ARM by using the multitasking processing capacity of multiple microprocessors and system services of a real-time operating system (RTOS. As a paradigm, an onboard embedded controller is designed and developed for the AGV with a camera detecting guiding landmarks, and the entire procedure has a high efficiency and a clear hierarchy. A vision guidance experiment for our AGV is carried out in a space-limited laboratory environment to verify the perception capacity and the onboard intelligence of the agent-oriented embedded control system.

Visual Localization across Seasons Using Sequence Matching Based on Multi-Feature Combination

Directory of Open Access Journals (Sweden)

Yongliang Qiao

2017-10-01

Full Text Available Visual localization is widely used in autonomous navigation system and Advanced Driver Assistance Systems (ADAS. However, visual-based localization in seasonal changing situations is one of the most challenging topics in computer vision and the intelligent vehicle community. The difficulty of this task is related to the strong appearance changes that occur in scenes due to weather or season changes. In this paper, a place recognition based visual localization method is proposed, which realizes the localization by identifying previously visited places using the sequence matching method. It operates by matching query image sequences to an image database acquired previously (video acquired during traveling period. In this method, in order to improve matching accuracy, multi-feature is constructed by combining a global GIST descriptor and local binary feature CSLBP (Center-symmetric local binary patterns to represent image sequence. Then, similarity measurement according to Chi-square distance is used for effective sequences matching. For experimental evaluation, the relationship between image sequence length and sequences matching performance is studied. To show its effectiveness, the proposed method is tested and evaluated in four seasons outdoor environments. The results have shown improved precision–recall performance against the state-of-the-art SeqSLAM algorithm.

A Versatile Embedded Platform for EMG Acquisition and Gesture Recognition.

Science.gov (United States)

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.

Using Multi-Viewpoint Contracts for Negotiation of Embedded Software Updates

Directory of Open Access Journals (Sweden)

Sönke Holthusen

2016-05-01

Full Text Available In this paper we address the issue of change after deployment in safety-critical embedded system applications. Our goal is to substitute lab-based verification with in-field formal analysis to determine whether an update may be safely applied. This is challenging because it requires an automated process able to handle multiple viewpoints such as functional correctness, timing, etc. For this purpose, we propose an original methodology for contract-based negotiation of software updates. The use of contracts allows us to cleanly split the verification effort between the lab and the field. In addition, we show how to rely on existing viewpoint-specific methods for update negotiation. We illustrate our approach on a concrete example inspired by the automotive domain.

Hierarchical programming language for modal multi-rate real-time stream processing applications

NARCIS (Netherlands)

Geuns, S.J.; Hausmans, J.P.H.M.; Bekooij, Marco Jan Gerrit

2014-01-01

Modal multi-rate stream processing applications with real-time constraints which are executed on multi-core embedded systems often cannot be conveniently specified using current programming languages. An important issue is that sequential programming languages do not allow for convenient programming

Dependency Parsing with Transformed Feature

Directory of Open Access Journals (Sweden)

Fuxiang Wu

2017-01-01

Full Text Available Dependency parsing is an important subtask of natural language processing. In this paper, we propose an embedding feature transforming method for graph-based parsing, transform-based parsing, which directly utilizes the inner similarity of the features to extract information from all feature strings including the un-indexed strings and alleviate the feature sparse problem. The model transforms the extracted features to transformed features via applying a feature weight matrix, which consists of similarities between the feature strings. Since the matrix is usually rank-deficient because of similar feature strings, it would influence the strength of constraints. However, it is proven that the duplicate transformed features do not degrade the optimization algorithm: the margin infused relaxed algorithm. Moreover, this problem can be alleviated by reducing the number of the nearest transformed features of a feature. In addition, to further improve the parsing accuracy, a fusion parser is introduced to integrate transformed and original features. Our experiments verify that both transform-based and fusion parser improve the parsing accuracy compared to the corresponding feature-based parser.

Embedded systems handbook networked embedded systems

CERN Document Server

Zurawski, Richard

2009-01-01

Considered a standard industry resource, the Embedded Systems Handbook provided researchers and technicians with the authoritative information needed to launch a wealth of diverse applications, including those in automotive electronics, industrial automated systems, and building automation and control. Now a new resource is required to report on current developments and provide a technical reference for those looking to move the field forward yet again. Divided into two volumes to accommodate this growth, the Embedded Systems Handbook, Second Edition presents a comprehensive view on this area

Java Source Code Analysis for API Migration to Embedded Systems

Energy Technology Data Exchange (ETDEWEB)

Winter, Victor [Univ. of Nebraska, Omaha, NE (United States); McCoy, James A. [Sandia National Lab. (SNL-NM), Albuquerque, NM (United States); Guerrero, Jonathan [Univ. of Nebraska, Omaha, NE (United States); Reinke, Carl Werner [Sandia National Lab. (SNL-NM), Albuquerque, NM (United States); Perry, James Thomas [Sandia National Lab. (SNL-NM), Albuquerque, NM (United States)

2015-02-01

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 by 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.

a Probabilistic Embedding Clustering Method for Urban Structure Detection

Science.gov (United States)

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.

A PROBABILISTIC EMBEDDING CLUSTERING METHOD FOR URBAN STRUCTURE DETECTION

Directory of Open Access Journals (Sweden)

X. Lin

2017-09-01

Full Text Available 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.

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

Science.gov (United States)

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

Embedded Linux in het onderwijs

NARCIS (Netherlands)

Dr Ruud Ermers

2008-01-01

Embedded Linux wordt bij steeds meer grote bedrijven ingevoerd als embedded operating system. Binnen de opleiding Technische Informatica van Fontys Hogeschool ICT is Embedded Linux geïntroduceerd in samenwerking met het lectoraat Architectuur van Embedded Systemen. Embedded Linux is als vakgebied

Design of Networks-on-Chip for Real-Time Multi-Processor Systems-on-Chip

DEFF Research Database (Denmark)

Sparsø, Jens

2012-01-01

This paper addresses the design of networks-on-chips for use in multi-processor systems-on-chips - the hardware platforms used in embedded systems. These platforms typically have to guarantee real-time properties, and as the network is a shared resource, it has to provide service guarantees...... (bandwidth and/or latency) to different communication flows. The paper reviews some past work in this field and the lessons learned, and the paper discusses ongoing research conducted as part of the project "Time-predictable Multi-Core Architecture for Embedded Systems" (T-CREST), supported by the European...

Nanofluidic Device with Embedded Nanopore

Science.gov (United States)

Zhang, Yuning; Reisner, Walter

2014-03-01

Nanofluidic based devices are robust methods for biomolecular sensing and single DNA manipulation. Nanopore-based DNA sensing has attractive features that make it a leading candidate as a single-molecule DNA sequencing technology. Nanochannel based extension of DNA, combined with enzymatic or denaturation-based barcoding schemes, is already a powerful approach for genome analysis. We believe that there is revolutionary potential in devices that combine nanochannels with nanpore detectors. In particular, due to the fast translocation of a DNA molecule through a standard nanopore configuration, there is an unfavorable trade-off between signal and sequence resolution. With a combined nanochannel-nanopore device, based on embedding a nanopore inside a nanochannel, we can in principle gain independent control over both DNA translocation speed and sensing signal, solving the key draw-back of the standard nanopore configuration. We demonstrate that we can detect - using fluorescent microscopy - successful translocation of DNA from the nanochannel out through the nanopore, a possible method to 'select' a given barcode for further analysis. We also show that in equilibrium DNA will not escape through an embedded sub-persistence length nanopore until a certain voltage bias is added.

Radiomic features for prostate cancer detection on MRI differ between the transition and peripheral zones: Preliminary findings from a multi-institutional study.

Science.gov (United States)

Ginsburg, Shoshana B; Algohary, Ahmad; Pahwa, Shivani; Gulani, Vikas; Ponsky, Lee; Aronen, Hannu J; Boström, Peter J; Böhm, Maret; Haynes, Anne-Maree; Brenner, Phillip; Delprado, Warick; Thompson, James; Pulbrock, Marley; Taimen, Pekka; Villani, Robert; Stricker, Phillip; Rastinehad, Ardeshir R; Jambor, Ivan; Madabhushi, Anant

2017-07-01

To evaluate in a multi-institutional study whether radiomic features useful for prostate cancer (PCa) detection from 3 Tesla (T) multi-parametric MRI (mpMRI) in the transition zone (TZ) differ from those in the peripheral zone (PZ). 3T mpMRI, including T2-weighted (T2w), apparent diffusion coefficient (ADC) maps, and dynamic contrast-enhanced MRI (DCE-MRI), were retrospectively obtained from 80 patients at three institutions. This study was approved by the institutional review board of each participating institution. First-order statistical, co-occurrence, and wavelet features were extracted from T2w MRI and ADC maps, and contrast kinetic features were extracted from DCE-MRI. Feature selection was performed to identify 10 features for PCa detection in the TZ and PZ, respectively. Two logistic regression classifiers used these features to detect PCa and were evaluated by area under the receiver-operating characteristic curve (AUC). Classifier performance was compared with a zone-ignorant classifier. Radiomic features that were identified as useful for PCa detection differed between TZ and PZ. When classification was performed on a per-voxel basis, a PZ-specific classifier detected PZ tumors on an independent test set with significantly higher accuracy (AUC = 0.61-0.71) than a zone-ignorant classifier trained to detect cancer throughout the entire prostate (P  0.14) were obtained for all institutions. A zone-aware classifier significantly improves the accuracy of cancer detection in the PZ. 3 Technical Efficacy: Stage 2 J. MAGN. RESON. IMAGING 2017;46:184-193. © 2016 International Society for Magnetic Resonance in Medicine.

Toward Optimal Manifold Hashing via Discrete Locally Linear Embedding.

Science.gov (United States)

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.

Embedding beyond electrostatics

DEFF Research Database (Denmark)

Nåbo, Lina J.; Olsen, Jógvan Magnus Haugaard; Holmgaard List, Nanna

2016-01-01

We study excited states of cholesterol in solution and show that, in this specific case, solute wave-function confinement is the main effect of the solvent. This is rationalized on the basis of the polarizable density embedding scheme, which in addition to polarizable embedding includes non-electrostatic...... repulsion that effectively confines the solute wave function to its cavity. We illustrate how the inclusion of non-electrostatic repulsion results in a successful identification of the intense π → π∗ transition, which was not possible using an embedding method that only includes electrostatics....... This underlines the importance of non-electrostatic repulsion in quantum-mechanical embedding-based methods....

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

Science.gov (United States)

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…

The data embedding method

Energy Technology Data Exchange (ETDEWEB)

Sandford, M.T. II; Bradley, J.N.; Handel, T.G.

1996-06-01

Data embedding is a new steganographic method for combining digital information sets. This paper describes the data embedding method and gives examples of its application using software written in the C-programming language. Sandford and Handel produced a computer program (BMPEMBED, Ver. 1.51 written for IBM PC/AT or compatible, MS/DOS Ver. 3.3 or later) that implements data embedding in an application for digital imagery. Information is embedded into, and extracted from, Truecolor or color-pallet images in Microsoft{reg_sign} bitmap (.BMP) format. Hiding data in the noise component of a host, by means of an algorithm that modifies or replaces the noise bits, is termed {open_quote}steganography.{close_quote} Data embedding differs markedly from conventional steganography, because it uses the noise component of the host to insert information with few or no modifications to the host data values or their statistical properties. Consequently, the entropy of the host data is affected little by using data embedding to add information. The data embedding method applies to host data compressed with transform, or {open_quote}lossy{close_quote} compression algorithms, as for example ones based on discrete cosine transform and wavelet functions. Analysis of the host noise generates a key required for embedding and extracting the auxiliary data from the combined data. The key is stored easily in the combined data. Images without the key cannot be processed to extract the embedded information. To provide security for the embedded data, one can remove the key from the combined data and manage it separately. The image key can be encrypted and stored in the combined data or transmitted separately as a ciphertext much smaller in size than the embedded data. The key size is typically ten to one-hundred bytes, and it is in data an analysis algorithm.

Designing a Web Spam Classifier Based on Feature Fusion in the Layered Multi-Population Genetic Programming Framework

Directory of Open Access Journals (Sweden)

Amir Hosein KEYHANIPOUR

2013-11-01

Full Text Available Nowadays, Web spam pages are a critical challenge for Web retrieval systems which have drastic influence on the performance of such systems. Although these systems try to combat the impact of spam pages on their final results list, spammers increasingly use more sophisticated techniques to increase the number of views for their intended pages in order to have more commercial success. This paper employs the recently proposed Layered Multi-population Genetic Programming model for Web spam detection task as well application of correlation coefficient analysis for feature space reduction. Based on our tentative results, the designed classifier, which is based on a combination of easy to compute features, has a very reasonable performance in comparison with similar methods.

Extreme learning machine based optimal embedding location finder for image steganography.

Directory of Open Access Journals (Sweden)

Hayfaa Abdulzahra Atee

Full Text Available 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.

Morphing the feature-based multi-blocks of normative/healthy vertebral geometries to scoliosis vertebral geometries: development of personalized finite element models.

Science.gov (United States)

Hadagali, Prasannaah; Peters, James R; Balasubramanian, Sriram

2018-03-12

Personalized Finite Element (FE) models and hexahedral elements are preferred for biomechanical investigations. Feature-based multi-block methods are used to develop anatomically accurate personalized FE models with hexahedral mesh. It is tedious to manually construct multi-blocks for large number of geometries on an individual basis to develop personalized FE models. Mesh-morphing method mitigates the aforementioned tediousness in meshing personalized geometries every time, but leads to element warping and loss of geometrical data. Such issues increase in magnitude when normative spine FE model is morphed to scoliosis-affected spinal geometry. The only way to bypass the issue of hex-mesh distortion or loss of geometry as a result of morphing is to rely on manually constructing the multi-blocks for scoliosis-affected spine geometry of each individual, which is time intensive. A method to semi-automate the construction of multi-blocks on the geometry of scoliosis vertebrae from the existing multi-blocks of normative vertebrae is demonstrated in this paper. High-quality hexahedral elements were generated on the scoliosis vertebrae from the morphed multi-blocks of normative vertebrae. Time taken was 3 months to construct the multi-blocks for normative spine and less than a day for scoliosis. Efforts taken to construct multi-blocks on personalized scoliosis spinal geometries are significantly reduced by morphing existing multi-blocks.

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

Science.gov (United States)

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

Embedded engineering education

CERN Document Server

Kaštelan, Ivan; Temerinac, Miodrag; Barak, Moshe; Sruk, Vlado

2016-01-01

This book focuses on the outcome of the European research project “FP7-ICT-2011-8 / 317882: Embedded Engineering Learning Platform” E2LP. Additionally, some experiences and researches outside this project have been included. This book provides information about the achieved results of the E2LP project as well as some broader views about the embedded engineering education. It captures project results and applications, methodologies, and evaluations. It leads to the history of computer architectures, brings a touch of the future in education tools and provides a valuable resource for anyone interested in embedded engineering education concepts, experiences and material. The book contents 12 original contributions and will open a broader discussion about the necessary knowledge and appropriate learning methods for the new profile of embedded engineers. As a result, the proposed Embedded Computer Engineering Learning Platform will help to educate a sufficient number of future engineers in Europe, capable of d...

Visual Tracking via Feature Tensor Multimanifold Discriminate Analysis

Directory of Open Access Journals (Sweden)

Ting-quan Deng

2014-01-01

Full Text Available In the visual tracking scenarios, if there are multiple objects, due to the interference of similar objects, tracking may fail in the progress of occlusion to separation. To address this problem, this paper proposed a visual tracking algorithm with discrimination through multimanifold learning. Color-gradient-based feature tensor was used to describe object appearance for accommodation of partial occlusion. A prior multimanifold tensor dataset is established through the template matching tracking algorithm. For the purpose of discrimination, tensor distance was defined to determine the intramanifold and intermanifold neighborhood relationship in multimanifold space. Then multimanifold discriminate analysis was employed to construct multilinear projection matrices of submanifolds. Finally, object states were obtained by combining with sequence inference. Meanwhile, the multimanifold dataset and manifold learning embedded projection should be updated online. Experiments were conducted on two real visual surveillance sequences to evaluate the proposed algorithm with three state-of-the-art tracking methods qualitatively and quantitatively. Experimental results show that the proposed algorithm can achieve effective and robust effect in multi-similar-object mutual occlusion scenarios.

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

Science.gov (United States)

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.

Adaptive GDDA-BLAST: fast and efficient algorithm for protein sequence embedding.

Directory of Open Access Journals (Sweden)

Yoojin Hong

2010-10-01

Full Text Available A major computational challenge in the genomic era is annotating structure/function to the vast quantities of sequence information that is now available. This problem is illustrated by the fact that most proteins lack comprehensive annotations, even when experimental evidence exists. We previously theorized that embedded-alignment profiles (simply "alignment profiles" hereafter provide a quantitative method that is capable of relating the structural and functional properties of proteins, as well as their evolutionary relationships. A key feature of alignment profiles lies in the interoperability of data format (e.g., alignment information, physio-chemical information, genomic information, etc.. Indeed, we have demonstrated that the Position Specific Scoring Matrices (PSSMs are an informative M-dimension that is scored by quantitatively measuring the embedded or unmodified sequence alignments. Moreover, the information obtained from these alignments is informative, and remains so even in the "twilight zone" of sequence similarity (<25% identity. Although our previous embedding strategy was powerful, it suffered from contaminating alignments (embedded AND unmodified and high computational costs. Herein, we describe the logic and algorithmic process for a heuristic embedding strategy named "Adaptive GDDA-BLAST." Adaptive GDDA-BLAST is, on average, up to 19 times faster than, but has similar sensitivity to our previous method. Further, data are provided to demonstrate the benefits of embedded-alignment measurements in terms of detecting structural homology in highly divergent protein sequences and isolating secondary structural elements of transmembrane and ankyrin-repeat domains. Together, these advances allow further exploration of the embedded alignment data space within sufficiently large data sets to eventually induce relevant statistical inferences. We show that sequence embedding could serve as one of the vehicles for measurement of low

Embedded Processor Laboratory

Data.gov (United States)

Federal Laboratory Consortium — The Embedded Processor Laboratory provides the means to design, develop, fabricate, and test embedded computers for missile guidance electronics systems in support...

Multi-Stage Feature Selection by Using Genetic Algorithms for Fault Diagnosis in Gearboxes Based on Vibration Signal

Directory of Open Access Journals (Sweden)

Mariela Cerrada

2015-09-01

Full Text Available There are growing demands for condition-based monitoring of gearboxes, and techniques to improve the reliability, effectiveness and accuracy for fault diagnosis are considered valuable contributions. Feature selection is still an important aspect in machine learning-based diagnosis in order to reach good performance in the diagnosis system. The main aim of this research is to propose a multi-stage feature selection mechanism for selecting the best set of condition parameters on the time, frequency and time-frequency domains, which are extracted from vibration signals for fault diagnosis purposes in gearboxes. The selection is based on genetic algorithms, proposing in each stage a new subset of the best features regarding the classifier performance in a supervised environment. The selected features are augmented at each stage and used as input for a neural network classifier in the next step, while a new subset of feature candidates is treated by the selection process. As a result, the inherent exploration and exploitation of the genetic algorithms for finding the best solutions of the selection problem are locally focused. The Sensors 2015, 15 23904 approach is tested on a dataset from a real test bed with several fault classes under different running conditions of load and velocity. The model performance for diagnosis is over 98%.

Trends in languages for embedded systems

International Nuclear Information System (INIS)

Boasson, M.

1986-01-01

Characteristics of embedded systems are discussed. In particular, the role of the computer in such systems is highlighted. Special emphasis is placed on the different requirements different kinds of systems may place on program execution. From such requirements necessary programming constructs are derived and in an overview of currently used languages it is shown how evolution led to modern languages like Ada and CHILL. With the advent of cheap, fast and small processing units, exploitation of parallelism for enhancing system performance is becoming increasingly tempting. However, few languages support the design of such multi-processor systems. Some methods for dealing with this problem are discussed. Finally, systems architectures and associated languages for the use of techniques originally developed for AI research are adumbrated. (Auth.)

Embedded defects

International Nuclear Information System (INIS)

Barriola, M.; Vachaspati, T.; Bucher, M.

1994-01-01

We give a prescription for embedding classical solutions and, in particular, topological defects in field theories which are invariant under symmetry groups that are not necessarily simple. After providing examples of embedded defects in field theories based on simple groups, we consider the electroweak model and show that it contains the Z string and a one-parameter family of strings called the W(α) string. It is argued that although the members of this family are gauge equivalent when considered in isolation, each member becomes physically distinct when multistring configurations are considered. We then turn to the issue of stability of embedded defects and demonstrate the instability of a large class of such solutions in the absence of bound states or condensates. The Z string is shown to be unstable for all values of the Higgs boson mass when θ W =π/4. W strings are also shown to be unstable for a large range of parameters. Embedded monopoles suffer from the Brandt-Neri-Coleman instability. Finally, we connect the electroweak string solutions to the sphaleron

Laser Cladding of Embedded Sensors for Thermal Barrier Coating Applications

Directory of Open Access Journals (Sweden)

Yanli Zhang

2018-05-01

Full Text Available The accurate real-time monitoring of surface or internal temperatures of thermal barrier coatings (TBCs in hostile environments presents significant benefits to the efficient and safe operation of gas turbines. A new method for fabricating high-temperature K-type thermocouple sensors on gas turbine engines using coaxial laser cladding technology has been developed. The deposition of the thermocouple sensors was optimized to provide minimal intrusive features to the TBC, which is beneficial for the operational reliability of the protective coatings. Notably, this avoids a melt pool on the TBC surface. Sensors were deposited onto standard yttria-stabilized zirconia (7–8 wt % YSZ coated substrates; subsequently, they were embedded with second YSZ layers by the Atmospheric Plasma Spray (APS process. Morphology of cladded thermocouples before and after embedding was optimized in terms of topography and internal homogeneity, respectively. The dimensions of the cladded thermocouple were in the order of 200 microns in thickness and width. The thermal and electrical response of the cladded thermocouple was tested before and after embedding in temperatures ranging from ambient to approximately 450 °C in a furnace. Seebeck coefficients of bared and embedded thermocouples were also calculated correspondingly, and the results were compared to that of a commercial standard K-type thermocouple, which demonstrates that laser cladding is a prospective technology for manufacturing microsensors on the surface of or even embedded into functional coatings.

Intrusion detection model using fusion of chi-square feature selection and multi class SVM

Directory of Open Access Journals (Sweden)

Ikram Sumaiya Thaseen

2017-10-01

Full Text Available Intrusion detection is a promising area of research in the domain of security with the rapid development of internet in everyday life. Many intrusion detection systems (IDS employ a sole classifier algorithm for classifying network traffic as normal or abnormal. Due to the large amount of data, these sole classifier models fail to achieve a high attack detection rate with reduced false alarm rate. However by applying dimensionality reduction, data can be efficiently reduced to an optimal set of attributes without loss of information and then classified accurately using a multi class modeling technique for identifying the different network attacks. In this paper, we propose an intrusion detection model using chi-square feature selection and multi class support vector machine (SVM. A parameter tuning technique is adopted for optimization of Radial Basis Function kernel parameter namely gamma represented by ‘ϒ’ and over fitting constant ‘C’. These are the two important parameters required for the SVM model. The main idea behind this model is to construct a multi class SVM which has not been adopted for IDS so far to decrease the training and testing time and increase the individual classification accuracy of the network attacks. The investigational results on NSL-KDD dataset which is an enhanced version of KDDCup 1999 dataset shows that our proposed approach results in a better detection rate and reduced false alarm rate. An experimentation on the computational time required for training and testing is also carried out for usage in time critical applications.

Polymorphic Embedding of DSLs

DEFF Research Database (Denmark)

Hofer, Christian; Ostermann, Klaus; Rendel, Tillmann

2008-01-01

propose polymorphic embedding of DSLs, where many different interpretations of a DSL can be provided as reusable components, and show how polymorphic embedding can be realized in the programming language Scala. With polymorphic embedding, the static type-safety, modularity, composability and rapid...

Investigating the multi-causal and complex nature of the accident causal influence of construction project features.

Science.gov (United States)

Manu, Patrick A; Ankrah, Nii A; Proverbs, David G; Suresh, Subashini

2012-09-01

Construction project features (CPFs) are organisational, physical and operational attributes that characterise construction projects. Although previous studies have examined the accident causal influence of CPFs, the multi-causal attribute of this causal phenomenon still remain elusive and thus requires further investigation. Aiming to shed light on this facet of the accident causal phenomenon of CPFs, this study examines relevant literature and crystallises the attained insight of the multi-causal attribute by a graphical model which is subsequently operationalised by a derived mathematical risk expression that offers a systematic approach for evaluating the potential of CPFs to cause harm and consequently their health and safety (H&S) risk implications. The graphical model and the risk expression put forth by the study thus advance current understanding of the accident causal phenomenon of CPFs and they present an opportunity for project participants to manage the H&S risk associated with CPFs from the early stages of project procurement. Copyright © 2011 Elsevier Ltd. All rights reserved.

Visual processing in adolescents with autism spectrum disorders: Evidence from embedded figures and configural superiority tests

OpenAIRE

Dillen, Claudia; Steyaert, Jean; Op de Beeck, Hans; Boets, Bart

2015-01-01

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 l...

3D printed fluidics with embedded analytic functionality for automated reaction optimisation

OpenAIRE

Andrew J. Capel; Andrew Wright; Matthew J. Harding; George W. Weaver; Yuqi Li; Russell A. Harris; Steve Edmondson; Ruth D. Goodridge; Steven D. R. Christie

2017-01-01

Additive manufacturing or ‘3D printing’ is being developed as a novel manufacturing process for the production of bespoke micro and milli-scale fluidic devices. When coupled with online monitoring and optimisation software, this offers an advanced, customised method for performing automated chemical synthesis. This paper reports the use of two additive manufacturing processes, stereolithography and selective laser melting, to create multi-functional fluidic devices with embedded reaction moni...

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

Science.gov (United States)

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

Time-dependent embedding

OpenAIRE

Inglesfield, J. E.

2007-01-01

A method of solving the time-dependent Schr\\"odinger equation is presented, in which a finite region of space is treated explicitly, with the boundary conditions for matching the wave-functions on to the rest of the system replaced by an embedding term added on to the Hamiltonian. This time-dependent embedding term is derived from the Fourier transform of the energy-dependent embedding potential, which embeds the time-independent Schr\\"odinger equation. Results are presented for a one-dimensi...

Field tests on partial embedment effects (embedment effect tests on soil-structure interaction)

International Nuclear Information System (INIS)

Kurimoto, O.; Tsunoda, T.; Inoue, T.; Izumi, M.; Kusakabe, K.; Akino, K.

1993-01-01

A series of Model Tests of Embedment Effect on Reactor Buildings has been carried out by the Nuclear Power Engineering Corporation (NUPEC), under the sponsorship of the Ministry of International Trade and lndustry (MITI) of Japan. The nuclear reactor buildings are partially embedded due to conditions for the construction or building arrangement in Japan. It is necessary to verify the partial embedment effects by experiments and analytical studies in order to incorporate the effects in the seismic design. Forced vibration tests, therefore, were performed using a model with several types of embedment. Correlated simulation analyses were also performed and the characteristics of partial embedment effects on soil-structure interaction were evaluated. (author)

Embedded Systems Design: Optimization Challenges

DEFF Research Database (Denmark)

Pop, Paul

2005-01-01

Summary form only given. Embedded systems are everywhere: from alarm clocks to PDAs, from mobile phones to cars, almost all the devices we use are controlled by embedded systems. Over 99% of the microprocessors produced today are used in embedded systems, and recently the number of embedded systems...

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

Science.gov (United States)

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

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

Science.gov (United States)

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.

Multi-tasking and Arduino : why and how?

NARCIS (Netherlands)

Feijs, L.M.G.; Chen, L.L.; Djajadiningrat, T.; Feijs, L.M.G.; Fraser, S.; Hu, J.; Kyffin, S.; Steffen, D.

2013-01-01

In this article I argue that it is important to develop experiential prototypes which have multi-tasking capabilities. At the same time I show that for embedded prototype software based on the popular Arduino platform this is not too difficult. The approach is explained and illustrated using

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

Science.gov (United States)

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…

Classification of Urban Feature from Unmanned Aerial Vehicle Images Using Gasvm Integration and Multi-Scale Segmentation

Science.gov (United States)

Modiri, M.; Salehabadi, A.; Mohebbi, M.; Hashemi, A. M.; Masumi, M.

2015-12-01

The use of UAV in the application of photogrammetry to obtain cover images and achieve the main objectives of the photogrammetric mapping has been a boom in the region. The images taken from REGGIOLO region in the province of, Italy Reggio -Emilia by UAV with non-metric camera Canon Ixus and with an average height of 139.42 meters were used to classify urban feature. Using the software provided SURE and cover images of the study area, to produce dense point cloud, DSM and Artvqvtv spatial resolution of 10 cm was prepared. DTM area using Adaptive TIN filtering algorithm was developed. NDSM area was prepared with using the difference between DSM and DTM and a separate features in the image stack. In order to extract features, using simultaneous occurrence matrix features mean, variance, homogeneity, contrast, dissimilarity, entropy, second moment, and correlation for each of the RGB band image was used Orthophoto area. Classes used to classify urban problems, including buildings, trees and tall vegetation, grass and vegetation short, paved road and is impervious surfaces. Class consists of impervious surfaces such as pavement conditions, the cement, the car, the roof is stored. In order to pixel-based classification and selection of optimal features of classification was GASVM pixel basis. In order to achieve the classification results with higher accuracy and spectral composition informations, texture, and shape conceptual image featureOrthophoto area was fencing. The segmentation of multi-scale segmentation method was used.it belonged class. Search results using the proposed classification of urban feature, suggests the suitability of this method of classification complications UAV is a city using images. The overall accuracy and kappa coefficient method proposed in this study, respectively, 47/93% and 84/91% was.

An optimal set of features for predicting type IV secretion system effector proteins for a subset of species based on a multi-level feature selection approach.

Directory of Open Access Journals (Sweden)

Zhila Esna Ashari

Full Text Available Type IV secretion systems (T4SS are multi-protein complexes in a number of bacterial pathogens that can translocate proteins and DNA to the host. Most T4SSs function in conjugation and translocate DNA; however, approximately 13% function to secrete proteins, delivering effector proteins into the cytosol of eukaryotic host cells. Upon entry, these effectors manipulate the host cell's machinery for their own benefit, which can result in serious illness or death of the host. For this reason recognition of T4SS effectors has become an important subject. Much previous work has focused on verifying effectors experimentally, a costly endeavor in terms of money, time, and effort. Having good predictions for effectors will help to focus experimental validations and decrease testing costs. In recent years, several scoring and machine learning-based methods have been suggested for the purpose of predicting T4SS effector proteins. These methods have used different sets of features for prediction, and their predictions have been inconsistent. In this paper, an optimal set of features is presented for predicting T4SS effector proteins using a statistical approach. A thorough literature search was performed to find features that have been proposed. Feature values were calculated for datasets of known effectors and non-effectors for T4SS-containing pathogens for four genera with a sufficient number of known effectors, Legionella pneumophila, Coxiella burnetii, Brucella spp, and Bartonella spp. The features were ranked, and less important features were filtered out. Correlations between remaining features were removed, and dimensional reduction was accomplished using principal component analysis and factor analysis. Finally, the optimal features for each pathogen were chosen by building logistic regression models and evaluating each model. The results based on evaluation of our logistic regression models confirm the effectiveness of our four optimal sets of

Integrated development environment for multi-core systems

Directory of Open Access Journals (Sweden)

Krunić Momčilo V.

2014-01-01

Full Text Available Development of the software application that provides comfortable working environment of embedded software applications was always a difficult task to achieve. To reach this goal it was necessary to integrate all specific tools designed for that purpose. This paper describes Integrated Development Environment (IDE that was developed to meet all specific needs of a software development for the family of multi-core target platforms designed for a digital signal processing in Cirrus Logic Company. Eclipse platform and RCP (Rich Client Platform was used as a basis, because it provides an extensible plug-in system for customizing the development environment. CLIDE (Cirrus Logic Integrated Development Environment represent the epilog of that effort, reliable IDE used for development of embedded applications. Validation of the solution is accomplished thru 2641 J Unit tests that validate most of the CLIDE's functionalities. Developed IDE (CLIDE significantly increases a quality of a software development for multi-core systems and reduces time-to-market, thereby justifying development costs.

Extraction of multi-scale landslide morphological features based on local Gi* using airborne LiDAR-derived DEM

Science.gov (United States)

Shi, Wenzhong; Deng, Susu; Xu, Wenbing

2018-02-01

For automatic landslide detection, landslide morphological features should be quantitatively expressed and extracted. High-resolution Digital Elevation Models (DEMs) derived from airborne Light Detection and Ranging (LiDAR) data allow fine-scale morphological features to be extracted, but noise in DEMs influences morphological feature extraction, and the multi-scale nature of landslide features should be considered. This paper proposes a method to extract landslide morphological features characterized by homogeneous spatial patterns. Both profile and tangential curvature are utilized to quantify land surface morphology, and a local Gi* statistic is calculated for each cell to identify significant patterns of clustering of similar morphometric values. The method was tested on both synthetic surfaces simulating natural terrain and airborne LiDAR data acquired over an area dominated by shallow debris slides and flows. The test results of the synthetic data indicate that the concave and convex morphologies of the simulated terrain features at different scales and distinctness could be recognized using the proposed method, even when random noise was added to the synthetic data. In the test area, cells with large local Gi* values were extracted at a specified significance level from the profile and the tangential curvature image generated from the LiDAR-derived 1-m DEM. The morphologies of landslide main scarps, source areas and trails were clearly indicated, and the morphological features were represented by clusters of extracted cells. A comparison with the morphological feature extraction method based on curvature thresholds proved the proposed method's robustness to DEM noise. When verified against a landslide inventory, the morphological features of almost all recent (historical (> 10 years) landslides were extracted. This finding indicates that the proposed method can facilitate landslide detection, although the cell clusters extracted from curvature images should

Enhanced performance of microfluidic soft pressure sensors with embedded solid microspheres

Science.gov (United States)

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.

Improving developer productivity with C++ embedded domain specific languages

Science.gov (United States)

Kozacik, Stephen; Chao, Evenie; Paolini, Aaron; Bonnett, James; Kelmelis, Eric

2017-05-01

Domain-specific languages are a useful tool for productivity allowing domain experts to program using familiar concepts and vocabulary while benefiting from performance choices made by computing experts. Embedding the domain specific language into an existing language allows easy interoperability with non-domain-specific code and use of standard compilers and build systems. In C++, this is enabled through the template and preprocessor features. C++ embedded domain specific languages (EDSLs) allow the user to write simple, safe, performant, domain specific code that has access to all the low-level functionality that C and C++ offer as well as the diverse set of libraries available in the C/C++ ecosystem. In this paper, we will discuss several tools available for building EDSLs in C++ and show examples of projects successfully leveraging EDSLs. Modern C++ has added many useful new features to the language which we have leveraged to further extend the capability of EDSLs. At EM Photonics, we have used EDSLs to allow developers to transparently benefit from using high performance computing (HPC) hardware. We will show ways EDSLs combine with existing technologies and EM Photonics high performance tools and libraries to produce clean, short, high performance code in ways that were not previously possible.

A simplified analysis of dynamic interaction between soil and embedded structure

International Nuclear Information System (INIS)

Shimomura, Y.; Ikeda, Y.

1993-01-01

The simplified method of obtaining interaction stiffnesses associated with the embedded part of structures has been proposed in our previous paper. In this method, the stiffnesses are considered for three directional components, that is, lateral axis, shear and rotation, which relate to the side surface of an embedded structure. Novak et al. have derived the laterally axial and rotational stiffnesses based on the plane strain approximation for a horizontal soil layer. For practical purpose, we have given the approximate expression of the soil stiffnesses. The paper aims to capture the applicability of the approximate expression of the frequency dependent stiffnesses. The validity of approximate expression of the soil stiffnesses is discussed by their frequency dependency and the dynamic earth pressures, and the application to a multi-layered soil. The results from this proposed method are compared with the more exact results from the original 3-dimensional thin layer approach. (author)

Multi-view Multi-sparsity Kernel Reconstruction for Multi-class Image Classification

KAUST Repository

Zhu, Xiaofeng

2015-05-28

This paper addresses the problem of multi-class image classification by proposing a novel multi-view multi-sparsity kernel reconstruction (MMKR for short) model. Given images (including test images and training images) representing with multiple visual features, the MMKR first maps them into a high-dimensional space, e.g., a reproducing kernel Hilbert space (RKHS), where test images are then linearly reconstructed by some representative training images, rather than all of them. Furthermore a classification rule is proposed to classify test images. Experimental results on real datasets show the effectiveness of the proposed MMKR while comparing to state-of-the-art algorithms.

Embedded software verification and debugging

CERN Document Server

Winterholer, Markus

2017-01-01

This book provides comprehensive coverage of verification and debugging techniques for embedded software, which is frequently used in safety critical applications (e.g., automotive), where failures are unacceptable. Since the verification of complex systems needs to encompass the verification of both hardware and embedded software modules, this book focuses on verification and debugging approaches for embedded software with hardware dependencies. Coverage includes the entire flow of design, verification and debugging of embedded software and all key approaches to debugging, dynamic, static, and hybrid verification. This book discusses the current, industrial embedded software verification flow, as well as emerging trends with focus on formal and hybrid verification and debugging approaches. Includes in a single source the entire flow of design, verification and debugging of embedded software; Addresses the main techniques that are currently being used in the industry for assuring the quality of embedded softw...

Consensus embedding: theory, algorithms and application to segmentation and classification of biomedical data

Directory of Open Access Journals (Sweden)

Viswanath Satish

2012-02-01

Full Text Available Abstract 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

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

Science.gov (United States)

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.

Isometric embeddings of 2-spheres by embedding flow for applications in numerical relativity

International Nuclear Information System (INIS)

Jasiulek, Michael; Korzyński, Mikołaj

2012-01-01

We present a numerical method for solving Weyl's embedding problem which consists in finding a global isometric embedding of a positively curved and positive-definite spherical 2-metric into the Euclidean 3-space. The method is based on a construction introduced by Weingarten and was used in Nirenberg's proof of Weyl's conjecture. The target embedding results as the endpoint of an embedding flow in R 3 beginning at the unit sphere's embedding. We employ spectral methods to handle functions on the surface and to solve various (non)linear elliptic PDEs. The code requires no additional input or steering from the operator and its convergence is guaranteed by the Nirenberg arguments. Possible applications in 3 + 1 numerical relativity range from quasi-local mass and momentum measures to coarse-graining in inhomogeneous cosmological models. (paper)

Multi-source feature extraction and target recognition in wireless sensor networks based on adaptive distributed wavelet compression algorithms

Science.gov (United States)

Hortos, William S.

2008-04-01

Proposed distributed wavelet-based algorithms are a means to compress sensor data received at the nodes forming a wireless sensor network (WSN) by exchanging information between neighboring sensor nodes. Local collaboration among nodes compacts the measurements, yielding a reduced fused set with equivalent information at far fewer nodes. Nodes may be equipped with multiple sensor types, each capable of sensing distinct phenomena: thermal, humidity, chemical, voltage, or image signals with low or no frequency content as well as audio, seismic or video signals within defined frequency ranges. Compression of the multi-source data through wavelet-based methods, distributed at active nodes, reduces downstream processing and storage requirements along the paths to sink nodes; it also enables noise suppression and more energy-efficient query routing within the WSN. Targets are first detected by the multiple sensors; then wavelet compression and data fusion are applied to the target returns, followed by feature extraction from the reduced data; feature data are input to target recognition/classification routines; targets are tracked during their sojourns through the area monitored by the WSN. Algorithms to perform these tasks are implemented in a distributed manner, based on a partition of the WSN into clusters of nodes. In this work, a scheme of collaborative processing is applied for hierarchical data aggregation and decorrelation, based on the sensor data itself and any redundant information, enabled by a distributed, in-cluster wavelet transform with lifting that allows multiple levels of resolution. The wavelet-based compression algorithm significantly decreases RF bandwidth and other resource use in target processing tasks. Following wavelet compression, features are extracted. The objective of feature extraction is to maximize the probabilities of correct target classification based on multi-source sensor measurements, while minimizing the resource expenditures at

An Architectural Style for Optimizing System Qualities in Adaptive Embedded Systems using Multi-Objective Optimization

NARCIS (Netherlands)

de Roo, Arjan; Sözer, Hasan; Aksit, Mehmet

Customers of today's complex embedded systems demand the optimization of multiple system qualities under varying operational conditions. To be able to influence the system qualities, the system must have parameters that can be adapted. Constraints may be defined on the value of these parameters.

An enhanced PSO-DEFS based feature selection with biometric authentication for identification of diabetic retinopathy

Directory of Open Access Journals (Sweden)

Umarani Balakrishnan

2016-11-01

Full Text Available Recently, automatic diagnosis of diabetic retinopathy (DR from the retinal image is the most significant research topic in the medical applications. Diabetic macular edema (DME is the major reason for the loss of vision in patients suffering from DR. Early identification of the DR enables to prevent the vision loss and encourage diabetic control activities. Many techniques are developed to diagnose the DR. The major drawbacks of the existing techniques are low accuracy and high time complexity. To overcome these issues, this paper proposes an enhanced particle swarm optimization-differential evolution feature selection (PSO-DEFS based feature selection approach with biometric authentication for the identification of DR. Initially, a hybrid median filter (HMF is used for pre-processing the input images. Then, the pre-processed images are embedded with each other by using least significant bit (LSB for authentication purpose. Simultaneously, the image features are extracted using convoluted local tetra pattern (CLTrP and Tamura features. Feature selection is performed using PSO-DEFS and PSO-gravitational search algorithm (PSO-GSA to reduce time complexity. Based on some performance metrics, the PSO-DEFS is chosen as a better choice for feature selection. The feature selection is performed based on the fitness value. A multi-relevance vector machine (M-RVM is introduced to classify the 13 normal and 62 abnormal images among 75 images from 60 patients. Finally, the DR patients are further classified by M-RVM. The experimental results exhibit that the proposed approach achieves better accuracy, sensitivity, and specificity than the existing techniques.

the syntax of multi-word expressions in yorulish code-mixing

African Journals Online (AJOL)

Dr. Obadele Kambon

posively sampled from standard dictionaries and textbooks on English and ... Multi-word expressions (MWEs) are words that are usually collocated every- .... The expressions with deep/embedded meaning as opposed to those with literal ...

A Self-embedding Robust Digital Watermarking Algorithm with Blind Detection

Directory of Open Access Journals (Sweden)

Gong Yunfeng

2014-08-01

Full Text Available In order to achieve the perfectly blind detection of robustness watermarking algorithm, a novel self-embedding robust digital watermarking algorithm with blind detection is proposed in this paper. Firstly the original image is divided to not overlap image blocks and then decomposable coefficients are obtained by lifting-based wavelet transform in every image blocks. Secondly the low-frequency coefficients of block images are selected and then approximately represented as a product of a base matrix and a coefficient matrix using NMF. Then the feature vector represent original image is obtained by quantizing coefficient matrix, and finally the adaptive quantization of the robustness watermark is embedded in the low-frequency coefficients of LWT. Experimental results show that the scheme is robust against common signal processing attacks, meanwhile perfect blind detection is achieve.

Developing a multimodal biometric authentication system using soft computing methods.

Science.gov (United States)

Malcangi, Mario

2015-01-01

Robust personal authentication is becoming ever more important in computer-based applications. Among a variety of methods, biometric offers several advantages, mainly in embedded system applications. Hard and soft multi-biometric, combined with hard and soft computing methods, can be applied to improve the personal authentication process and to generalize the applicability. This chapter describes the embedded implementation of a multi-biometric (voiceprint and fingerprint) multimodal identification system based on hard computing methods (DSP) for feature extraction and matching, an artificial neural network (ANN) for soft feature pattern matching, and a fuzzy logic engine (FLE) for data fusion and decision.

Embedded security system for multi-modal surveillance in a railway carriage

Science.gov (United States)

Zouaoui, Rhalem; Audigier, Romaric; Ambellouis, Sébastien; Capman, François; Benhadda, Hamid; Joudrier, Stéphanie; Sodoyer, David; Lamarque, Thierry

2015-10-01

Public transport security is one of the main priorities of the public authorities when fighting against crime and terrorism. In this context, there is a great demand for autonomous systems able to detect abnormal events such as violent acts aboard passenger cars and intrusions when the train is parked at the depot. To this end, we present an innovative approach which aims at providing efficient automatic event detection by fusing video and audio analytics and reducing the false alarm rate compared to classical stand-alone video detection. The multi-modal system is composed of two microphones and one camera and integrates onboard video and audio analytics and fusion capabilities. On the one hand, for detecting intrusion, the system relies on the fusion of "unusual" audio events detection with intrusion detections from video processing. The audio analysis consists in modeling the normal ambience and detecting deviation from the trained models during testing. This unsupervised approach is based on clustering of automatically extracted segments of acoustic features and statistical Gaussian Mixture Model (GMM) modeling of each cluster. The intrusion detection is based on the three-dimensional (3D) detection and tracking of individuals in the videos. On the other hand, for violent events detection, the system fuses unsupervised and supervised audio algorithms with video event detection. The supervised audio technique detects specific events such as shouts. A GMM is used to catch the formant structure of a shout signal. Video analytics use an original approach for detecting aggressive motion by focusing on erratic motion patterns specific to violent events. As data with violent events is not easily available, a normality model with structured motions from non-violent videos is learned for one-class classification. A fusion algorithm based on Dempster-Shafer's theory analyses the asynchronous detection outputs and computes the degree of belief of each probable event.

Modelling and simulation of multi-phase effects on X-ray elasticity constants

CERN Document Server

Freour, S; Guillen, R; François, M X

2003-01-01

This paper deals with the calculation of X-ray Elasticity Constants (XEC) of phases embedded in multi-phase polycrystals. A three scales (macroscopic, pseudo-macroscopic, mesoscopic) model based on the classical self-consistent formalism is developed in order to analyse multi-phase effects on XEC values. Simulations are performed for cubic or hexagonal crystallographic structure phases embedded in several two-phases materials. In fact, it is demonstrated that XEC vary with the macroscopic stiffness of the whole polycrystal. In consequence, the constants of one particular phase depend on the elastic behaviour and the volume fraction of all the phases constituting the material. Now, XEC play a leading role in pseudo-macroscopic stresses determination by X-Ray Diffraction (XRD) methods. In this work, a quantitative analysis of the multi-phase effects on stresses determination by XRD methods was performed. Numerical results will be compared and discussed. (Abstract Copyright [2003], Wiley Periodicals, Inc.)

CT and MR imaging features of mucinous tubular and spindle cell carcinoma of the kidneys. A multi-institutional review

Energy Technology Data Exchange (ETDEWEB)

Cornelis, F.; Grenier, N. [Pellegrin Hospital, Department of Radiology, Bordeaux (France); Ambrosetti, D. [Pasteur Hospital, Department of Pathology, Nice (France); Rocher, L. [Kremlin-Bicetre Hospital, Department of Radiology, Paris (France); Derchi, L.E. [University of Genoa, IRCCS AOU Ospedale, San Martino IST, Department of Health Sciences (DISSAL), Genoa (Italy); Renard, B.; Puech, P. [Claude Huriez Hospital, Department of Radiology, Lille (France); Claudon, M. [Brabois Hospital, Department of Radiology, Vandoeuvre-les-Nancy (France); Rouviere, O. [E. Herriot Hospital, Department of Radiology, Lyon (France); Ferlicot, S. [Kremlin-Bicetre Hospital, Department of Pathology, Paris (France); Roy, C. [Civil Hospital, Department of Radiology, Strasbourg (France); Yacoub, M. [Pellegrin Hospital, Department of Pathology, Bordeaux (France); Bernhard, J.C. [Pellegrin Hospital, Department of Urologic Surgery, Bordeaux (France)

2017-03-15

Mucinous tubular and spindle cell carcinoma (MTSCC) of the kidney is a recently identified renal malignancy. Diagnosis of this rare subtype of renal tumour can be challenging for pathologists, and as such, any additional data would be helpful to improve diagnostic reliability. As imaging features of this new and rare sub-type have not yet been clearly described, the purpose of this study was to describe the main radiologic features on computed tomography (CT) and magnetic resonance imaging (MRI), based jointly on the literature and findings from a multi-institutional retrospective review of pathology and imaging databases. Using a combination of CT/MRI features, diagnosis of MTSCC could be suggested in many cases. A combination of slow enhancement with plateau on dynamic contrast-enhanced CT/MRI, intermediate to high T2 signal intensity contrasting with low apparent diffusion coefficient values on MRI appeared evocative of this diagnosis. (orig.)

A feature extraction algorithm based on corner and spots in self-driving vehicles

Directory of Open Access Journals (Sweden)

Yupeng FENG

2017-06-01

Full Text Available To solve the poor real-time performance problem of the visual odometry based on embedded system with limited computing resources, an image matching method based on Harris and SIFT is proposed, namely the Harris-SIFT algorithm. On the basis of the review of SIFT algorithm, the principle of Harris-SIFT algorithm is provided. First, Harris algorithm is used to extract the corners of the image as candidate feature points, and scale invariant feature transform (SIFT features are extracted from those candidate feature points. At last, through an example, the algorithm is simulated by Matlab, then the complexity and other performance of the algorithm are analyzed. The experimental results show that the proposed method reduces the computational complexity and improves the speed of feature extraction. Harris-SIFT algorithm can be used in the real-time vision odometer system, and will bring about a wide application of visual odometry in embedded navigation system.

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

Science.gov (United States)

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.

WIMS/PANTHER analysis of UO2/MOX cores using embedded super-cells

International Nuclear Information System (INIS)

Knight, M.; Bryce, P.; Hall, S.

2012-01-01

This paper describes a method of analysing PWR UO 2 MOX cores with WIMS/PANTHER. Embedded super-cells, run within the reactor code, are used to correct the standard methodology of using 2-group smeared data from single assembly lattice calculations. In many other codes the weakness of this standard approach has been improved for MOX by imposing a more realistic environment in the lattice code, or by improving the sophistication of the reactor code. In this approach an intermediate set of calculations is introduced, leaving both lattice and reactor calculations broadly unchanged. The essence of the approach is that the whole core is broken down into a set of 'embedded' super-cells, each extending over just four quarter assemblies, with zero leakage imposed at the assembly mid-lines. Each supercell is solved twice, first with a detailed multi-group pin-by-pin solution, and then with the standard single assembly approach. Correction factors are defined by comparing the two solutions, and these can be applied in whole core calculations. The restriction that all such calculations are modelled with zero leakage means that they are independent of each other and of the core-wide flux shape. This allows parallel pre-calculation for the entire cycle once the loading pattern has been determined, in much the same way that single assembly lattice calculations can be pre-calculated once the range of fuel types is known. Comparisons against a whole core pin-by-pin reference demonstrates that the embedding process does not introduce a significant error, even after burnup and refuelling. Comparisons against a WIMS reference demonstrate that a pin-by-pin multi-group diffusion solution is capable of capturing the main interface effects. This therefore defines a practical approach for achieving results close to lattice code accuracy, but broadly at the cost of a standard reactor calculation. (authors)

Crack growth monitoring in composite materials using embedded optical Fiber Bragg Grating sensor

DEFF Research Database (Denmark)

Pereira, Gilmar Ferreira; Mikkelsen, Lars Pilgaard; McGugan, Malcolm

2015-01-01

In this paper a novel method to assess a crack growing/damage event in fiber reinforced plastic, or adhesive using Fiber Bragg Grating (FBG) sensors embedded in a host material is shown. Different features of the crack mechanism that induce a change in the FBG response were identified. Double...

CVXPY: A Python-Embedded Modeling Language for Convex Optimization

OpenAIRE

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.

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

Science.gov (United States)

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.

Measuring Performance of Soft Real-Time Tasks on Multi-core Systems

OpenAIRE

Rafiq, Salman

2011-01-01

Multi-core platforms are well established, and they are slowly moving into the area of embedded and real-time systems. Nowadays to take advantage of multi-core systems in terms of throughput, soft real-time applications are run together with general purpose applications under an operating system such as Linux. But due to shared hardware resources in multi-core architectures, it is likely that these applications will interfere and compete with each other. This can cause slower response times f...

A real-time spike sorting method based on the embedded GPU.

Science.gov (United States)

Zelan Yang; Kedi Xu; Xiang Tian; Shaomin Zhang; Xiaoxiang Zheng

2017-07-01

Microelectrode arrays with hundreds of channels have been widely used to acquire neuron population signals in neuroscience studies. Online spike sorting is becoming one of the most important challenges for high-throughput neural signal acquisition systems. Graphic processing unit (GPU) with high parallel computing capability might provide an alternative solution for increasing real-time computational demands on spike sorting. This study reported a method of real-time spike sorting through computing unified device architecture (CUDA) which was implemented on an embedded GPU (NVIDIA JETSON Tegra K1, TK1). The sorting approach is based on the principal component analysis (PCA) and K-means. By analyzing the parallelism of each process, the method was further optimized in the thread memory model of GPU. Our results showed that the GPU-based classifier on TK1 is 37.92 times faster than the MATLAB-based classifier on PC while their accuracies were the same with each other. The high-performance computing features of embedded GPU demonstrated in our studies suggested that the embedded GPU provide a promising platform for the real-time neural signal processing.

Spectral embedding-based registration (SERg) for multimodal fusion of prostate histology and MRI

Science.gov (United States)

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

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

Science.gov (United States)

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…

A 3D Printing Model Watermarking Algorithm Based on 3D Slicing and Feature Points

Directory of Open Access Journals (Sweden)

Giao N. Pham

2018-02-01

Full Text Available With the increase of three-dimensional (3D printing applications in many areas of life, a large amount of 3D printing data is copied, shared, and used several times without any permission from the original providers. Therefore, copyright protection and ownership identification for 3D printing data in communications or commercial transactions are practical issues. This paper presents a novel watermarking algorithm for 3D printing models based on embedding watermark data into the feature points of a 3D printing model. Feature points are determined and computed by the 3D slicing process along the Z axis of a 3D printing model. The watermark data is embedded into a feature point of a 3D printing model by changing the vector length of the feature point in OXY space based on the reference length. The x and y coordinates of the feature point will be then changed according to the changed vector length that has been embedded with a watermark. Experimental results verified that the proposed algorithm is invisible and robust to geometric attacks, such as rotation, scaling, and translation. The proposed algorithm provides a better method than the conventional works, and the accuracy of the proposed algorithm is much higher than previous methods.

Implementing eco friendly highly reliable upload feature using multi 3G service

Science.gov (United States)

Tanutama, Lukas; Wijaya, Rico

2017-12-01

The current trend of eco friendly Internet access is preferred. In this research the understanding of eco friendly is minimum power consumption. The devices that are selected have operationally low power consumption and normally have no power consumption as they are hibernating during idle state. To have the reliability a router of a router that has internal load balancing feature will provide the improvement of previous research on multi 3G services for broadband lines. Previous studies emphasized on accessing and downloading information files from Public Cloud residing Web Servers. The demand is not only for speed but high reliability of access as well. High reliability will mean mitigating both direct and indirect high cost due to repeated attempts of uploading and downloading the large files. Nomadic and mobile computer users need viable solution. Following solution for downloading information has been proposed and tested. The solution is promising. The result is now extended to providing reliable access line by means of redundancy and automatic reconfiguration for uploading and downloading large information files to a Web Server in the Cloud. The technique is taking advantage of internal load balancing feature to provision a redundant line acting as a backup line. A router that has the ability to provide load balancing to several WAN lines is chosen. The WAN lines are constructed using multiple 3G lines. The router supports the accessing Internet with more than one 3G access line which increases the reliability and availability of the Internet access as the second line immediately takes over if the first line is disturbed.

A fuzzy feature fusion method for auto-segmentation of gliomas with multi-modality diffusion and perfusion magnetic resonance images in radiotherapy.

Science.gov (United States)

Guo, Lu; Wang, Ping; Sun, Ranran; Yang, Chengwen; Zhang, Ning; Guo, Yu; Feng, Yuanming

2018-02-19

The diffusion and perfusion magnetic resonance (MR) images can provide functional information about tumour and enable more sensitive detection of the tumour extent. We aimed to develop a fuzzy feature fusion method for auto-segmentation of gliomas in radiotherapy planning using multi-parametric functional MR images including apparent diffusion coefficient (ADC), fractional anisotropy (FA) and relative cerebral blood volume (rCBV). For each functional modality, one histogram-based fuzzy model was created to transform image volume into a fuzzy feature space. Based on the fuzzy fusion result of the three fuzzy feature spaces, regions with high possibility belonging to tumour were generated automatically. The auto-segmentations of tumour in structural MR images were added in final auto-segmented gross tumour volume (GTV). For evaluation, one radiation oncologist delineated GTVs for nine patients with all modalities. Comparisons between manually delineated and auto-segmented GTVs showed that, the mean volume difference was 8.69% (±5.62%); the mean Dice's similarity coefficient (DSC) was 0.88 (±0.02); the mean sensitivity and specificity of auto-segmentation was 0.87 (±0.04) and 0.98 (±0.01) respectively. High accuracy and efficiency can be achieved with the new method, which shows potential of utilizing functional multi-parametric MR images for target definition in precision radiation treatment planning for patients with gliomas.

Stego Keys Performance on Feature Based Coding Method in Text Domain

Directory of Open Access Journals (Sweden)

Din Roshidi

2017-01-01

Full Text Available A main critical factor on embedding process in any text steganography method is a key used known as stego key. This factor will be influenced the success of the embedding process of text steganography method to hide a message from third party or any adversary. One of the important aspects on embedding process in text steganography method is the fitness performance of the stego key. Three parameters of the fitness performance of the stego key have been identified such as capacity ratio, embedded fitness ratio and saving space ratio. It is because a better as capacity ratio, embedded fitness ratio and saving space ratio offers of any stego key; a more message can be hidden. Therefore, main objective of this paper is to analyze three features coding based namely CALP, VERT and QUAD of stego keys in text steganography on their capacity ratio, embedded fitness ratio and saving space ratio. It is found that CALP method give a good effort performance compared to VERT and QUAD methods.

Mechanistic Features of Nanodiamonds in the Lapping of Magnetic Heads

Directory of Open Access Journals (Sweden)

Xionghua Jiang

2014-01-01

Full Text Available Nanodiamonds, which are the main components of slurry in the precision lapping process of magnetic heads, play an important role in surface quality. This paper studies the mechanistic features of nanodiamond embedment into a Sn plate in the lapping process. This is the first study to develop mathematical models for nanodiamond embedment. Such models can predict the optimum parameters for particle embedment. From the modeling calculations, the embedded pressure satisfies p0=3/2·W/πa2 and the indentation depth satisfies δ=k1P/HV. Calculation results reveal that the largest embedded pressure is 731.48 GPa and the critical indentation depth δ is 7 nm. Atomic force microscopy (AFM, scanning electron microscopy (SEM, and Auger electron spectroscopy (AES were used to carry out surface quality detection and analysis of the disk head. Both the formation of black spots on the surface and the removal rate have an important correlation with the size of nanodiamonds. The results demonstrate that an improved removal rate (21 nm·min−1 can be obtained with 100 nm diamonds embedded in the plate.

Mechanistic features of nanodiamonds in the lapping of magnetic heads.

Science.gov (United States)

Jiang, Xionghua; Chen, Zhenxing; Wolfram, Joy; Yang, Zhizhou

2014-01-01

Nanodiamonds, which are the main components of slurry in the precision lapping process of magnetic heads, play an important role in surface quality. This paper studies the mechanistic features of nanodiamond embedment into a Sn plate in the lapping process. This is the first study to develop mathematical models for nanodiamond embedment. Such models can predict the optimum parameters for particle embedment. From the modeling calculations, the embedded pressure satisfies p 0 = (3/2) · (W/πa (2)) and the indentation depth satisfies δ = k1√P/HV. Calculation results reveal that the largest embedded pressure is 731.48 GPa and the critical indentation depth δ is 7 nm. Atomic force microscopy (AFM), scanning electron microscopy (SEM), and Auger electron spectroscopy (AES) were used to carry out surface quality detection and analysis of the disk head. Both the formation of black spots on the surface and the removal rate have an important correlation with the size of nanodiamonds. The results demonstrate that an improved removal rate (21 nm · min(-1)) can be obtained with 100 nm diamonds embedded in the plate.

Controllable edge feature sharpening for dental applications.

Science.gov (United States)

Fan, Ran; Jin, Xiaogang

2014-01-01

This paper presents a new approach to sharpen blurred edge features in scanned tooth preparation surfaces generated by structured-light scanners. It aims to efficiently enhance the edge features so that the embedded feature lines can be easily identified in dental CAD systems, and to avoid unnatural oversharpening geometry. We first separate the feature regions using graph-cut segmentation, which does not require a user-defined threshold. Then, we filter the face normal vectors to propagate the geometry from the smooth region to the feature region. In order to control the degree of the sharpness, we propose a feature distance measure which is based on normal tensor voting. Finally, the vertex positions are updated according to the modified face normal vectors. We have applied the approach to scanned tooth preparation models. The results show that the blurred edge features are enhanced without unnatural oversharpening geometry.

Controllable Edge Feature Sharpening for Dental Applications

Directory of Open Access Journals (Sweden)

Ran Fan

2014-01-01

Full Text Available This paper presents a new approach to sharpen blurred edge features in scanned tooth preparation surfaces generated by structured-light scanners. It aims to efficiently enhance the edge features so that the embedded feature lines can be easily identified in dental CAD systems, and to avoid unnatural oversharpening geometry. We first separate the feature regions using graph-cut segmentation, which does not require a user-defined threshold. Then, we filter the face normal vectors to propagate the geometry from the smooth region to the feature region. In order to control the degree of the sharpness, we propose a feature distance measure which is based on normal tensor voting. Finally, the vertex positions are updated according to the modified face normal vectors. We have applied the approach to scanned tooth preparation models. The results show that the blurred edge features are enhanced without unnatural oversharpening geometry.

Multi-bi- and tri-stability using nonlinear plasmonic Fano resonators

KAUST Repository

Amin, Muhammad

2013-09-01

A plasmonic Fano resonator embedding Kerr nonlinearity is used to achieve multi-bi- and tri-stability. Fano resonance is obtained by inducing higher-order plasmon modes on metallic surfaces via geometrical symmetry breaking. The presence of the multiple higher order plasmon modes provides the means for producing multi-bi- or tri-stability in the response of the resonator when it is loaded with a material with Kerr nonlinearity. The multi-stability in the response of the proposed resonator enables its use in three-state all optical memory and switching applications. © 2013 IEEE.

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

Science.gov (United States)

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…

Exterior orientation of CBERS-2B imagery using multi-feature control and orbital data

Science.gov (United States)

Marcato Junior, J.; Tommaselli, A. M. G.

2013-05-01

The major contribution of this paper relates to the practical advantages of combining Ground Control Points (GCPs), Ground Control Lines (GCLs) and orbital data to estimate the exterior orientation parameters of images collected by CBERS-2B (China-Brazil Earth Resources Satellite) HRC (High-resolution Camera) and CCD (High-resolution CCD Camera) sensors. Although the CBERS-2B is no longer operational, its images are still being used in Brazil, and the next generations of the CBERS satellite will have sensors with similar technical features, which motivates the study presented in this paper. The mathematical models that relate the object and image spaces are based on collinearity (for points) and coplanarity (for lines) conditions. These models were created in an in-house developed software package called TMS (Triangulation with Multiple Sensors) with multi-feature control (GCPs and GCLs). Experiments on a block of four CBERS-2B HRC images and on one CBERS-2B CCD image were performed using both models. It was observed that the combination of GCPs and GCLs provided better bundle block adjustment results than conventional bundle adjustment using only GCPs. The results also demonstrate the advantages of using primarily orbital data when the number of control entities is reduced.

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

Science.gov (United States)

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.

Research on oral test modeling based on multi-feature fusion

Science.gov (United States)

Shi, Yuliang; Tao, Yiyue; Lei, Jun

2018-04-01

In this paper, the spectrum of speech signal is taken as an input of feature extraction. The advantage of PCNN in image segmentation and other processing is used to process the speech spectrum and extract features. And a new method combining speech signal processing and image processing is explored. At the same time of using the features of the speech map, adding the MFCC to establish the spectral features and integrating them with the features of the spectrogram to further improve the accuracy of the spoken language recognition. Considering that the input features are more complicated and distinguishable, we use Support Vector Machine (SVM) to construct the classifier, and then compare the extracted test voice features with the standard voice features to achieve the spoken standard detection. Experiments show that the method of extracting features from spectrograms using PCNN is feasible, and the fusion of image features and spectral features can improve the detection accuracy.

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

Science.gov (United States)

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.

Embedding Complementarity in HCI Methods

DEFF Research Database (Denmark)

Nielsen, Janni; Yssing, Carsten; Tweddell Levinsen, Karin

2007-01-01

Differences in cultural contexts constitute differences in cognition, and research has shown that different cultures may use different cognitive tools for perception and reasoning. The cultural embeddings are significant in relation to HCI, because the cultural context is also embedded in the tec......Differences in cultural contexts constitute differences in cognition, and research has shown that different cultures may use different cognitive tools for perception and reasoning. The cultural embeddings are significant in relation to HCI, because the cultural context is also embedded...

Smart Multicore Embedded Systems

DEFF Research Database (Denmark)

This book provides a single-source reference to the state-of-the-art of high-level programming models and compilation tool-chains for embedded system platforms. The authors address challenges faced by programmers developing software to implement parallel applications in embedded systems, where very...... specificities of various embedded systems from different industries. Parallel programming tool-chains are described that take as input parameters both the application and the platform model, then determine relevant transformations and mapping decisions on the concrete platform, minimizing user intervention...... and hiding the difficulties related to the correct and efficient use of memory hierarchy and low level code generation. Describes tools and programming models for multicore embedded systems Emphasizes throughout performance per watt scalability Discusses realistic limits of software parallelization Enables...

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

Science.gov (United States)

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.

The Hidden Flow Structure and Metric Space of Network Embedding Algorithms Based on Random Walks.

Science.gov (United States)

Gu, Weiwei; Gong, Li; Lou, Xiaodan; Zhang, Jiang

2017-10-13

Network embedding which encodes all vertices in a network as a set of numerical vectors in accordance with it's local and global structures, has drawn widespread attention. Network embedding not only learns significant features of a network, such as the clustering and linking prediction but also learns the latent vector representation of the nodes which provides theoretical support for a variety of applications, such as visualization, link prediction, node classification, and recommendation. As the latest progress of the research, several algorithms based on random walks have been devised. Although those algorithms have drawn much attention for their high scores in learning efficiency and accuracy, there is still a lack of theoretical explanation, and the transparency of those algorithms has been doubted. Here, we propose an approach based on the open-flow network model to reveal the underlying flow structure and its hidden metric space of different random walk strategies on networks. We show that the essence of embedding based on random walks is the latent metric structure defined on the open-flow network. This not only deepens our understanding of random- walk-based embedding algorithms but also helps in finding new potential applications in network embedding.

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

Science.gov (United States)

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.

Communicating embedded systems networks applications

CERN Document Server

Krief, Francine

2013-01-01

Embedded systems become more and more complex and require having some knowledge in various disciplines such as electronics, data processing, telecommunications and networks. Without detailing all the aspects related to the design of embedded systems, this book, which was written by specialists in electronics, data processing and telecommunications and networks, gives an interesting point of view of communication techniques and problems in embedded systems. This choice is easily justified by the fact that embedded systems are today massively communicating and that telecommunications and network

Advances in embedded computer vision

CERN Document Server

Kisacanin, Branislav

2014-01-01

This illuminating collection offers a fresh look at the very latest advances in the field of embedded computer vision. Emerging areas covered by this comprehensive text/reference include the embedded realization of 3D vision technologies for a variety of applications, such as stereo cameras on mobile devices. Recent trends towards the development of small unmanned aerial vehicles (UAVs) with embedded image and video processing algorithms are also examined. The authoritative insights range from historical perspectives to future developments, reviewing embedded implementation, tools, technolog

Nanomechanical Optical Fiber with Embedded Electrodes Actuated by Joule Heating.

Science.gov (United States)

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.

Nanomechanical Optical Fiber with Embedded Electrodes Actuated by Joule Heating

Science.gov (United States)

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

2014-01-01

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. PMID:28788148

Model Checking Feature Interactions

DEFF Research Database (Denmark)

Le Guilly, Thibaut; Olsen, Petur; Pedersen, Thomas

2015-01-01

This paper presents an offline approach to analyzing feature interactions in embedded systems. The approach consists of a systematic process to gather the necessary information about system components and their models. The model is first specified in terms of predicates, before being refined to t...... to timed automata. The consistency of the model is verified at different development stages, and the correct linkage between the predicates and their semantic model is checked. The approach is illustrated on a use case from home automation....

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

Science.gov (United States)

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.

Real-Time Video Convolutional Face Finder on Embedded Platforms

Directory of Open Access Journals (Sweden)

Mamalet Franck

2007-01-01

Full Text Available A high-level optimization methodology is applied for implementing the well-known convolutional face finder (CFF algorithm for real-time applications on mobile phones, such as teleconferencing, advanced user interfaces, image indexing, and security access control. CFF is based on a feature extraction and classification technique which consists of a pipeline of convolutions and subsampling operations. The design of embedded systems requires a good trade-off between performance and code size due to the limited amount of available resources. The followed methodology copes with the main drawbacks of the original implementation of CFF such as floating-point computation and memory allocation, in order to allow parallelism exploitation and perform algorithm optimizations. Experimental results show that our embedded face detection system can accurately locate faces with less computational load and memory cost. It runs on a 275 MHz Starcore DSP at 35 QCIF images/s with state-of-the-art detection rates and very low false alarm rates.

Real-Time Video Convolutional Face Finder on Embedded Platforms

Directory of Open Access Journals (Sweden)

Franck Mamalet

2007-03-01

Full Text Available A high-level optimization methodology is applied for implementing the well-known convolutional face finder (CFF algorithm for real-time applications on mobile phones, such as teleconferencing, advanced user interfaces, image indexing, and security access control. CFF is based on a feature extraction and classification technique which consists of a pipeline of convolutions and subsampling operations. The design of embedded systems requires a good trade-off between performance and code size due to the limited amount of available resources. The followed methodology copes with the main drawbacks of the original implementation of CFF such as floating-point computation and memory allocation, in order to allow parallelism exploitation and perform algorithm optimizations. Experimental results show that our embedded face detection system can accurately locate faces with less computational load and memory cost. It runs on a 275 MHz Starcore DSP at 35 QCIF images/s with state-of-the-art detection rates and very low false alarm rates.

Design Methodologies for Secure Embedded Systems

CERN Document Server

Biedermann, Alexander

2011-01-01

Embedded systems have been almost invisibly pervading our daily lives for several decades. They facilitate smooth operations in avionics, automotive electronics, or telecommunication. New problems arise by the increasing employment, interconnection, and communication of embedded systems in heterogeneous environments: How secure are these embedded systems against attacks or breakdowns? Therefore, how can embedded systems be designed to be more secure? And how can embedded systems autonomically react to threats? Facing these questions, Sorin A. Huss is significantly involved in the exploration o

Generative embedding for model-based classification of fMRI data.

Directory of Open Access Journals (Sweden)

Kay H Brodersen

2011-06-01

Full Text Available Decoding models, such as those underlying multivariate classification algorithms, have been increasingly used to infer cognitive or clinical brain states from measures of brain activity obtained by functional magnetic resonance imaging (fMRI. The practicality of current classifiers, however, is restricted by two major challenges. First, due to the high data dimensionality and low sample size, algorithms struggle to separate informative from uninformative features, resulting in poor generalization performance. Second, popular discriminative methods such as support vector machines (SVMs rarely afford mechanistic interpretability. In this paper, we address these issues by proposing a novel generative-embedding approach that incorporates neurobiologically interpretable generative models into discriminative classifiers. Our approach extends previous work on trial-by-trial classification for electrophysiological recordings to subject-by-subject classification for fMRI and offers two key advantages over conventional methods: it may provide more accurate predictions by exploiting discriminative information encoded in 'hidden' physiological quantities such as synaptic connection strengths; and it affords mechanistic interpretability of clinical classifications. Here, we introduce generative embedding for fMRI using a combination of dynamic causal models (DCMs and SVMs. We propose a general procedure of DCM-based generative embedding for subject-wise classification, provide a concrete implementation, and suggest good-practice guidelines for unbiased application of generative embedding in the context of fMRI. We illustrate the utility of our approach by a clinical example in which we classify moderately aphasic patients and healthy controls using a DCM of thalamo-temporal regions during speech processing. Generative embedding achieves a near-perfect balanced classification accuracy of 98% and significantly outperforms conventional activation-based and

Persistent topological features of dynamical systems

Energy Technology Data Exchange (ETDEWEB)

Maletić, Slobodan, E-mail: slobodan@hitsz.edu.cn [Shenzhen Graduate School, Harbin Institute of Technology, Shenzhen (China); Institute of Nuclear Sciences Vinča, University of Belgrade, Belgrade (Serbia); Zhao, Yi, E-mail: zhao.yi@hitsz.edu.cn [Shenzhen Graduate School, Harbin Institute of Technology, Shenzhen (China); Rajković, Milan, E-mail: milanr@vinca.rs [Institute of Nuclear Sciences Vinča, University of Belgrade, Belgrade (Serbia)

2016-05-15

Inspired by an early work of Muldoon et al., Physica D 65, 1–16 (1993), we present a general method for constructing simplicial complex from observed time series of dynamical systems based on the delay coordinate reconstruction procedure. The obtained simplicial complex preserves all pertinent topological features of the reconstructed phase space, and it may be analyzed from topological, combinatorial, and algebraic aspects. In focus of this study is the computation of homology of the invariant set of some well known dynamical systems that display chaotic behavior. Persistent homology of simplicial complex and its relationship with the embedding dimensions are examined by studying the lifetime of topological features and topological noise. The consistency of topological properties for different dynamic regimes and embedding dimensions is examined. The obtained results shed new light on the topological properties of the reconstructed phase space and open up new possibilities for application of advanced topological methods. The method presented here may be used as a generic method for constructing simplicial complex from a scalar time series that has a number of advantages compared to the mapping of the same time series to a complex network.

An Embedded Sensor Node Microcontroller with Crypto-Processors.

Science.gov (United States)

Panić, Goran; Stecklina, Oliver; Stamenković, Zoran

2016-04-27

Wireless sensor network applications range from industrial automation and control, agricultural and environmental protection, to surveillance and medicine. In most applications, data are highly sensitive and must be protected from any type of attack and abuse. Security challenges in wireless sensor networks are mainly defined by the power and computing resources of sensor devices, memory size, quality of radio channels and susceptibility to physical capture. In this article, an embedded sensor node microcontroller designed to support sensor network applications with severe security demands is presented. It features a low power 16-bitprocessor core supported by a number of hardware accelerators designed to perform complex operations required by advanced crypto algorithms. The microcontroller integrates an embedded Flash and an 8-channel 12-bit analog-to-digital converter making it a good solution for low-power sensor nodes. The article discusses the most important security topics in wireless sensor networks and presents the architecture of the proposed hardware solution. Furthermore, it gives details on the chip implementation, verification and hardware evaluation. Finally, the chip power dissipation and performance figures are estimated and analyzed.

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

Science.gov (United States)

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.

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

Science.gov (United States)

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.

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

Science.gov (United States)

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.

Learning slow features for behavior analysis

NARCIS (Netherlands)

Zafeiriou, Lazaros; Nicolaou, Mihalis A.; Zafeiriou, Stefanos; Nikitids, Symeon; Pantic, Maja

2013-01-01

A recently introduced latent feature learning technique for time varying dynamic phenomena analysis is the socalled Slow Feature Analysis (SFA). SFA is a deterministic component analysis technique for multi-dimensional sequences that by minimizing the variance of the first order time derivative

WIMS/PANTHER analysis of UO{sub 2}/MOX cores using embedded super-cells

Energy Technology Data Exchange (ETDEWEB)

Knight, M.; Bryce, P. [EDF Energy, Barnett Way, Barnwood, Gloucester (United Kingdom); Hall, S. [Advanced Modelling and Computation Group, Imperial College, London (United Kingdom)

2012-07-01

This paper describes a method of analysing PWR UO{sub 2}MOX cores with WIMS/PANTHER. Embedded super-cells, run within the reactor code, are used to correct the standard methodology of using 2-group smeared data from single assembly lattice calculations. In many other codes the weakness of this standard approach has been improved for MOX by imposing a more realistic environment in the lattice code, or by improving the sophistication of the reactor code. In this approach an intermediate set of calculations is introduced, leaving both lattice and reactor calculations broadly unchanged. The essence of the approach is that the whole core is broken down into a set of 'embedded' super-cells, each extending over just four quarter assemblies, with zero leakage imposed at the assembly mid-lines. Each supercell is solved twice, first with a detailed multi-group pin-by-pin solution, and then with the standard single assembly approach. Correction factors are defined by comparing the two solutions, and these can be applied in whole core calculations. The restriction that all such calculations are modelled with zero leakage means that they are independent of each other and of the core-wide flux shape. This allows parallel pre-calculation for the entire cycle once the loading pattern has been determined, in much the same way that single assembly lattice calculations can be pre-calculated once the range of fuel types is known. Comparisons against a whole core pin-by-pin reference demonstrates that the embedding process does not introduce a significant error, even after burnup and refuelling. Comparisons against a WIMS reference demonstrate that a pin-by-pin multi-group diffusion solution is capable of capturing the main interface effects. This therefore defines a practical approach for achieving results close to lattice code accuracy, but broadly at the cost of a standard reactor calculation. (authors)

Design optimization of embedded ultrasonic transducers for concrete structures assessment.

Science.gov (United States)

Dumoulin, Cédric; Deraemaeker, Arnaud

2017-08-01

In the last decades, the field of structural health monitoring and damage detection has been intensively explored. Active vibration techniques allow to excite structures at high frequency vibrations which are sensitive to small damage. Piezoelectric PZT transducers are perfect candidates for such testing due to their small size, low cost and large bandwidth. Current ultrasonic systems are based on external piezoelectric transducers which need to be placed on two faces of the concrete specimen. The limited accessibility of in-service structures makes such an arrangement often impractical. An alternative is to embed permanently low-cost transducers inside the structure. Such types of transducers have been applied successfully for the in-situ estimation of the P-wave velocity in fresh concrete, and for crack monitoring. Up to now, the design of such transducers was essentially based on trial and error, or in a few cases, on the limitation of the acoustic impedance mismatch between the PZT and concrete. In the present study, we explore the working principles of embedded piezoelectric transducers which are found to be significantly different from external transducers. One of the major challenges concerning embedded transducers is to produce very low cost transducers. We show that a practical way to achieve this imperative is to consider the radial mode of actuation of bulk PZT elements. This is done by developing a simple finite element model of a piezoelectric transducer embedded in an infinite medium. The model is coupled with a multi-objective genetic algorithm which is used to design specific ultrasonic embedded transducers both for hard and fresh concrete monitoring. The results show the efficiency of the approach and a few designs are proposed which are optimal for hard concrete, fresh concrete, or both, in a given frequency band of interest. Copyright © 2017 Elsevier B.V. All rights reserved.

Efficient and Secure Fingerprint Verification for Embedded Devices

Directory of Open Access Journals (Sweden)

Sakiyama Kazuo

2006-01-01

Full Text Available This paper describes a secure and memory-efficient embedded fingerprint verification system. It shows how a fingerprint verification module originally developed to run on a workstation can be transformed and optimized in a systematic way to run real-time on an embedded device with limited memory and computation power. A complete fingerprint recognition module is a complex application that requires in the order of 1000 M unoptimized floating-point instruction cycles. The goal is to run both the minutiae extraction and the matching engines on a small embedded processor, in our case a 50 MHz LEON-2 softcore. It does require optimization and acceleration techniques at each design step. In order to speed up the fingerprint signal processing phase, we propose acceleration techniques at the algorithm level, at the software level to reduce the execution cycle number, and at the hardware level to distribute the system work load. Thirdly, a memory trace map-based memory reduction strategy is used for lowering the system memory requirement. Lastly, at the hardware level, it requires the development of specialized coprocessors. As results of these optimizations, we achieve a 65% reduction on the execution time and a 67% reduction on the memory storage requirement for the minutiae extraction process, compared against the reference implementation. The complete operation, that is, fingerprint capture, feature extraction, and matching, can be done in real-time of less than 4 seconds

Embedded random matrix ensembles in quantum physics

CERN Document Server

Kota, V K B

2014-01-01

Although used with increasing frequency in many branches of physics, random matrix ensembles are not always sufficiently specific to account for important features of the physical system at hand. One refinement which retains the basic stochastic approach but allows for such features consists in the use of embedded ensembles.  The present text is an exhaustive introduction to and survey of this important field. Starting with an easy-to-read introduction to general random matrix theory, the text then develops the necessary concepts from the beginning, accompanying the reader to the frontiers of present-day research. With some notable exceptions, to date these ensembles have primarily been applied in nuclear spectroscopy. A characteristic example is the use of a random two-body interaction in the framework of the nuclear shell model. Yet, topics in atomic physics, mesoscopic physics, quantum information science and statistical mechanics of isolated finite quantum systems can also be addressed using these ensemb...

Quasi-local mass via isometric embeddings: a review from a geometric perspective

International Nuclear Information System (INIS)

Miao, Pengzi

2015-01-01

In this paper, we review geometric aspects of quasi-local energies proposed by Brown–York, Liu–Yau, and Wang–Yau. These quasi-local energy functions, having the important positivity property, share a common feature that they are defined via the canonical Hamiltonian approach, and therefore an isometric embedding of the two-surface into a background space is used as a reference. (topical review)

Electronics for embedded systems

CERN Document Server

Bindal, Ahmet

2017-01-01

This book provides semester-length coverage of electronics for embedded systems, covering most common analog and digital circuit-related issues encountered while designing embedded system hardware. It is written for students and young professionals who have basic circuit theory background and want to learn more about passive circuits, diode and bipolar transistor circuits, the state-of-the-art CMOS logic family and its interface with older logic families such as TTL, sensors and sensor physics, operational amplifier circuits to condition sensor signals, data converters and various circuits used in electro-mechanical device control in embedded systems. The book also provides numerous hardware design examples by integrating the topics learned in earlier chapters. The last chapter extensively reviews the combinational and sequential logic design principles to be able to design the digital part of embedded system hardware.

Pedestrian count estimation using texture feature with spatial distribution

Directory of Open Access Journals (Sweden)

Hongyu Hu

2016-12-01

Full Text Available We present a novel pedestrian count estimation approach based on global image descriptors formed from multi-scale texture features that considers spatial distribution. For regions of interest, local texture features are represented based on histograms of multi-scale block local binary pattern, which jointly constitute the feature vector of the whole image. Therefore, to achieve an effective estimation of pedestrian count, principal component analysis is used to reduce the dimension of the global representation features, and a fitting model between image global features and pedestrian count is constructed via support vector regression. The experimental result shows that the proposed method exhibits high accuracy on pedestrian count estimation and can be applied well in the real world.

Vibration measurement on composite material with embedded optical fiber based on phase-OTDR

Science.gov (United States)

Franciscangelis, C.; Margulis, W.; Floridia, C.; Rosolem, J. B.; Salgado, F. C.; Nyman, T.; Petersson, M.; Hallander, P.; Hällstrom, S.; Söderquist, I.; Fruett, F.

2017-04-01

Distributed sensors based on phase-optical time-domain reflectometry (phase-OTDR) are suitable for aircraft health monitoring due to electromagnetic interference immunity, small dimensions, low weight and flexibility. These features allow the fiber embedment into aircraft structures in a nearly non-intrusive way to measure vibrations along its length. The capability of measuring vibrations on avionics structures is of interest for what concerns the study of material fatigue or the occurrence of undesirable phenomena like flutter. In this work, we employed the phase-OTDR technique to measure vibrations ranging from some dozens of Hz to kHz in two layers of composite material board with embedded polyimide coating 0.24 numerical aperture single-mode optical fiber.

Brauer type embedding problems

CERN Document Server

Ledet, Arne

2005-01-01

This monograph is concerned with Galois theoretical embedding problems of so-called Brauer type with a focus on 2-groups and on finding explicit criteria for solvability and explicit constructions of the solutions. The advantage of considering Brauer type embedding problems is their comparatively simple condition for solvability in the form of an obstruction in the Brauer group of the ground field. This book presupposes knowledge of classical Galois theory and the attendant algebra. Before considering questions of reducing the embedding problems and reformulating the solvability criteria, the

Embedded Systems Design with FPGAs

CERN Document Server

Pnevmatikatos, Dionisios; Sklavos, Nicolas

2013-01-01

This book presents methodologies for modern applications of embedded systems design, using field programmable gate array (FPGA) devices.  Coverage includes state-of-the-art research from academia and industry on a wide range of topics, including advanced electronic design automation (EDA), novel system architectures, embedded processors, arithmetic, dynamic reconfiguration and applications. Describes a variety of methodologies for modern embedded systems design;  Implements methodologies presented on FPGAs; Covers a wide variety of applications for reconfigurable embedded systems, including Bioinformatics, Communications and networking, Application acceleration, Medical solutions, Experiments for high energy physics, Astronomy, Aerospace, Biologically inspired systems and Computational fluid dynamics (CFD).

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

Directory of Open Access Journals (Sweden)

Carolina Micolini

2017-11-01

Full Text Available 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.

'No one to trust': the cultural embedding of atomism in financial markets.

Science.gov (United States)

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.

Permutation entropy with vector embedding delays

Science.gov (United States)

Little, Douglas J.; Kane, Deb M.

2017-12-01

Permutation entropy (PE) is a statistic used widely for the detection of structure within a time series. Embedding delay times at which the PE is reduced are characteristic timescales for which such structure exists. Here, a generalized scheme is investigated where embedding delays are represented by vectors rather than scalars, permitting PE to be calculated over a (D -1 ) -dimensional space, where D is the embedding dimension. This scheme is applied to numerically generated noise, sine wave and logistic map series, and experimental data sets taken from a vertical-cavity surface emitting laser exhibiting temporally localized pulse structures within the round-trip time of the laser cavity. Results are visualized as PE maps as a function of embedding delay, with low PE values indicating combinations of embedding delays where correlation structure is present. It is demonstrated that vector embedding delays enable identification of structure that is ambiguous or masked, when the embedding delay is constrained to scalar form.

FAILSAFE Health Management for Embedded Systems

Science.gov (United States)

Horvath, Gregory A.; Wagner, David A.; Wen, Hui Ying; Barry, Matthew

2010-01-01

The FAILSAFE project is developing concepts and prototype implementations for software health management in mission- critical, real-time embedded systems. The project unites features of the industry-standard ARINC 653 Avionics Application Software Standard Interface and JPL s Mission Data System (MDS) technology (see figure). The ARINC 653 standard establishes requirements for the services provided by partitioned, real-time operating systems. The MDS technology provides a state analysis method, canonical architecture, and software framework that facilitates the design and implementation of software-intensive complex systems. The MDS technology has been used to provide the health management function for an ARINC 653 application implementation. In particular, the focus is on showing how this combination enables reasoning about, and recovering from, application software problems.

Smart Multicore Embedded Systems

DEFF Research Database (Denmark)

This book provides a single-source reference to the state-of-the-art of high-level programming models and compilation tool-chains for embedded system platforms. The authors address challenges faced by programmers developing software to implement parallel applications in embedded systems, where ve...

A distributed framework for inter-domain virtual network embedding

Science.gov (United States)

Wang, Zihua; Han, Yanni; Lin, Tao; Tang, Hui

2013-03-01

Network virtualization has been a promising technology for overcoming the Internet impasse. A main challenge in network virtualization is the efficient assignment of virtual resources. Existing work focused on intra-domain solutions whereas inter-domain situation is more practical in realistic setting. In this paper, we present a distributed inter-domain framework for mapping virtual networks to physical networks which can ameliorate the performance of the virtual network embedding. The distributed framework is based on a Multi-agent approach. A set of messages for information exchange is defined. We design different operations and IPTV use scenarios to validate the advantages of our framework. Use cases shows that our framework can solve the inter-domain problem efficiently.

Improved features of MARS 1.4 and verification

Energy Technology Data Exchange (ETDEWEB)

Lee, Won Jae; Chung, Bub Don; Jeong, Jae Jun; Ha, Kwi Seok [Korea Atomic Energy Research Institute, Taejon (Korea)

1999-09-01

MARS 1.4 code has been developed as a basic code frame for multi-dimensional thermal-hydraulic analysis of light water reactor transients. This report describes the newly improved features of MARS 1.4 and their verification results. The new features of MARS 1.4 include the implementation of point kinetics model in the 3D module, the coupled heat structure model, the extension of control functions and input check functions in the 3D module, the implementation of new features of RELAP5/MOD3.2.2 -version, the addition of automatic initialization function for fuel 3-D analysis and the unification of material properties and forcing functions, etc. These features have been implemented in the code in order to extend the code modeling capability and to enhance the user friendliness. Among these features, this report describes the implementation of new features of RELAP5/MOD3.3.3-version such as reflood model and critical heat flux models, etc., the automatic initialization function, the unification of material properties and forcing functions and the other code improvements and error corrections, which were not reported in the previous report. Through the verification calculations, the new features of MARS 1.4 have been verified well implemented in the code. In conclusion, MARS 1.4 code has been developed and verified as implemented in the code. In conclusion, MARS 1.4 code has been developed and verified as a multi-dimensional system thermal-hydraulic analysis tool. And, it can play its role as a basic code frame for the future development of a multi-purpose consolidated code, MARS 2.x, for coupled analysis of multi-dimensional system thermal hydraulics, 3D core kinetics, core CHF and containment as well as for further improvement of thermal-hydraulic and numerical models. 4 refs., 10 figs. (Author)

Implementation of density functional embedding theory within the projector-augmented-wave method and applications to semiconductor defect states

International Nuclear Information System (INIS)

Yu, Kuang; Libisch, Florian; Carter, Emily A.

2015-01-01

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 our 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

Experimental study of reinforced concrete pile caps with external, embedded and partially embedded socket with smooth interface

Directory of Open Access Journals (Sweden)

R. Barros

Full Text Available On Precast concrete structures the column foundation connections can occur through the socket foundation, which can be embedded, partially embedded or external, with socket walls over the pile caps. This paper presents an experimental study about two pile caps reinforced concrete with external, partially embedded and embedded socket submitted to central load, using 1:2 scaled models. In the analyzed models, the smooth interface between the socket walls and column was considered. The results are compared to a reference model that presents monolithic connections between the column and pile cap. It is observed that the ultimate load of pile cap with external sockets has the same magnitude as the reference pile cap, but the ultimate load of models with partially embedded and embedded socket present less magnitude than the reference model.

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

Science.gov (United States)

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.

Multi-feature classifiers for burst detection in single EEG channels from preterm infants

Science.gov (United States)

Navarro, X.; Porée, F.; Kuchenbuch, M.; Chavez, M.; Beuchée, Alain; Carrault, G.

2017-08-01

Objective. The study of electroencephalographic (EEG) bursts in preterm infants provides valuable information about maturation or prognostication after perinatal asphyxia. Over the last two decades, a number of works proposed algorithms to automatically detect EEG bursts in preterm infants, but they were designed for populations under 35 weeks of post menstrual age (PMA). However, as the brain activity evolves rapidly during postnatal life, these solutions might be under-performing with increasing PMA. In this work we focused on preterm infants reaching term ages (PMA  ⩾36 weeks) using multi-feature classification on a single EEG channel. Approach. Five EEG burst detectors relying on different machine learning approaches were compared: logistic regression (LR), linear discriminant analysis (LDA), k-nearest neighbors (kNN), support vector machines (SVM) and thresholding (Th). Classifiers were trained by visually labeled EEG recordings from 14 very preterm infants (born after 28 weeks of gestation) with 36-41 weeks PMA. Main results. The most performing classifiers reached about 95% accuracy (kNN, SVM and LR) whereas Th obtained 84%. Compared to human-automatic agreements, LR provided the highest scores (Cohen’s kappa  =  0.71) using only three EEG features. Applying this classifier in an unlabeled database of 21 infants  ⩾36 weeks PMA, we found that long EEG bursts and short inter-burst periods are characteristic of infants with the highest PMA and weights. Significance. In view of these results, LR-based burst detection could be a suitable tool to study maturation in monitoring or portable devices using a single EEG channel.

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

Science.gov (United States)

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.

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

Science.gov (United States)

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.

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

Science.gov (United States)

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.

Poincare ball embeddings of the optical geometry

International Nuclear Information System (INIS)

Abramowicz, M A; Bengtsson, I; Karas, V; Rosquist, K

2002-01-01

It is shown that the optical geometry of the Reissner-Nordstroem exterior metric can be embedded in a hyperbolic space all the way down to its outer horizon. The adopted embedding procedure removes a breakdown of flat-space embeddings which occurs outside the horizon, at and below the Buchdahl-Bondi limit (R/M=9/4 in the Schwarzschild case). In particular, the horizon can be captured in the optical geometry embedding diagram. Moreover, by using the compact Poincare ball representation of the hyperbolic space, the embedding diagram can cover the whole extent of radius from spatial infinity down to the horizon. Attention is drawn to the advantages of such embeddings in an appropriately curved space: this approach gives compact embeddings and it clearly distinguishes the case of an extremal black hole from a non-extremal one in terms of the topology of the embedded horizon

Homomorphic embeddings in n-groups

Directory of Open Access Journals (Sweden)

Mona Cristescu

2013-06-01

Full Text Available We prove that an cancellative n-groupoid A can be homotopic embedded in an n-group if and only if in A are satisfied all n-ary Malcev conditions. Now we shall prove that in the presence of associative law we obtain homomorphic embeddings. Furthermore, if A has a lateral identity a such embeddings is assured by a subset of n-ary Malcev conditions - unary Malcev conditions.

PET image reconstruction using multi-parametric anato-functional priors

Science.gov (United States)

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

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

Directory of Open Access Journals (Sweden)

Vasileios Karyotis

2018-04-01

Full Text Available 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.

Sparse representation of multi parametric DCE-MRI features using K-SVD for classifying gene expression based breast cancer recurrence risk

Science.gov (United States)

Mahrooghy, Majid; Ashraf, Ahmed B.; Daye, Dania; Mies, Carolyn; Rosen, Mark; Feldman, Michael; Kontos, Despina

2014-03-01

We evaluate the prognostic value of sparse representation-based features by applying the K-SVD algorithm on multiparametric kinetic, textural, and morphologic features in breast dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI). K-SVD is an iterative dimensionality reduction method that optimally reduces the initial feature space by updating the dictionary columns jointly with the sparse representation coefficients. Therefore, by using K-SVD, we not only provide sparse representation of the features and condense the information in a few coefficients but also we reduce the dimensionality. The extracted K-SVD features are evaluated by a machine learning algorithm including a logistic regression classifier for the task of classifying high versus low breast cancer recurrence risk as determined by a validated gene expression assay. The features are evaluated using ROC curve analysis and leave one-out cross validation for different sparse representation and dimensionality reduction numbers. Optimal sparse representation is obtained when the number of dictionary elements is 4 (K=4) and maximum non-zero coefficients is 2 (L=2). We compare K-SVD with ANOVA based feature selection for the same prognostic features. The ROC results show that the AUC of the K-SVD based (K=4, L=2), the ANOVA based, and the original features (i.e., no dimensionality reduction) are 0.78, 0.71. and 0.68, respectively. From the results, it can be inferred that by using sparse representation of the originally extracted multi-parametric, high-dimensional data, we can condense the information on a few coefficients with the highest predictive value. In addition, the dimensionality reduction introduced by K-SVD can prevent models from over-fitting.

Energy-aware embedded classifier design for real-time emotion analysis.

Science.gov (United States)

Padmanabhan, Manoj; Murali, Srinivasan; Rincon, Francisco; Atienza, David

2015-01-01

Detection and classification of human emotions from multiple bio-signals has a wide variety of applications. Though electronic devices are available in the market today that acquire multiple body signals, the classification of human emotions in real-time, adapted to the tight energy budgets of wearable embedded systems is a big challenge. In this paper we present an embedded classifier for real-time emotion classification. We propose a system that operates at different energy budgeted modes, depending on the available energy, where each mode is constrained by an operating energy bound. The classifier has an offline training phase where feature selection is performed for each operating mode, with an energy-budget aware algorithm that we propose. Across the different operating modes, the classification accuracy ranges from 95% - 75% and 89% - 70% for arousal and valence respectively. The accuracy is traded off for less power consumption, which results in an increased battery life of up to 7.7 times (from 146.1 to 1126.9 hours).

Spectral embedding based active contour (SEAC): application to breast lesion segmentation on DCE-MRI

Science.gov (United States)

Agner, Shannon C.; Xu, Jun; Rosen, Mark; Karthigeyan, Sudha; Englander, Sarah; Madabhushi, Anant

2011-03-01

Spectral embedding (SE), a graph-based manifold learning method, has previously been shown to be useful in high dimensional data classification. In this work, we present a novel SE based active contour (SEAC) segmentation scheme and demonstrate its applications in lesion segmentation on breast dynamic contrast enhance magnetic resonance imaging (DCE-MRI). In this work, we employ SE on DCE-MRI on a per voxel basis to embed the high dimensional time series intensity vector into a reduced dimensional space, where the reduced embedding space is characterized by the principal eigenvectors. The orthogonal eigenvector-based data representation allows for computation of strong tensor gradients in the spectrally embedded space and also yields improved region statistics that serve as optimal stopping criteria for SEAC. We demonstrate both analytically and empirically that the tensor gradients in the spectrally embedded space are stronger than the corresponding gradients in the original grayscale intensity space. On a total of 50 breast DCE-MRI studies, SEAC yielded a mean absolute difference (MAD) of 3.2+/-2.1 pixels and mean Dice similarity coefficient (DSC) of 0.74+/-0.13 compared to manual ground truth segmentation. An active contour in conjunction with fuzzy c-means (FCM+AC), a commonly used segmentation method for breast DCE-MRI, produced a corresponding MAD of 7.2+/-7.4 pixels and mean DSC of 0.58+/-0.32. In conjunction with a set of 6 quantitative morphological features automatically extracted from the SEAC derived lesion boundary, a support vector machine (SVM) classifier yielded an area under the curve (AUC) of 0.73, for discriminating between 10 benign and 30 malignant lesions; the corresponding SVM classifier with the FCM+AC derived morphological features yielded an AUC of 0.65.

Towards a multi-agent system for regulated information exchange in crime investigations

NARCIS (Netherlands)

Dijkstra, Pieter; Prakken, H.; Vey Mestdagh, C.N.J. de

2005-01-01

This paper outlines a multi-agent architecture for regulated information exchange of crime investigation data between police forces. Interactions between police officers about information exchange are analysed as negotiation dialogues with embedded persuasion dialogues. An architecture is then

Schedulability-Driven Partitioning and Mapping for Multi-Cluster Real-Time Systems

DEFF Research Database (Denmark)

Pop, Paul; Eles, Petru; Peng, Zebo

2004-01-01

We present an approach to partitioning and mapping for multi-cluster embedded systems consisting of time-triggered and event-triggered clusters, interconnected via gateways. We have proposed a schedulability analysis for such systems, including a worst-case queuing delay analysis for the gateways...

Enzyme-sharing as a cause of multi-stationarity in signalling systems

DEFF Research Database (Denmark)

Feliu, Elisenda; Wiuf, Carsten

2012-01-01

Multi-stationarity in biological systems is a mechanism of cellular decision-making. In particular, signalling pathways regulated by protein phosphorylation display features that facilitate a variety of responses to different biological inputs. The features that lead to multi-stationarity are of ......Multi-stationarity in biological systems is a mechanism of cellular decision-making. In particular, signalling pathways regulated by protein phosphorylation display features that facilitate a variety of responses to different biological inputs. The features that lead to multi......-stationarity are of particular interest to determine, as well as the stability, properties of the steady states. In this paper, we determine conditions for the emergence of multi-stationarity in small motifs without feedback that repeatedly occur in signalling pathways. We derive an explicit mathematical relationship ¿ between...... identify characteristics of the motifs that lead to multi-stationarity, and extend the view that multi-stationarity in signalling pathways arises from multi-site phosphorylation. Our approach relies on mass-action kinetics, and the conclusions are drawn in full generality without resorting to simulations...

Multi-objective optimization of generalized reliability design problems using feature models-A concept for early design stages

International Nuclear Information System (INIS)

Limbourg, Philipp; Kochs, Hans-Dieter

2008-01-01

Reliability optimization problems such as the redundancy allocation problem (RAP) have been of considerable interest in the past. However, due to the restrictions of the design space formulation, they may not be applicable in all practical design problems. A method with high modelling freedom for rapid design screening is desirable, especially in early design stages. This work presents a novel approach to reliability optimization. Feature modelling, a specification method originating from software engineering, is applied for the fast specification and enumeration of complex design spaces. It is shown how feature models can not only describe arbitrary RAPs but also much more complex design problems. The design screening is accomplished by a multi-objective evolutionary algorithm for probabilistic objectives. Comparing averages or medians may hide the true characteristics of this distributions. Therefore the algorithm uses solely the probability of a system dominating another to achieve the Pareto optimal set. We illustrate the approach by specifying a RAP and a more complex design space and screening them with the evolutionary algorithm

Carbon nano tubes embedded in polymer nano fibers

International Nuclear Information System (INIS)

Dror, Y.; Kedem, S.; Khalfin, R.L.; Paz, Y.; Cohenl, Y.; Salalha, Y.; Yarin, A.L.; Zussman, A.

2004-01-01

Full Text: The electro spinning process was used successfully to embed Multi-walled carbon nano tubes (MWCNTs) and single-walled carbon nano tubes (SWCNTs) in a matrix of poly(ethylene oxide) (PEO) forming composite nano fibers. Initial dispersion of SWCNTs in water was achieved by the use of an amphphilic alternating copolymer of styrene and sodium maleate. MWNT dispersion was achieved by ionic and nonionic surfactants. The distribution and conformation of the nano tubes in the nano fibers were studied by transmission electron microscopy (TEM). Oxygen plasma etching was used to expose the nano tubes within the nano fibers to facilitate direct observation. Nano tube alignment within the nano fibers was shown to depend strongly on the quality of the initial dispersions. Well-dispersed and separated nano tubes were embedded in a straight and aligned form while entangled non-separated nano tubes were incorporated as dense aggregates. X-ray diffraction demonstrated a high degree of orientation of the PEO crystals in the electro spun nano fibers with embedded SWCNTs, whereas incorporation of MVCNTs had a detrimental effect on the polymer orientation. Composite polymer nano fibers containing dispersed phases of nanometric TiO 2 particles and MWCNTs were also prepared electro spinning. In this case, the polymer matrix was poly(acrylonitrile) (PAN). The morphology and possible applications of these composite nano fibers will be discussed

Correlation coefficient based supervised locally linear embedding for pulmonary nodule recognition.

Science.gov (United States)

Wu, Panpan; Xia, Kewen; Yu, Hengyong

2016-11-01

Dimensionality reduction techniques are developed to suppress the negative effects of high dimensional feature space of lung CT images on classification performance in computer aided detection (CAD) systems for pulmonary nodule detection. An improved supervised locally linear embedding (SLLE) algorithm is proposed based on the concept of correlation coefficient. The Spearman's rank correlation coefficient is introduced to adjust the distance metric in the SLLE algorithm to ensure that more suitable neighborhood points could be identified, and thus to enhance the discriminating power of embedded data. The proposed Spearman's rank correlation coefficient based SLLE (SC(2)SLLE) is implemented and validated in our pilot CAD system using a clinical dataset collected from the publicly available lung image database consortium and image database resource initiative (LICD-IDRI). Particularly, a representative CAD system for solitary pulmonary nodule detection is designed and implemented. After a sequential medical image processing steps, 64 nodules and 140 non-nodules are extracted, and 34 representative features are calculated. The SC(2)SLLE, as well as SLLE and LLE algorithm, are applied to reduce the dimensionality. Several quantitative measurements are also used to evaluate and compare the performances. Using a 5-fold cross-validation methodology, the proposed algorithm achieves 87.65% accuracy, 79.23% sensitivity, 91.43% specificity, and 8.57% false positive rate, on average. Experimental results indicate that the proposed algorithm outperforms the original locally linear embedding and SLLE coupled with the support vector machine (SVM) classifier. Based on the preliminary results from a limited number of nodules in our dataset, this study demonstrates the great potential to improve the performance of a CAD system for nodule detection using the proposed SC(2)SLLE. Copyright © 2016 Elsevier Ireland Ltd. All rights reserved.

Uniform competency-based local feature extraction for remote sensing images

Science.gov (United States)

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.

Development of MARS for multi-dimensional and multi-purpose thermal-hydraulic system analysis

Energy Technology Data Exchange (ETDEWEB)

Lee, Won Jae; Chung, Bub Dong; Kim, Kyung Doo; Hwang, Moon Kyu; Jeong, Jae Jun; Ha, Kwi Seok; Joo, Han Gyu [Korea Atomic Energy Research Institute, T/H Safety Research Team, Yusung, Daejeon (Korea)

2000-10-01

MARS (Multi-dimensional Analysis of Reactor Safety) code is being developed by KAERI for the realistic thermal-hydraulic simulation of light water reactor system transients. MARS 1.4 has been developed as a final version of basic code frame for the multi-dimensional analysis of system thermal-hydraulics. Since MARS 1.3, MARS 1.4 has been improved to have the enhanced code capability and user friendliness through the unification of input/output features, code models and code functions, and through the code modernization. Further improvements of thermal-hydraulic models, numerical method and user friendliness are being carried out for the enhanced code accuracy. As a multi-purpose safety analysis code system, a coupled analysis system, MARS/MASTER/CONTEMPT, has been developed using multiple DLL (Dynamic Link Library) techniques of Windows system. This code system enables the coupled, that is, more realistic analysis of multi-dimensional thermal-hydraulics (MARS 2.0), three-dimensional core kinetics (MASTER) and containment thermal-hydraulics (CONTEMPT). This paper discusses the MARS development program, and the developmental progress of the MARS 1.4 and the MARS/MASTER/CONTEMPT focusing on major features of the codes and their verification. It also discusses thermal hydraulic models and new code features under development. (author)

Development of MARS for multi-dimensional and multi-purpose thermal-hydraulic system analysis

International Nuclear Information System (INIS)

Lee, Won Jae; Chung, Bub Dong; Kim, Kyung Doo; Hwang, Moon Kyu; Jeong, Jae Jun; Ha, Kwi Seok; Joo, Han Gyu

2000-01-01

MARS (Multi-dimensional Analysis of Reactor Safety) code is being developed by KAERI for the realistic thermal-hydraulic simulation of light water reactor system transients. MARS 1.4 has been developed as a final version of basic code frame for the multi-dimensional analysis of system thermal-hydraulics. Since MARS 1.3, MARS 1.4 has been improved to have the enhanced code capability and user friendliness through the unification of input/output features, code models and code functions, and through the code modernization. Further improvements of thermal-hydraulic models, numerical method and user friendliness are being carried out for the enhanced code accuracy. As a multi-purpose safety analysis code system, a coupled analysis system, MARS/MASTER/CONTEMPT, has been developed using multiple DLL (Dynamic Link Library) techniques of Windows system. This code system enables the coupled, that is, more realistic analysis of multi-dimensional thermal-hydraulics (MARS 2.0), three-dimensional core kinetics (MASTER) and containment thermal-hydraulics (CONTEMPT). This paper discusses the MARS development program, and the developmental progress of the MARS 1.4 and the MARS/MASTER/CONTEMPT focusing on major features of the codes and their verification. It also discusses thermal hydraulic models and new code features under development. (author)

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

Science.gov (United States)

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…

Algorithmic foundation of multi-scale spatial representation

CERN Document Server

Li, Zhilin

2006-01-01

With the widespread use of GIS, multi-scale representation has become an important issue in the realm of spatial data handling. However, no book to date has systematically tackled the different aspects of this discipline. Emphasizing map generalization, Algorithmic Foundation of Multi-Scale Spatial Representation addresses the mathematical basis of multi-scale representation, specifically, the algorithmic foundation.Using easy-to-understand language, the author focuses on geometric transformations, with each chapter surveying a particular spatial feature. After an introduction to the essential operations required for geometric transformations as well as some mathematical and theoretical background, the book describes algorithms for a class of point features/clusters. It then examines algorithms for individual line features, such as the reduction of data points, smoothing (filtering), and scale-driven generalization, followed by a discussion of algorithms for a class of line features including contours, hydrog...

Hybrid modelling of soil-structure interaction for embedded structures

International Nuclear Information System (INIS)

Gupta, S.; Penzien, J.

1981-01-01

The basic methods currently being used for the analysis of soil-structure interaction fail to properly model three-dimensional embedded structures with flexible foundations. A hybrid model for the analysis of soil-structure interaction is developed in this investigation which takes advantage of the desirable features of both the finite element and substructure methods and which minimizes their undesirable features. The hybrid model is obtained by partitioning the total soil-structure system into a nearfield and a far-field with a smooth hemispherical interface. The near-field consists of the structure and a finite region of soil immediately surrounding its base. The entire near-field may be modelled in three-dimensional form using the finite element method; thus, taking advantage of its ability to model irregular geometries, and the non-linear soil behavior in the immediate vicinity of the structure. (orig./WL)

CASPER: Embedding Power Estimation and Hardware-Controlled Power Management in a Cycle-Accurate Micro-Architecture Simulation Platform for Many-Core Multi-Threading Heterogeneous Processors

Directory of Open Access Journals (Sweden)

Arun Ravindran

2012-02-01

Full Text Available Despite the promising performance improvement observed in emerging many-core architectures in high performance processors, high power consumption prohibitively affects their use and marketability in the low-energy sectors, such as embedded processors, network processors and application specific instruction processors (ASIPs. While most chip architects design power-efficient processors by finding an optimal power-performance balance in their design, some use sophisticated on-chip autonomous power management units, which dynamically reduce the voltage or frequencies of idle cores and hence extend battery life and reduce operating costs. For large scale designs of many-core processors, a holistic approach integrating both these techniques at different levels of abstraction can potentially achieve maximal power savings. In this paper we present CASPER, a robust instruction trace driven cycle-accurate many-core multi-threading micro-architecture simulation platform where we have incorporated power estimation models of a wide variety of tunable many-core micro-architectural design parameters, thus enabling processor architects to explore a sufficiently large design space and achieve power-efficient designs. Additionally CASPER is designed to accommodate cycle-accurate models of hardware controlled power management units, enabling architects to experiment with and evaluate different autonomous power-saving mechanisms to study the run-time power-performance trade-offs in embedded many-core processors. We have implemented two such techniques in CASPER–Chipwide Dynamic Voltage and Frequency Scaling, and Performance Aware Core-Specific Frequency Scaling, which show average power savings of 35.9% and 26.2% on a baseline 4-core SPARC based architecture respectively. This power saving data accounts for the power consumption of the power management units themselves. The CASPER simulation platform also provides users with complete support of SPARCV9

Performance evaluation of multi-channel wireless mesh networks with embedded systems.

Science.gov (United States)

Lam, Jun Huy; Lee, Sang-Gon; Tan, Whye Kit

2012-01-01

Many commercial wireless mesh network (WMN) products are available in the marketplace with their own proprietary standards, but interoperability among the different vendors is not possible. Open source communities have their own WMN implementation in accordance with the IEEE 802.11s draft standard, Linux open80211s project and FreeBSD WMN implementation. While some studies have focused on the test bed of WMNs based on the open80211s project, none are based on the FreeBSD. In this paper, we built an embedded system using the FreeBSD WMN implementation that utilizes two channels and evaluated its performance. This implementation allows the legacy system to connect to the WMN independent of the type of platform and distributes the load between the two non-overlapping channels. One channel is used for the backhaul connection and the other one is used to connect to the stations to wireless mesh network. By using the power efficient 802.11 technology, this device can also be used as a gateway for the wireless sensor network (WSN).

Structure of the Einstein tensor for class-1 embedded space time

Energy Technology Data Exchange (ETDEWEB)

Krause, J [Universidad Central de Venezuela, Caracas

1976-04-11

Continuing previous work, some features of the flat embedding theory of class-1 curved space-time are further discussed. In the two-metric formalism provided by the embedding approach the Gauss tensor obtains as the flat-covariant gradient of a fundamental vector potential. The Einstein tensor is then examined in terms of the Gauss tensor. It is proved that the Einstein tensor is divergence free in flat space-time, i.e. a true Lorentz-covariant conservation law for the Einstein tensor is shown to hold. The form of the Einstein tensor in flat space-time also appears as a canonical energy-momentum tensor of the vector potential. The corresponding Lagrangian density, however, does not provide us with a set of field equations for the fundamental vector potential; indeed, the Euler-Lagrange ''equations'' collapse to a useless identity, while the Lagrangian density has the form of a flat divergence.

Pruning techniques for multi-objective system-level design space exploration

NARCIS (Netherlands)

Piscitelli, R.

2014-01-01

System-level design space exploration (DSE), which is performed early in the design process, is of eminent importance to the design of complex multi-processor embedded system architectures. During system-level DSE, system parameters like, e.g., the number and type of processors, the type and size of

A computational environment for long-term multi-feature and multi-algorithm seizure prediction.

Science.gov (United States)

Teixeira, C A; Direito, B; Costa, R P; Valderrama, M; Feldwisch-Drentrup, H; Nikolopoulos, S; Le Van Quyen, M; Schelter, B; Dourado, A

2010-01-01

The daily life of epilepsy patients is constrained by the possibility of occurrence of seizures. Until now, seizures cannot be predicted with sufficient sensitivity and specificity. Most of the seizure prediction studies have been focused on a small number of patients, and frequently assuming unrealistic hypothesis. This paper adopts the view that for an appropriate development of reliable predictors one should consider long-term recordings and several features and algorithms integrated in one software tool. A computational environment, based on Matlab (®), is presented, aiming to be an innovative tool for seizure prediction. It results from the need of a powerful and flexible tool for long-term EEG/ECG analysis by multiple features and algorithms. After being extracted, features can be subjected to several reduction and selection methods, and then used for prediction. The predictions can be conducted based on optimized thresholds or by applying computational intelligence methods. One important aspect is the integrated evaluation of the seizure prediction characteristic of the developed predictors.

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

Science.gov (United States)

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...

Method for independent strain and temperature measurement in polymeric tensile test specimen using embedded FBG sensors

DEFF Research Database (Denmark)

Pereira, Gilmar Ferreira; McGugan, Malcolm; Mikkelsen, Lars Pilgaard

2016-01-01

to calculate independently the strain and temperature are presented in the article, together with a measurement resolution study. This multi-parameter measurement method was applied to an epoxy tensile specimen, tested in a unidirectional tensile test machine with a temperature controlled cabinet. A full......A novel method to obtain independent strain and temperature measurements using embedded Fibre Bragg Grating (FBG) in polymeric tensile test specimens is presented in this paper. The FBG strain and temperature cross-sensitivity was decoupled using two single mode FBG sensors, which were embedded...... of temperature, from 40 C to -10 C. The consistency of the expected theoretical results with the calibration procedure and the experimental validation shows that this proposed method is applicable to measure accurate strain and temperature in polymers during static or fatigue tensile testing. Two different...

Embedded Linux projects using Yocto project cookbook

CERN Document Server

González, Alex

2015-01-01

If you are an embedded developer learning about embedded Linux with some experience with the Yocto project, this book is the ideal way to become proficient and broaden your knowledge with examples that are immediately applicable to your embedded developments. Experienced embedded Yocto developers will find new insight into working methodologies and ARM specific development competence.

A multi-approach feature extractions for iris recognition

Science.gov (United States)

Sanpachai, H.; Settapong, M.

2014-04-01

Biometrics is a promising technique that is used to identify individual traits and characteristics. Iris recognition is one of the most reliable biometric methods. As iris texture and color is fully developed within a year of birth, it remains unchanged throughout a person's life. Contrary to fingerprint, which can be altered due to several aspects including accidental damage, dry or oily skin and dust. Although iris recognition has been studied for more than a decade, there are limited commercial products available due to its arduous requirement such as camera resolution, hardware size, expensive equipment and computational complexity. However, at the present time, technology has overcome these obstacles. Iris recognition can be done through several sequential steps which include pre-processing, features extractions, post-processing, and matching stage. In this paper, we adopted the directional high-low pass filter for feature extraction. A box-counting fractal dimension and Iris code have been proposed as feature representations. Our approach has been tested on CASIA Iris Image database and the results are considered successful.

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

Science.gov (United States)

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…

Statistical analyses of conserved features of genomic islands in bacteria.

Science.gov (United States)

Guo, F-B; Xia, Z-K; Wei, W; Zhao, H-L

2014-03-17

We performed statistical analyses of five conserved features of genomic islands of bacteria. Analyses were made based on 104 known genomic islands, which were identified by comparative methods. Four of these features include sequence size, abnormal G+C content, flanking tRNA gene, and embedded mobility gene, which are frequently investigated. One relatively new feature, G+C homogeneity, was also investigated. Among the 104 known genomic islands, 88.5% were found to fall in the typical length of 10-200 kb and 80.8% had G+C deviations with absolute values larger than 2%. For the 88 genomic islands whose hosts have been sequenced and annotated, 52.3% of them were found to have flanking tRNA genes and 64.7% had embedded mobility genes. For the homogeneity feature, 85% had an h homogeneity index less than 0.1, indicating that their G+C content is relatively uniform. Taking all the five features into account, 87.5% of 88 genomic islands had three of them. Only one genomic island had only one conserved feature and none of the genomic islands had zero features. These statistical results should help to understand the general structure of known genomic islands. We found that larger genomic islands tend to have relatively small G+C deviations relative to absolute values. For example, the absolute G+C deviations of 9 genomic islands longer than 100,000 bp were all less than 5%. This is a novel but reasonable result given that larger genomic islands should have greater restrictions in their G+C contents, in order to maintain the stable G+C content of the recipient genome.

Crack Growth Monitoring by Embedded Optical Fibre Bragg Grating Sensors: Fibre Reinforced Plastic Crack Growing Detection

DEFF Research Database (Denmark)

Pereira, Gilmar Ferreira; Mikkelsen, Lars Pilgaard; McGugan, Malcolm

2015-01-01

This article presents a novel method to asses a crack growing/damage event in fibre reinforced plastic, or adhesive using Fibre Bragg Grating (FBG) sensors embedded in a host material. Different features of the crack mechanism that induce a change in the FBG response were identified. Double Canti...

Beyond assemblies: system convergence and multi-materiality.

Science.gov (United States)

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.

Beyond assemblies: system convergence and multi-materiality

International Nuclear Information System (INIS)

Wiscombe, Tom

2012-01-01

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.

Scalar-Tensor Black Holes Embedded in an Expanding Universe

Science.gov (United States)

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.

Scalar-Tensor Black Holes Embedded in an Expanding Universe

Directory of Open Access Journals (Sweden)

Daria Tretyakova

2018-02-01

Full Text Available 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 black holes, 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 issues that are not fully investigated.

Discriminative Multi-View Interactive Image Re-Ranking.

Science.gov (United States)

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.

Polarizable Density Embedding

DEFF Research Database (Denmark)

Olsen, Jógvan Magnus Haugaard; Steinmann, Casper; Ruud, Kenneth

2015-01-01

We present a new QM/QM/MM-based model for calculating molecular properties and excited states of solute-solvent systems. We denote this new approach the polarizable density embedding (PDE) model and it represents an extension of our previously developed polarizable embedding (PE) strategy. The PDE...... model is a focused computational approach in which a core region of the system studied is represented by a quantum-chemical method, whereas the environment is divided into two other regions: an inner and an outer region. Molecules belonging to the inner region are described by their exact densities...

MS-XANES studies on the interface effect of semiconductor InSb nanoparticles embedded in a-SiO2 matrix

International Nuclear Information System (INIS)

Chen Dongliang; Wu Ziyu; Wei Shiqiang

2006-01-01

The interface effect of semiconductor InSb nanoparticles (NPs) embedded in a-SiO 2 matrix was investigated via multi-scattering XANES simulations. The results show that the white line increase and broadening to higher energies of InSb NPs embedded in a-SiO 2 host matrix are mainly due to the interaction of InSb NPs and a-SiO 2 matrix. It can be interpreted as both a local single-site effect on μ 0 (E) due to the effect of a-SiO 2 matrix on Sb intra-atomic potential and the increase in 5p-hole population due to 5p-electron depletion in Sb for the InSb NPs embedded in SiO 2 matrix. On the other hand, our result reveals evidently that it is not reasonable to estimate the 5p-hole counts only according to the intensity of the white line due to the interface effect of nanoparticles. (authors)

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

Science.gov (United States)

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).

Specification and Verification of Distributed Embedded Systems: A Traffic Intersection Product Family

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José Meseguer

2010-09-01

Full Text Available Distributed embedded systems (DESs are no longer the exception; they are the rule in many application areas such as avionics, the automotive industry, traffic systems, sensor networks, and medical devices. Formal DES specification and verification is challenging due to state space explosion and the need to support real-time features. This paper reports on an extensive industry-based case study involving a DES product family for a pedestrian and car 4-way traffic intersection in which autonomous devices communicate by asynchronous message passing without a centralized controller. All the safety requirements and a liveness requirement informally specified in the requirements document have been formally verified using Real-Time Maude and its model checking features.

New technique of skin embedded wire double-sided laser beam welding

Science.gov (United States)

Han, Bing; Tao, Wang; Chen, Yanbin

2017-06-01

In the aircraft industry, double-sided laser beam welding is an approved method for producing skin-stringer T-joints on aircraft fuselage panels. As for the welding of new generation aluminum-lithium alloys, however, this technique is limited because of high hot cracking susceptibility and strengthening elements' uneven distributions within weld. In the present study, a new technique of skin embedded wire double-sided laser beam welding (LBW) has been developed to fabricate T-joints consisting of 2.0 mm thick 2060-T8/2099-T83 aluminum-lithium alloys using eutectic alloy AA4047 filler wire. Necessary dimension parameters of the novel groove were reasonably designed for achieving crack-free welds. Comparisons were made between the new technique welded T-joint and conventional T-joint mainly on microstructure, hot crack, elements distribution features and mechanical properties within weld. Excellent crack-free microstructure, uniform distribution of silicon and superior tensile properties within weld were found in the new skin embedded wire double-sided LBW T-joints.

Features of the non-collinear one-phonon anomalous light scattering controlled by elastic waves with elevated linear losses: potentials for multi-frequency parallel spectrum analysis of radio-wave signals.

Science.gov (United States)

Shcherbakov, Alexandre S; Arellanes, Adan Omar

2017-12-01

During subsequent development of the recently proposed multi-frequency parallel spectrometer for precise spectrum analysis of wideband radio-wave signals, we study potentials of new acousto-optical cells exploiting selected crystalline materials at the limits of their capabilities. Characterizing these wide-aperture cells is non-trivial due to new features inherent in the chosen regime of an advanced non-collinear one-phonon anomalous light scattering by elastic waves with significantly elevated acoustic losses. These features can be observed simpler in uniaxial, tetragonal, and trigonal crystals possessing linear acoustic attenuation. We demonstrate that formerly studied additional degree of freedom, revealed initially for multi-phonon regimes of acousto-optical interaction, can be identified within the one-phonon geometry as well and exploited for designing new cells. We clarify the role of varying the central acoustic frequency and acoustic attenuation using the identified degree of freedom. Therewith, we are strongly restricted by a linear regime of acousto-optical interaction to avoid the origin of multi-phonon processes within carrying out a multi-frequency parallel spectrum analysis of radio-wave signals. Proof-of-principle experiments confirm the developed approaches and illustrate their applicability to innovative technique for an advanced spectrum analysis of wideband radio-wave signals with the improved resolution in an extended frequency range.

A study on embedded resistor components fabricated by laser micro-cladding and rapid prototype

International Nuclear Information System (INIS)

Li Huiling; Zeng Xiaoyan

2006-01-01

With the rapid development of IC and packaging, electronic devices are required to be smaller, to have a high-density integration, to become multifunction and to be of lower cost and high-reliability. Thick-film technology is not able to meet the current developing demands because of its shortcomings, such as the limit of pattern resolution, the severe torsion and delay of high-speed signal transmission. The speed and quality of signal transmission will be improved if embedded resistor components are directly integrated in the multiplayer substrate of multi-chip or laminated module, and high-density integration and reliability are achieved because the short interconnection and the less soldering point. In this paper, a technique named laser micro-cladding and rapid prototype is used to directly fabricate embedded resistor units on the multiplayer ceramic substrate without using a mask and high-temperature sintering, and without trimming resistor, which will simplify processing and decrease cost as well as improving high-speed and reliable performance

Prognosis Essay Scoring and Article Relevancy Using Multi-Text Features and Machine Learning

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Arif Mehmood

2017-01-01

Full Text Available This study develops a model for essay scoring and article relevancy. Essay scoring is a costly process when we consider the time spent by an evaluator. It may lead to inequalities of the effort by various evaluators to apply the same evaluation criteria. Bibliometric research uses the evaluation criteria to find relevancy of articles instead. Researchers mostly face relevancy issues while searching articles. Therefore, they classify the articles manually. However, manual classification is burdensome due to time needed for evaluation. The proposed model performs automatic essay evaluation using multi-text features and ensemble machine learning. The proposed method is implemented in two data sets: a Kaggle short answer data set for essay scoring that includes four ranges of disciplines (Science, Biology, English, and English language Arts, and a bibliometric data set having IoT (Internet of Things and non-IoT classes. The efficacy of the model is measured against the Tandalla and AutoP approach using Cohen’s kappa. The model achieves kappa values of 0.80 and 0.83 for the first and second data sets, respectively. Kappa values show that the proposed model has better performance than those of earlier approaches.

Enhancement in visible light-responsive photocatalytic activity by embedding Cu-doped ZnO nanoparticles on multi-walled carbon nanotubes

Energy Technology Data Exchange (ETDEWEB)

Ahmad, M., E-mail: mzkhm73@gmail.com [Department of Physics, Bahauddin Zakariya University, Multan 60800 (Pakistan); State Key Laboratory of Silicon Materials, Department of Materials Science and Engineering, Zhejiang University, Hangzhou 310027 (China); Ahmed, E., E-mail: profejaz@gmail.com [Department of Physics, Bahauddin Zakariya University, Multan 60800 (Pakistan); Hong, Z.L.; Jiao, X.L. [State Key Laboratory of Silicon Materials, Department of Materials Science and Engineering, Zhejiang University, Hangzhou 310027 (China); Abbas, T. [Institute of Industrial Control System, Rawalpindi (Pakistan); Khalid, N.R. [Department of Physics, Bahauddin Zakariya University, Multan 60800 (Pakistan); State Key Laboratory of Silicon Materials, Department of Materials Science and Engineering, Zhejiang University, Hangzhou 310027 (China)

2013-11-15

Copper doped ZnO nanoparticles embedded on multi-walled carbon nanotubes (CNTs) were successfully synthesized using a facile, nontoxic sol method. The resulting visible light-responsive Cu-doped ZnO/CNTs composites were characterized using powder X-ray diffraction (XRD), high resolution transmission electron microscope (HR-TEM), transmission electron microscope (TEM), scanning electron microscope (SEM) with energy dispersive X-ray analysis (EDX), X-ray photoelectron spectroscopy (XPS) and Brunauer Emmett Teller (BET) surface area analyzer. Optical properties of Cu-doped ZnO/CNTs nanocomposites, studied using UV–vis diffuse reflectance spectroscopy and photoluminescence spectroscopy (PL), which exhibited extended light absorption in visible light region and possessed better charge separation capability, respectively as compared to Cu-doped ZnO, pure ZnO and ZnO/CNTs composite. The photocatalytic activity was tested by degradation of methyl orange (MO) dye under visible light irradiation. The results demonstrated that Cu-doped ZnO/CNTs nanocomposites effectively bleached out MO, showing an impressive photocatalytic enhancement over ZnO, commercial ZnO, Cu-doped ZnO nanoparticles and ZnO/CNTs nanocomposites. Chemical oxygen demand (COD) of textile wastewater was also measured before and after the photocatalysis experiment under sunlight to evaluate the mineralization of wastewater. The significant decrease in COD of the treated effluent revealed a complete destruction of the organic molecules along with color removal. This dramatically enhanced photoactivity of nanocomposite photocatalysts was attributed to greater adsorptivity of dyes, extended light absorption and increased charge separation efficiency due to excellent electrical properties of carbon nanotubes and the large surface area.

Superheating and supercooling of Ge nanocrystals embedded in SiO2

International Nuclear Information System (INIS)

Xu, Q; Sharp, I D; Yuan, C W; Yi, D O; Liao, C Y; Glaeser, A M; Minor, A M; Beeman, J W; Ridgway, M C; Kluth, P; Iii, J W Ager; Chrzan, D C; Haller, E E

2007-01-01

Free-standing nanocrystals exhibit a size-dependant thermodynamic melting point reduction relative to the bulk melting point that is governed by the surface free energy. The presence of an encapsulating matrix, however, alters the interface free energy of nanocrystals and their thermodynamic melting point can either increase or decrease relative to bulk. Furthermore, kinetic contributions can significantly alter the melting behaviours of embedded nanoscale materials. To study the effect of an encapsulating matrix on the melting behaviour of nanocrystals, we performed in situ electron diffraction measurements on Ge nanocrystals embedded in a silicon dioxide matrix. Ge nanocrystals were formed by multi-energy ion implantation into a 500 nm thick silica thin film on a silicon substrate followed by thermal annealing at 900 deg. C for 1 h. We present results demonstrating that Ge nanocrystals embedded in SiO 2 exhibit a 470 K melting/solidification hysteresis that is approximately symmetric about the bulk melting point. This unique behaviour, which is thought to be impossible for bulk materials, is well described using a classical thermodynamic model that predicts both kinetic supercooling and kinetic superheating. The presence of the silica matrix suppresses surface pre-melting of nanocrystals. Therefore, heterogeneous nucleation of both the liquid phase and the solid phase are required during the heating and cooling cycle. The magnitude of melting hysteresis is governed primarily by the value of the liquid Ge/solid Ge interface free energy, whereas the relative values of the solid Ge/matrix and liquid Ge/matrix interface free energies govern the position of the hysteresis loop in absolute temperature

Joint Multi-scale Convolution Neural Network for Scene Classification of High Resolution Remote Sensing Imagery

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ZHENG Zhuo

2018-05-01

Full Text Available High resolution remote sensing imagery scene classification is important for automatic complex scene recognition, which is the key technology for military and disaster relief, etc. In this paper, we propose a novel joint multi-scale convolution neural network (JMCNN method using a limited amount of image data for high resolution remote sensing imagery scene classification. Different from traditional convolutional neural network, the proposed JMCNN is an end-to-end training model with joint enhanced high-level feature representation, which includes multi-channel feature extractor, joint multi-scale feature fusion and Softmax classifier. Multi-channel and scale convolutional extractors are used to extract scene middle features, firstly. Then, in order to achieve enhanced high-level feature representation in a limit dataset, joint multi-scale feature fusion is proposed to combine multi-channel and scale features using two feature fusions. Finally, enhanced high-level feature representation can be used for classification by Softmax. Experiments were conducted using two limit public UCM and SIRI datasets. Compared to state-of-the-art methods, the JMCNN achieved improved performance and great robustness with average accuracies of 89.3% and 88.3% on the two datasets.