Authentication of Smartphone Users Based on Activity Recognition and Mobile Sensing

PubMed Central

Ehatisham-ul-Haq, Muhammad; Azam, Muhammad Awais; Loo, Jonathan; Shuang, Kai; Islam, Syed; Naeem, Usman; Amin, Yasar

2017-01-01

Smartphones are context-aware devices that provide a compelling platform for ubiquitous computing and assist users in accomplishing many of their routine tasks anytime and anywhere, such as sending and receiving emails. The nature of tasks conducted with these devices has evolved with the exponential increase in the sensing and computing capabilities of a smartphone. Due to the ease of use and convenience, many users tend to store their private data, such as personal identifiers and bank account details, on their smartphone. However, this sensitive data can be vulnerable if the device gets stolen or lost. A traditional approach for protecting this type of data on mobile devices is to authenticate users with mechanisms such as PINs, passwords, and fingerprint recognition. However, these techniques are vulnerable to user compliance and a plethora of attacks, such as smudge attacks. The work in this paper addresses these challenges by proposing a novel authentication framework, which is based on recognizing the behavioral traits of smartphone users using the embedded sensors of smartphone, such as Accelerometer, Gyroscope and Magnetometer. The proposed framework also provides a platform for carrying out multi-class smart user authentication, which provides different levels of access to a wide range of smartphone users. This work has been validated with a series of experiments, which demonstrate the effectiveness of the proposed framework. PMID:28878177

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

PubMed

Hamada, Megumi

2017-10-01

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

Factors that influence the performance of experienced speech recognition users.

PubMed

Koester, Heidi Horstmann

2006-01-01

Performance on automatic speech recognition (ASR) systems for users with physical disabilities varies widely between individuals. The goal of this study was to discover some key factors that account for that variation. Using data from 23 experienced ASR users with physical disabilities, the effect of 20 different independent variables on recognition accuracy and text entry rate with ASR was measured using bivariate and multivariate analyses. The results show that use of appropriate correction strategies had the strongest influence on user performance with ASR. The amount of time the user spent on his or her computer, the user's manual typing speed, and the speed with which the ASR system recognized speech were all positively associated with better performance. The amount or perceived adequacy of ASR training did not have a significant impact on performance for this user group.

[Research progress of multi-model medical image fusion and recognition].

PubMed

Zhou, Tao; Lu, Huiling; Chen, Zhiqiang; Ma, Jingxian

2013-10-01

Medical image fusion and recognition has a wide range of applications, such as focal location, cancer staging and treatment effect assessment. Multi-model medical image fusion and recognition are analyzed and summarized in this paper. Firstly, the question of multi-model medical image fusion and recognition is discussed, and its advantage and key steps are discussed. Secondly, three fusion strategies are reviewed from the point of algorithm, and four fusion recognition structures are discussed. Thirdly, difficulties, challenges and possible future research direction are discussed.

Can soft biometric traits assist user recognition?

NASA Astrophysics Data System (ADS)

Jain, Anil K.; Dass, Sarat C.; Nandakumar, Karthik

2004-08-01

Biometrics is rapidly gaining acceptance as the technology that can meet the ever increasing need for security in critical applications. Biometric systems automatically recognize individuals based on their physiological and behavioral characteristics. Hence, the fundamental requirement of any biometric recognition system is a human trait having several desirable properties like universality, distinctiveness, permanence, collectability, acceptability, and resistance to circumvention. However, a human characteristic that possesses all these properties has not yet been identified. As a result, none of the existing biometric systems provide perfect recognition and there is a scope for improving the performance of these systems. Although characteristics like gender, ethnicity, age, height, weight and eye color are not unique and reliable, they provide some information about the user. We refer to these characteristics as "soft" biometric traits and argue that these traits can complement the identity information provided by the primary biometric identifiers like fingerprint and face. This paper presents the motivation for utilizing soft biometric information and analyzes how the soft biometric traits can be automatically extracted and incorporated in the decision making process of the primary biometric system. Preliminary experiments were conducted on a fingerprint database of 160 users by synthetically generating soft biometric traits like gender, ethnicity, and height based on known statistics. The results show that the use of additional soft biometric user information significantly improves (approximately 6%) the recognition performance of the fingerprint biometric system.

User-Independent Motion State Recognition Using Smartphone Sensors

PubMed Central

Gu, Fuqiang; Kealy, Allison; Khoshelham, Kourosh; Shang, Jianga

2015-01-01

The recognition of locomotion activities (e.g., walking, running, still) is important for a wide range of applications like indoor positioning, navigation, location-based services, and health monitoring. Recently, there has been a growing interest in activity recognition using accelerometer data. However, when utilizing only acceleration-based features, it is difficult to differentiate varying vertical motion states from horizontal motion states especially when conducting user-independent classification. In this paper, we also make use of the newly emerging barometer built in modern smartphones, and propose a novel feature called pressure derivative from the barometer readings for user motion state recognition, which is proven to be effective for distinguishing vertical motion states and does not depend on specific users’ data. Seven types of motion states are defined and six commonly-used classifiers are compared. In addition, we utilize the motion state history and the characteristics of people’s motion to improve the classification accuracies of those classifiers. Experimental results show that by using the historical information and human’s motion characteristics, we can achieve user-independent motion state classification with an accuracy of up to 90.7%. In addition, we analyze the influence of the window size and smartphone pose on the accuracy. PMID:26690163

Multi-Task Convolutional Neural Network for Pose-Invariant Face Recognition

NASA Astrophysics Data System (ADS)

Yin, Xi; Liu, Xiaoming

2018-02-01

This paper explores multi-task learning (MTL) for face recognition. We answer the questions of how and why MTL can improve the face recognition performance. First, we propose a multi-task Convolutional Neural Network (CNN) for face recognition where identity classification is the main task and pose, illumination, and expression estimations are the side tasks. Second, we develop a dynamic-weighting scheme to automatically assign the loss weight to each side task, which is a crucial problem in MTL. Third, we propose a pose-directed multi-task CNN by grouping different poses to learn pose-specific identity features, simultaneously across all poses. Last but not least, we propose an energy-based weight analysis method to explore how CNN-based MTL works. We observe that the side tasks serve as regularizations to disentangle the variations from the learnt identity features. Extensive experiments on the entire Multi-PIE dataset demonstrate the effectiveness of the proposed approach. To the best of our knowledge, this is the first work using all data in Multi-PIE for face recognition. Our approach is also applicable to in-the-wild datasets for pose-invariant face recognition and achieves comparable or better performance than state of the art on LFW, CFP, and IJB-A datasets.

Action tagging in a multi-user indoor environment for behavioural analysis purposes.

PubMed

Guerra, Claudio; Bianchi, Valentina; De Munari, Ilaria; Ciampolini, Paolo

2015-01-01

EU population is getting older, so that ICT-based solutions are expected to provide support in the challenges implied by the demographic change. At the University of Parma an AAL (Ambient Assisted Living) system, named CARDEA, has been developed. In this paper a new feature of the system is introduced, in which environmental and personal (i.e., wearable) sensors coexist, providing an accurate picture of the user's activity and needs. Environmental devices may greatly help in performing activity recognition and behavioral analysis tasks. However, in a multi-user environment, this implies the need of attributing environmental sensors outcome to a specific user, i.e., identifying the user when he performs a task detected by an environmental device. We implemented such an "action tagging" feature, based on information fusion, within the CARDEA environment, as an inexpensive, alternative solution to the problematic issue of indoor locationing.

Knowledge Engineering Aspects of Affective Bi-Modal Educational Applications

NASA Astrophysics Data System (ADS)

Alepis, Efthymios; Virvou, Maria; Kabassi, Katerina

This paper analyses the knowledge and software engineering aspects of educational applications that provide affective bi-modal human-computer interaction. For this purpose, a system that provides affective interaction based on evidence from two different modes has been developed. More specifically, the system's inferences about students' emotions are based on user input evidence from the keyboard and the microphone. Evidence from these two modes is combined by a user modelling component that incorporates user stereotypes as well as a multi criteria decision making theory. The mechanism that integrates the inferences from the two modes has been based on the results of two empirical studies that were conducted in the context of knowledge engineering of the system. The evaluation of the developed system showed significant improvements in the recognition of the emotional states of users.

The Suitability of Cloud-Based Speech Recognition Engines for Language Learning

ERIC Educational Resources Information Center

Daniels, Paul; Iwago, Koji

2017-01-01

As online automatic speech recognition (ASR) engines become more accurate and more widely implemented with call software, it becomes important to evaluate the effectiveness and the accuracy of these recognition engines using authentic speech samples. This study investigates two of the most prominent cloud-based speech recognition engines--Apple's…

Using Markov Chains and Multi-Objective Optimization for Energy-Efficient Context Recognition.

PubMed

Janko, Vito; Luštrek, Mitja

2017-12-29

The recognition of the user's context with wearable sensing systems is a common problem in ubiquitous computing. However, the typically small battery of such systems often makes continuous recognition impractical. The strain on the battery can be reduced if the sensor setting is adapted to each context. We propose a method that efficiently finds near-optimal sensor settings for each context. It uses Markov chains to simulate the behavior of the system in different configurations and the multi-objective genetic algorithm to find a set of good non-dominated configurations. The method was evaluated on three real-life datasets and found good trade-offs between the system's energy expenditure and the system's accuracy. One of the solutions, for example, consumed five-times less energy than the default one, while sacrificing only two percentage points of accuracy.

User engineering: A new look at system engineering

NASA Technical Reports Server (NTRS)

Mclaughlin, Larry L.

1987-01-01

User Engineering is a new System Engineering perspective responsible for defining and maintaining the user view of the system. Its elements are a process to guide the project and customer, a multidisciplinary team including hard and soft sciences, rapid prototyping tools to build user interfaces quickly and modify them frequently at low cost, and a prototyping center for involving users and designers in an iterative way. The main consideration is reducing the risk that the end user will not or cannot effectively use the system. The process begins with user analysis to produce cognitive and work style models, and task analysis to produce user work functions and scenarios. These become major drivers of the human computer interface design which is presented and reviewed as an interactive prototype by users. Feedback is rapid and productive, and user effectiveness can be measured and observed before the system is built and fielded. Requirements are derived via the prototype and baselined early to serve as an input to the architecture and software design.

Clustered Multi-Task Learning for Automatic Radar Target Recognition

PubMed Central

Li, Cong; Bao, Weimin; Xu, Luping; Zhang, Hua

2017-01-01

Model training is a key technique for radar target recognition. Traditional model training algorithms in the framework of single task leaning ignore the relationships among multiple tasks, which degrades the recognition performance. In this paper, we propose a clustered multi-task learning, which can reveal and share the multi-task relationships for radar target recognition. To further make full use of these relationships, the latent multi-task relationships in the projection space are taken into consideration. Specifically, a constraint term in the projection space is proposed, the main idea of which is that multiple tasks within a close cluster should be close to each other in the projection space. In the proposed method, the cluster structures and multi-task relationships can be autonomously learned and utilized in both of the original and projected space. In view of the nonlinear characteristics of radar targets, the proposed method is extended to a non-linear kernel version and the corresponding non-linear multi-task solving method is proposed. Comprehensive experimental studies on simulated high-resolution range profile dataset and MSTAR SAR public database verify the superiority of the proposed method to some related algorithms. PMID:28953267

University multi-user facility survey-2010.

PubMed

Riley, Melissa B

2011-12-01

Multi-user facilities serve as a resource for many universities. In 2010, a survey was conducted investigating possible changes and successful characteristics of multi-user facilities, as well as identifying problems in facilities. Over 300 surveys were e-mailed to persons identified from university websites as being involved with multi-user facilities. Complete responses were received from 36 facilities with an average of 20 years of operation. Facilities were associated with specific departments (22%), colleges (22%), and university research centers (8.3%) or were not affiliated with any department or college within the university (47%). The five most important factors to succeed as a multi-user facility were: 1) maintaining an experienced, professional staff in an open atmosphere; 2) university-level support providing partial funding; 3) broad client base; 4) instrument training programs; and 5) an effective leader and engaged strategic advisory group. The most significant problems were: 1) inadequate university financial support and commitment; 2) problems recovering full service costs from university subsidies and user fees; 3) availability of funds to repair and upgrade equipment; 4) inability to retain highly qualified staff; and 5) unqualified users dirtying/damaging equipment. Further information related to these issues and to fee structure was solicited. Overall, there appeared to be a decline in university support for facilities and more emphasis on securing income by serving clients outside of the institution and by obtaining grants from entities outside of the university.

A system for activity recognition using multi-sensor fusion.

PubMed

Gao, Lei; Bourke, Alan K; Nelson, John

2011-01-01

This paper proposes a system for activity recognition using multi-sensor fusion. In this system, four sensors are attached to the waist, chest, thigh, and side of the body. In the study we present two solutions for factors that affect the activity recognition accuracy: the calibration drift and the sensor orientation changing. The datasets used to evaluate this system were collected from 8 subjects who were asked to perform 8 scripted normal activities of daily living (ADL), three times each. The Naïve Bayes classifier using multi-sensor fusion is adopted and achieves 70.88%-97.66% recognition accuracies for 1-4 sensors.

Sub-pattern based multi-manifold discriminant analysis for face recognition

NASA Astrophysics Data System (ADS)

Dai, Jiangyan; Guo, Changlu; Zhou, Wei; Shi, Yanjiao; Cong, Lin; Yi, Yugen

2018-04-01

In this paper, we present a Sub-pattern based Multi-manifold Discriminant Analysis (SpMMDA) algorithm for face recognition. Unlike existing Multi-manifold Discriminant Analysis (MMDA) approach which is based on holistic information of face image for recognition, SpMMDA operates on sub-images partitioned from the original face image and then extracts the discriminative local feature from the sub-images separately. Moreover, the structure information of different sub-images from the same face image is considered in the proposed method with the aim of further improve the recognition performance. Extensive experiments on three standard face databases (Extended YaleB, CMU PIE and AR) demonstrate that the proposed method is effective and outperforms some other sub-pattern based face recognition methods.

Multi-modal gesture recognition using integrated model of motion, audio and video

NASA Astrophysics Data System (ADS)

Goutsu, Yusuke; Kobayashi, Takaki; Obara, Junya; Kusajima, Ikuo; Takeichi, Kazunari; Takano, Wataru; Nakamura, Yoshihiko

2015-07-01

Gesture recognition is used in many practical applications such as human-robot interaction, medical rehabilitation and sign language. With increasing motion sensor development, multiple data sources have become available, which leads to the rise of multi-modal gesture recognition. Since our previous approach to gesture recognition depends on a unimodal system, it is difficult to classify similar motion patterns. In order to solve this problem, a novel approach which integrates motion, audio and video models is proposed by using dataset captured by Kinect. The proposed system can recognize observed gestures by using three models. Recognition results of three models are integrated by using the proposed framework and the output becomes the final result. The motion and audio models are learned by using Hidden Markov Model. Random Forest which is the video classifier is used to learn the video model. In the experiments to test the performances of the proposed system, the motion and audio models most suitable for gesture recognition are chosen by varying feature vectors and learning methods. Additionally, the unimodal and multi-modal models are compared with respect to recognition accuracy. All the experiments are conducted on dataset provided by the competition organizer of MMGRC, which is a workshop for Multi-Modal Gesture Recognition Challenge. The comparison results show that the multi-modal model composed of three models scores the highest recognition rate. This improvement of recognition accuracy means that the complementary relationship among three models improves the accuracy of gesture recognition. The proposed system provides the application technology to understand human actions of daily life more precisely.

Probabilistic Multi-Scale, Multi-Level, Multi-Disciplinary Analysis and Optimization of Engine Structures

NASA Technical Reports Server (NTRS)

Chamis, Christos C.; Abumeri, Galib H.

2000-01-01

Aircraft engines are assemblies of dynamically interacting components. Engine updates to keep present aircraft flying safely and engines for new aircraft are progressively required to operate in more demanding technological and environmental requirements. Designs to effectively meet those requirements are necessarily collections of multi-scale, multi-level, multi-disciplinary analysis and optimization methods and probabilistic methods are necessary to quantify respective uncertainties. These types of methods are the only ones that can formally evaluate advanced composite designs which satisfy those progressively demanding requirements while assuring minimum cost, maximum reliability and maximum durability. Recent research activities at NASA Glenn Research Center have focused on developing multi-scale, multi-level, multidisciplinary analysis and optimization methods. Multi-scale refers to formal methods which describe complex material behavior metal or composite; multi-level refers to integration of participating disciplines to describe a structural response at the scale of interest; multidisciplinary refers to open-ended for various existing and yet to be developed discipline constructs required to formally predict/describe a structural response in engine operating environments. For example, these include but are not limited to: multi-factor models for material behavior, multi-scale composite mechanics, general purpose structural analysis, progressive structural fracture for evaluating durability and integrity, noise and acoustic fatigue, emission requirements, hot fluid mechanics, heat-transfer and probabilistic simulations. Many of these, as well as others, are encompassed in an integrated computer code identified as Engine Structures Technology Benefits Estimator (EST/BEST) or Multi-faceted/Engine Structures Optimization (MP/ESTOP). The discipline modules integrated in MP/ESTOP include: engine cycle (thermodynamics), engine weights, internal fluid mechanics

Ambient and smartphone sensor assisted ADL recognition in multi-inhabitant smart environments.

PubMed

Roy, Nirmalya; Misra, Archan; Cook, Diane

2016-02-01

Activity recognition in smart environments is an evolving research problem due to the advancement and proliferation of sensing, monitoring and actuation technologies to make it possible for large scale and real deployment. While activities in smart home are interleaved, complex and volatile; the number of inhabitants in the environment is also dynamic. A key challenge in designing robust smart home activity recognition approaches is to exploit the users' spatiotemporal behavior and location, focus on the availability of multitude of devices capable of providing different dimensions of information and fulfill the underpinning needs for scaling the system beyond a single user or a home environment. In this paper, we propose a hybrid approach for recognizing complex activities of daily living (ADL), that lie in between the two extremes of intensive use of body-worn sensors and the use of ambient sensors. Our approach harnesses the power of simple ambient sensors (e.g., motion sensors) to provide additional 'hidden' context (e.g., room-level location) of an individual, and then combines this context with smartphone-based sensing of micro-level postural/locomotive states. The major novelty is our focus on multi-inhabitant environments, where we show how the use of spatiotemporal constraints along with multitude of data sources can be used to significantly improve the accuracy and computational overhead of traditional activity recognition based approaches such as coupled-hidden Markov models. Experimental results on two separate smart home datasets demonstrate that this approach improves the accuracy of complex ADL classification by over 30 %, compared to pure smartphone-based solutions.

Ambient and smartphone sensor assisted ADL recognition in multi-inhabitant smart environments

PubMed Central

Misra, Archan; Cook, Diane

2016-01-01

Activity recognition in smart environments is an evolving research problem due to the advancement and proliferation of sensing, monitoring and actuation technologies to make it possible for large scale and real deployment. While activities in smart home are interleaved, complex and volatile; the number of inhabitants in the environment is also dynamic. A key challenge in designing robust smart home activity recognition approaches is to exploit the users' spatiotemporal behavior and location, focus on the availability of multitude of devices capable of providing different dimensions of information and fulfill the underpinning needs for scaling the system beyond a single user or a home environment. In this paper, we propose a hybrid approach for recognizing complex activities of daily living (ADL), that lie in between the two extremes of intensive use of body-worn sensors and the use of ambient sensors. Our approach harnesses the power of simple ambient sensors (e.g., motion sensors) to provide additional ‘hidden’ context (e.g., room-level location) of an individual, and then combines this context with smartphone-based sensing of micro-level postural/locomotive states. The major novelty is our focus on multi-inhabitant environments, where we show how the use of spatiotemporal constraints along with multitude of data sources can be used to significantly improve the accuracy and computational overhead of traditional activity recognition based approaches such as coupled-hidden Markov models. Experimental results on two separate smart home datasets demonstrate that this approach improves the accuracy of complex ADL classification by over 30 %, compared to pure smartphone-based solutions. PMID:27042240

Face Averages Enhance User Recognition for Smartphone Security

PubMed Central

Robertson, David J.; Kramer, Robin S. S.; Burton, A. Mike

2015-01-01

Our recognition of familiar faces is excellent, and generalises across viewing conditions. However, unfamiliar face recognition is much poorer. For this reason, automatic face recognition systems might benefit from incorporating the advantages of familiarity. Here we put this to the test using the face verification system available on a popular smartphone (the Samsung Galaxy). In two experiments we tested the recognition performance of the smartphone when it was encoded with an individual’s ‘face-average’ – a representation derived from theories of human face perception. This technique significantly improved performance for both unconstrained celebrity images (Experiment 1) and for real faces (Experiment 2): users could unlock their phones more reliably when the device stored an average of the user’s face than when they stored a single image. This advantage was consistent across a wide variety of everyday viewing conditions. Furthermore, the benefit did not reduce the rejection of imposter faces. This benefit is brought about solely by consideration of suitable representations for automatic face recognition, and we argue that this is just as important as development of matching algorithms themselves. We propose that this representation could significantly improve recognition rates in everyday settings. PMID:25807251

A multi-view face recognition system based on cascade face detector and improved Dlib

NASA Astrophysics Data System (ADS)

Zhou, Hongjun; Chen, Pei; Shen, Wei

2018-03-01

In this research, we present a framework for multi-view face detect and recognition system based on cascade face detector and improved Dlib. This method is aimed to solve the problems of low efficiency and low accuracy in multi-view face recognition, to build a multi-view face recognition system, and to discover a suitable monitoring scheme. For face detection, the cascade face detector is used to extracted the Haar-like feature from the training samples, and Haar-like feature is used to train a cascade classifier by combining Adaboost algorithm. Next, for face recognition, we proposed an improved distance model based on Dlib to improve the accuracy of multiview face recognition. Furthermore, we applied this proposed method into recognizing face images taken from different viewing directions, including horizontal view, overlooks view, and looking-up view, and researched a suitable monitoring scheme. This method works well for multi-view face recognition, and it is also simulated and tested, showing satisfactory experimental results.

Enhanced iris recognition method based on multi-unit iris images

NASA Astrophysics Data System (ADS)

Shin, Kwang Yong; Kim, Yeong Gon; Park, Kang Ryoung

2013-04-01

For the purpose of biometric person identification, iris recognition uses the unique characteristics of the patterns of the iris; that is, the eye region between the pupil and the sclera. When obtaining an iris image, the iris's image is frequently rotated because of the user's head roll toward the left or right shoulder. As the rotation of the iris image leads to circular shifting of the iris features, the accuracy of iris recognition is degraded. To solve this problem, conventional iris recognition methods use shifting of the iris feature codes to perform the matching. However, this increases the computational complexity and level of false acceptance error. To solve these problems, we propose a novel iris recognition method based on multi-unit iris images. Our method is novel in the following five ways compared with previous methods. First, to detect both eyes, we use Adaboost and a rapid eye detector (RED) based on the iris shape feature and integral imaging. Both eyes are detected using RED in the approximate candidate region that consists of the binocular region, which is determined by the Adaboost detector. Second, we classify the detected eyes into the left and right eyes, because the iris patterns in the left and right eyes in the same person are different, and they are therefore considered as different classes. We can improve the accuracy of iris recognition using this pre-classification of the left and right eyes. Third, by measuring the angle of head roll using the two center positions of the left and right pupils, detected by two circular edge detectors, we obtain the information of the iris rotation angle. Fourth, in order to reduce the error and processing time of iris recognition, adaptive bit-shifting based on the measured iris rotation angle is used in feature matching. Fifth, the recognition accuracy is enhanced by the score fusion of the left and right irises. Experimental results on the iris open database of low-resolution images showed that the

A multi-views multi-learners approach towards dysarthric speech recognition using multi-nets artificial neural networks.

PubMed

Shahamiri, Seyed Reza; Salim, Siti Salwah Binti

2014-09-01

Automatic speech recognition (ASR) can be very helpful for speakers who suffer from dysarthria, a neurological disability that damages the control of motor speech articulators. Although a few attempts have been made to apply ASR technologies to sufferers of dysarthria, previous studies show that such ASR systems have not attained an adequate level of performance. In this study, a dysarthric multi-networks speech recognizer (DM-NSR) model is provided using a realization of multi-views multi-learners approach called multi-nets artificial neural networks, which tolerates variability of dysarthric speech. In particular, the DM-NSR model employs several ANNs (as learners) to approximate the likelihood of ASR vocabulary words and to deal with the complexity of dysarthric speech. The proposed DM-NSR approach was presented as both speaker-dependent and speaker-independent paradigms. In order to highlight the performance of the proposed model over legacy models, multi-views single-learner models of the DM-NSRs were also provided and their efficiencies were compared in detail. Moreover, a comparison among the prominent dysarthric ASR methods and the proposed one is provided. The results show that the DM-NSR recorded improved recognition rate by up to 24.67% and the error rate was reduced by up to 8.63% over the reference model.

Recognition vs Reverse Engineering in Boolean Concepts Learning

ERIC Educational Resources Information Center

Shafat, Gabriel; Levin, Ilya

2012-01-01

This paper deals with two types of logical problems--recognition problems and reverse engineering problems, and with the interrelations between these types of problems. The recognition problems are modeled in the form of a visual representation of various objects in a common pattern, with a composition of represented objects in the pattern.…

Multi-layer sparse representation for weighted LBP-patches based facial expression recognition.

PubMed

Jia, Qi; Gao, Xinkai; Guo, He; Luo, Zhongxuan; Wang, Yi

2015-03-19

In this paper, a novel facial expression recognition method based on sparse representation is proposed. Most contemporary facial expression recognition systems suffer from limited ability to handle image nuisances such as low resolution and noise. Especially for low intensity expression, most of the existing training methods have quite low recognition rates. Motivated by sparse representation, the problem can be solved by finding sparse coefficients of the test image by the whole training set. Deriving an effective facial representation from original face images is a vital step for successful facial expression recognition. We evaluate facial representation based on weighted local binary patterns, and Fisher separation criterion is used to calculate the weighs of patches. A multi-layer sparse representation framework is proposed for multi-intensity facial expression recognition, especially for low-intensity expressions and noisy expressions in reality, which is a critical problem but seldom addressed in the existing works. To this end, several experiments based on low-resolution and multi-intensity expressions are carried out. Promising results on publicly available databases demonstrate the potential of the proposed approach.

Phonological Activation in Multi-Syllabic Sord Recognition

ERIC Educational Resources Information Center

Lee, Chang H.

2007-01-01

Three experiments were conducted to test the phonological recoding hypothesis in visual word recognition. Most studies on this issue have been conducted using mono-syllabic words, eventually constructing various models of phonological processing. Yet in many languages including English, the majority of words are multi-syllabic words. English…

Multi-dimension feature fusion for action recognition

NASA Astrophysics Data System (ADS)

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

2018-04-01

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

A Case Study in User Support for Managing OpenSim Based Multi User Learning Environments

ERIC Educational Resources Information Center

Perera, Indika; Miller, Alan; Allison, Colin

2017-01-01

Immersive 3D Multi User Learning Environments (MULE) have shown sufficient success to warrant their consideration as a mainstream educational paradigm. These are based on 3D Multi User Virtual Environment platforms (MUVE), and although they have been used for various innovative educational projects their complex permission systems and large…

Nuclear Engine System Simulation (NESS). Volume 1: Program user's guide

NASA Astrophysics Data System (ADS)

Pelaccio, Dennis G.; Scheil, Christine M.; Petrosky, Lyman J.

1993-03-01

A Nuclear Thermal Propulsion (NTP) engine system design analysis tool is required to support current and future Space Exploration Initiative (SEI) propulsion and vehicle design studies. Currently available NTP engine design models are those developed during the NERVA program in the 1960's and early 1970's and are highly unique to that design or are modifications of current liquid propulsion system design models. To date, NTP engine-based liquid design models lack integrated design of key NTP engine design features in the areas of reactor, shielding, multi-propellant capability, and multi-redundant pump feed fuel systems. Additionally, since the SEI effort is in the initial development stage, a robust, verified NTP analysis design tool could be of great use to the community. This effort developed an NTP engine system design analysis program (tool), known as the Nuclear Engine System Simulation (NESS) program, to support ongoing and future engine system and stage design study efforts. In this effort, Science Applications International Corporation's (SAIC) NTP version of the Expanded Liquid Engine Simulation (ELES) program was modified extensively to include Westinghouse Electric Corporation's near-term solid-core reactor design model. The ELES program has extensive capability to conduct preliminary system design analysis of liquid rocket systems and vehicles. The program is modular in nature and is versatile in terms of modeling state-of-the-art component and system options as discussed. The Westinghouse reactor design model, which was integrated in the NESS program, is based on the near-term solid-core ENABLER NTP reactor design concept. This program is now capable of accurately modeling (characterizing) a complete near-term solid-core NTP engine system in great detail, for a number of design options, in an efficient manner. The following discussion summarizes the overall analysis methodology, key assumptions, and capabilities associated with the NESS presents an

Nuclear Engine System Simulation (NESS). Volume 1: Program user's guide

NASA Technical Reports Server (NTRS)

Pelaccio, Dennis G.; Scheil, Christine M.; Petrosky, Lyman J.

1993-01-01

A Nuclear Thermal Propulsion (NTP) engine system design analysis tool is required to support current and future Space Exploration Initiative (SEI) propulsion and vehicle design studies. Currently available NTP engine design models are those developed during the NERVA program in the 1960's and early 1970's and are highly unique to that design or are modifications of current liquid propulsion system design models. To date, NTP engine-based liquid design models lack integrated design of key NTP engine design features in the areas of reactor, shielding, multi-propellant capability, and multi-redundant pump feed fuel systems. Additionally, since the SEI effort is in the initial development stage, a robust, verified NTP analysis design tool could be of great use to the community. This effort developed an NTP engine system design analysis program (tool), known as the Nuclear Engine System Simulation (NESS) program, to support ongoing and future engine system and stage design study efforts. In this effort, Science Applications International Corporation's (SAIC) NTP version of the Expanded Liquid Engine Simulation (ELES) program was modified extensively to include Westinghouse Electric Corporation's near-term solid-core reactor design model. The ELES program has extensive capability to conduct preliminary system design analysis of liquid rocket systems and vehicles. The program is modular in nature and is versatile in terms of modeling state-of-the-art component and system options as discussed. The Westinghouse reactor design model, which was integrated in the NESS program, is based on the near-term solid-core ENABLER NTP reactor design concept. This program is now capable of accurately modeling (characterizing) a complete near-term solid-core NTP engine system in great detail, for a number of design options, in an efficient manner. The following discussion summarizes the overall analysis methodology, key assumptions, and capabilities associated with the NESS presents an

Usability testing of a prototype multi-user telehealth kiosk.

PubMed

Courtney, Karen L; Matthews, Judith T; McMillan, Julie M; Person Mecca, Laurel; Smailagic, Asim; Siewiorek, Daniel

2015-01-01

The overall purpose of this study was to learn how community-dwelling older adults would interact with our prototype multi-user telehealth kiosk and their views about its usability. Seven subjects participated in laboratory-based usability sessions to evaluate the physical design, appearance, functionality and perceived ease of use of a multi-user telehealth kiosk prototype. During usability testing participants recommended 18 new features (29% of comments), identified 15 software errors (23% of comments) and 29 user interface errors (47% of comments).

Extreme C2 and Multi-Touch, Multi-User Collaborative User Interfaces

DTIC Science & Technology

2008-06-01

Organization: Office of the Chief Engineer , Space and Naval Warfare Systems Center Charleston Address: PO Box 190022 N. Charleston, SC 29419 843...collaborative development technique can increase the adaptability and quality of software, something of high value in the complex domain of enterprise...concept to C2 should be able to produce similar benefits for planning in military operations, particularly complex, multi- faceted operations. This

Evaluation of accelerometer based multi-sensor versus single-sensor activity recognition systems.

PubMed

Gao, Lei; Bourke, A K; Nelson, John

2014-06-01

Physical activity has a positive impact on people's well-being and it had been shown to decrease the occurrence of chronic diseases in the older adult population. To date, a substantial amount of research studies exist, which focus on activity recognition using inertial sensors. Many of these studies adopt a single sensor approach and focus on proposing novel features combined with complex classifiers to improve the overall recognition accuracy. In addition, the implementation of the advanced feature extraction algorithms and the complex classifiers exceed the computing ability of most current wearable sensor platforms. This paper proposes a method to adopt multiple sensors on distributed body locations to overcome this problem. The objective of the proposed system is to achieve higher recognition accuracy with "light-weight" signal processing algorithms, which run on a distributed computing based sensor system comprised of computationally efficient nodes. For analysing and evaluating the multi-sensor system, eight subjects were recruited to perform eight normal scripted activities in different life scenarios, each repeated three times. Thus a total of 192 activities were recorded resulting in 864 separate annotated activity states. The methods for designing such a multi-sensor system required consideration of the following: signal pre-processing algorithms, sampling rate, feature selection and classifier selection. Each has been investigated and the most appropriate approach is selected to achieve a trade-off between recognition accuracy and computing execution time. A comparison of six different systems, which employ single or multiple sensors, is presented. The experimental results illustrate that the proposed multi-sensor system can achieve an overall recognition accuracy of 96.4% by adopting the mean and variance features, using the Decision Tree classifier. The results demonstrate that elaborate classifiers and feature sets are not required to achieve high

Multi-stream face recognition for crime-fighting

NASA Astrophysics Data System (ADS)

Jassim, Sabah A.; Sellahewa, Harin

2007-04-01

Automatic face recognition (AFR) is a challenging task that is increasingly becoming the preferred biometric trait for identification and has the potential of becoming an essential tool in the fight against crime and terrorism. Closed-circuit television (CCTV) cameras have increasingly been used over the last few years for surveillance in public places such as airports, train stations and shopping centers. They are used to detect and prevent crime, shoplifting, public disorder and terrorism. The work of law-enforcing and intelligence agencies is becoming more reliant on the use of databases of biometric data for large section of the population. Face is one of the most natural biometric traits that can be used for identification and surveillance. However, variations in lighting conditions, facial expressions, face size and pose are a great obstacle to AFR. This paper is concerned with using waveletbased face recognition schemes in the presence of variations of expressions and illumination. In particular, we will investigate the use of a combination of wavelet frequency channels for a multi-stream face recognition using various wavelet subbands as different face signal streams. The proposed schemes extend our recently developed face veri.cation scheme for implementation on mobile devices. We shall present experimental results on the performance of our proposed schemes for a number of face databases including a new AV database recorded on a PDA. By analyzing the various experimental data, we shall demonstrate that the multi-stream approach is robust against variations in illumination and facial expressions than the previous single-stream approach.

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

NASA Astrophysics Data System (ADS)

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.

Multi-task learning with group information for human action recognition

NASA Astrophysics Data System (ADS)

Qian, Li; Wu, Song; Pu, Nan; Xu, Shulin; Xiao, Guoqiang

2018-04-01

Human action recognition is an important and challenging task in computer vision research, due to the variations in human motion performance, interpersonal differences and recording settings. In this paper, we propose a novel multi-task learning framework with group information (MTL-GI) for accurate and efficient human action recognition. Specifically, we firstly obtain group information through calculating the mutual information according to the latent relationship between Gaussian components and action categories, and clustering similar action categories into the same group by affinity propagation clustering. Additionally, in order to explore the relationships of related tasks, we incorporate group information into multi-task learning. Experimental results evaluated on two popular benchmarks (UCF50 and HMDB51 datasets) demonstrate the superiority of our proposed MTL-GI framework.

Robust Face Recognition via Multi-Scale Patch-Based Matrix Regression.

PubMed

Gao, Guangwei; Yang, Jian; Jing, Xiaoyuan; Huang, Pu; Hua, Juliang; Yue, Dong

2016-01-01

In many real-world applications such as smart card solutions, law enforcement, surveillance and access control, the limited training sample size is the most fundamental problem. By making use of the low-rank structural information of the reconstructed error image, the so-called nuclear norm-based matrix regression has been demonstrated to be effective for robust face recognition with continuous occlusions. However, the recognition performance of nuclear norm-based matrix regression degrades greatly in the face of the small sample size problem. An alternative solution to tackle this problem is performing matrix regression on each patch and then integrating the outputs from all patches. However, it is difficult to set an optimal patch size across different databases. To fully utilize the complementary information from different patch scales for the final decision, we propose a multi-scale patch-based matrix regression scheme based on which the ensemble of multi-scale outputs can be achieved optimally. Extensive experiments on benchmark face databases validate the effectiveness and robustness of our method, which outperforms several state-of-the-art patch-based face recognition algorithms.

Multi-Directional Multi-Level Dual-Cross Patterns for Robust Face Recognition.

PubMed

Ding, Changxing; Choi, Jonghyun; Tao, Dacheng; Davis, Larry S

2016-03-01

To perform unconstrained face recognition robust to variations in illumination, pose and expression, this paper presents a new scheme to extract "Multi-Directional Multi-Level Dual-Cross Patterns" (MDML-DCPs) from face images. Specifically, the MDML-DCPs scheme exploits the first derivative of Gaussian operator to reduce the impact of differences in illumination and then computes the DCP feature at both the holistic and component levels. DCP is a novel face image descriptor inspired by the unique textural structure of human faces. It is computationally efficient and only doubles the cost of computing local binary patterns, yet is extremely robust to pose and expression variations. MDML-DCPs comprehensively yet efficiently encodes the invariant characteristics of a face image from multiple levels into patterns that are highly discriminative of inter-personal differences but robust to intra-personal variations. Experimental results on the FERET, CAS-PERL-R1, FRGC 2.0, and LFW databases indicate that DCP outperforms the state-of-the-art local descriptors (e.g., LBP, LTP, LPQ, POEM, tLBP, and LGXP) for both face identification and face verification tasks. More impressively, the best performance is achieved on the challenging LFW and FRGC 2.0 databases by deploying MDML-DCPs in a simple recognition scheme.

Users matter : multi-agent systems model of high performance computing cluster users.

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

North, M. J.; Hood, C. S.; Decision and Information Sciences

2005-01-01

High performance computing clusters have been a critical resource for computational science for over a decade and have more recently become integral to large-scale industrial analysis. Despite their well-specified components, the aggregate behavior of clusters is poorly understood. The difficulties arise from complicated interactions between cluster components during operation. These interactions have been studied by many researchers, some of whom have identified the need for holistic multi-scale modeling that simultaneously includes network level, operating system level, process level, and user level behaviors. Each of these levels presents its own modeling challenges, but the user level is the most complex duemore » to the adaptability of human beings. In this vein, there are several major user modeling goals, namely descriptive modeling, predictive modeling and automated weakness discovery. This study shows how multi-agent techniques were used to simulate a large-scale computing cluster at each of these levels.« less

2.5D multi-view gait recognition based on point cloud registration.

PubMed

Tang, Jin; Luo, Jian; Tjahjadi, Tardi; Gao, Yan

2014-03-28

This paper presents a method for modeling a 2.5-dimensional (2.5D) human body and extracting the gait features for identifying the human subject. To achieve view-invariant gait recognition, a multi-view synthesizing method based on point cloud registration (MVSM) to generate multi-view training galleries is proposed. The concept of a density and curvature-based Color Gait Curvature Image is introduced to map 2.5D data onto a 2D space to enable data dimension reduction by discrete cosine transform and 2D principle component analysis. Gait recognition is achieved via a 2.5D view-invariant gait recognition method based on point cloud registration. Experimental results on the in-house database captured by a Microsoft Kinect camera show a significant performance gain when using MVSM.

2.5D Multi-View Gait Recognition Based on Point Cloud Registration

PubMed Central

Tang, Jin; Luo, Jian; Tjahjadi, Tardi; Gao, Yan

2014-01-01

This paper presents a method for modeling a 2.5-dimensional (2.5D) human body and extracting the gait features for identifying the human subject. To achieve view-invariant gait recognition, a multi-view synthesizing method based on point cloud registration (MVSM) to generate multi-view training galleries is proposed. The concept of a density and curvature-based Color Gait Curvature Image is introduced to map 2.5D data onto a 2D space to enable data dimension reduction by discrete cosine transform and 2D principle component analysis. Gait recognition is achieved via a 2.5D view-invariant gait recognition method based on point cloud registration. Experimental results on the in-house database captured by a Microsoft Kinect camera show a significant performance gain when using MVSM. PMID:24686727

Multi-physics CFD simulations in engineering

NASA Astrophysics Data System (ADS)

Yamamoto, Makoto

2013-08-01

Nowadays Computational Fluid Dynamics (CFD) software is adopted as a design and analysis tool in a great number of engineering fields. We can say that single-physics CFD has been sufficiently matured in the practical point of view. The main target of existing CFD software is single-phase flows such as water and air. However, many multi-physics problems exist in engineering. Most of them consist of flow and other physics, and the interactions between different physics are very important. Obviously, multi-physics phenomena are critical in developing machines and processes. A multi-physics phenomenon seems to be very complex, and it is so difficult to be predicted by adding other physics to flow phenomenon. Therefore, multi-physics CFD techniques are still under research and development. This would be caused from the facts that processing speed of current computers is not fast enough for conducting a multi-physics simulation, and furthermore physical models except for flow physics have not been suitably established. Therefore, in near future, we have to develop various physical models and efficient CFD techniques, in order to success multi-physics simulations in engineering. In the present paper, I will describe the present states of multi-physics CFD simulations, and then show some numerical results such as ice accretion and electro-chemical machining process of a three-dimensional compressor blade which were obtained in my laboratory. Multi-physics CFD simulations would be a key technology in near future.

HD-MTL: Hierarchical Deep Multi-Task Learning for Large-Scale Visual Recognition.

PubMed

Fan, Jianping; Zhao, Tianyi; Kuang, Zhenzhong; Zheng, Yu; Zhang, Ji; Yu, Jun; Peng, Jinye

2017-02-09

In this paper, a hierarchical deep multi-task learning (HD-MTL) algorithm is developed to support large-scale visual recognition (e.g., recognizing thousands or even tens of thousands of atomic object classes automatically). First, multiple sets of multi-level deep features are extracted from different layers of deep convolutional neural networks (deep CNNs), and they are used to achieve more effective accomplishment of the coarseto- fine tasks for hierarchical visual recognition. A visual tree is then learned by assigning the visually-similar atomic object classes with similar learning complexities into the same group, which can provide a good environment for determining the interrelated learning tasks automatically. By leveraging the inter-task relatedness (inter-class similarities) to learn more discriminative group-specific deep representations, our deep multi-task learning algorithm can train more discriminative node classifiers for distinguishing the visually-similar atomic object classes effectively. Our hierarchical deep multi-task learning (HD-MTL) algorithm can integrate two discriminative regularization terms to control the inter-level error propagation effectively, and it can provide an end-to-end approach for jointly learning more representative deep CNNs (for image representation) and more discriminative tree classifier (for large-scale visual recognition) and updating them simultaneously. Our incremental deep learning algorithms can effectively adapt both the deep CNNs and the tree classifier to the new training images and the new object classes. Our experimental results have demonstrated that our HD-MTL algorithm can achieve very competitive results on improving the accuracy rates for large-scale visual recognition.

Multi-objects recognition for distributed intelligent sensor networks

NASA Astrophysics Data System (ADS)

He, Haibo; Chen, Sheng; Cao, Yuan; Desai, Sachi; Hohil, Myron E.

2008-04-01

This paper proposes an innovative approach for multi-objects recognition for homeland security and defense based intelligent sensor networks. Unlike the conventional way of information analysis, data mining in such networks is typically characterized with high information ambiguity/uncertainty, data redundancy, high dimensionality and real-time constrains. Furthermore, since a typical military based network normally includes multiple mobile sensor platforms, ground forces, fortified tanks, combat flights, and other resources, it is critical to develop intelligent data mining approaches to fuse different information resources to understand dynamic environments, to support decision making processes, and finally to achieve the goals. This paper aims to address these issues with a focus on multi-objects recognition. Instead of classifying a single object as in the traditional image classification problems, the proposed method can automatically learn multiple objectives simultaneously. Image segmentation techniques are used to identify the interesting regions in the field, which correspond to multiple objects such as soldiers or tanks. Since different objects will come with different feature sizes, we propose a feature scaling method to represent each object in the same number of dimensions. This is achieved by linear/nonlinear scaling and sampling techniques. Finally, support vector machine (SVM) based learning algorithms are developed to learn and build the associations for different objects, and such knowledge will be adaptively accumulated for objects recognition in the testing stage. We test the effectiveness of proposed method in different simulated military environments.

ASERA: A Spectrum Eye Recognition Assistant

NASA Astrophysics Data System (ADS)

Yuan, Hailong; Zhang, Haotong; Zhang, Yanxia; Lei, Yajuan; Dong, Yiqiao; Zhao, Yongheng

2018-04-01

ASERA, ASpectrum Eye Recognition Assistant, aids in quasar spectral recognition and redshift measurement and can also be used to recognize various types of spectra of stars, galaxies and AGNs (Active Galactic Nucleus). This interactive software allows users to visualize observed spectra, superimpose template spectra from the Sloan Digital Sky Survey (SDSS), and interactively access related spectral line information. ASERA is an efficient and user-friendly semi-automated toolkit for the accurate classification of spectra observed by LAMOST (the Large Sky Area Multi-object Fiber Spectroscopic Telescope) and is available as a standalone Java application and as a Java applet. The software offers several functions, including wavelength and flux scale settings, zoom in and out, redshift estimation, and spectral line identification.

Multi-User Hardware Solutions to Combustion Science ISS Research

NASA Technical Reports Server (NTRS)

Otero, Angel M.

2001-01-01

In response to the budget environment and to expand on the International Space Station (ISS) Fluids and Combustion Facility (FCF) Combustion Integrated Rack (CIR), common hardware approach, the NASA Combustion Science Program shifted focus in 1999 from single investigator PI (Principal Investigator)-specific hardware to multi-user 'Minifacilities'. These mini-facilities would take the CIR common hardware philosophy to the next level. The approach that was developed re-arranged all the investigations in the program into sub-fields of research. Then common requirements within these subfields were used to develop a common system that would then be complemented by a few PI-specific components. The sub-fields of research selected were droplet combustion, solids and fire safety, and gaseous fuels. From these research areas three mini-facilities have sprung: the Multi-user Droplet Combustion Apparatus (MDCA) for droplet research, Flow Enclosure for Novel Investigations in Combustion of Solids (FEANICS) for solids and fire safety, and the Multi-user Gaseous Fuels Apparatus (MGFA) for gaseous fuels. These mini-facilities will develop common Chamber Insert Assemblies (CIA) and diagnostics for the respective investigators complementing the capability provided by CIR. Presently there are four investigators for MDCA, six for FEANICS, and four for MGFA. The goal of these multi-user facilities is to drive the cost per PI down after the initial development investment is made. Each of these mini-facilities will become a fixture of future Combustion Science NASA Research Announcements (NRAs), enabling investigators to propose against an existing capability. Additionally, an investigation is provided the opportunity to enhance the existing capability to bridge the gap between the capability and their specific science requirements. This multi-user development approach will enable the Combustion Science Program to drive cost per investigation down while drastically reducing the time

Research on multi-user encrypted search scheme in cloud environment

NASA Astrophysics Data System (ADS)

Yu, Zonghua; Lin, Sui

2017-05-01

Aiming at the existing problems of multi-user encrypted search scheme in cloud computing environment, a basic multi-user encrypted scheme is proposed firstly, and then the basic scheme is extended to an anonymous hierarchical management authority. Compared with most of the existing schemes, the scheme not only to achieve the protection of keyword information, but also to achieve the protection of user identity privacy; the same time, data owners can directly control the user query permissions, rather than the cloud server. In addition, through the use of a special query key generation rules, to achieve the hierarchical management of the user's query permissions. The safety analysis shows that the scheme is safe and that the performance analysis and experimental data show that the scheme is practicable.

Error Rates in Users of Automatic Face Recognition Software

PubMed Central

White, David; Dunn, James D.; Schmid, Alexandra C.; Kemp, Richard I.

2015-01-01

In recent years, wide deployment of automatic face recognition systems has been accompanied by substantial gains in algorithm performance. However, benchmarking tests designed to evaluate these systems do not account for the errors of human operators, who are often an integral part of face recognition solutions in forensic and security settings. This causes a mismatch between evaluation tests and operational accuracy. We address this by measuring user performance in a face recognition system used to screen passport applications for identity fraud. Experiment 1 measured target detection accuracy in algorithm-generated ‘candidate lists’ selected from a large database of passport images. Accuracy was notably poorer than in previous studies of unfamiliar face matching: participants made over 50% errors for adult target faces, and over 60% when matching images of children. Experiment 2 then compared performance of student participants to trained passport officers–who use the system in their daily work–and found equivalent performance in these groups. Encouragingly, a group of highly trained and experienced “facial examiners” outperformed these groups by 20 percentage points. We conclude that human performance curtails accuracy of face recognition systems–potentially reducing benchmark estimates by 50% in operational settings. Mere practise does not attenuate these limits, but superior performance of trained examiners suggests that recruitment and selection of human operators, in combination with effective training and mentorship, can improve the operational accuracy of face recognition systems. PMID:26465631

Modular Engine Noise Component Prediction System (MCP) Program Users' Guide

NASA Technical Reports Server (NTRS)

Golub, Robert A. (Technical Monitor); Herkes, William H.; Reed, David H.

2004-01-01

This is a user's manual for Modular Engine Noise Component Prediction System (MCP). This computer code allows the user to predict turbofan engine noise estimates. The program is based on an empirical procedure that has evolved over many years at The Boeing Company. The data used to develop the procedure include both full-scale engine data and small-scale model data, and include testing done by Boeing, by the engine manufacturers, and by NASA. In order to generate a noise estimate, the user specifies the appropriate engine properties (including both geometry and performance parameters), the microphone locations, the atmospheric conditions, and certain data processing options. The version of the program described here allows the user to predict three components: inlet-radiated fan noise, aft-radiated fan noise, and jet noise. MCP predicts one-third octave band noise levels over the frequency range of 50 to 10,000 Hertz. It also calculates overall sound pressure levels and certain subjective noise metrics (e.g., perceived noise levels).

Some effects of stress on users of a voice recognition system: A preliminary inquiry

NASA Astrophysics Data System (ADS)

French, B. A.

1983-03-01

Recent work with Automatic Speech Recognition has focused on applications and productivity considerations in the man-machine interface. This thesis is an attempt to see if placing users of such equipment under time-induced stress has an effect on their percent correct recognition rates. Subjects were given a message-handling task of fixed length and allowed progressively shorter times to attempt to complete it. Questionnaire responses indicate stress levels increased with decreased time-allowance; recognition rates decreased as time was reduced.

Exploring the Educational Potential of Three-Dimensional Multi-User Virtual Worlds for STEM Education: A Mixed-Method Systematic Literature Review

ERIC Educational Resources Information Center

Pellas, Nikolaos; Kazanidis, Ioannis; Konstantinou, Nikolaos; Georgiou, Georgia

2017-01-01

The present literature review builds on the results of 50 research articles published from 2000 until 2016. All these studies have successfully accomplished various learning tasks in the domain of Science, Technology, Engineering, and Mathematics (STEM) education using three-dimensional (3-D) multi-user virtual worlds for Primary, Secondary and…

Does quality of life depend on speech recognition performance for adult cochlear implant users?

PubMed

Capretta, Natalie R; Moberly, Aaron C

2016-03-01

Current postoperative clinical outcome measures for adults receiving cochlear implants (CIs) consist of testing speech recognition, primarily under quiet conditions. However, it is strongly suspected that results on these measures may not adequately reflect patients' quality of life (QOL) using their implants. This study aimed to evaluate whether QOL for CI users depends on speech recognition performance. Twenty-three postlingually deafened adults with CIs were assessed. Participants were tested for speech recognition (Central Institute for the Deaf word and AzBio sentence recognition in quiet) and completed three QOL measures-the Nijmegen Cochlear Implant Questionnaire; either the Hearing Handicap Inventory for Adults or the Hearing Handicap Inventory for the Elderly; and the Speech, Spatial and Qualities of Hearing Scale questionnaires-to assess a variety of QOL factors. Correlations were sought between speech recognition and QOL scores. Demographics, audiologic history, language, and cognitive skills were also examined as potential predictors of QOL. Only a few QOL scores significantly correlated with postoperative sentence or word recognition in quiet, and correlations were primarily isolated to speech-related subscales on QOL measures. Poorer pre- and postoperative unaided hearing predicted better QOL. Socioeconomic status, duration of deafness, age at implantation, duration of CI use, reading ability, vocabulary size, and cognitive status did not consistently predict QOL scores. For adult, postlingually deafened CI users, clinical speech recognition measures in quiet do not correlate broadly with QOL. Results suggest the need for additional outcome measures of the benefits and limitations of cochlear implantation. 4. Laryngoscope, 126:699-706, 2016. © 2015 The American Laryngological, Rhinological and Otological Society, Inc.

Multi-User Space Link Extension (SLE) System

NASA Technical Reports Server (NTRS)

Perkins, Toby

2013-01-01

The Multi-User Space (MUS) Link Extension system, a software and data system, provides Space Link Extension (SLE) users with three space data transfer services in timely, complete, and offline modes as applicable according to standards defined by the Consultative Committee for Space Data Systems (CCSDS). MUS radically reduces the schedule, cost, and risk of implementing a new SLE user system, minimizes operating costs with a lights-out approach to SLE, and is designed to require no sustaining engineering expense during its lifetime unless changes in the CCSDS SLE standards, combined with new provider implementations, force changes. No software modification to MUS needs to be made to support a new mission. Any systems engineer with Linux experience can begin testing SLE user service instances with MUS starting from a personal computer (PC) within five days. For flight operators, MUS provides a familiar-looking Web page for entering SLE configuration data received from SLE. Operators can also use the Web page to back up a space mission's entire set of up to approximately 500 SLE service instances in less than five seconds, or to restore or transfer from another system the same amount of data from a MUS backup file in about the same amount of time. Missions operate each MUS SLE service instance independently by sending it MUS directives, which are legible, plain ASCII strings. MUS directives are usually (but not necessarily) sent through a TCP-IP (Transmission Control Protocol Internet Protocol) socket from a MOC (Mission Operations Center) or POCC (Payload Operations Control Center) system, under scripted control, during "lights-out" spacecraft operation. MUS permits the flight operations team to configure independently each of its data interfaces; not only commands and telemetry, but also MUS status messages to the MOC. Interfaces can use single- or multiple-client TCP/IP server sockets, TCP/IP client sockets, temporary disk files, the system log, or standard in

Effects of electrical stimulation rate on speech recognition in cochlear implant users.

PubMed

Park, Sung Hye; Kim, Eunoak; Lee, Hyo-Jeong; Kim, Hyung-Jong

2012-04-01

The stimulus signals delivered in cochlear implant (CI) systems are generally derived by sampling the temporal envelope of each channel at some constant rate and using its intensity to control the stimulation current level delivered to the corresponding electrode site. The objective of the study was to investigate speech recognition performance of cochlear implant users in quiet and noisy environments using either moderate or high rates of electrical stimulations. Six post-lingually deafened adult users of the Nucleus CI24 cochlear implant (Contour® electrode array, Cochlear™, Macquarie Park, Australia) with the Freedom® speech processor participated in the study. Stimulation rates of 900 and 2400 pulses-per-second/channel (pps/ch) were used after both stimulation programs were balanced for loudness. Monosyllabic word and sentence recognition scores in quiet and noisy environments were evaluated for each stimulation program after two months of practice. Subjects were also asked to respond to a questionnaire to examine their preference to any stimulation rate in different hearing conditions. Word recognition scores for monosyllabic words in quiet conditions with the 900 stimulation rate was better than that of the 2400 stimulation rate, although no significant differences between them were found for sentence test in noise. A survey questionnaire indicated that most subjects preferred the 900 stimulation rate to the 2400 stimulation rate, especially in quiet conditions. Most subjects indicated a preference for 900 pps/ch rate in quiet conditions. It is recommended to remap at 900 pps/ch for those CI users whose performance in quiet conditions is less than ideal.

The Fluids And Combustion Facility Combustion Integrated Rack And The Multi-User Droplet Combustion Apparatus: Microgravity Combustion Science Using Modular Multi-User Hardware

NASA Technical Reports Server (NTRS)

OMalley, Terence F.; Myhre, Craig A.

2000-01-01

The Fluids and Combustion Facility (FCF) is a multi-rack payload planned for the International Space Station (ISS) that will enable the study of fluid physics and combustion science in a microgravity environment. The Combustion Integrated Rack (CIR) is one of two International Standard Payload Racks of the FCF and is being designed primarily to support combustion science experiments. The Multi-user Droplet Combustion Apparatus (MDCA) is a multi-user apparatus designed to accommodate four different droplet combustion science experiments and is the first payload for CIR. The CIR will function independently until the later launch of the Fluids Integrated Rack component of the FCF. This paper provides an overview of the capabilities and the development status of the CIR and MDCA.

Facial Emotion Recognition: A Survey and Real-World User Experiences in Mixed Reality

PubMed Central

Mehta, Dhwani; Siddiqui, Mohammad Faridul Haque

2018-01-01

Extensive possibilities of applications have made emotion recognition ineluctable and challenging in the field of computer science. The use of non-verbal cues such as gestures, body movement, and facial expressions convey the feeling and the feedback to the user. This discipline of Human–Computer Interaction places reliance on the algorithmic robustness and the sensitivity of the sensor to ameliorate the recognition. Sensors play a significant role in accurate detection by providing a very high-quality input, hence increasing the efficiency and the reliability of the system. Automatic recognition of human emotions would help in teaching social intelligence in the machines. This paper presents a brief study of the various approaches and the techniques of emotion recognition. The survey covers a succinct review of the databases that are considered as data sets for algorithms detecting the emotions by facial expressions. Later, mixed reality device Microsoft HoloLens (MHL) is introduced for observing emotion recognition in Augmented Reality (AR). A brief introduction of its sensors, their application in emotion recognition and some preliminary results of emotion recognition using MHL are presented. The paper then concludes by comparing results of emotion recognition by the MHL and a regular webcam. PMID:29389845

Facial Emotion Recognition: A Survey and Real-World User Experiences in Mixed Reality.

PubMed

Mehta, Dhwani; Siddiqui, Mohammad Faridul Haque; Javaid, Ahmad Y

2018-02-01

Extensive possibilities of applications have made emotion recognition ineluctable and challenging in the field of computer science. The use of non-verbal cues such as gestures, body movement, and facial expressions convey the feeling and the feedback to the user. This discipline of Human-Computer Interaction places reliance on the algorithmic robustness and the sensitivity of the sensor to ameliorate the recognition. Sensors play a significant role in accurate detection by providing a very high-quality input, hence increasing the efficiency and the reliability of the system. Automatic recognition of human emotions would help in teaching social intelligence in the machines. This paper presents a brief study of the various approaches and the techniques of emotion recognition. The survey covers a succinct review of the databases that are considered as data sets for algorithms detecting the emotions by facial expressions. Later, mixed reality device Microsoft HoloLens (MHL) is introduced for observing emotion recognition in Augmented Reality (AR). A brief introduction of its sensors, their application in emotion recognition and some preliminary results of emotion recognition using MHL are presented. The paper then concludes by comparing results of emotion recognition by the MHL and a regular webcam.

Proposal for Implementing Multi-User Database (MUD) Technology in an Academic Library.

ERIC Educational Resources Information Center

Filby, A. M. Iliana

1996-01-01

Explores the use of MOO (multi-user object oriented) virtual environments in academic libraries to enhance reference services. Highlights include the development of multi-user database (MUD) technology from gaming to non-recreational settings; programming issues; collaborative MOOs; MOOs as distinguished from other types of virtual reality; audio…

Software Engineering for User Interfaces. Technical Report.

ERIC Educational Resources Information Center

Draper, Stephen W.; Norman, Donald A.

The discipline of software engineering can be extended in a natural way to deal with the issues raised by a systematic approach to the design of human-machine interfaces. The user should be treated as part of the system being designed and projects should be organized to take into account the current lack of a priori knowledge of user interface…

Can I order a burger at rnacdonalds.com? Visual similarity effects of multi-letter combinations at the early stages of word recognition.

PubMed

Marcet, Ana; Perea, Manuel

2018-05-01

Previous research has shown that early in the word recognition process, there is some degree of uncertainty concerning letter identity and letter position. Here, we examined whether this uncertainty also extends to the mapping of letter features onto letters, as predicted by the Bayesian Reader (Norris & Kinoshita, 2012). Indeed, anecdotal evidence suggests that nonwords containing multi-letter homoglyphs (e.g., rn→m), such as docurnent, can be confusable with their base word. We conducted 2 masked priming lexical decision experiments in which the words/nonwords contained a middle letter that was visually similar to a multi-letter homoglyph (e.g., docurnent [rn-m], presiclent [cl-d]). Three types of primes were employed: identity, multi-letter homoglyph, and orthographic control. We used 2 commonly used fonts: Tahoma in Experiment 1 and Calibri in Experiment 2. Results in both experiments showed faster word identification times in the homoglyph condition than in the control condition (e.g., docurnento-DOCUMENTO faster than docusnento-DOCUMENTO). Furthermore, the homoglyph condition produced nearly the same latencies as the identity condition. These findings have important implications not only at a theoretical level (models of printed word recognition) but also at an applied level (Internet administrators/users). (PsycINFO Database Record (c) 2018 APA, all rights reserved).

Mirror self-recognition: a review and critique of attempts to promote and engineer self-recognition in primates.

PubMed

Anderson, James R; Gallup, Gordon G

2015-10-01

We review research on reactions to mirrors and self-recognition in nonhuman primates, focusing on methodological issues. Starting with the initial demonstration in chimpanzees in 1970 and subsequent attempts to extend this to other species, self-recognition in great apes is discussed with emphasis on spontaneous manifestations of mirror-guided self-exploration as well as spontaneous use of the mirror to investigate foreign marks on otherwise nonvisible body parts-the mark test. Attempts to show self-recognition in other primates are examined with particular reference to the lack of convincing examples of spontaneous mirror-guided self-exploration, and efforts to engineer positive mark test responses by modifying the test or using conditioning techniques. Despite intensive efforts to demonstrate self-recognition in other primates, we conclude that to date there is no compelling evidence that prosimians, monkeys, or lesser apes-gibbons and siamangs-are capable of mirror self-recognition.

Effects of Electrical Stimulation Rate on Speech Recognition in Cochlear Implant Users

PubMed Central

Park, Sung Hye; Kim, Eunoak; Lee, Hyo-Jeong

2012-01-01

Background and Objectives The stimulus signals delivered in cochlear implant (CI) systems are generally derived by sampling the temporal envelope of each channel at some constant rate and using its intensity to control the stimulation current level delivered to the corresponding electrode site. The objective of the study was to investigate speech recognition performance of cochlear implant users in quiet and noisy environments using either moderate or high rates of electrical stimulations. Materials and Methods Six post-lingually deafened adult users of the Nucleus CI24 cochlear implant (Contour® electrode array, Cochlear™, Macquarie Park, Australia) with the Freedom® speech processor participated in the study. Stimulation rates of 900 and 2400 pulses-per-second/channel (pps/ch) were used after both stimulation programs were balanced for loudness. Monosyllabic word and sentence recognition scores in quiet and noisy environments were evaluated for each stimulation program after two months of practice. Subjects were also asked to respond to a questionnaire to examine their preference to any stimulation rate in different hearing conditions. Results Word recognition scores for monosyllabic words in quiet conditions with the 900 stimulation rate was better than that of the 2400 stimulation rate, although no significant differences between them were found for sentence test in noise. A survey questionnaire indicated that most subjects preferred the 900 stimulation rate to the 2400 stimulation rate, especially in quiet conditions. Conclusions Most subjects indicated a preference for 900 pps/ch rate in quiet conditions. It is recommended to remap at 900 pps/ch for those CI users whose performance in quiet conditions is less than ideal. PMID:24653862

Usability engineering for augmented reality: employing user-based studies to inform design.

PubMed

Gabbard, Joseph L; Swan, J Edward

2008-01-01

A major challenge, and thus opportunity, in the field of human-computer interaction and specifically usability engineering is designing effective user interfaces for emerging technologies that have no established design guidelines or interaction metaphors or introduce completely new ways for users to perceive and interact with technology and the world around them. Clearly, augmented reality is one such emerging technology. We propose a usability engineering approach that employs user-based studies to inform design, by iteratively inserting a series of user-based studies into a traditional usability engineering lifecycle to better inform initial user interface designs. We present an exemplar user-based study conducted to gain insight into how users perceive text in outdoor augmented reality settings and to derive implications for design in outdoor augmented reality. We also describe lessons learned from our experiences conducting user-based studies as part of the design process.

The biometric recognition on contactless multi-spectrum finger images

NASA Astrophysics Data System (ADS)

Kang, Wenxiong; Chen, Xiaopeng; Wu, Qiuxia

2015-01-01

This paper presents a novel multimodal biometric system based on contactless multi-spectrum finger images, which aims to deal with the limitations of unimodal biometrics. The chief merits of the system are the richness of the permissible texture and the ease of data access. We constructed a multi-spectrum instrument to simultaneously acquire three different types of biometrics from a finger: contactless fingerprint, finger vein, and knuckleprint. On the basis of the samples with these characteristics, a moderate database was built for the evaluation of our system. Considering the real-time requirements and the respective characteristics of the three biometrics, the block local binary patterns algorithm was used to extract features and match for the fingerprints and finger veins, while the Oriented FAST and Rotated BRIEF algorithm was applied for knuckleprints. Finally, score-level fusion was performed on the matching results from the aforementioned three types of biometrics. The experiments showed that our proposed multimodal biometric recognition system achieves an equal error rate of 0.109%, which is 88.9%, 94.6%, and 89.7% lower than the individual fingerprint, knuckleprint, and finger vein recognitions, respectively. Nevertheless, our proposed system also satisfies the real-time requirements of the applications.

System Engineering Strategy for Distributed Multi-Purpose Simulation Architectures

NASA Technical Reports Server (NTRS)

Bhula, Dlilpkumar; Kurt, Cindy Marie; Luty, Roger

2007-01-01

This paper describes the system engineering approach used to develop distributed multi-purpose simulations. The multi-purpose simulation architecture focuses on user needs, operations, flexibility, cost and maintenance. This approach was used to develop an International Space Station (ISS) simulator, which is called the International Space Station Integrated Simulation (ISIS)1. The ISIS runs unmodified ISS flight software, system models, and the astronaut command and control interface in an open system design that allows for rapid integration of multiple ISS models. The initial intent of ISIS was to provide a distributed system that allows access to ISS flight software and models for the creation, test, and validation of crew and ground controller procedures. This capability reduces the cost and scheduling issues associated with utilizing standalone simulators in fixed locations, and facilitates discovering unknowns and errors earlier in the development lifecycle. Since its inception, the flexible architecture of the ISIS has allowed its purpose to evolve to include ground operator system and display training, flight software modification testing, and as a realistic test bed for Exploration automation technology research and development.

Managing a Safe and Successful Multi-User Spaceport

NASA Technical Reports Server (NTRS)

Dacko, Taylor; Ketterer, Kirk; Meade, Phillip

2016-01-01

Encouraged by the creation of the Office of Commercial Space Transportation within the U.S. Federal Aviation Administration (FAA) in 1984 and the Commercial Space Act of 1998, the National Aeronautics and Space Administration (NASA) now relies on an extensive network of support from commercial companies and organizations. At NASA's Kennedy Space Center (KSC), this collaboration opens competitive opportunities for launch providers, including repurposing underutilized Shuttle Program resources, constructing new facilities, and utilizing center services and laboratories. The resulting multi-user spaceport fosters diverse activity, though it engenders risk from hazards associated with various spaceflight processing activities. The KSC Safety & Mission Assurance (S&MA) Directorate, in coordination with the center's Spaceport Integration and Center Planning & Development organizations, has developed a novel approach to protect NASA's workforce, critical assets, and the public from hazardous, space-related activity associated with KSC's multi-user spaceport. For NASA KSC S&MA, the transformation to a multi-user spaceport required implementing methods to foster safe and successful commercial activity while resolving challenges involving: Retirement of the Space Shuttle program; Co-location of multiple NASA programs; Relationships between the NASA programs; Complex relationships between NASA programs and commercial partner operations in exclusive-use facilities; Complex relationships between NASA programs and commercial partner operations in shared-use facilities. NASA KSC S&MA challenges were met with long-term planning and solutions involving cooperation with the Spaceport Integration and Services Directorate. This directorate is responsible for managing active commercial partnerships with customer advocacy and services management, providing a dedicated and consistent level of support to a wide array of commercial operations. This paper explores these solutions, their

The ATLAS multi-user upgrade and potential applications

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

Mustapha, B.; Nolen, J. A.; Savard, G.

With the recent integration of the CARIBU-EBIS charge breeder into the ATLAS accelerator system to provide for more pure and efficient charge breeding of radioactive beams, a multi-user upgrade of the ATLAS facility is being proposed to serve multiple users simultaneously. ATLAS was the first superconducting ion linac in the world and is the US DOE low-energy Nuclear Physics National User Facility. The proposed upgrade will take advantage of the continuous-wave nature of ATLAS and the pulsed nature of the EBIS charge breeder in order to simultaneously accelerate two beams with very close mass-to-charge ratios; one stable from the existingmore » ECR ion source and one radioactive from the newly commissioned EBIS charge breeder. In addition to enhancing the nuclear physics program, beam extraction at different points along the linac will open up the opportunity for other potential applications; for instance, material irradiation studies at ~ 1 MeV/u and isotope production at ~ 6 MeV/u or at the full ATLAS energy of ~ 15 MeV/u. The concept and proposed implementation of the ATLAS multi-user upgrade will be presented. Future plans to enhance the flexibility of this upgrade will also be presented.« less

The ATLAS multi-user upgrade and potential applications

NASA Astrophysics Data System (ADS)

Mustapha, B.; Nolen, J. A.; Savard, G.; Ostroumov, P. N.

2017-12-01

With the recent integration of the CARIBU-EBIS charge breeder into the ATLAS accelerator system to provide for more pure and efficient charge breeding of radioactive beams, a multi-user upgrade of the ATLAS facility is being proposed to serve multiple users simultaneously. ATLAS was the first superconducting ion linac in the world and is the US DOE low-energy Nuclear Physics National User Facility. The proposed upgrade will take advantage of the continuous-wave nature of ATLAS and the pulsed nature of the EBIS charge breeder in order to simultaneously accelerate two beams with very close mass-to-charge ratios; one stable from the existing ECR ion source and one radioactive from the newly commissioned EBIS charge breeder. In addition to enhancing the nuclear physics program, beam extraction at different points along the linac will open up the opportunity for other potential applications; for instance, material irradiation studies at ~1 MeV/u, isotope production and radiobiological studies at ~6 MeV/u and at the full ATLAS energy of ~15 MeV/u. The concept and proposed implementation of the ATLAS multi-user upgrade will be discussed. Future plans to enhance the flexibility of this upgrade will be presented.

The Navy/NASA Engine Program (NNEP89): A user's manual

NASA Technical Reports Server (NTRS)

Plencner, Robert M.; Snyder, Christopher A.

1991-01-01

An engine simulation computer code called NNEP89 was written to perform 1-D steady state thermodynamic analysis of turbine engine cycles. By using a very flexible method of input, a set of standard components are connected at execution time to simulate almost any turbine engine configuration that the user could imagine. The code was used to simulate a wide range of engine cycles from turboshafts and turboprops to air turborockets and supersonic cruise variable cycle engines. Off design performance is calculated through the use of component performance maps. A chemical equilibrium model is incorporated to adequately predict chemical dissociation as well as model virtually any fuel. NNEP89 is written in standard FORTRAN77 with clear structured programming and extensive internal documentation. The standard FORTRAN77 programming allows it to be installed onto most mainframe computers and workstations without modification. The NNEP89 code was derived from the Navy/NASA Engine program (NNEP). NNEP89 provides many improvements and enhancements to the original NNEP code and incorporates features which make it easier to use for the novice user. This is a comprehensive user's guide for the NNEP89 code.

SCOUSE: Semi-automated multi-COmponent Universal Spectral-line fitting Engine

NASA Astrophysics Data System (ADS)

Henshaw, J. D.; Longmore, S. N.; Kruijssen, J. M. D.; Davies, B.; Bally, J.; Barnes, A.; Battersby, C.; Burton, M.; Cunningham, M. R.; Dale, J. E.; Ginsburg, A.; Immer, K.; Jones, P. A.; Kendrew, S.; Mills, E. A. C.; Molinari, S.; Moore, T. J. T.; Ott, J.; Pillai, T.; Rathborne, J.; Schilke, P.; Schmiedeke, A.; Testi, L.; Walker, D.; Walsh, A.; Zhang, Q.

2016-01-01

The Semi-automated multi-COmponent Universal Spectral-line fitting Engine (SCOUSE) is a spectral line fitting algorithm that fits Gaussian files to spectral line emission. It identifies the spatial area over which to fit the data and generates a grid of spectral averaging areas (SAAs). The spatially averaged spectra are fitted according to user-provided tolerance levels, and the best fit is selected using the Akaike Information Criterion, which weights the chisq of a best-fitting solution according to the number of free-parameters. A more detailed inspection of the spectra can be performed to improve the fit through an iterative process, after which SCOUSE integrates the new solutions into the solution file.

Awareness of Malicious Social Engineering among Facebook Users

ERIC Educational Resources Information Center

Slonka, Kevin J.

2014-01-01

With the rapid growth of Facebook, the social networking website is becoming a lucrative target for malicious activity. Users of Facebook therefore should be aware of various malicious attacks and know how to identify them. This research analyzed Facebook users' level of understanding in the domain of malicious social engineering on Facebook. The…

Self-recognition in corals facilitates deep-sea habitat engineering

USGS Publications Warehouse

Hennige, Sebastian J; Morrison, Cheryl L.; Form, Armin U.; Buscher, Janina; Kamenos, Nicholas A.; Roberts, J. Murray

2014-01-01

The ability of coral reefs to engineer complex three-dimensional habitats is central to their success and the rich biodiversity they support. In tropical reefs, encrusting coralline algae bind together substrates and dead coral framework to make continuous reef structures, but beyond the photic zone, the cold-water coral Lophelia pertusa also forms large biogenic reefs, facilitated by skeletal fusion. Skeletal fusion in tropical corals can occur in closely related or juvenile individuals as a result of non-aggressive skeletal overgrowth or allogeneic tissue fusion, but contact reactions in many species result in mortality if there is no ‘self-recognition’ on a broad species level. This study reveals areas of ‘flawless’ skeletal fusion in Lophelia pertusa, potentially facilitated by allogeneic tissue fusion, are identified as having small aragonitic crystals or low levels of crystal organisation, and strong molecular bonding. Regardless of the mechanism, the recognition of ‘self’ between adjacent L. pertusa colonies leads to no observable mortality, facilitates ecosystem engineering and reduces aggression-related energetic expenditure in an environment where energy conservation is crucial. The potential for self-recognition at a species level, and subsequent skeletal fusion in framework-forming cold-water corals is an important first step in understanding their significance as ecological engineers in deep-seas worldwide.

An adaptive Hidden Markov Model for activity recognition based on a wearable multi-sensor device

USDA-ARS?s Scientific Manuscript database

Human activity recognition is important in the study of personal health, wellness and lifestyle. In order to acquire human activity information from the personal space, many wearable multi-sensor devices have been developed. In this paper, a novel technique for automatic activity recognition based o...

Design and test of a hybrid foot force sensing and GPS system for richer user mobility activity recognition.

PubMed

Zhang, Zelun; Poslad, Stefan

2013-11-01

Wearable and accompanied sensors and devices are increasingly being used for user activity recognition. However, typical GPS-based and accelerometer-based (ACC) methods face three main challenges: a low recognition accuracy; a coarse recognition capability, i.e., they cannot recognise both human posture (during travelling) and transportation mode simultaneously, and a relatively high computational complexity. Here, a new GPS and Foot-Force (GPS + FF) sensor method is proposed to overcome these challenges that leverages a set of wearable FF sensors in combination with GPS, e.g., in a mobile phone. User mobility activities that can be recognised include both daily user postures and common transportation modes: sitting, standing, walking, cycling, bus passenger, car passenger (including private cars and taxis) and car driver. The novelty of this work is that our approach provides a more comprehensive recognition capability in terms of reliably recognising both human posture and transportation mode simultaneously during travel. In addition, by comparing the new GPS + FF method with both an ACC method (62% accuracy) and a GPS + ACC based method (70% accuracy) as baseline methods, it obtains a higher accuracy (95%) with less computational complexity, when tested on a dataset obtained from ten individuals.

Design and Test of a Hybrid Foot Force Sensing and GPS System for Richer User Mobility Activity Recognition

PubMed Central

Zhang, Zelun; Poslad, Stefan

2013-01-01

Wearable and accompanied sensors and devices are increasingly being used for user activity recognition. However, typical GPS-based and accelerometer-based (ACC) methods face three main challenges: a low recognition accuracy; a coarse recognition capability, i.e., they cannot recognise both human posture (during travelling) and transportation mode simultaneously, and a relatively high computational complexity. Here, a new GPS and Foot-Force (GPS + FF) sensor method is proposed to overcome these challenges that leverages a set of wearable FF sensors in combination with GPS, e.g., in a mobile phone. User mobility activities that can be recognised include both daily user postures and common transportation modes: sitting, standing, walking, cycling, bus passenger, car passenger (including private cars and taxis) and car driver. The novelty of this work is that our approach provides a more comprehensive recognition capability in terms of reliably recognising both human posture and transportation mode simultaneously during travel. In addition, by comparing the new GPS + FF method with both an ACC method (62% accuracy) and a GPS + ACC based method (70% accuracy) as baseline methods, it obtains a higher accuracy (95%) with less computational complexity, when tested on a dataset obtained from ten individuals. PMID:24189333

Speech recognition systems on the Cell Broadband Engine

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

Liu, Y; Jones, H; Vaidya, S

In this paper we describe our design, implementation, and first results of a prototype connected-phoneme-based speech recognition system on the Cell Broadband Engine{trademark} (Cell/B.E.). Automatic speech recognition decodes speech samples into plain text (other representations are possible) and must process samples at real-time rates. Fortunately, the computational tasks involved in this pipeline are highly data-parallel and can receive significant hardware acceleration from vector-streaming architectures such as the Cell/B.E. Identifying and exploiting these parallelism opportunities is challenging, but also critical to improving system performance. We observed, from our initial performance timings, that a single Cell/B.E. processor can recognize speech from thousandsmore » of simultaneous voice channels in real time--a channel density that is orders-of-magnitude greater than the capacity of existing software speech recognizers based on CPUs (central processing units). This result emphasizes the potential for Cell/B.E.-based speech recognition and will likely lead to the future development of production speech systems using Cell/B.E. clusters.« less

Point spread function engineering for iris recognition system design.

PubMed

Ashok, Amit; Neifeld, Mark A

2010-04-01

Undersampling in the detector array degrades the performance of iris-recognition imaging systems. We find that an undersampling of 8 x 8 reduces the iris-recognition performance by nearly a factor of 4 (on CASIA iris database), as measured by the false rejection ratio (FRR) metric. We employ optical point spread function (PSF) engineering via a Zernike phase mask in conjunction with multiple subpixel shifted image measurements (frames) to mitigate the effect of undersampling. A task-specific optimization framework is used to engineer the optical PSF and optimize the postprocessing parameters to minimize the FRR. The optimized Zernike phase enhanced lens (ZPEL) imager design with one frame yields an improvement of nearly 33% relative to a thin observation module by bounded optics (TOMBO) imager with one frame. With four frames the optimized ZPEL imager achieves a FRR equal to that of the conventional imager without undersampling. Further, the ZPEL imager design using 16 frames yields a FRR that is actually 15% lower than that obtained with the conventional imager without undersampling.

Multi-texture local ternary pattern for face recognition

NASA Astrophysics Data System (ADS)

Essa, Almabrok; Asari, Vijayan

2017-05-01

In imagery and pattern analysis domain a variety of descriptors have been proposed and employed for different computer vision applications like face detection and recognition. Many of them are affected under different conditions during the image acquisition process such as variations in illumination and presence of noise, because they totally rely on the image intensity values to encode the image information. To overcome these problems, a novel technique named Multi-Texture Local Ternary Pattern (MTLTP) is proposed in this paper. MTLTP combines the edges and corners based on the local ternary pattern strategy to extract the local texture features of the input image. Then returns a spatial histogram feature vector which is the descriptor for each image that we use to recognize a human being. Experimental results using a k-nearest neighbors classifier (k-NN) on two publicly available datasets justify our algorithm for efficient face recognition in the presence of extreme variations of illumination/lighting environments and slight variation of pose conditions.

What Do Our Users Want? Perspectives on Understanding and Meeting User Needs for Multi-Mission Data Services

NASA Technical Reports Server (NTRS)

McGuire, Robert E.; Candey, Robert M.; Bilitza, D.

2006-01-01

The Sun-Earth Connection Active Archive (SECAA) project of NASA's Space Physics Data Facility operates a range of unique and heavily used multi-mission data services in support of the large-scale science objectives of the Great Observatory, including services such as CDAWeb, the CDAWeb Plus client, SSCWeb, OMNIweb and the CDF data format. In developing and operating these services, we have encountered and continue to struggle with a wide range of issues such as balancing scope and functionality with simplicity and ease of use, understanding the effectiveness of our choices and identifying areas most important for further improvement. In this paper, we will review our key services and then discuss some of our observations and new approaches to understanding and meeting user data service requirements. Some observations are obvious but may still have substantial implications; e.g. functionality without information content is of little user interest, which has led to our recent emphasis on development of web services interfaces, so the content and functionality we already serve is readily and fully available as a building block for new services. Some observations require careful design and tradeoffs; e.g. users will complain when they are offered interfaces with limited options but users are also easily intimidated and become lost when offered extensive options for customization. Some observations remain highly challenging; e.g. a comprehensive multi-mission, multi-source view of all data and services available easily produces a daunting list, but a more selective view can easily lead users to overlook available and relevant data. It is often difficult to obtain and meaningfully interpret measures of true productive usage and overall user satisfaction, even with a variety of techniques including statistics, citations, case studies, user feedback and advisory committees. Most of these issues will apply to and may even be more acute for distributed implementation

Quantum pattern recognition with multi-neuron interactions

NASA Astrophysics Data System (ADS)

Fard, E. Rezaei; Aghayar, K.; Amniat-Talab, M.

2018-03-01

We present a quantum neural network with multi-neuron interactions for pattern recognition tasks by a combination of extended classic Hopfield network and adiabatic quantum computation. This scheme can be used as an associative memory to retrieve partial patterns with any number of unknown bits. Also, we propose a preprocessing approach to classifying the pattern space S to suppress spurious patterns. The results of pattern clustering show that for pattern association, the number of weights (η ) should equal the numbers of unknown bits in the input pattern ( d). It is also remarkable that associative memory function depends on the location of unknown bits apart from the d and load parameter α.

Privacy and Security in Multi-User Health Kiosks

PubMed Central

TAKYI, HAROLD; WATZLAF, VALERIE; MATTHEWS, JUDITH TABOLT; ZHOU, LEMING; DEALMEIDA, DILHARI

2017-01-01

Enforcement of the Health Insurance Portability and Accountability Act (HIPAA) and the Health Information Technology for Economic and Clinical Health Act (HITECH) has gotten stricter and penalties have become more severe in response to a significant increase in computer-related information breaches in recent years. With health information said to be worth twice as much as other forms of information on the underground market, making preservation of privacy and security an integral part of health technology development, rather than an afterthought, not only mitigates risks but also helps to ensure HIPAA and HITECH compliance. This paper provides a guide, based on the Office for Civil Rights (OCR) audit protocol, for creating and maintaining an audit checklist for multi-user health kiosks. Implementation of selected audit elements for a multi-user health kiosk designed for use by community-residing older adults illustrates how the guide can be applied. PMID:28814990

User Activity Recognition in Smart Homes Using Pattern Clustering Applied to Temporal ANN Algorithm.

PubMed

Bourobou, Serge Thomas Mickala; Yoo, Younghwan

2015-05-21

This paper discusses the possibility of recognizing and predicting user activities in the IoT (Internet of Things) based smart environment. The activity recognition is usually done through two steps: activity pattern clustering and activity type decision. Although many related works have been suggested, they had some limited performance because they focused only on one part between the two steps. This paper tries to find the best combination of a pattern clustering method and an activity decision algorithm among various existing works. For the first step, in order to classify so varied and complex user activities, we use a relevant and efficient unsupervised learning method called the K-pattern clustering algorithm. In the second step, the training of smart environment for recognizing and predicting user activities inside his/her personal space is done by utilizing the artificial neural network based on the Allen's temporal relations. The experimental results show that our combined method provides the higher recognition accuracy for various activities, as compared with other data mining classification algorithms. Furthermore, it is more appropriate for a dynamic environment like an IoT based smart home.

User Activity Recognition in Smart Homes Using Pattern Clustering Applied to Temporal ANN Algorithm

PubMed Central

Bourobou, Serge Thomas Mickala; Yoo, Younghwan

2015-01-01

This paper discusses the possibility of recognizing and predicting user activities in the IoT (Internet of Things) based smart environment. The activity recognition is usually done through two steps: activity pattern clustering and activity type decision. Although many related works have been suggested, they had some limited performance because they focused only on one part between the two steps. This paper tries to find the best combination of a pattern clustering method and an activity decision algorithm among various existing works. For the first step, in order to classify so varied and complex user activities, we use a relevant and efficient unsupervised learning method called the K-pattern clustering algorithm. In the second step, the training of smart environment for recognizing and predicting user activities inside his/her personal space is done by utilizing the artificial neural network based on the Allen’s temporal relations. The experimental results show that our combined method provides the higher recognition accuracy for various activities, as compared with other data mining classification algorithms. Furthermore, it is more appropriate for a dynamic environment like an IoT based smart home. PMID:26007738

Human Activity Recognition from Smart-Phone Sensor Data using a Multi-Class Ensemble Learning in Home Monitoring.

PubMed

Ghose, Soumya; Mitra, Jhimli; Karunanithi, Mohan; Dowling, Jason

2015-01-01

Home monitoring of chronically ill or elderly patient can reduce frequent hospitalisations and hence provide improved quality of care at a reduced cost to the community, therefore reducing the burden on the healthcare system. Activity recognition of such patients is of high importance in such a design. In this work, a system for automatic human physical activity recognition from smart-phone inertial sensors data is proposed. An ensemble of decision trees framework is adopted to train and predict the multi-class human activity system. A comparison of our proposed method with a multi-class traditional support vector machine shows significant improvement in activity recognition accuracies.

Predicting user click behaviour in search engine advertisements

NASA Astrophysics Data System (ADS)

Daryaie Zanjani, Mohammad; Khadivi, Shahram

2015-10-01

According to the specific requirements and interests of users, search engines select and display advertisements that match user needs and have higher probability of attracting users' attention based on their previous search history. New objects such as user, advertisement or query cause a deterioration of precision in targeted advertising due to their lack of history. This article surveys this challenge. In the case of new objects, we first extract similar observed objects to the new object and then we use their history as the history of new object. Similarity between objects is measured based on correlation, which is a relation between user and advertisement when the advertisement is displayed to the user. This method is used for all objects, so it has helped us to accurately select relevant advertisements for users' queries. In our proposed model, we assume that similar users behave in a similar manner. We find that users with few queries are similar to new users. We will show that correlation between users and advertisements' keywords is high. Thus, users who pay attention to advertisements' keywords, click similar advertisements. In addition, users who pay attention to specific brand names might have similar behaviours too.

Tool for Turbine Engine Closed-Loop Transient Analysis (TTECTrA) Users' Guide

NASA Technical Reports Server (NTRS)

Csank, Jeffrey T.; Zinnecker, Alicia M.

2014-01-01

The tool for turbine engine closed-loop transient analysis (TTECTrA) is a semi-automated control design tool for subsonic aircraft engine simulations. At a specific flight condition, TTECTrA produces a basic controller designed to meet user-defined goals and containing only the fundamental limiters that affect the transient performance of the engine. The purpose of this tool is to provide the user a preliminary estimate of the transient performance of an engine model without the need to design a full nonlinear controller.

Pharmacologic suppression of target cell recognition by engineered T cells expressing chimeric T-cell receptors.

PubMed

Alvarez-Vallina, L; Yañez, R; Blanco, B; Gil, M; Russell, S J

2000-04-01

Adoptive therapy with autologous T cells expressing chimeric T-cell receptors (chTCRs) is of potential interest for the treatment of malignancy. To limit possible T-cell-mediated damage to normal tissues that weakly express the targeted tumor antigen (Ag), we have tested a strategy for the suppression of target cell recognition by engineered T cells. Jurkat T cells were transduced with an anti-hapten chTCR tinder the control of a tetracycline-suppressible promoter and were shown to respond to Ag-positive (hapten-coated) but not to Ag-negative target cells. The engineered T cells were then reacted with hapten-coated target cells at different effector to target cell ratios before and after exposure to tetracycline. When the engineered T cells were treated with tetracycline, expression of the chTCR was greatly decreased and recognition of the hapten-coated target cells was completely suppressed. Tetracycline-mediated suppression of target cell recognition by engineered T cells may be a useful strategy to limit the toxicity of the approach to cancer gene therapy.

Multi-cylinder hot gas engine

DOEpatents

Corey, John A.

1985-01-01

A multi-cylinder hot gas engine having an equal angle, V-shaped engine block in which two banks of parallel, equal length, equally sized cylinders are formed together with annular regenerator/cooler units surrounding each cylinder, and wherein the pistons are connected to a single crankshaft. The hot gas engine further includes an annular heater head disposed around a central circular combustor volume having a new balanced-flow hot-working-fluid manifold assembly that provides optimum balanced flow of the working fluid through the heater head working fluid passageways which are connected between each of the cylinders and their respective associated annular regenerator units. This balanced flow provides even heater head temperatures and, therefore, maximum average working fluid temperature for best operating efficiency with the use of a single crankshaft V-shaped engine block.

Developing a Natural User Interface and Facial Recognition System With OpenCV and the Microsoft Kinect

NASA Technical Reports Server (NTRS)

Gutensohn, Michael

2018-01-01

The task for this project was to design, develop, test, and deploy a facial recognition system for the Kennedy Space Center Augmented/Virtual Reality Lab. This system will serve as a means of user authentication as part of the NUI of the lab. The overarching goal is to create a seamless user interface that will allow the user to initiate and interact with AR and VR experiences without ever needing to use a mouse or keyboard at any step in the process.

A joint precoding scheme for indoor downlink multi-user MIMO VLC systems

NASA Astrophysics Data System (ADS)

Zhao, Qiong; Fan, Yangyu; Kang, Bochao

2017-11-01

In this study, we aim to improve the system performance and reduce the implementation complexity of precoding scheme for visible light communication (VLC) systems. By incorporating the power-method algorithm and the block diagonalization (BD) algorithm, we propose a joint precoding scheme for indoor downlink multi-user multi-input-multi-output (MU-MIMO) VLC systems. In this scheme, we apply the BD algorithm to eliminate the co-channel interference (CCI) among users firstly. Secondly, the power-method algorithm is used to search the precoding weight for each user based on the optimal criterion of signal to interference plus noise ratio (SINR) maximization. Finally, the optical power restrictions of VLC systems are taken into account to constrain the precoding weight matrix. Comprehensive computer simulations in two scenarios indicate that the proposed scheme always has better bit error rate (BER) performance and lower computation complexity than that of the traditional scheme.

A Multi-User Microcomputer System for Small Libraries.

ERIC Educational Resources Information Center

Leggate, Peter

1988-01-01

Describes the development of Bookshelf, a multi-user microcomputer system for small libraries that uses an integrated software package. The discussion covers the design parameters of the package, which were based on a survey of seven small libraries, and some characteristics of the software. (three notes with references) (CLB)

Neuromorphic Hardware Architecture Using the Neural Engineering Framework for Pattern Recognition.

PubMed

Wang, Runchun; Thakur, Chetan Singh; Cohen, Gregory; Hamilton, Tara Julia; Tapson, Jonathan; van Schaik, Andre

2017-06-01

We present a hardware architecture that uses the neural engineering framework (NEF) to implement large-scale neural networks on field programmable gate arrays (FPGAs) for performing massively parallel real-time pattern recognition. NEF is a framework that is capable of synthesising large-scale cognitive systems from subnetworks and we have previously presented an FPGA implementation of the NEF that successfully performs nonlinear mathematical computations. That work was developed based on a compact digital neural core, which consists of 64 neurons that are instantiated by a single physical neuron using a time-multiplexing approach. We have now scaled this approach up to build a pattern recognition system by combining identical neural cores together. As a proof of concept, we have developed a handwritten digit recognition system using the MNIST database and achieved a recognition rate of 96.55%. The system is implemented on a state-of-the-art FPGA and can process 5.12 million digits per second. The architecture and hardware optimisations presented offer high-speed and resource-efficient means for performing high-speed, neuromorphic, and massively parallel pattern recognition and classification tasks.

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

PubMed

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

2016-12-02

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

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

PubMed Central

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

2016-01-01

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

Rotary engine performance computer program (RCEMAP and RCEMAPPC): User's guide

NASA Technical Reports Server (NTRS)

Bartrand, Timothy A.; Willis, Edward A.

1993-01-01

This report is a user's guide for a computer code that simulates the performance of several rotary combustion engine configurations. It is intended to assist prospective users in getting started with RCEMAP and/or RCEMAPPC. RCEMAP (Rotary Combustion Engine performance MAP generating code) is the mainframe version, while RCEMAPPC is a simplified subset designed for the personal computer, or PC, environment. Both versions are based on an open, zero-dimensional combustion system model for the prediction of instantaneous pressures, temperature, chemical composition and other in-chamber thermodynamic properties. Both versions predict overall engine performance and thermal characteristics, including bmep, bsfc, exhaust gas temperature, average material temperatures, and turbocharger operating conditions. Required inputs include engine geometry, materials, constants for use in the combustion heat release model, and turbomachinery maps. Illustrative examples and sample input files for both versions are included.

SVM-Fold: a tool for discriminative multi-class protein fold and superfamily recognition

PubMed Central

Melvin, Iain; Ie, Eugene; Kuang, Rui; Weston, Jason; Stafford, William Noble; Leslie, Christina

2007-01-01

Background Predicting a protein's structural class from its amino acid sequence is a fundamental problem in computational biology. Much recent work has focused on developing new representations for protein sequences, called string kernels, for use with support vector machine (SVM) classifiers. However, while some of these approaches exhibit state-of-the-art performance at the binary protein classification problem, i.e. discriminating between a particular protein class and all other classes, few of these studies have addressed the real problem of multi-class superfamily or fold recognition. Moreover, there are only limited software tools and systems for SVM-based protein classification available to the bioinformatics community. Results We present a new multi-class SVM-based protein fold and superfamily recognition system and web server called SVM-Fold, which can be found at . Our system uses an efficient implementation of a state-of-the-art string kernel for sequence profiles, called the profile kernel, where the underlying feature representation is a histogram of inexact matching k-mer frequencies. We also employ a novel machine learning approach to solve the difficult multi-class problem of classifying a sequence of amino acids into one of many known protein structural classes. Binary one-vs-the-rest SVM classifiers that are trained to recognize individual structural classes yield prediction scores that are not comparable, so that standard "one-vs-all" classification fails to perform well. Moreover, SVMs for classes at different levels of the protein structural hierarchy may make useful predictions, but one-vs-all does not try to combine these multiple predictions. To deal with these problems, our method learns relative weights between one-vs-the-rest classifiers and encodes information about the protein structural hierarchy for multi-class prediction. In large-scale benchmark results based on the SCOP database, our code weighting approach significantly improves

The Pandora multi-algorithm approach to automated pattern recognition in LAr TPC detectors

NASA Astrophysics Data System (ADS)

Marshall, J. S.; Blake, A. S. T.; Thomson, M. A.; Escudero, L.; de Vries, J.; Weston, J.; MicroBooNE Collaboration

2017-09-01

The development and operation of Liquid Argon Time Projection Chambers (LAr TPCs) for neutrino physics has created a need for new approaches to pattern recognition, in order to fully exploit the superb imaging capabilities offered by this technology. The Pandora Software Development Kit provides functionality to aid the process of designing, implementing and running pattern recognition algorithms. It promotes the use of a multi-algorithm approach to pattern recognition: individual algorithms each address a specific task in a particular topology; a series of many tens of algorithms then carefully builds-up a picture of the event. The input to the Pandora pattern recognition is a list of 2D Hits. The output from the chain of over 70 algorithms is a hierarchy of reconstructed 3D Particles, each with an identified particle type, vertex and direction.

An Exemplar-Based Multi-View Domain Generalization Framework for Visual Recognition.

PubMed

Niu, Li; Li, Wen; Xu, Dong; Cai, Jianfei

2018-02-01

In this paper, we propose a new exemplar-based multi-view domain generalization (EMVDG) framework for visual recognition by learning robust classifier that are able to generalize well to arbitrary target domain based on the training samples with multiple types of features (i.e., multi-view features). In this framework, we aim to address two issues simultaneously. First, the distribution of training samples (i.e., the source domain) is often considerably different from that of testing samples (i.e., the target domain), so the performance of the classifiers learnt on the source domain may drop significantly on the target domain. Moreover, the testing data are often unseen during the training procedure. Second, when the training data are associated with multi-view features, the recognition performance can be further improved by exploiting the relation among multiple types of features. To address the first issue, considering that it has been shown that fusing multiple SVM classifiers can enhance the domain generalization ability, we build our EMVDG framework upon exemplar SVMs (ESVMs), in which a set of ESVM classifiers are learnt with each one trained based on one positive training sample and all the negative training samples. When the source domain contains multiple latent domains, the learnt ESVM classifiers are expected to be grouped into multiple clusters. To address the second issue, we propose two approaches under the EMVDG framework based on the consensus principle and the complementary principle, respectively. Specifically, we propose an EMVDG_CO method by adding a co-regularizer to enforce the cluster structures of ESVM classifiers on different views to be consistent based on the consensus principle. Inspired by multiple kernel learning, we also propose another EMVDG_MK method by fusing the ESVM classifiers from different views based on the complementary principle. In addition, we further extend our EMVDG framework to exemplar-based multi-view domain

User Experience of a Mobile Speaking Application with Automatic Speech Recognition for EFL Learning

ERIC Educational Resources Information Center

Ahn, Tae youn; Lee, Sangmin-Michelle

2016-01-01

With the spread of mobile devices, mobile phones have enormous potential regarding their pedagogical use in language education. The goal of this study is to analyse user experience of a mobile-based learning system that is enhanced by speech recognition technology for the improvement of EFL (English as a foreign language) learners' speaking…

MDMA (Ecstasy) use is associated with reduced BOLD signal change during semantic recognition in abstinent human polydrug users: a preliminary fMRI study

PubMed Central

Raj, Vidya; Liang, Han-Chun; Woodward, Neil D.; Bauernfeind, Amy L.; Lee, Junghee; Dietrich, Mary; Park, Sohee; Cowan, Ronald L.

2011-01-01

Objectives MDMA users have impaired verbal memory, and voxel-based morphometry has demonstrated decreased gray matter in Brodmann area (BA) 18, 21 and 45. Because these regions play a role in verbal memory, we hypothesized that MDMA users would show altered brain activation in these areas during performance of an fMRI task that probed semantic verbal memory. Methods Polysubstance users enriched for MDMA exposure participated in a semantic memory encoding and recognition fMRI task that activated left BA 9, 18, 21/22 and 45. Primary outcomes were percent BOLD signal change in left BA 9, 18, 21/22 and 45, accuracy and response time. Results During semantic recognition, lifetime MDMA use was associated with decreased activation in left BA 9, 18 and 21/22 but not 45. This was partly influenced by contributions from cannabis and cocaine use. MDMA exposure was not associated with accuracy or response time during the semantic recognition task. Conclusions During semantic recognition, MDMA exposure is associated with reduced regional brain activation in regions mediating verbal memory. These findings partially overlap with prior structural evidence for reduced gray matter in MDMA users and may, in part, explain the consistent verbal memory impairments observed in other studies of MDMA users. PMID:19304866

Combining users' activity survey and simulators to evaluate human activity recognition systems.

PubMed

Azkune, Gorka; Almeida, Aitor; López-de-Ipiña, Diego; Chen, Liming

2015-04-08

Evaluating human activity recognition systems usually implies following expensive and time-consuming methodologies, where experiments with humans are run with the consequent ethical and legal issues. We propose a novel evaluation methodology to overcome the enumerated problems, which is based on surveys for users and a synthetic dataset generator tool. Surveys allow capturing how different users perform activities of daily living, while the synthetic dataset generator is used to create properly labelled activity datasets modelled with the information extracted from surveys. Important aspects, such as sensor noise, varying time lapses and user erratic behaviour, can also be simulated using the tool. The proposed methodology is shown to have very important advantages that allow researchers to carry out their work more efficiently. To evaluate the approach, a synthetic dataset generated following the proposed methodology is compared to a real dataset computing the similarity between sensor occurrence frequencies. It is concluded that the similarity between both datasets is more than significant.

Nonlinear dynamic simulation of single- and multi-spool core engines

NASA Technical Reports Server (NTRS)

Schobeiri, T.; Lippke, C.; Abouelkheir, M.

1993-01-01

In this paper a new computational method for accurate simulation of the nonlinear dynamic behavior of single- and multi-spool core engines, turbofan engines, and power generation gas turbine engines is presented. In order to perform the simulation, a modularly structured computer code has been developed which includes individual mathematical modules representing various engine components. The generic structure of the code enables the dynamic simulation of arbitrary engine configurations ranging from single-spool thrust generation to multi-spool thrust/power generation engines under adverse dynamic operating conditions. For precise simulation of turbine and compressor components, row-by-row calculation procedures were implemented that account for the specific turbine and compressor cascade and blade geometry and characteristics. The dynamic behavior of the subject engine is calculated by solving a number of systems of partial differential equations, which describe the unsteady behavior of the individual components. In order to ensure the capability, accuracy, robustness, and reliability of the code, comprehensive critical performance assessment and validation tests were performed. As representatives, three different transient cases with single- and multi-spool thrust and power generation engines were simulated. The transient cases range from operating with a prescribed fuel schedule, to extreme load changes, to generator and turbine shut down.

PIA: An Intuitive Protein Inference Engine with a Web-Based User Interface.

PubMed

Uszkoreit, Julian; Maerkens, Alexandra; Perez-Riverol, Yasset; Meyer, Helmut E; Marcus, Katrin; Stephan, Christian; Kohlbacher, Oliver; Eisenacher, Martin

2015-07-02

Protein inference connects the peptide spectrum matches (PSMs) obtained from database search engines back to proteins, which are typically at the heart of most proteomics studies. Different search engines yield different PSMs and thus different protein lists. Analysis of results from one or multiple search engines is often hampered by different data exchange formats and lack of convenient and intuitive user interfaces. We present PIA, a flexible software suite for combining PSMs from different search engine runs and turning these into consistent results. PIA can be integrated into proteomics data analysis workflows in several ways. A user-friendly graphical user interface can be run either locally or (e.g., for larger core facilities) from a central server. For automated data processing, stand-alone tools are available. PIA implements several established protein inference algorithms and can combine results from different search engines seamlessly. On several benchmark data sets, we show that PIA can identify a larger number of proteins at the same protein FDR when compared to that using inference based on a single search engine. PIA supports the majority of established search engines and data in the mzIdentML standard format. It is implemented in Java and freely available at https://github.com/mpc-bioinformatics/pia.

Multi-sensor physical activity recognition in free-living.

PubMed

Ellis, Katherine; Godbole, Suneeta; Kerr, Jacqueline; Lanckriet, Gert

Physical activity monitoring in free-living populations has many applications for public health research, weight-loss interventions, context-aware recommendation systems and assistive technologies. We present a system for physical activity recognition that is learned from a free-living dataset of 40 women who wore multiple sensors for seven days. The multi-level classification system first learns low-level codebook representations for each sensor and uses a random forest classifier to produce minute-level probabilities for each activity class. Then a higher-level HMM layer learns patterns of transitions and durations of activities over time to smooth the minute-level predictions. [Formula: see text].

The research of multi-frame target recognition based on laser active imaging

NASA Astrophysics Data System (ADS)

Wang, Can-jin; Sun, Tao; Wang, Tin-feng; Chen, Juan

2013-09-01

Laser active imaging is fit to conditions such as no difference in temperature between target and background, pitch-black night, bad visibility. Also it can be used to detect a faint target in long range or small target in deep space, which has advantage of high definition and good contrast. In one word, it is immune to environment. However, due to the affect of long distance, limited laser energy and atmospheric backscatter, it is impossible to illuminate the whole scene at the same time. It means that the target in every single frame is unevenly or partly illuminated, which make the recognition more difficult. At the same time the speckle noise which is common in laser active imaging blurs the images . In this paper we do some research on laser active imaging and propose a new target recognition method based on multi-frame images . Firstly, multi pulses of laser is used to obtain sub-images for different parts of scene. A denoising method combined homomorphic filter with wavelet domain SURE is used to suppress speckle noise. And blind deconvolution is introduced to obtain low-noise and clear sub-images. Then these sub-images are registered and stitched to combine a completely and uniformly illuminated scene image. After that, a new target recognition method based on contour moments is proposed. Firstly, canny operator is used to obtain contours. For each contour, seven invariant Hu moments are calculated to generate the feature vectors. At last the feature vectors are input into double hidden layers BP neural network for classification . Experiments results indicate that the proposed algorithm could achieve a high recognition rate and satisfactory real-time performance for laser active imaging.

Modeling Spoken Word Recognition Performance by Pediatric Cochlear Implant Users using Feature Identification

PubMed Central

Frisch, Stefan A.; Pisoni, David B.

2012-01-01

Objective Computational simulations were carried out to evaluate the appropriateness of several psycholinguistic theories of spoken word recognition for children who use cochlear implants. These models also investigate the interrelations of commonly used measures of closed-set and open-set tests of speech perception. Design A software simulation of phoneme recognition performance was developed that uses feature identification scores as input. Two simulations of lexical access were developed. In one, early phoneme decisions are used in a lexical search to find the best matching candidate. In the second, phoneme decisions are made only when lexical access occurs. Simulated phoneme and word identification performance was then applied to behavioral data from the Phonetically Balanced Kindergarten test and Lexical Neighborhood Test of open-set word recognition. Simulations of performance were evaluated for children with prelingual sensorineural hearing loss who use cochlear implants with the MPEAK or SPEAK coding strategies. Results Open-set word recognition performance can be successfully predicted using feature identification scores. In addition, we observed no qualitative differences in performance between children using MPEAK and SPEAK, suggesting that both groups of children process spoken words similarly despite differences in input. Word recognition ability was best predicted in the model in which phoneme decisions were delayed until lexical access. Conclusions Closed-set feature identification and open-set word recognition focus on different, but related, levels of language processing. Additional insight for clinical intervention may be achieved by collecting both types of data. The most successful model of performance is consistent with current psycholinguistic theories of spoken word recognition. Thus it appears that the cognitive process of spoken word recognition is fundamentally the same for pediatric cochlear implant users and children and adults with

Multi-stream face recognition on dedicated mobile devices for crime-fighting

NASA Astrophysics Data System (ADS)

Jassim, Sabah A.; Sellahewa, Harin

2006-09-01

Automatic face recognition is a useful tool in the fight against crime and terrorism. Technological advance in mobile communication systems and multi-application mobile devices enable the creation of hybrid platforms for active and passive surveillance. A dedicated mobile device that incorporates audio-visual sensors would not only complement existing networks of fixed surveillance devices (e.g. CCTV) but could also provide wide geographical coverage in almost any situation and anywhere. Such a device can hold a small portion of a law-enforcing agency biometric database that consist of audio and/or visual data of a number of suspects/wanted or missing persons who are expected to be in a local geographical area. This will assist law-enforcing officers on the ground in identifying persons whose biometric templates are downloaded onto their devices. Biometric data on the device can be regularly updated which will reduce the number of faces an officer has to remember. Such a dedicated device would act as an active/passive mobile surveillance unit that incorporate automatic identification. This paper is concerned with the feasibility of using wavelet-based face recognition schemes on such devices. The proposed schemes extend our recently developed face verification scheme for implementation on a currently available PDA. In particular we will investigate the use of a combination of wavelet frequency channels for multi-stream face recognition. We shall present experimental results on the performance of our proposed schemes for a number of publicly available face databases including a new AV database of videos recorded on a PDA.

Early Sign Language Experience Goes Along with an Increased Cross-modal Gain for Affective Prosodic Recognition in Congenitally Deaf CI Users.

PubMed

Fengler, Ineke; Delfau, Pia-Céline; Röder, Brigitte

2018-04-01

It is yet unclear whether congenitally deaf cochlear implant (CD CI) users' visual and multisensory emotion perception is influenced by their history in sign language acquisition. We hypothesized that early-signing CD CI users, relative to late-signing CD CI users and hearing, non-signing controls, show better facial expression recognition and rely more on the facial cues of audio-visual emotional stimuli. Two groups of young adult CD CI users-early signers (ES CI users; n = 11) and late signers (LS CI users; n = 10)-and a group of hearing, non-signing, age-matched controls (n = 12) performed an emotion recognition task with auditory, visual, and cross-modal emotionally congruent and incongruent speech stimuli. On different trials, participants categorized either the facial or the vocal expressions. The ES CI users more accurately recognized affective prosody than the LS CI users in the presence of congruent facial information. Furthermore, the ES CI users, but not the LS CI users, gained more than the controls from congruent visual stimuli when recognizing affective prosody. Both CI groups performed overall worse than the controls in recognizing affective prosody. These results suggest that early sign language experience affects multisensory emotion perception in CD CI users.

An Object-Oriented Graphical User Interface for a Reusable Rocket Engine Intelligent Control System

NASA Technical Reports Server (NTRS)

Litt, Jonathan S.; Musgrave, Jeffrey L.; Guo, Ten-Huei; Paxson, Daniel E.; Wong, Edmond; Saus, Joseph R.; Merrill, Walter C.

1994-01-01

An intelligent control system for reusable rocket engines under development at NASA Lewis Research Center requires a graphical user interface to allow observation of the closed-loop system in operation. The simulation testbed consists of a real-time engine simulation computer, a controls computer, and several auxiliary computers for diagnostics and coordination. The system is set up so that the simulation computer could be replaced by the real engine and the change would be transparent to the control system. Because of the hard real-time requirement of the control computer, putting a graphical user interface on it was not an option. Thus, a separate computer used strictly for the graphical user interface was warranted. An object-oriented LISP-based graphical user interface has been developed on a Texas Instruments Explorer 2+ to indicate the condition of the engine to the observer through plots, animation, interactive graphics, and text.

ASERA: A spectrum eye recognition assistant for quasar spectra

NASA Astrophysics Data System (ADS)

Yuan, Hailong; Zhang, Haotong; Zhang, Yanxia; Lei, Yajuan; Dong, Yiqiao; Zhao, Yongheng

2013-11-01

Spectral type recognition is an important and fundamental step of large sky survey projects in the data reduction for further scientific research, like parameter measurement and statistic work. It tends out to be a huge job to manually inspect the low quality spectra produced from the massive spectroscopic survey, where the automatic pipeline may not provide confident type classification results. In order to improve the efficiency and effectiveness of spectral classification, we develop a semi-automated toolkit named ASERA, ASpectrum Eye Recognition Assistant. The main purpose of ASERA is to help the user in quasar spectral recognition and redshift measurement. Furthermore it can also be used to recognize various types of spectra of stars, galaxies and AGNs (Active Galactic Nucleus). It is an interactive software allowing the user to visualize observed spectra, superimpose template spectra from the Sloan Digital Sky Survey (SDSS), and interactively access related spectral line information. It is an efficient and user-friendly toolkit for the accurate classification of spectra observed by LAMOST (the Large Sky Area Multi-object Fiber Spectroscopic Telescope). The toolkit is available in two modes: a Java standalone application and a Java applet. ASERA has a few functions, such as wavelength and flux scale setting, zoom in and out, redshift estimation, spectral line identification, which helps user to improve the spectral classification accuracy especially for low quality spectra and reduce the labor of eyeball check. The function and performance of this tool is displayed through the recognition of several quasar spectra and a late type stellar spectrum from the LAMOST Pilot survey. Its future expansion capabilities are discussed.

Recognition-driven chemical labeling of endogenous proteins in multi-molecular crowding in live cells.

PubMed

Amaike, Kazuma; Tamura, Tomonori; Hamachi, Itaru

2017-11-14

Endogenous protein labeling is one of the most invaluable methods for studying the bona fide functions of proteins in live cells. However, multi-molecular crowding conditions, such as those that occur in live cells, hamper the highly selective chemical labeling of a protein of interest (POI). We herein describe how the efficient coupling of molecular recognition with a chemical reaction is crucial for selective protein labeling. Recognition-driven protein labeling is carried out by a synthetic labeling reagent containing a protein (recognition) ligand, a reporter tag, and a reactive moiety. The molecular recognition of a POI can be used to greatly enhance the reaction kinetics and protein selectivity, even under live cell conditions. In this review, we also briefly discuss how such selective chemical labeling of an endogenous protein can have a variety of applications at the interface of chemistry and biology.

Adaptation and development of software simulation methodologies for cardiovascular engineering: present and future challenges from an end-user perspective

PubMed Central

Díaz-Zuccarini, V.; Narracott, A.J.; Burriesci, G.; Zervides, C.; Rafiroiu, D.; Jones, D.; Hose, D.R.; Lawford, P.V.

2009-01-01

This paper describes the use of diverse software tools in cardiovascular applications. These tools were primarily developed in the field of engineering and the applications presented push the boundaries of the software to address events related to venous and arterial valve closure, exploration of dynamic boundary conditions or the inclusion of multi-scale boundary conditions from protein to organ levels. The future of cardiovascular research and the challenges that modellers and clinicians face from validation to clinical uptake are discussed from an end-user perspective. PMID:19487202

Adaptation and development of software simulation methodologies for cardiovascular engineering: present and future challenges from an end-user perspective.

PubMed

Díaz-Zuccarini, V; Narracott, A J; Burriesci, G; Zervides, C; Rafiroiu, D; Jones, D; Hose, D R; Lawford, P V

2009-07-13

This paper describes the use of diverse software tools in cardiovascular applications. These tools were primarily developed in the field of engineering and the applications presented push the boundaries of the software to address events related to venous and arterial valve closure, exploration of dynamic boundary conditions or the inclusion of multi-scale boundary conditions from protein to organ levels. The future of cardiovascular research and the challenges that modellers and clinicians face from validation to clinical uptake are discussed from an end-user perspective.

Implementation and preliminary evaluation of 'C-tone': A novel algorithm to improve lexical tone recognition in Mandarin-speaking cochlear implant users.

PubMed

Ping, Lichuan; Wang, Ningyuan; Tang, Guofang; Lu, Thomas; Yin, Li; Tu, Wenhe; Fu, Qian-Jie

2017-09-01

Because of limited spectral resolution, Mandarin-speaking cochlear implant (CI) users have difficulty perceiving fundamental frequency (F0) cues that are important to lexical tone recognition. To improve Mandarin tone recognition in CI users, we implemented and evaluated a novel real-time algorithm (C-tone) to enhance the amplitude contour, which is strongly correlated with the F0 contour. The C-tone algorithm was implemented in clinical processors and evaluated in eight users of the Nurotron NSP-60 CI system. Subjects were given 2 weeks of experience with C-tone. Recognition of Chinese tones, monosyllables, and disyllables in quiet was measured with and without the C-tone algorithm. Subjective quality ratings were also obtained for C-tone. After 2 weeks of experience with C-tone, there were small but significant improvements in recognition of lexical tones, monosyllables, and disyllables (P < 0.05 in all cases). Among lexical tones, the largest improvements were observed for Tone 3 (falling-rising) and the smallest for Tone 4 (falling). Improvements with C-tone were greater for disyllables than for monosyllables. Subjective quality ratings showed no strong preference for or against C-tone, except for perception of own voice, where C-tone was preferred. The real-time C-tone algorithm provided small but significant improvements for speech performance in quiet with no change in sound quality. Pre-processing algorithms to reduce noise and better real-time F0 extraction would improve the benefits of C-tone in complex listening environments. Chinese CI users' speech recognition in quiet can be significantly improved by modifying the amplitude contour to better resemble the F0 contour.

Multi-Sector Sustainability Browser (MSSB) User Manual: A ...

EPA Pesticide Factsheets

EPA’s Sustainable and Healthy Communities (SHC) Research Program is developing methodologies, resources, and tools to assist community members and local decision makers in implementing policy choices that facilitate sustainable approaches in managing their resources affecting the built environment, natural environment, and human health. In order to assist communities and decision makers in implementing sustainable practices, EPA is developing computer-based systems including models, databases, web tools, and web browsers to help communities decide upon approaches that support their desired outcomes. Communities need access to resources that will allow them to achieve their sustainability objectives through intelligent decisions in four key sustainability areas: • Land Use • Buildings and Infrastructure • Transportation • Materials Management (i.e., Municipal Solid Waste [MSW] processing and disposal) The Multi-Sector Sustainability Browser (MSSB) is designed to support sustainable decision-making for communities, local and regional planners, and policy and decision makers. Document is an EPA Technical Report, which is the user manual for the Multi-Sector Sustainability Browser (MSSB) tool. The purpose of the document is to provide basic guidance on use of the tool for users

Early Sign Language Experience Goes along with an Increased Cross-Modal Gain for Affective Prosodic Recognition in Congenitally Deaf CI Users

ERIC Educational Resources Information Center

Fengler, Ineke; Delfau, Pia-Céline; Röder, Brigitte

2018-01-01

It is yet unclear whether congenitally deaf cochlear implant (CD CI) users' visual and multisensory emotion perception is influenced by their history in sign language acquisition. We hypothesized that early-signing CD CI users, relative to late-signing CD CI users and hearing, non-signing controls, show better facial expression recognition and…

Introducing ORACLE: Library Processing in a Multi-User Environment.

ERIC Educational Resources Information Center

Queensland Library Board, Brisbane (Australia).

Currently being developed by the State Library of Queensland, Australia, ORACLE (On-Line Retrieval of Acquisitions, Cataloguing, and Circulation Details for Library Enquiries) is a computerized library system designed to provide rapid processing of library materials in a multi-user environment. It is based on the Australian MARC format and fully…

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

NASA Astrophysics Data System (ADS)

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.

Track Everything: Limiting Prior Knowledge in Online Multi-Object Recognition.

PubMed

Wong, Sebastien C; Stamatescu, Victor; Gatt, Adam; Kearney, David; Lee, Ivan; McDonnell, Mark D

2017-10-01

This paper addresses the problem of online tracking and classification of multiple objects in an image sequence. Our proposed solution is to first track all objects in the scene without relying on object-specific prior knowledge, which in other systems can take the form of hand-crafted features or user-based track initialization. We then classify the tracked objects with a fast-learning image classifier, that is based on a shallow convolutional neural network architecture and demonstrate that object recognition improves when this is combined with object state information from the tracking algorithm. We argue that by transferring the use of prior knowledge from the detection and tracking stages to the classification stage, we can design a robust, general purpose object recognition system with the ability to detect and track a variety of object types. We describe our biologically inspired implementation, which adaptively learns the shape and motion of tracked objects, and apply it to the Neovision2 Tower benchmark data set, which contains multiple object types. An experimental evaluation demonstrates that our approach is competitive with the state-of-the-art video object recognition systems that do make use of object-specific prior knowledge in detection and tracking, while providing additional practical advantages by virtue of its generality.

Cabana Multi-User Spaceport Tour of KSC

NASA Image and Video Library

2017-02-17

Kennedy Space Center Director Bob Cabana speaks to members of the news media on the balcony of Operations Support Building II describing the site's transition from a primarily government-only facility to a premier, multi-user spaceport. In the background is the Vehicle Assembly Building (VAB). Modifications were recently completed in the VAB where new work platforms were installed to support processing of NASA's Space Launch System rocket designed to send the Orion spacecraft on missions beyond low-Earth orbit.

Motorcycle Start-stop System based on Intelligent Biometric Voice Recognition

NASA Astrophysics Data System (ADS)

Winda, A.; E Byan, W. R.; Sofyan; Armansyah; Zariantin, D. L.; Josep, B. G.

2017-03-01

Current mechanical key in the motorcycle is prone to bulgary, being stolen or misplaced. Intelligent biometric voice recognition as means to replace this mechanism is proposed as an alternative. The proposed system will decide whether the voice is belong to the user or not and the word utter by the user is ‘On’ or ‘Off’. The decision voice will be sent to Arduino in order to start or stop the engine. The recorded voice is processed in order to get some features which later be used as input to the proposed system. The Mel-Frequency Ceptral Coefficient (MFCC) is adopted as a feature extraction technique. The extracted feature is the used as input to the SVM-based identifier. Experimental results confirm the effectiveness of the proposed intelligent voice recognition and word recognition system. It show that the proposed method produces a good training and testing accuracy, 99.31% and 99.43%, respectively. Moreover, the proposed system shows the performance of false rejection rate (FRR) and false acceptance rate (FAR) accuracy of 0.18% and 17.58%, respectively. In the intelligent word recognition shows that the training and testing accuracy are 100% and 96.3%, respectively.

Multi-user investigation organizer

NASA Technical Reports Server (NTRS)

Panontin, Tina L. (Inventor); Williams, James F. (Inventor); Carvalho, Robert E. (Inventor); Sturken, Ian (Inventor); Wolfe, Shawn R. (Inventor); Gawdiak, Yuri O. (Inventor); Keller, Richard M. (Inventor)

2009-01-01

A system that allows a team of geographically dispersed users to collaboratively analyze a mishap event. The system includes a reconfigurable ontology, including instances that are related to and characterize the mishap, a semantic network that receives, indexes and stores, for retrieval, viewing and editing, the instances and links between the instances, a network browser interface for retrieving and viewing screens that present the instances and links to other instances and that allow editing thereof, and a rule-based inference engine, including a collection of rules associated with establishment of links between the instances. A possible conclusion arising from analysis of the mishap event may be characterized as one or more of: not a credible conclusion; an unlikely conclusion; a credible conclusion; conclusion needs analysis; conclusion needs supporting data; conclusion proposed to be closed; and an un-reviewed conclusion.

User acceptance of intelligent avionics: A study of automatic-aided target recognition

NASA Technical Reports Server (NTRS)

Becker, Curtis A.; Hayes, Brian C.; Gorman, Patrick C.

1991-01-01

User acceptance of new support systems typically was evaluated after the systems were specified, designed, and built. The current study attempts to assess user acceptance of an Automatic-Aided Target Recognition (ATR) system using an emulation of such a proposed system. The detection accuracy and false alarm level of the ATR system were varied systematically, and subjects rated the tactical value of systems exhibiting different performance levels. Both detection accuracy and false alarm level affected the subjects' ratings. The data from two experiments suggest a cut-off point in ATR performance below which the subjects saw little tactical value in the system. An ATR system seems to have obvious tactical value only if it functions at a correct detection rate of 0.7 or better with a false alarm level of 0.167 false alarms per square degree or fewer.

Center Planning and Development: Multi-User Spaceport Initiatives

NASA Technical Reports Server (NTRS)

Kennedy, Christopher John

2015-01-01

The Vehicle Assembly building at NASAs Kennedy Space Center has been used since 1966 to vertically assemble every launch vehicle, since the Apollo Program, launched from Launch Complex 39 (LC-39). After the cancellation of the Constellation Program in 2010 and the retirement of the Space Shuttle Program in 2011, the VAB faced an uncertain future. As the Space Launch System (SLS) gained a foothold as the future of American spaceflight to deep space, NASA was only using a portion of the VABs initial potential. With three high bays connected to the Crawler Way transportation system, the potential exists for up to three rockets to be simultaneously processed for launch. The Kennedy Space Center (KSC) Master plan, supported by the Center Planning and Development (CPD) Directorate, is guiding Kennedy toward a 21st century multi-user spaceport. This concept will maintain Kennedy as the United States premier gateway to space and provide multi-user operations through partnerships with the commercial aerospace industry. Commercial aerospace companies, now tasked with transporting cargo and, in the future, astronauts to the International Space Station (ISS) via the Commercial Resupply Service (CRS) and Commercial Crew Program (CCP), are a rapidly growing industry with increasing capabilities to make launch operations more economical for both private companies and the government. Commercial operations to Low Earth Orbit allow the government to focus on travel to farther destinations through the SLS Program. With LC-39B designated as a multi-use launch pad, companies seeking to use it will require an integration facility to assemble, integrate, and test their launch vehicle. An Announcement for Proposals (AFP) was released in June, beginning the process of finding a non-NASA user for High Bay 2 (HB2) and the Mobile Launcher Platforms (MLPs). An Industry Day, a business meeting and tour for interested companies and organizations, was also arranged to identify and answer any

DEEP SPACE: High Resolution VR Platform for Multi-user Interactive Narratives

NASA Astrophysics Data System (ADS)

Kuka, Daniela; Elias, Oliver; Martins, Ronald; Lindinger, Christopher; Pramböck, Andreas; Jalsovec, Andreas; Maresch, Pascal; Hörtner, Horst; Brandl, Peter

DEEP SPACE is a large-scale platform for interactive, stereoscopic and high resolution content. The spatial and the system design of DEEP SPACE are facing constraints of CAVETM-like systems in respect to multi-user interactive storytelling. To be used as research platform and as public exhibition space for many people, DEEP SPACE is capable to process interactive, stereoscopic applications on two projection walls with a size of 16 by 9 meters and a resolution of four times 1080p (4K) each. The processed applications are ranging from Virtual Reality (VR)-environments to 3D-movies to computationally intensive 2D-productions. In this paper, we are describing DEEP SPACE as an experimental VR platform for multi-user interactive storytelling. We are focusing on the system design relevant for the platform, including the integration of the Apple iPod Touch technology as VR control, and a special case study that is demonstrating the research efforts in the field of multi-user interactive storytelling. The described case study, entitled "Papyrate's Island", provides a prototypical scenario of how physical drawings may impact on digital narratives. In this special case, DEEP SPACE helps us to explore the hypothesis that drawing, a primordial human creative skill, gives us access to entirely new creative possibilities in the domain of interactive storytelling.

A rank-based Prediction Algorithm of Learning User's Intention

NASA Astrophysics Data System (ADS)

Shen, Jie; Gao, Ying; Chen, Cang; Gong, HaiPing

Internet search has become an important part in people's daily life. People can find many types of information to meet different needs through search engines on the Internet. There are two issues for the current search engines: first, the users should predetermine the types of information they want and then change to the appropriate types of search engine interfaces. Second, most search engines can support multiple kinds of search functions, each function has its own separate search interface. While users need different types of information, they must switch between different interfaces. In practice, most queries are corresponding to various types of information results. These queries can search the relevant results in various search engines, such as query "Palace" contains the websites about the introduction of the National Palace Museum, blog, Wikipedia, some pictures and video information. This paper presents a new aggregative algorithm for all kinds of search results. It can filter and sort the search results by learning three aspects about the query words, search results and search history logs to achieve the purpose of detecting user's intention. Experiments demonstrate that this rank-based method for multi-types of search results is effective. It can meet the user's search needs well, enhance user's satisfaction, provide an effective and rational model for optimizing search engines and improve user's search experience.

OpenGL in Multi-User Web-Based Applications

NASA Astrophysics Data System (ADS)

Szostek, K.; Piórkowski, A.

In this article construction and potential of OpenGL multi-user web-based application are presented. The most common technologies like: .NET ASP, Java and Mono were used with specific OpenGL libraries to visualize tree-dimensional medical data. The most important conclusion of this work is that server side applications can easily take advantage of fast GPU and produce efficient results of advanced computation just like the visualization.

Multi-responsive hydrogels for drug delivery and tissue engineering applications

PubMed Central

Knipe, Jennifer M.; Peppas, Nicholas A.

2014-01-01

Multi-responsive hydrogels, or ‘intelligent’ hydrogels that respond to more than one environmental stimulus, have demonstrated great utility as a regenerative biomaterial in recent years. They are structured biocompatible materials that provide specific and distinct responses to varied physiological or externally applied stimuli. As evidenced by a burgeoning number of investigators, multi-responsive hydrogels are endowed with tunable, controllable and even biomimetic behavior well-suited for drug delivery and tissue engineering or regenerative growth applications. This article encompasses recent developments and challenges regarding supramolecular, layer-by-layer assembled and covalently cross-linked multi-responsive hydrogel networks and their application to drug delivery and tissue engineering. PMID:26816625

Cabana Multi-User Spaceport Tour of KSC

NASA Image and Video Library

2017-02-17

Nancy Bray, director of Spaceport Integration and Services at NASA's Kennedy Space Center, speaks to members of the news media on the balcony of Operations Support Building II describing the site's transition from a primarily government-only facility to a premier, multi-user spaceport. In the background is the Vehicle Assembly Building (VAB). Modifications were recently completed in the VAB where new work platforms were installed to support processing of NASA's Space Launch System rocket designed to send the Orion spacecraft on missions beyond low-Earth orbit.

Cabana Multi-User Spaceport Tour of KSC

NASA Image and Video Library

2017-02-17

Tom Engler, director of Center Planning and Development at NASA's Kennedy Space Center, speaks to members of the news media on the balcony of Operations Support Building II describing the site's transition from a primarily government-only facility to a premier, multi-user spaceport. In the background is the Vehicle Assembly Building (VAB). Modifications were recently completed in the VAB where new work platforms were installed to support processing of NASA's Space Launch System rocket designed to send the Orion spacecraft on missions beyond low-Earth orbit.

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

PubMed

Li, Hongqiang; Yuan, Danyang; Wang, Youxi; Cui, Dianyin; Cao, Lu

2016-10-20

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.

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

PubMed Central

Li, Hongqiang; Yuan, Danyang; Wang, Youxi; Cui, Dianyin; Cao, Lu

2016-01-01

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. PMID:27775596

Modeling of Multi-Tube Pulse Detonation Engine Operation

NASA Technical Reports Server (NTRS)

Ebrahimi, Houshang B.; Mohanraj, Rajendran; Merkle, Charles L.

2001-01-01

The present paper explores some preliminary issues concerning the operational characteristics of multiple-tube pulsed detonation engines (PDEs). The study is based on a two-dimensional analysis of the first-pulse operation of two detonation tubes exhausting through a common nozzle. Computations are first performed to assess isolated tube behavior followed by results for multi-tube flow phenomena. The computations are based on an eight-species, finite-rate transient flow-field model. The results serve as an important precursor to understanding appropriate propellant fill procedures and shock wave propagation in multi-tube, multi-dimensional simulations. Differences in behavior between single and multi-tube PDE models are discussed, The influence of multi-tube geometry and the preferred times for injecting the fresh propellant mixture during multi-tube PDE operation are studied.

Tools and Methods for Risk Management in Multi-Site Engineering Projects

NASA Astrophysics Data System (ADS)

Zhou, Mingwei; Nemes, Laszlo; Reidsema, Carl; Ahmed, Ammar; Kayis, Berman

In today's highly global business environment, engineering and manufacturing projects often involve two or more geographically dispersed units or departments, research centers or companies. This paper attempts to identify the requirements for risk management in a multi-site engineering project environment, and presents a review of the state-of-the-art tools and methods that can be used to manage risks in multi-site engineering projects. This leads to the development of a risk management roadmap, which will underpin the design and implementation of an intelligent risk mapping system.

Multi-Level Adaptation in End-User Development of 3D Virtual Chemistry Experiments

ERIC Educational Resources Information Center

Liu, Chang; Zhong, Ying

2014-01-01

Multi-level adaptation in end-user development (EUD) is an effective way to enable non-technical end users such as educators to gradually introduce more functionality with increasing complexity to 3D virtual learning environments developed by themselves using EUD approaches. Parameterization, integration, and extension are three levels of…

Structural tailoring of engine blades (STAEBL) user's manual

NASA Technical Reports Server (NTRS)

Brown, K. W.

1985-01-01

This User's Manual contains instructions and demonstration case to prepare input data, run, and modify the Structural Tailoring of Engine Blades (STAEBL) computer code. STAEBL was developed to perform engine fan and compressor blade numerical optimizations. This blade optimization seeks a minimum weight or cost design that satisfies realistic blade design constraints, by tuning one to twenty design variables. The STAEBL constraint analyses include blade stresses, vibratory response, flutter, and foreign object damage. Blade design variables include airfoil thickness at several locations, blade chord, and construction variables: hole size for hollow blades, and composite material layup for composite blades.

Multi-Fuel Rotary Engine for General Aviation Aircraft

NASA Technical Reports Server (NTRS)

Jones, C.; Ellis, D. R.; Meng, P. R.

1983-01-01

Design studies, conducted for NASA, of Advanced Multi-fuel General Aviation and Commuter Aircraft Rotary Stratified Charge Engines are summarized. Conceptual design studies of an advanced engine sized to provide 186/250 shaft KW/HP under cruise conditions at 7620/25,000 m/ft. altitude were performed. Relevant engine development background covering both prior and recent engine test results of the direct injected unthrottled rotary engine technology, including the capability to interchangeably operate on gasoline, diesel fuel, kerosene, or aviation jet fuel, are presented and related to growth predictions. Aircraft studies, using these resultant growth engines, define anticipated system effects of the performance and power density improvements for both single engine and twin engine airplanes. The calculated results indicate superior system performance and 30 to 35% fuel economy improvement for the Rotary-engine airplanes as compared to equivalent airframe concept designs with current baseline engines. The research and technology activities required to attain the projected engine performance levels are also discussed.

Surface EMG signals based motion intent recognition using multi-layer ELM

NASA Astrophysics Data System (ADS)

Wang, Jianhui; Qi, Lin; Wang, Xiao

2017-11-01

The upper-limb rehabilitation robot is regard as a useful tool to help patients with hemiplegic to do repetitive exercise. The surface electromyography (sEMG) contains motion information as the electric signals are generated and related to nerve-muscle motion. These sEMG signals, representing human's intentions of active motions, are introduced into the rehabilitation robot system to recognize upper-limb movements. Traditionally, the feature extraction is an indispensable part of drawing significant information from original signals, which is a tedious task requiring rich and related experience. This paper employs a deep learning scheme to extract the internal features of the sEMG signals using an advanced Extreme Learning Machine based auto-encoder (ELMAE). The mathematical information contained in the multi-layer structure of the ELM-AE is used as the high-level representation of the internal features of the sEMG signals, and thus a simple ELM can post-process the extracted features, formulating the entire multi-layer ELM (ML-ELM) algorithm. The method is employed for the sEMG based neural intentions recognition afterwards. The case studies show the adopted deep learning algorithm (ELM-AE) is capable of yielding higher classification accuracy compared to the Principle Component Analysis (PCA) scheme in 5 different types of upper-limb motions. This indicates the effectiveness and the learning capability of the ML-ELM in such motion intent recognition applications.

NETL - Supercomputing: NETL Simulation Based Engineering User Center (SBEUC)

ScienceCinema

None

2018-02-07

NETL's Simulation-Based Engineering User Center, or SBEUC, integrates one of the world's largest high-performance computers with an advanced visualization center. The SBEUC offers a collaborative environment among researchers at NETL sites and those working through the NETL-Regional University Alliance.

NETL - Supercomputing: NETL Simulation Based Engineering User Center (SBEUC)

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

None

2013-09-30

NETL's Simulation-Based Engineering User Center, or SBEUC, integrates one of the world's largest high-performance computers with an advanced visualization center. The SBEUC offers a collaborative environment among researchers at NETL sites and those working through the NETL-Regional University Alliance.

Multi-channel feature dictionaries for RGB-D object recognition

NASA Astrophysics Data System (ADS)

Lan, Xiaodong; Li, Qiming; Chong, Mina; Song, Jian; Li, Jun

2018-04-01

Hierarchical matching pursuit (HMP) is a popular feature learning method for RGB-D object recognition. However, the feature representation with only one dictionary for RGB channels in HMP does not capture sufficient visual information. In this paper, we propose multi-channel feature dictionaries based feature learning method for RGB-D object recognition. The process of feature extraction in the proposed method consists of two layers. The K-SVD algorithm is used to learn dictionaries in sparse coding of these two layers. In the first-layer, we obtain features by performing max pooling on sparse codes of pixels in a cell. And the obtained features of cells in a patch are concatenated to generate patch jointly features. Then, patch jointly features in the first-layer are used to learn the dictionary and sparse codes in the second-layer. Finally, spatial pyramid pooling can be applied to the patch jointly features of any layer to generate the final object features in our method. Experimental results show that our method with first or second-layer features can obtain a comparable or better performance than some published state-of-the-art methods.

Context-based user grouping for multi-casting in heterogeneous radio networks

NASA Astrophysics Data System (ADS)

Mannweiler, C.; Klein, A.; Schneider, J.; Schotten, H. D.

2011-08-01

Along with the rise of sophisticated smartphones and smart spaces, the availability of both static and dynamic context information has steadily been increasing in recent years. Due to the popularity of social networks, these data are complemented by profile information about individual users. Making use of this information by classifying users in wireless networks enables targeted content and advertisement delivery as well as optimizing network resources, in particular bandwidth utilization, by facilitating group-based multi-casting. In this paper, we present the design and implementation of a web service for advanced user classification based on user, network, and environmental context information. The service employs simple and advanced clustering algorithms for forming classes of users. Available service functionalities include group formation, context-aware adaptation, and deletion as well as the exposure of group characteristics. Moreover, the results of a performance evaluation, where the service has been integrated in a simulator modeling user behavior in heterogeneous wireless systems, are presented.

Multi-year Content Analysis of User Facility Related Publications

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

Patton, Robert M; Stahl, Christopher G; Hines, Jayson

2013-01-01

Scientific user facilities provide resources and support that enable scientists to conduct experiments or simulations pertinent to their respective research. Consequently, it is critical to have an informed understanding of the impact and contributions that these facilities have on scientific discoveries. Leveraging insight into scientific publications that acknowledge the use of these facilities enables more informed decisions by facility management and sponsors in regard to policy, resource allocation, and influencing the direction of science as well as more effectively understand the impact of a scientific user facility. This work discusses preliminary results of mining scientific publications that utilized resources atmore » the Oak Ridge Leadership Computing Facility (OLCF) at Oak Ridge National Laboratory (ORNL). These results show promise in identifying and leveraging multi-year trends and providing a higher resolution view of the impact that a scientific user facility may have on scientific discoveries.« less

Lexical-Access Ability and Cognitive Predictors of Speech Recognition in Noise in Adult Cochlear Implant Users

PubMed Central

Smits, Cas; Merkus, Paul; Festen, Joost M.; Goverts, S. Theo

2017-01-01

Not all of the variance in speech-recognition performance of cochlear implant (CI) users can be explained by biographic and auditory factors. In normal-hearing listeners, linguistic and cognitive factors determine most of speech-in-noise performance. The current study explored specifically the influence of visually measured lexical-access ability compared with other cognitive factors on speech recognition of 24 postlingually deafened CI users. Speech-recognition performance was measured with monosyllables in quiet (consonant-vowel-consonant [CVC]), sentences-in-noise (SIN), and digit-triplets in noise (DIN). In addition to a composite variable of lexical-access ability (LA), measured with a lexical-decision test (LDT) and word-naming task, vocabulary size, working-memory capacity (Reading Span test [RSpan]), and a visual analogue of the SIN test (text reception threshold test) were measured. The DIN test was used to correct for auditory factors in SIN thresholds by taking the difference between SIN and DIN: SRTdiff. Correlation analyses revealed that duration of hearing loss (dHL) was related to SIN thresholds. Better working-memory capacity was related to SIN and SRTdiff scores. LDT reaction time was positively correlated with SRTdiff scores. No significant relationships were found for CVC or DIN scores with the predictor variables. Regression analyses showed that together with dHL, RSpan explained 55% of the variance in SIN thresholds. When controlling for auditory performance, LA, LDT, and RSpan separately explained, together with dHL, respectively 37%, 36%, and 46% of the variance in SRTdiff outcome. The results suggest that poor verbal working-memory capacity and to a lesser extent poor lexical-access ability limit speech-recognition ability in listeners with a CI. PMID:29205095

Nuclear Engine System Simulation (NESS). Version 2.0: Program user's guide

NASA Technical Reports Server (NTRS)

Pelaccio, Dennis G.; Scheil, Christine M.; Petrosky, Lyman

1993-01-01

This Program User's Guide discusses the Nuclear Thermal Propulsion (NTP) engine system design features and capabilities modeled in the Nuclear Engine System Simulation (NESS): Version 2.0 program (referred to as NESS throughout the remainder of this document), as well as its operation. NESS was upgraded to include many new modeling capabilities not available in the original version delivered to NASA LeRC in Dec. 1991, NESS's new features include the following: (1) an improved input format; (2) an advanced solid-core NERVA-type reactor system model (ENABLER 2); (3) a bleed-cycle engine system option; (4) an axial-turbopump design option; (5) an automated pump-out turbopump assembly sizing option; (6) an off-design gas generator engine cycle design option; (7) updated hydrogen properties; (8) an improved output format; and (9) personal computer operation capability. Sample design cases are presented in the user's guide that demonstrate many of the new features associated with this upgraded version of NESS, as well as design modeling features associated with the original version of NESS.

Ball-scale based hierarchical multi-object recognition in 3D medical images

NASA Astrophysics Data System (ADS)

Bağci, Ulas; Udupa, Jayaram K.; Chen, Xinjian

2010-03-01

This paper investigates, using prior shape models and the concept of ball scale (b-scale), ways of automatically recognizing objects in 3D images without performing elaborate searches or optimization. That is, the goal is to place the model in a single shot close to the right pose (position, orientation, and scale) in a given image so that the model boundaries fall in the close vicinity of object boundaries in the image. This is achieved via the following set of key ideas: (a) A semi-automatic way of constructing a multi-object shape model assembly. (b) A novel strategy of encoding, via b-scale, the pose relationship between objects in the training images and their intensity patterns captured in b-scale images. (c) A hierarchical mechanism of positioning the model, in a one-shot way, in a given image from a knowledge of the learnt pose relationship and the b-scale image of the given image to be segmented. The evaluation results on a set of 20 routine clinical abdominal female and male CT data sets indicate the following: (1) Incorporating a large number of objects improves the recognition accuracy dramatically. (2) The recognition algorithm can be thought as a hierarchical framework such that quick replacement of the model assembly is defined as coarse recognition and delineation itself is known as finest recognition. (3) Scale yields useful information about the relationship between the model assembly and any given image such that the recognition results in a placement of the model close to the actual pose without doing any elaborate searches or optimization. (4) Effective object recognition can make delineation most accurate.

Multi-scale Imaging of Cellular and Sub-cellular Structures using Scanning Probe Recognition Microscopy.

NASA Astrophysics Data System (ADS)

Chen, Q.; Rice, A. F.

2005-03-01

Scanning Probe Recognition Microscopy is a new scanning probe capability under development within our group to reliably return to and directly interact with a specific nanobiological feature of interest. In previous work, we have successfully recognized and classified tubular versus globular biological objects from experimental atomic force microscope images using a method based on normalized central moments [ref. 1]. In this paper we extend this work to include recognition schemes appropriate for cellular and sub-cellular structures. Globular cells containing tubular actin filaments are under investigation. Thus there are differences in external/internal shapes and scales. Continuous Wavelet Transform with a differential Gaussian mother wavelet is employed for multi- scale analysis. [ref. 1] Q. Chen, V. Ayres and L. Udpa, ``Biological Investigation Using Scanning Probe Recognition Microscopy,'' Proceedings 3rd IEEE Conference on Nanotechnology, vol. 2, p 863-865 (2003).

Authenticated multi-user quantum key distribution with single particles

NASA Astrophysics Data System (ADS)

Lin, Song; Wang, Hui; Guo, Gong-De; Ye, Guo-Hua; Du, Hong-Zhen; Liu, Xiao-Fen

2016-03-01

Quantum key distribution (QKD) has been growing rapidly in recent years and becomes one of the hottest issues in quantum information science. During the implementation of QKD on a network, identity authentication has been one main problem. In this paper, an efficient authenticated multi-user quantum key distribution (MQKD) protocol with single particles is proposed. In this protocol, any two users on a quantum network can perform mutual authentication and share a secure session key with the assistance of a semi-honest center. Meanwhile, the particles, which are used as quantum information carriers, are not required to be stored, therefore the proposed protocol is feasible with current technology. Finally, security analysis shows that this protocol is secure in theory.

Double-Windows-Based Motion Recognition in Multi-Floor Buildings Assisted by a Built-In Barometer.

PubMed

Liu, Maolin; Li, Huaiyu; Wang, Yuan; Li, Fei; Chen, Xiuwan

2018-04-01

Accelerometers, gyroscopes and magnetometers in smartphones are often used to recognize human motions. Since it is difficult to distinguish between vertical motions and horizontal motions in the data provided by these built-in sensors, the vertical motion recognition accuracy is relatively low. The emergence of a built-in barometer in smartphones improves the accuracy of motion recognition in the vertical direction. However, there is a lack of quantitative analysis and modelling of the barometer signals, which is the basis of barometer's application to motion recognition, and a problem of imbalanced data also exists. This work focuses on using the barometers inside smartphones for vertical motion recognition in multi-floor buildings through modelling and feature extraction of pressure signals. A novel double-windows pressure feature extraction method, which adopts two sliding time windows of different length, is proposed to balance recognition accuracy and response time. Then, a random forest classifier correlation rule is further designed to weaken the impact of imbalanced data on recognition accuracy. The results demonstrate that the recognition accuracy can reach 95.05% when pressure features and the improved random forest classifier are adopted. Specifically, the recognition accuracy of the stair and elevator motions is significantly improved with enhanced response time. The proposed approach proves effective and accurate, providing a robust strategy for increasing accuracy of vertical motions.

Urdu Nasta'liq text recognition using implicit segmentation based on multi-dimensional long short term memory neural networks.

PubMed

Naz, Saeeda; Umar, Arif Iqbal; Ahmed, Riaz; Razzak, Muhammad Imran; Rashid, Sheikh Faisal; Shafait, Faisal

2016-01-01

The recognition of Arabic script and its derivatives such as Urdu, Persian, Pashto etc. is a difficult task due to complexity of this script. Particularly, Urdu text recognition is more difficult due to its Nasta'liq writing style. Nasta'liq writing style inherits complex calligraphic nature, which presents major issues to recognition of Urdu text owing to diagonality in writing, high cursiveness, context sensitivity and overlapping of characters. Therefore, the work done for recognition of Arabic script cannot be directly applied to Urdu recognition. We present Multi-dimensional Long Short Term Memory (MDLSTM) Recurrent Neural Networks with an output layer designed for sequence labeling for recognition of printed Urdu text-lines written in the Nasta'liq writing style. Experiments show that MDLSTM attained a recognition accuracy of 98% for the unconstrained Urdu Nasta'liq printed text, which significantly outperforms the state-of-the-art techniques.

Scene Recognition for Indoor Localization Using a Multi-Sensor Fusion Approach.

PubMed

Liu, Mengyun; Chen, Ruizhi; Li, Deren; Chen, Yujin; Guo, Guangyi; Cao, Zhipeng; Pan, Yuanjin

2017-12-08

After decades of research, there is still no solution for indoor localization like the GNSS (Global Navigation Satellite System) solution for outdoor environments. The major reasons for this phenomenon are the complex spatial topology and RF transmission environment. To deal with these problems, an indoor scene constrained method for localization is proposed in this paper, which is inspired by the visual cognition ability of the human brain and the progress in the computer vision field regarding high-level image understanding. Furthermore, a multi-sensor fusion method is implemented on a commercial smartphone including cameras, WiFi and inertial sensors. Compared to former research, the camera on a smartphone is used to "see" which scene the user is in. With this information, a particle filter algorithm constrained by scene information is adopted to determine the final location. For indoor scene recognition, we take advantage of deep learning that has been proven to be highly effective in the computer vision community. For particle filter, both WiFi and magnetic field signals are used to update the weights of particles. Similar to other fingerprinting localization methods, there are two stages in the proposed system, offline training and online localization. In the offline stage, an indoor scene model is trained by Caffe (one of the most popular open source frameworks for deep learning) and a fingerprint database is constructed by user trajectories in different scenes. To reduce the volume requirement of training data for deep learning, a fine-tuned method is adopted for model training. In the online stage, a camera in a smartphone is used to recognize the initial scene. Then a particle filter algorithm is used to fuse the sensor data and determine the final location. To prove the effectiveness of the proposed method, an Android client and a web server are implemented. The Android client is used to collect data and locate a user. The web server is developed for

Scene Recognition for Indoor Localization Using a Multi-Sensor Fusion Approach

PubMed Central

Chen, Ruizhi; Li, Deren; Chen, Yujin; Guo, Guangyi; Cao, Zhipeng

2017-01-01

After decades of research, there is still no solution for indoor localization like the GNSS (Global Navigation Satellite System) solution for outdoor environments. The major reasons for this phenomenon are the complex spatial topology and RF transmission environment. To deal with these problems, an indoor scene constrained method for localization is proposed in this paper, which is inspired by the visual cognition ability of the human brain and the progress in the computer vision field regarding high-level image understanding. Furthermore, a multi-sensor fusion method is implemented on a commercial smartphone including cameras, WiFi and inertial sensors. Compared to former research, the camera on a smartphone is used to “see” which scene the user is in. With this information, a particle filter algorithm constrained by scene information is adopted to determine the final location. For indoor scene recognition, we take advantage of deep learning that has been proven to be highly effective in the computer vision community. For particle filter, both WiFi and magnetic field signals are used to update the weights of particles. Similar to other fingerprinting localization methods, there are two stages in the proposed system, offline training and online localization. In the offline stage, an indoor scene model is trained by Caffe (one of the most popular open source frameworks for deep learning) and a fingerprint database is constructed by user trajectories in different scenes. To reduce the volume requirement of training data for deep learning, a fine-tuned method is adopted for model training. In the online stage, a camera in a smartphone is used to recognize the initial scene. Then a particle filter algorithm is used to fuse the sensor data and determine the final location. To prove the effectiveness of the proposed method, an Android client and a web server are implemented. The Android client is used to collect data and locate a user. The web server is developed for

Combining engineered cell-sensors with multi-agent systems to realize smart environment

NASA Astrophysics Data System (ADS)

Chen, Mei

2013-03-01

The connection of everything in a sensory and an intelligent way is a pursuit in smart environment. This paper introduces the engineered cell-sensors into the multi-agent systems to realize the smart environment. The seamless interface with the natural environment and strong information-processing ability of cell with the achievements of synthetic biology make the construction of engineered cell-sensors possible. However, the engineered cell-sensors are only simple-functional and unreliable computational entities. Therefore how to combine engineered cell-sensors with digital device is a key problem in order to realize the smart environment. We give the abstract structure and interaction modes of the engineered cell-sensors in order to introduce engineered cell-sensors into multi-agent systems. We believe that the introduction of engineered cell-sensors will push forward the development of the smart environment.

Design and performance of a large vocabulary discrete word recognition system. Volume 2: Appendixes. [flow charts and users manual

NASA Technical Reports Server (NTRS)

1973-01-01

The users manual for the word recognition computer program contains flow charts of the logical diagram, the memory map for templates, the speech analyzer card arrangement, minicomputer input/output routines, and assembly language program listings.

Multi-Wavelength Optical Pyrometry Investigation for Turbine Engine Applications.

NASA Astrophysics Data System (ADS)

Estevadeordal, Jordi; Nirmalan, Nirm; Wang, Guanghua; Thermal Systems Team

2011-11-01

An investigation of optical Pyrometry using multiple wavelengths and its application to turbine engine is presented. Current turbine engine Pyrometers are typically broadband Si-detector line-of-sight (LOS) systems. They identify hot spots and spall areas in blades and bucket passages by detection of bursts of higher voltage signals. However, the single color signal can be misleading for estimating temperature and emissivity variations in these bursts. Results of the radiant temperature, multi-color temperature and apparent emissivity are presented for turbine engine applications. For example, the results indicate that spall regions can be characterized using multi-wavelength techniques by showing that the temperature typically drops and the emissivity increases and that differentiates from the emissivity of the normal regions. Burst signals are analyzed with multicolor algorithms and changes in the LOS hot-gas-path properties and in the suction side, trailing edge, pressure side, fillet and platform surfaces characterized.

Staff regard towards working with substance users: a European multi-centre study.

PubMed

Gilchrist, Gail; Moskalewicz, Jacek; Slezakova, Silvia; Okruhlica, Lubomir; Torrens, Marta; Vajd, Rajko; Baldacchino, Alex

2011-06-01

To compare regard for working with different patient groups (including substance users) among different professional groups in different health-care settings in eight European countries. A multi-centre, cross-sectional comparative study. Primary care, general psychiatry and specialist addiction services in Bulgaria, Greece, Italy, Poland, Scotland, Slovakia, Slovenia and Spain. A multi-disciplinary convenience sample of 866 professionals (physicians, psychiatrists, psychologists, nurses and social workers) from 253 services. The Medical Condition Regard Scale measured regard for working with different patient groups. Multi-factor between-subjects analysis of variance determined the factors associated with regard for each condition by country and all countries. Regard for working with alcohol (mean score alcohol: 45.35, 95% CI 44.76, 45.95) and drug users (mean score drugs: 43.67, 95% CI 42.98, 44.36) was consistently lower than for other patient groups (mean score diabetes: 50.19, 95% CI 49.71, 50.66; mean score depression: 51.34, 95% CI 50.89, 51.79) across all countries participating in the study, particularly among staff from primary care compared to general psychiatry or specialist addiction services (P<0.001). After controlling for sex of staff, profession and duration of time working in profession, treatment entry point and country remained the only statistically significant variables associated with regard for working with alcohol and drug users. Health professionals appear to ascribe lower status to working with substance users than helping other patient groups, particularly in primary care; the effect is larger in some countries than others. © 2011 The Authors, Addiction © 2011 Society for the Study of Addiction.

Distributed Compressive CSIT Estimation and Feedback for FDD Multi-User Massive MIMO Systems

NASA Astrophysics Data System (ADS)

Rao, Xiongbin; Lau, Vincent K. N.

2014-06-01

To fully utilize the spatial multiplexing gains or array gains of massive MIMO, the channel state information must be obtained at the transmitter side (CSIT). However, conventional CSIT estimation approaches are not suitable for FDD massive MIMO systems because of the overwhelming training and feedback overhead. In this paper, we consider multi-user massive MIMO systems and deploy the compressive sensing (CS) technique to reduce the training as well as the feedback overhead in the CSIT estimation. The multi-user massive MIMO systems exhibits a hidden joint sparsity structure in the user channel matrices due to the shared local scatterers in the physical propagation environment. As such, instead of naively applying the conventional CS to the CSIT estimation, we propose a distributed compressive CSIT estimation scheme so that the compressed measurements are observed at the users locally, while the CSIT recovery is performed at the base station jointly. A joint orthogonal matching pursuit recovery algorithm is proposed to perform the CSIT recovery, with the capability of exploiting the hidden joint sparsity in the user channel matrices. We analyze the obtained CSIT quality in terms of the normalized mean absolute error, and through the closed-form expressions, we obtain simple insights into how the joint channel sparsity can be exploited to improve the CSIT recovery performance.

Light Multi-Reflex Engine

NASA Astrophysics Data System (ADS)

Bolonkin, A.

The purpose of this article is to call attention to the revolutionary idea of multi-reflection. This idea allows the design of new engines, space propulsion systems, storage of a beam and solar energy, transmission of energy over millions of kilometers, a new weapon, etc. This method and its main innovations were offered by the author in 1983 in the former USSR. Now the author shows in a series of articles the huge possibilities of this idea in many fields such as space, aviation, energy, energy transmission, beam amplification, light transformation and so on. This article considers the direct transfer of light beam energy to mechanical energy and back.

Impact of user influence on information multi-step communication in a micro-blog

NASA Astrophysics Data System (ADS)

Wu, Yue; Hu, Yong; He, Xiao-Hai; Deng, Ken

2014-06-01

User influence is generally considered as one of the most critical factors that affect information cascading spreading. Based on this common assumption, this paper proposes a theoretical model to examine user influence on the information multi-step communication in a micro-blog. The multi-steps of information communication are divided into first-step and non-first-step, and user influence is classified into five dimensions. Actual data from the Sina micro-blog is collected to construct the model by means of an approach based on structural equations that uses the Partial Least Squares (PLS) technique. Our experimental results indicate that the dimensions of the number of fans and their authority significantly impact the information of first-step communication. Leader rank has a positive impact on both first-step and non-first-step communication. Moreover, global centrality and weight of friends are positively related to the information non-first-step communication, but authority is found to have much less relation to it.

Structural Basis for the Altered PAM Recognition by Engineered CRISPR-Cpf1.

PubMed

Nishimasu, Hiroshi; Yamano, Takashi; Gao, Linyi; Zhang, Feng; Ishitani, Ryuichiro; Nureki, Osamu

2017-07-06

The RNA-guided Cpf1 nuclease cleaves double-stranded DNA targets complementary to the CRISPR RNA (crRNA), and it has been harnessed for genome editing technologies. Recently, Acidaminococcus sp. BV3L6 (AsCpf1) was engineered to recognize altered DNA sequences as the protospacer adjacent motif (PAM), thereby expanding the target range of Cpf1-mediated genome editing. Whereas wild-type AsCpf1 recognizes the TTTV PAM, the RVR (S542R/K548V/N552R) and RR (S542R/K607R) variants can efficiently recognize the TATV and TYCV PAMs, respectively. However, their PAM recognition mechanisms remained unknown. Here we present the 2.0 Å resolution crystal structures of the RVR and RR variants bound to a crRNA and its target DNA. The structures revealed that the RVR and RR variants primarily recognize the PAM-complementary nucleotides via the substituted residues. Our high-resolution structures delineated the altered PAM recognition mechanisms of the AsCpf1 variants, providing a basis for the further engineering of CRISPR-Cpf1. Copyright © 2017 Elsevier Inc. All rights reserved.

A practical approach for writer-dependent symbol recognition using a writer-independent symbol recognizer.

PubMed

LaViola, Joseph J; Zeleznik, Robert C

2007-11-01

We present a practical technique for using a writer-independent recognition engine to improve the accuracy and speed while reducing the training requirements of a writer-dependent symbol recognizer. Our writer-dependent recognizer uses a set of binary classifiers based on the AdaBoost learning algorithm, one for each possible pairwise symbol comparison. Each classifier consists of a set of weak learners, one of which is based on a writer-independent handwriting recognizer. During online recognition, we also use the n-best list of the writer-independent recognizer to prune the set of possible symbols and thus reduce the number of required binary classifications. In this paper, we describe the geometric and statistical features used in our recognizer and our all-pairs classification algorithm. We also present the results of experiments that quantify the effect incorporating a writer-independent recognition engine into a writer-dependent recognizer has on accuracy, speed, and user training time.

A MultiAir®/MultiFuel Approach to Enhancing Engine System Efficiency

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

Reese, Ronald

2015-05-20

FCA US LLC (formally known as Chrysler Group LLC, and hereinafter “Chrysler”) was awarded an American Recovery and Reinvestment Act (ARRA) funded project by the Department of Energy (DOE) titled “A MultiAir®/MultiFuel Approach to Enhancing Engine System Efficiency” (hereinafter “project”). This award was issued after Chrysler submitted a proposal for Funding Opportunity Announcement DE-FOA- 0000079, “Systems Level Technology Development, Integration, and Demonstration for Efficient Class 8 Trucks (SuperTruck) and Advanced Technology Powertrains for Light-Duty Vehicles (ATP-LD).” Chrysler started work on this project on June 01, 2010 and completed testing activities on August 30, 2014. Overall objectives of this project were;more » Demonstrate a 25% improvement in combined Federal Test Procedure (FTP) City and Highway fuel economy over a 2009 Chrysler minivan; Accelerate the development of highly efficient engine and powertrain systems for light-duty vehicles, while meeting future emissions standards; and Create and retain jobs in accordance with the American Recovery and Reinvestment Act of 2009« less

Database Access Manager for the Software Engineering Laboratory (DAMSEL) user's guide

NASA Technical Reports Server (NTRS)

1990-01-01

Operating instructions for the Database Access Manager for the Software Engineering Laboratory (DAMSEL) system are presented. Step-by-step instructions for performing various data entry and report generation activities are included. Sample sessions showing the user interface display screens are also included. Instructions for generating reports are accompanied by sample outputs for each of the reports. The document groups the available software functions by the classes of users that may access them.

Engineering of Data Acquiring Mobile Software and Sustainable End-User Applications

NASA Technical Reports Server (NTRS)

Smith, Benton T.

2013-01-01

The criteria for which data acquiring software and its supporting infrastructure should be designed should take the following two points into account: the reusability and organization of stored online and remote data and content, and an assessment on whether abandoning a platform optimized design in favor for a multi-platform solution significantly reduces the performance of an end-user application. Furthermore, in-house applications that control or process instrument acquired data for end-users should be designed with a communication and control interface such that the application's modules can be reused as plug-in modular components in greater software systems. The application of the above mentioned is applied using two loosely related projects: a mobile application, and a website containing live and simulated data. For the intelligent devices mobile application AIDM, the end-user interface have a platform and data type optimized design, while the database and back-end applications store this information in an organized manner and manage access to that data to only to authorized user end application(s). Finally, the content for the website was derived from a database such that the content can be included and uniform to all applications accessing the content. With these projects being ongoing, I have concluded from my research that the applicable methods presented are feasible for both projects, and that a multi-platform design for the mobile application only marginally drop the performance of the mobile application.

A software for managing chemical processes in a multi-user laboratory

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

Camino, Fernando E.

Here, we report a software for logging chemical processes in a multi-user laboratory, which implements a work flow designed to reduce hazardous situations associated with the disposal of chemicals in incompatible waste containers. The software allows users to perform only those processes displayed in their list of authorized chemical processes and provides the location and label code of waste containers, among other useful information. The software has been used for six years in the cleanroom of the Center for Functional Nanomaterials at Brookhaven National Laboratory and has been an important factor for the excellent safety record of the Center.

A software for managing chemical processes in a multi-user laboratory

DOE PAGES

Camino, Fernando E.

2016-10-26

Here, we report a software for logging chemical processes in a multi-user laboratory, which implements a work flow designed to reduce hazardous situations associated with the disposal of chemicals in incompatible waste containers. The software allows users to perform only those processes displayed in their list of authorized chemical processes and provides the location and label code of waste containers, among other useful information. The software has been used for six years in the cleanroom of the Center for Functional Nanomaterials at Brookhaven National Laboratory and has been an important factor for the excellent safety record of the Center.

The Development of a Graphical User Interface Engine for the Convenient Use of the HL7 Version 2.x Interface Engine

PubMed Central

Kim, Hwa Sun; Cho, Hune

2011-01-01

Objectives The Health Level Seven Interface Engine (HL7 IE), developed by Kyungpook National University, has been employed in health information systems, however users without a background in programming have reported difficulties in using it. Therefore, we developed a graphical user interface (GUI) engine to make the use of the HL7 IE more convenient. Methods The GUI engine was directly connected with the HL7 IE to handle the HL7 version 2.x messages. Furthermore, the information exchange rules (called the mapping data), represented by a conceptual graph in the GUI engine, were transformed into program objects that were made available to the HL7 IE; the mapping data were stored as binary files for reuse. The usefulness of the GUI engine was examined through information exchange tests between an HL7 version 2.x message and a health information database system. Results Users could easily create HL7 version 2.x messages by creating a conceptual graph through the GUI engine without requiring assistance from programmers. In addition, time could be saved when creating new information exchange rules by reusing the stored mapping data. Conclusions The GUI engine was not able to incorporate information types (e.g., extensible markup language, XML) other than the HL7 version 2.x messages and the database, because it was designed exclusively for the HL7 IE protocol. However, in future work, by including additional parsers to manage XML-based information such as Continuity of Care Documents (CCD) and Continuity of Care Records (CCR), we plan to ensure that the GUI engine will be more widely accessible for the health field. PMID:22259723

The Development of a Graphical User Interface Engine for the Convenient Use of the HL7 Version 2.x Interface Engine.

PubMed

Kim, Hwa Sun; Cho, Hune; Lee, In Keun

2011-12-01

The Health Level Seven Interface Engine (HL7 IE), developed by Kyungpook National University, has been employed in health information systems, however users without a background in programming have reported difficulties in using it. Therefore, we developed a graphical user interface (GUI) engine to make the use of the HL7 IE more convenient. The GUI engine was directly connected with the HL7 IE to handle the HL7 version 2.x messages. Furthermore, the information exchange rules (called the mapping data), represented by a conceptual graph in the GUI engine, were transformed into program objects that were made available to the HL7 IE; the mapping data were stored as binary files for reuse. The usefulness of the GUI engine was examined through information exchange tests between an HL7 version 2.x message and a health information database system. Users could easily create HL7 version 2.x messages by creating a conceptual graph through the GUI engine without requiring assistance from programmers. In addition, time could be saved when creating new information exchange rules by reusing the stored mapping data. The GUI engine was not able to incorporate information types (e.g., extensible markup language, XML) other than the HL7 version 2.x messages and the database, because it was designed exclusively for the HL7 IE protocol. However, in future work, by including additional parsers to manage XML-based information such as Continuity of Care Documents (CCD) and Continuity of Care Records (CCR), we plan to ensure that the GUI engine will be more widely accessible for the health field.

Coupled multi-disciplinary simulation of composite engine structures in propulsion environment

NASA Technical Reports Server (NTRS)

Chamis, Christos C.; Singhal, Surendra N.

1992-01-01

A computational simulation procedure is described for the coupled response of multi-layered multi-material composite engine structural components which are subjected to simultaneous multi-disciplinary thermal, structural, vibration, and acoustic loadings including the effect of hostile environments. The simulation is based on a three dimensional finite element analysis technique in conjunction with structural mechanics codes and with acoustic analysis methods. The composite material behavior is assessed at the various composite scales, i.e., the laminate/ply/constituents (fiber/matrix), via a nonlinear material characterization model. Sample cases exhibiting nonlinear geometrical, material, loading, and environmental behavior of aircraft engine fan blades, are presented. Results for deformed shape, vibration frequency, mode shapes, and acoustic noise emitted from the fan blade, are discussed for their coupled effect in hot and humid environments. Results such as acoustic noise for coupled composite-mechanics/heat transfer/structural/vibration/acoustic analyses demonstrate the effectiveness of coupled multi-disciplinary computational simulation and the various advantages of composite materials compared to metals.

Performance Analysis of Diversity-Controlled Multi-User Superposition Transmission for 5G Wireless Networks.

PubMed

Yeom, Jeong Seon; Chu, Eunmi; Jung, Bang Chul; Jin, Hu

2018-02-10

In this paper, we propose a novel low-complexity multi-user superposition transmission (MUST) technique for 5G downlink networks, which allows multiple cell-edge users to be multiplexed with a single cell-center user. We call the proposed technique diversity-controlled MUST technique since the cell-center user enjoys the frequency diversity effect via signal repetition over multiple orthogonal frequency division multiplexing (OFDM) sub-carriers. We assume that a base station is equipped with a single antenna but users are equipped with multiple antennas. In addition, we assume that the quadrature phase shift keying (QPSK) modulation is used for users. We mathematically analyze the bit error rate (BER) of both cell-edge users and cell-center users, which is the first theoretical result in the literature to the best of our knowledge. The mathematical analysis is validated through extensive link-level simulations.

Listening for Recollection: A Multi-Voxel Pattern Analysis of Recognition Memory Retrieval Strategies

PubMed Central

Quamme, Joel R.; Weiss, David J.; Norman, Kenneth A.

2010-01-01

Recent studies of recognition memory indicate that subjects can strategically vary how much they rely on recollection of specific details vs. feelings of familiarity when making recognition judgments. One possible explanation of these results is that subjects can establish an internally directed attentional state (“listening for recollection”) that enhances retrieval of studied details; fluctuations in this attentional state over time should be associated with fluctuations in subjects’ recognition behavior. In this study, we used multi-voxel pattern analysis of fMRI data to identify brain regions that are involved in listening for recollection. We looked for brain regions that met the following criteria: (1) Distinct neural patterns should be present when subjects are instructed to rely on recollection vs. familiarity, and (2) fluctuations in these neural patterns should be related to recognition behavior in the manner predicted by dual-process theories of recognition: Specifically, the presence of the recollection pattern during the pre-stimulus interval (indicating that subjects are “listening for recollection” at that moment) should be associated with a selective decrease in false alarms to related lures. We found that pre-stimulus activity in the right supramarginal gyrus met all of these criteria, suggesting that this region proactively establishes an internally directed attentional state that fosters recollection. We also found other regions (e.g., left middle temporal gyrus) where the pattern of neural activity was related to subjects’ responding to related lures after stimulus onset (but not before), suggesting that these regions implement processes that are engaged in a reactive fashion to boost recollection. PMID:20740073

Pattern recognition with composite correlation filters designed with multi-object combinatorial optimization

DOE PAGES

Awwal, Abdul; Diaz-Ramirez, Victor H.; Cuevas, Andres; ...

2014-10-23

Composite correlation filters are used for solving a wide variety of pattern recognition problems. These filters are given by a combination of several training templates chosen by a designer in an ad hoc manner. In this work, we present a new approach for the design of composite filters based on multi-objective combinatorial optimization. Given a vast search space of training templates, an iterative algorithm is used to synthesize a filter with an optimized performance in terms of several competing criteria. Furthermore, by employing a suggested binary-search procedure a filter bank with a minimum number of filters can be constructed, formore » a prespecified trade-off of performance metrics. Computer simulation results obtained with the proposed method in recognizing geometrically distorted versions of a target in cluttered and noisy scenes are discussed and compared in terms of recognition performance and complexity with existing state-of-the-art filters.« less

Pattern recognition with composite correlation filters designed with multi-object combinatorial optimization

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

Awwal, Abdul; Diaz-Ramirez, Victor H.; Cuevas, Andres

Composite correlation filters are used for solving a wide variety of pattern recognition problems. These filters are given by a combination of several training templates chosen by a designer in an ad hoc manner. In this work, we present a new approach for the design of composite filters based on multi-objective combinatorial optimization. Given a vast search space of training templates, an iterative algorithm is used to synthesize a filter with an optimized performance in terms of several competing criteria. Furthermore, by employing a suggested binary-search procedure a filter bank with a minimum number of filters can be constructed, formore » a prespecified trade-off of performance metrics. Computer simulation results obtained with the proposed method in recognizing geometrically distorted versions of a target in cluttered and noisy scenes are discussed and compared in terms of recognition performance and complexity with existing state-of-the-art filters.« less

Using Markov Chains and Multi-Objective Optimization for Energy-Efficient Context Recognition †

PubMed Central

Janko, Vito

2017-01-01

The recognition of the user’s context with wearable sensing systems is a common problem in ubiquitous computing. However, the typically small battery of such systems often makes continuous recognition impractical. The strain on the battery can be reduced if the sensor setting is adapted to each context. We propose a method that efficiently finds near-optimal sensor settings for each context. It uses Markov chains to simulate the behavior of the system in different configurations and the multi-objective genetic algorithm to find a set of good non-dominated configurations. The method was evaluated on three real-life datasets and found good trade-offs between the system’s energy expenditure and the system’s accuracy. One of the solutions, for example, consumed five-times less energy than the default one, while sacrificing only two percentage points of accuracy. PMID:29286301

Changing drug users' risk environments: peer health advocates as multi-level community change agents.

PubMed

Weeks, Margaret R; Convey, Mark; Dickson-Gomez, Julia; Li, Jianghong; Radda, Kim; Martinez, Maria; Robles, Eduardo

2009-06-01

Peer delivered, social oriented HIV prevention intervention designs are increasingly popular for addressing broader contexts of health risk beyond a focus on individual factors. Such interventions have the potential to affect multiple social levels of risk and change, including at the individual, network, and community levels, and reflect social ecological principles of interaction across social levels over time. The iterative and feedback dynamic generated by this multi-level effect increases the likelihood for sustained health improvement initiated by those trained to deliver the peer intervention. The Risk Avoidance Partnership (RAP), conducted with heroin and cocaine/crack users in Hartford, Connecticut, exemplified this intervention design and illustrated the multi-level effect on drug users' risk and harm reduction at the individual level, the social network level, and the larger community level. Implications of the RAP program for designing effective prevention programs and for analyzing long-term change to reduce HIV transmission among high-risk groups are discussed from this ecological and multi-level intervention perspective.

Reasoning about Users' Actions in a Graphical User Interface.

ERIC Educational Resources Information Center

Virvou, Maria; Kabassi, Katerina

2002-01-01

Describes a graphical user interface called IFM (Intelligent File Manipulator) that provides intelligent help to users. Explains two underlying reasoning mechanisms, one an adaptation of human plausible reasoning and one that performs goal recognition based on the effects of users' commands; and presents results of an empirical study that…

Multi-Scale Validation of a Nanodiamond Drug Delivery System and Multi-Scale Engineering Education

ERIC Educational Resources Information Center

Schwalbe, Michelle Kristin

2010-01-01

This dissertation has two primary concerns: (i) evaluating the uncertainty and prediction capabilities of a nanodiamond drug delivery model using Bayesian calibration and bias correction, and (ii) determining conceptual difficulties of multi-scale analysis from an engineering education perspective. A Bayesian uncertainty quantification scheme…

A Spatially Constrained Multi-autoencoder Approach for Multivariate Geochemical Anomaly Recognition

NASA Astrophysics Data System (ADS)

Lirong, C.; Qingfeng, G.; Renguang, Z.; Yihui, X.

2017-12-01

Separating and recognizing geochemical anomalies from the geochemical background is one of the key tasks in geochemical exploration. Many methods have been developed, such as calculating the mean ±2 standard deviation, and fractal/multifractal models. In recent years, deep autoencoder, a deep learning approach, have been used for multivariate geochemical anomaly recognition. While being able to deal with the non-normal distributions of geochemical concentrations and the non-linear relationships among them, this self-supervised learning method does not take into account the spatial heterogeneity of geochemical background and the uncertainty induced by the randomly initialized weights of neurons, leading to ineffective recognition of weak anomalies. In this paper, we introduce a spatially constrained multi-autoencoder (SCMA) approach for multivariate geochemical anomaly recognition, which includes two steps: spatial partitioning and anomaly score computation. The first step divides the study area into multiple sub-regions to segregate the geochemical background, by grouping the geochemical samples through K-means clustering, spatial filtering, and spatial constraining rules. In the second step, for each sub-region, a group of autoencoder neural networks are constructed with an identical structure but different initial weights on neurons. Each autoencoder is trained using the geochemical samples within the corresponding sub-region to learn the sub-regional geochemical background. The best autoencoder of a group is chosen as the final model for the corresponding sub-region. The anomaly score at each location can then be calculated as the euclidean distance between the observed concentrations and reconstructed concentrations of geochemical elements.The experiments using the geochemical data and Fe deposits in the southwestern Fujian province of China showed that our SCMA approach greatly improved the recognition of weak anomalies, achieving the AUC of 0.89, compared

Functional Anatomy of Recognition of Chinese Multi-Character Words: Convergent Evidence from Effects of Transposable Nonwords, Lexicality, and Word Frequency.

PubMed

Lin, Nan; Yu, Xi; Zhao, Ying; Zhang, Mingxia

2016-01-01

This fMRI study aimed to identify the neural mechanisms underlying the recognition of Chinese multi-character words by partialling out the confounding effect of reaction time (RT). For this purpose, a special type of nonword-transposable nonword-was created by reversing the character orders of real words. These nonwords were included in a lexical decision task along with regular (non-transposable) nonwords and real words. Through conjunction analysis on the contrasts of transposable nonwords versus regular nonwords and words versus regular nonwords, the confounding effect of RT was eliminated, and the regions involved in word recognition were reliably identified. The word-frequency effect was also examined in emerged regions to further assess their functional roles in word processing. Results showed significant conjunctional effect and positive word-frequency effect in the bilateral inferior parietal lobules and posterior cingulate cortex, whereas only conjunctional effect was found in the anterior cingulate cortex. The roles of these brain regions in recognition of Chinese multi-character words were discussed.

Functional Anatomy of Recognition of Chinese Multi-Character Words: Convergent Evidence from Effects of Transposable Nonwords, Lexicality, and Word Frequency

PubMed Central

Lin, Nan; Yu, Xi; Zhao, Ying; Zhang, Mingxia

2016-01-01

This fMRI study aimed to identify the neural mechanisms underlying the recognition of Chinese multi-character words by partialling out the confounding effect of reaction time (RT). For this purpose, a special type of nonword—transposable nonword—was created by reversing the character orders of real words. These nonwords were included in a lexical decision task along with regular (non-transposable) nonwords and real words. Through conjunction analysis on the contrasts of transposable nonwords versus regular nonwords and words versus regular nonwords, the confounding effect of RT was eliminated, and the regions involved in word recognition were reliably identified. The word-frequency effect was also examined in emerged regions to further assess their functional roles in word processing. Results showed significant conjunctional effect and positive word-frequency effect in the bilateral inferior parietal lobules and posterior cingulate cortex, whereas only conjunctional effect was found in the anterior cingulate cortex. The roles of these brain regions in recognition of Chinese multi-character words were discussed. PMID:26901644

Performance Analysis of Diversity-Controlled Multi-User Superposition Transmission for 5G Wireless Networks

PubMed Central

Yeom, Jeong Seon; Jung, Bang Chul; Jin, Hu

2018-01-01

In this paper, we propose a novel low-complexity multi-user superposition transmission (MUST) technique for 5G downlink networks, which allows multiple cell-edge users to be multiplexed with a single cell-center user. We call the proposed technique diversity-controlled MUST technique since the cell-center user enjoys the frequency diversity effect via signal repetition over multiple orthogonal frequency division multiplexing (OFDM) sub-carriers. We assume that a base station is equipped with a single antenna but users are equipped with multiple antennas. In addition, we assume that the quadrature phase shift keying (QPSK) modulation is used for users. We mathematically analyze the bit error rate (BER) of both cell-edge users and cell-center users, which is the first theoretical result in the literature to the best of our knowledge. The mathematical analysis is validated through extensive link-level simulations. PMID:29439413

A Versatile Embedded Platform for EMG Acquisition and Gesture Recognition.

PubMed

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

2015-10-01

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

Multi-Criteria Approach in Multifunctional Building Design Process

NASA Astrophysics Data System (ADS)

Gerigk, Mateusz

2017-10-01

The paper presents new approach in multifunctional building design process. Publication defines problems related to the design of complex multifunctional buildings. Currently, contemporary urban areas are characterized by very intensive use of space. Today, buildings are being built bigger and contain more diverse functions to meet the needs of a large number of users in one capacity. The trends show the need for recognition of design objects in an organized structure, which must meet current design criteria. The design process in terms of the complex system is a theoretical model, which is the basis for optimization solutions for the entire life cycle of the building. From the concept phase through exploitation phase to disposal phase multipurpose spaces should guarantee aesthetics, functionality, system efficiency, system safety and environmental protection in the best possible way. The result of the analysis of the design process is presented as a theoretical model of the multifunctional structure. Recognition of multi-criteria model in the form of Cartesian product allows to create a holistic representation of the designed building in the form of a graph model. The proposed network is the theoretical base that can be used in the design process of complex engineering systems. The systematic multi-criteria approach makes possible to maintain control over the entire design process and to provide the best possible performance. With respect to current design requirements, there are no established design rules for multifunctional buildings in relation to their operating phase. Enrichment of the basic criteria with functional flexibility criterion makes it possible to extend the exploitation phase which brings advantages on many levels.

Real-time mental arithmetic task recognition from EEG signals.

PubMed

Wang, Qiang; Sourina, Olga

2013-03-01

Electroencephalography (EEG)-based monitoring the state of the user's brain functioning and giving her/him the visual/audio/tactile feedback is called neurofeedback technique, and it could allow the user to train the corresponding brain functions. It could provide an alternative way of treatment for some psychological disorders such as attention deficit hyperactivity disorder (ADHD), where concentration function deficit exists, autism spectrum disorder (ASD), or dyscalculia where the difficulty in learning and comprehending the arithmetic exists. In this paper, a novel method for multifractal analysis of EEG signals named generalized Higuchi fractal dimension spectrum (GHFDS) was proposed and applied in mental arithmetic task recognition from EEG signals. Other features such as power spectrum density (PSD), autoregressive model (AR), and statistical features were analyzed as well. The usage of the proposed fractal dimension spectrum of EEG signal in combination with other features improved the mental arithmetic task recognition accuracy in both multi-channel and one-channel subject-dependent algorithms up to 97.87% and 84.15% correspondingly. Based on the channel ranking, four channels were chosen which gave the accuracy up to 97.11%. Reliable real-time neurofeedback system could be implemented based on the algorithms proposed in this paper.

Breakthrough: NETL's Simulation-Based Engineering User Center (SBEUC)

ScienceCinema

Guenther, Chris

2018-05-23

The National Energy Technology Laboratory relies on supercomputers to develop many novel ideas that become tomorrow's energy solutions. Supercomputers provide a cost-effective, efficient platform for research and usher technologies into widespread use faster to bring benefits to the nation. In 2013, Secretary of Energy Dr. Ernest Moniz dedicated NETL's new supercomputer, the Simulation Based Engineering User Center, or SBEUC. The SBEUC is dedicated to fossil energy research and is a collaborative tool for all of NETL and our regional university partners.

Breakthrough: NETL's Simulation-Based Engineering User Center (SBEUC)

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

Guenther, Chris

The National Energy Technology Laboratory relies on supercomputers to develop many novel ideas that become tomorrow's energy solutions. Supercomputers provide a cost-effective, efficient platform for research and usher technologies into widespread use faster to bring benefits to the nation. In 2013, Secretary of Energy Dr. Ernest Moniz dedicated NETL's new supercomputer, the Simulation Based Engineering User Center, or SBEUC. The SBEUC is dedicated to fossil energy research and is a collaborative tool for all of NETL and our regional university partners.

Multi-Stage System for Automatic Target Recognition

NASA Technical Reports Server (NTRS)

Chao, Tien-Hsin; Lu, Thomas T.; Ye, David; Edens, Weston; Johnson, Oliver

2010-01-01

A multi-stage automated target recognition (ATR) system has been designed to perform computer vision tasks with adequate proficiency in mimicking human vision. The system is able to detect, identify, and track targets of interest. Potential regions of interest (ROIs) are first identified by the detection stage using an Optimum Trade-off Maximum Average Correlation Height (OT-MACH) filter combined with a wavelet transform. False positives are then eliminated by the verification stage using feature extraction methods in conjunction with neural networks. Feature extraction transforms the ROIs using filtering and binning algorithms to create feature vectors. A feedforward back-propagation neural network (NN) is then trained to classify each feature vector and to remove false positives. The system parameter optimizations process has been developed to adapt to various targets and datasets. The objective was to design an efficient computer vision system that can learn to detect multiple targets in large images with unknown backgrounds. Because the target size is small relative to the image size in this problem, there are many regions of the image that could potentially contain the target. A cursory analysis of every region can be computationally efficient, but may yield too many false positives. On the other hand, a detailed analysis of every region can yield better results, but may be computationally inefficient. The multi-stage ATR system was designed to achieve an optimal balance between accuracy and computational efficiency by incorporating both models. The detection stage first identifies potential ROIs where the target may be present by performing a fast Fourier domain OT-MACH filter-based correlation. Because threshold for this stage is chosen with the goal of detecting all true positives, a number of false positives are also detected as ROIs. The verification stage then transforms the regions of interest into feature space, and eliminates false positives using an

Improved Open-Microphone Speech Recognition

NASA Astrophysics Data System (ADS)

Abrash, Victor

2002-12-01

Many current and future NASA missions make extreme demands on mission personnel both in terms of work load and in performing under difficult environmental conditions. In situations where hands are impeded or needed for other tasks, eyes are busy attending to the environment, or tasks are sufficiently complex that ease of use of the interface becomes critical, spoken natural language dialog systems offer unique input and output modalities that can improve efficiency and safety. They also offer new capabilities that would not otherwise be available. For example, many NASA applications require astronauts to use computers in micro-gravity or while wearing space suits. Under these circumstances, command and control systems that allow users to issue commands or enter data in hands-and eyes-busy situations become critical. Speech recognition technology designed for current commercial applications limits the performance of the open-ended state-of-the-art dialog systems being developed at NASA. For example, today's recognition systems typically listen to user input only during short segments of the dialog, and user input outside of these short time windows is lost. Mistakes detecting the start and end times of user utterances can lead to mistakes in the recognition output, and the dialog system as a whole has no way to recover from this, or any other, recognition error. Systems also often require the user to signal when that user is going to speak, which is impractical in a hands-free environment, or only allow a system-initiated dialog requiring the user to speak immediately following a system prompt. In this project, SRI has developed software to enable speech recognition in a hands-free, open-microphone environment, eliminating the need for a push-to-talk button or other signaling mechanism. The software continuously captures a user's speech and makes it available to one or more recognizers. By constantly monitoring and storing the audio stream, it provides the spoken

Improved Open-Microphone Speech Recognition

NASA Technical Reports Server (NTRS)

Abrash, Victor

2002-01-01

Many current and future NASA missions make extreme demands on mission personnel both in terms of work load and in performing under difficult environmental conditions. In situations where hands are impeded or needed for other tasks, eyes are busy attending to the environment, or tasks are sufficiently complex that ease of use of the interface becomes critical, spoken natural language dialog systems offer unique input and output modalities that can improve efficiency and safety. They also offer new capabilities that would not otherwise be available. For example, many NASA applications require astronauts to use computers in micro-gravity or while wearing space suits. Under these circumstances, command and control systems that allow users to issue commands or enter data in hands-and eyes-busy situations become critical. Speech recognition technology designed for current commercial applications limits the performance of the open-ended state-of-the-art dialog systems being developed at NASA. For example, today's recognition systems typically listen to user input only during short segments of the dialog, and user input outside of these short time windows is lost. Mistakes detecting the start and end times of user utterances can lead to mistakes in the recognition output, and the dialog system as a whole has no way to recover from this, or any other, recognition error. Systems also often require the user to signal when that user is going to speak, which is impractical in a hands-free environment, or only allow a system-initiated dialog requiring the user to speak immediately following a system prompt. In this project, SRI has developed software to enable speech recognition in a hands-free, open-microphone environment, eliminating the need for a push-to-talk button or other signaling mechanism. The software continuously captures a user's speech and makes it available to one or more recognizers. By constantly monitoring and storing the audio stream, it provides the spoken

Multi-user distribution of polarization entangled photon pairs

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

Trapateau, J.; Orieux, A.; Diamanti, E.

We experimentally demonstrate multi-user distribution of polarization entanglement using commercial telecom wavelength division demultiplexers. The entangled photon pairs are generated from a broadband source based on spontaneous parametric down conversion in a periodically poled lithium niobate crystal using a double path setup employing a Michelson interferometer and active phase stabilisation. We test and compare demultiplexers based on various technologies and analyze the effect of their characteristics, such as losses and polarization dependence, on the quality of the distributed entanglement for three channel pairs of each demultiplexer. In all cases, we obtain a Bell inequality violation, whose value depends on themore » demultiplexer features. This demonstrates that entanglement can be distributed to at least three user pairs of a network from a single source. Additionally, we verify for the best demultiplexer that the violation is maintained when the pairs are distributed over a total channel attenuation corresponding to 20 km of optical fiber. These techniques are therefore suitable for resource-efficient practical implementations of entanglement-based quantum key distribution and other quantum communication network applications.« less

Protein hydrogels with engineered biomolecular recognition

NASA Astrophysics Data System (ADS)

Mi, Lixin

Extracellular matrices (ECMs) are the hydrated macromolecular gels in which cells migrate and proliferate and organize into tissues in vivo . The development of artificial ECM with the required mechanical, physico-chemical, and biological properties has long been a challenge in the biomaterial research field. In this dissertation, a novel set of bioactive protein hydrogels has been synthesized and characterized at both molecular and materials levels. The self-recognized and self-assembled protein copolymers have the ability to provide engineered biofunctionality through the controlled arrangement of bioactive domains on the nanoscale. Genetic engineering methods have been employed to synthesize these protein copolymers. Plasmid DNA carrying genes to express both di- and tri-block proteins have been constructed using molecular cloning techniques. These genes were expressed in bacterial E. coli to ensure homogeneous protein length and anticipated structure. Three diblock protein sequences having a leucine zipper construct on one end and polyelectrolyte (AGAGAGPEG)10 on the other, have been studied by circular dichroism, size-exclusion chromatography, analytical ultracentrifugation, and static light scattering to characterize their secondary structure, structural stability, and oligomeric state. The results show that ABC diblock mixtures form very stable heterotrimer aggregates via self-recognition and self-assembly of the coiled coil end domains. Tri-block proteins with two leucine zipper motif ends flanking the polyelectrolyte random coil in the middle have been investigated by circular dichroism and fluorescence spectroscopy, and the hydrogels formed by self-assembly of these tri-blocks have been studied using transmission electronic microscopy and diffusing wave spectroscopy. The reversible gelation behavior is the result of heterotrimeric aggregation of helices to form the physical crosslinks in the gel, with the polyelectrolyte region center block retaining

Multi-location laser ignition using a spatial light modulator towards improving automotive gasoline engine performance

NASA Astrophysics Data System (ADS)

Kuang, Zheng; Lyon, Elliott; Cheng, Hua; Page, Vincent; Shenton, Tom; Dearden, Geoff

2017-03-01

We report on a study into multi-location laser ignition (LI) with a Spatial Light Modulator (SLM), to improve the performance of a single cylinder automotive gasoline engine. Three questions are addressed: i/ How to deliver a multi-beam diffracted pattern into an engine cylinder, through a small opening, while avoiding clipping? ii/ How much incident energy can a SLM handle (optical damage threshold) and how many simultaneous beam foci could thus be created? ; iii/ Would the multi-location sparks created be sufficiently intense and stable to ignite an engine and, if so, what would be their effect on engine performance compared to single-location LI? Answers to these questions were determined as follows. Multi-beam diffracted patterns were created by applying computer generated holograms (CGHs) to the SLM. An optical system for the SLM was developed via modelling in ZEMAX, to cleanly deliver the multi-beam patterns into the combustion chamber without clipping. Optical damage experiments were carried out on Liquid Crystal on Silicon (LCoS) samples provided by the SLM manufacturer and the maximum safe pulse energy to avoid SLM damage found to be 60 mJ. Working within this limit, analysis of the multi-location laser induced sparks showed that diffracting into three identical beams gave slightly insufficient energy to guarantee 100% sparking, so subsequent engine experiments used 2 equal energy beams laterally spaced by 4 mm. The results showed that dual-location LI gave more stable combustion and higher engine power output than single-location LI, for increasingly lean air-fuel mixtures. The paper concludes by a discussion of how these results may be exploited.

Preservice Teachers Experience Reading Response Pedagogy in a Multi-User Virtual Environment

ERIC Educational Resources Information Center

Dooley, Caitlin McMunn; Calandra, Brendan; Harmon, Stephen

2014-01-01

This qualitative case study describes how 18 preservice teachers learned to nurture literary meaning-making via activities based on Louise Rosenblatt's Reader Response Theory within a multi-user virtual environment (MUVE). Participants re-created and responded to scenes from selected works of children's literature in Second Life as a way to…

2D/3D video content adaptation decision engine based on content classification and user assessment

NASA Astrophysics Data System (ADS)

Fernandes, Rui; Andrade, M. T.

2017-07-01

Multimedia adaptation depends on several factors, such as the content itself, the consumption device and its characteristics, the transport and access networks and the user. An adaptation decision engine, in order to provide the best possible Quality of Experience to a user, needs to have information about all variables that may influence its decision. For the aforementioned factors, we implement content classification, define device classes, consider limited bandwidth scenarios and categorize user preferences based on a subjective quality evaluation test. The results of these actions generate vital information to pass to the adaptation decision engine so that its operation may provide the indication of the most suitable adaptation to perform that delivers the best possible outcome for the user under the existing constraints.

Vehicle logo recognition using multi-level fusion model

NASA Astrophysics Data System (ADS)

Ming, Wei; Xiao, Jianli

2018-04-01

Vehicle logo recognition plays an important role in manufacturer identification and vehicle recognition. This paper proposes a new vehicle logo recognition algorithm. It has a hierarchical framework, which consists of two fusion levels. At the first level, a feature fusion model is employed to map the original features to a higher dimension feature space. In this space, the vehicle logos become more recognizable. At the second level, a weighted voting strategy is proposed to promote the accuracy and the robustness of the recognition results. To evaluate the performance of the proposed algorithm, extensive experiments are performed, which demonstrate that the proposed algorithm can achieve high recognition accuracy and work robustly.

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

PubMed

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.

An Enhanced Data Visualization Method for Diesel Engine Malfunction Classification Using Multi-Sensor Signals

PubMed Central

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

2015-01-01

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. PMID:26506347

The right view from the wrong location: depth perception in stereoscopic multi-user virtual environments.

PubMed

Pollock, Brice; Burton, Melissa; Kelly, Jonathan W; Gilbert, Stephen; Winer, Eliot

2012-04-01

Stereoscopic depth cues improve depth perception and increase immersion within virtual environments (VEs). However, improper display of these cues can distort perceived distances and directions. Consider a multi-user VE, where all users view identical stereoscopic images regardless of physical location. In this scenario, cues are typically customized for one "leader" equipped with a head-tracking device. This user stands at the center of projection (CoP) and all other users ("followers") view the scene from other locations and receive improper depth cues. This paper examines perceived depth distortion when viewing stereoscopic VEs from follower perspectives and the impact of these distortions on collaborative spatial judgments. Pairs of participants made collaborative depth judgments of virtual shapes viewed from the CoP or after displacement forward or backward. Forward and backward displacement caused perceived depth compression and expansion, respectively, with greater compression than expansion. Furthermore, distortion was less than predicted by a ray-intersection model of stereo geometry. Collaboration times were significantly longer when participants stood at different locations compared to the same location, and increased with greater perceived depth discrepancy between the two viewing locations. These findings advance our understanding of spatial distortions in multi-user VEs, and suggest a strategy for reducing distortion.

An interactive VR system based on full-body tracking and gesture recognition

NASA Astrophysics Data System (ADS)

Zeng, Xia; Sang, Xinzhu; Chen, Duo; Wang, Peng; Guo, Nan; Yan, Binbin; Wang, Kuiru

2016-10-01

Most current virtual reality (VR) interactions are realized with the hand-held input device which leads to a low degree of presence. There is other solutions using sensors like Leap Motion to recognize the gestures of users in order to interact in a more natural way, but the navigation in these systems is still a problem, because they fail to map the actual walking to virtual walking only with a partial body of the user represented in the synthetic environment. Therefore, we propose a system in which users can walk around in the virtual environment as a humanoid model, selecting menu items and manipulating with the virtual objects using natural hand gestures. With a Kinect depth camera, the system tracks the joints of the user, mapping them to a full virtual body which follows the move of the tracked user. The movements of the feet can be detected to determine whether the user is in walking state, so that the walking of model in the virtual world can be activated and stopped by means of animation control in Unity engine. This method frees the hands of users comparing to traditional navigation way using hand-held device. We use the point cloud data getting from Kinect depth camera to recognize the gestures of users, such as swiping, pressing and manipulating virtual objects. Combining the full body tracking and gestures recognition using Kinect, we achieve our interactive VR system in Unity engine with a high degree of presence.

Efficiently Multi-User Searchable Encryption Scheme with Attribute Revocation and Grant for Cloud Storage

PubMed Central

Wang, Shangping; Zhang, Xiaoxue; Zhang, Yaling

2016-01-01

Cipher-policy attribute-based encryption (CP-ABE) focus on the problem of access control, and keyword-based searchable encryption scheme focus on the problem of finding the files that the user interested in the cloud storage quickly. To design a searchable and attribute-based encryption scheme is a new challenge. In this paper, we propose an efficiently multi-user searchable attribute-based encryption scheme with attribute revocation and grant for cloud storage. In the new scheme the attribute revocation and grant processes of users are delegated to proxy server. Our scheme supports multi attribute are revoked and granted simultaneously. Moreover, the keyword searchable function is achieved in our proposed scheme. The security of our proposed scheme is reduced to the bilinear Diffie-Hellman (BDH) assumption. Furthermore, the scheme is proven to be secure under the security model of indistinguishability against selective ciphertext-policy and chosen plaintext attack (IND-sCP-CPA). And our scheme is also of semantic security under indistinguishability against chosen keyword attack (IND-CKA) in the random oracle model. PMID:27898703

Efficiently Multi-User Searchable Encryption Scheme with Attribute Revocation and Grant for Cloud Storage.

PubMed

Wang, Shangping; Zhang, Xiaoxue; Zhang, Yaling

2016-01-01

Cipher-policy attribute-based encryption (CP-ABE) focus on the problem of access control, and keyword-based searchable encryption scheme focus on the problem of finding the files that the user interested in the cloud storage quickly. To design a searchable and attribute-based encryption scheme is a new challenge. In this paper, we propose an efficiently multi-user searchable attribute-based encryption scheme with attribute revocation and grant for cloud storage. In the new scheme the attribute revocation and grant processes of users are delegated to proxy server. Our scheme supports multi attribute are revoked and granted simultaneously. Moreover, the keyword searchable function is achieved in our proposed scheme. The security of our proposed scheme is reduced to the bilinear Diffie-Hellman (BDH) assumption. Furthermore, the scheme is proven to be secure under the security model of indistinguishability against selective ciphertext-policy and chosen plaintext attack (IND-sCP-CPA). And our scheme is also of semantic security under indistinguishability against chosen keyword attack (IND-CKA) in the random oracle model.

Community Identity and User Engagement in a Multi-Community Landscape.

PubMed

Zhang, Justine; Hamilton, William L; Danescu-Niculescu-Mizil, Cristian; Jurafsky, Dan; Leskovec, Jure

2017-05-01

A community's identity defines and shapes its internal dynamics. Our current understanding of this interplay is mostly limited to glimpses gathered from isolated studies of individual communities. In this work we provide a systematic exploration of the nature of this relation across a wide variety of online communities. To this end we introduce a quantitative, language-based typology reflecting two key aspects of a community's identity: how distinctive , and how temporally dynamic it is. By mapping almost 300 Reddit communities into the landscape induced by this typology, we reveal regularities in how patterns of user engagement vary with the characteristics of a community. Our results suggest that the way new and existing users engage with a community depends strongly and systematically on the nature of the collective identity it fosters, in ways that are highly consequential to community maintainers. For example, communities with distinctive and highly dynamic identities are more likely to retain their users. However, such niche communities also exhibit much larger acculturation gaps between existing users and newcomers, which potentially hinder the integration of the latter. More generally, our methodology reveals differences in how various social phenomena manifest across communities, and shows that structuring the multi-community landscape can lead to a better understanding of the systematic nature of this diversity.

Stem Cell Hydrogel, Jump-Starting Zika Drug Discovery, and Engineering RNA Recognition.

PubMed

Kostic, Milka

2016-08-18

Every month the editors of Cell Chemical Biology bring you highlights of the most recent chemical biology literature that impressed them with creativity and potential for follow up work. Our August 2016 selection includes a description of hydrogels with self-tunable stiffness that are used to profile lipid metabolites during stems cell differentiation, a look at whether we can find a drug repurposing solution to Zika virus infection, and an engineered RNA recognition motif (RRM). Copyright © 2016. Published by Elsevier Ltd.

Engineering data compendium. Human perception and performance. User's guide

NASA Technical Reports Server (NTRS)

Boff, Kenneth R. (Editor); Lincoln, Janet E. (Editor)

1988-01-01

The concept underlying the Engineering Data Compendium was the product of a research and development program (Integrated Perceptual Information for Designers project) aimed at facilitating the application of basic research findings in human performance to the design and military crew systems. The principal objective was to develop a workable strategy for: (1) identifying and distilling information of potential value to system design from the existing research literature, and (2) presenting this technical information in a way that would aid its accessibility, interpretability, and applicability by systems designers. The present four volumes of the Engineering Data Compendium represent the first implementation of this strategy. This is the first volume, the User's Guide, containing a description of the program and instructions for its use.

Online handwritten mathematical expression recognition

NASA Astrophysics Data System (ADS)

Büyükbayrak, Hakan; Yanikoglu, Berrin; Erçil, Aytül

2007-01-01

We describe a system for recognizing online, handwritten mathematical expressions. The system is designed with a user-interface for writing scientific articles, supporting the recognition of basic mathematical expressions as well as integrals, summations, matrices etc. A feed-forward neural network recognizes symbols which are assumed to be single-stroke and a recursive algorithm parses the expression by combining neural network output and the structure of the expression. Preliminary results show that writer-dependent recognition rates are very high (99.8%) while writer-independent symbol recognition rates are lower (75%). The interface associated with the proposed system integrates the built-in recognition capabilities of the Microsoft's Tablet PC API for recognizing textual input and supports conversion of hand-drawn figures into PNG format. This enables the user to enter text, mathematics and draw figures in a single interface. After recognition, all output is combined into one LATEX code and compiled into a PDF file.

Horror Image Recognition Based on Context-Aware Multi-Instance Learning.

PubMed

Li, Bing; Xiong, Weihua; Wu, Ou; Hu, Weiming; Maybank, Stephen; Yan, Shuicheng

2015-12-01

Horror content sharing on the Web is a growing phenomenon that can interfere with our daily life and affect the mental health of those involved. As an important form of expression, horror images have their own characteristics that can evoke extreme emotions. In this paper, we present a novel context-aware multi-instance learning (CMIL) algorithm for horror image recognition. The CMIL algorithm identifies horror images and picks out the regions that cause the sensation of horror in these horror images. It obtains contextual cues among adjacent regions in an image using a random walk on a contextual graph. Borrowing the strength of the fuzzy support vector machine (FSVM), we define a heuristic optimization procedure based on the FSVM to search for the optimal classifier for the CMIL. To improve the initialization of the CMIL, we propose a novel visual saliency model based on the tensor analysis. The average saliency value of each segmented region is set as its initial fuzzy membership in the CMIL. The advantage of the tensor-based visual saliency model is that it not only adaptively selects features, but also dynamically determines fusion weights for saliency value combination from different feature subspaces. The effectiveness of the proposed CMIL model is demonstrated by its use in horror image recognition on two large-scale image sets collected from the Internet.

Comparison of bimodal and bilateral cochlear implant users on speech recognition with competing talker, music perception, affective prosody discrimination, and talker identification.

PubMed

Cullington, Helen E; Zeng, Fan-Gang

2011-02-01

Despite excellent performance in speech recognition in quiet, most cochlear implant users have great difficulty with speech recognition in noise, music perception, identifying tone of voice, and discriminating different talkers. This may be partly due to the pitch coding in cochlear implant speech processing. Most current speech processing strategies use only the envelope information; the temporal fine structure is discarded. One way to improve electric pitch perception is to use residual acoustic hearing via a hearing aid on the nonimplanted ear (bimodal hearing). This study aimed to test the hypothesis that bimodal users would perform better than bilateral cochlear implant users on tasks requiring good pitch perception. Four pitch-related tasks were used. 1. Hearing in Noise Test (HINT) sentences spoken by a male talker with a competing female, male, or child talker. 2. Montreal Battery of Evaluation of Amusia. This is a music test with six subtests examining pitch, rhythm and timing perception, and musical memory. 3. Aprosodia Battery. This has five subtests evaluating aspects of affective prosody and recognition of sarcasm. 4. Talker identification using vowels spoken by 10 different talkers (three men, three women, two boys, and two girls). Bilateral cochlear implant users were chosen as the comparison group. Thirteen bimodal and 13 bilateral adult cochlear implant users were recruited; all had good speech perception in quiet. There were no significant differences between the mean scores of the bimodal and bilateral groups on any of the tests, although the bimodal group did perform better than the bilateral group on almost all tests. Performance on the different pitch-related tasks was not correlated, meaning that if a subject performed one task well they would not necessarily perform well on another. The correlation between the bimodal users' hearing threshold levels in the aided ear and their performance on these tasks was weak. Although the bimodal cochlear

An end user evaluation of query formulation and results review tools in three medical meta-search engines.

PubMed

Leroy, Gondy; Xu, Jennifer; Chung, Wingyan; Eggers, Shauna; Chen, Hsinchun

2007-01-01

Retrieving sufficient relevant information online is difficult for many people because they use too few keywords to search and search engines do not provide many support tools. To further complicate the search, users often ignore support tools when available. Our goal is to evaluate in a realistic setting when users use support tools and how they perceive these tools. We compared three medical search engines with support tools that require more or less effort from users to form a query and evaluate results. We carried out an end user study with 23 users who were asked to find information, i.e., subtopics and supporting abstracts, for a given theme. We used a balanced within-subjects design and report on the effectiveness, efficiency and usability of the support tools from the end user perspective. We found significant differences in efficiency but did not find significant differences in effectiveness between the three search engines. Dynamic user support tools requiring less effort led to higher efficiency. Fewer searches were needed and more documents were found per search when both query reformulation and result review tools dynamically adjust to the user query. The query reformulation tool that provided a long list of keywords, dynamically adjusted to the user query, was used most often and led to more subtopics. As hypothesized, the dynamic result review tools were used more often and led to more subtopics than static ones. These results were corroborated by the usability questionnaires, which showed that support tools that dynamically optimize output were preferred.

Surgical PACS for the digital operating room. Systems engineering and specification of user requirements.

PubMed

Korb, Werner; Bohn, Stefan; Burgert, Oliver; Dietz, Andreas; Jacobs, Stephan; Falk, Volkmar; Meixensberger, Jürgen; Strauss, Gero; Trantakis, Christos; Lemke, Heinz U

2006-01-01

For better integration of surgical assist systems into the operating room, a common communication and processing plattform that is based on the users needs is needed. The development of such a system, a Surgical Picture Aquisition and Communication System (S-PACS), according the systems engineering cycle is oulined in this paper. The first two steps (concept and specification) for the engineering of the S-PACS are discussed.A method for the systematic integration of the users needs', the Quality Function Deployment (QFD), is presented. The properties of QFD for the underlying problem and first results are discussed. Finally, this leads to a first definition of an S-PACS system.

Single-Trial Classification of Multi-User P300-Based Brain-Computer Interface Using Riemannian Geometry.

PubMed

Korczowski, L; Congedo, M; Jutten, C

2015-08-01

The classification of electroencephalographic (EEG) data recorded from multiple users simultaneously is an important challenge in the field of Brain-Computer Interface (BCI). In this paper we compare different approaches for classification of single-trials Event-Related Potential (ERP) on two subjects playing a collaborative BCI game. The minimum distance to mean (MDM) classifier in a Riemannian framework is extended to use the diversity of the inter-subjects spatio-temporal statistics (MDM-hyper) or to merge multiple classifiers (MDM-multi). We show that both these classifiers outperform significantly the mean performance of the two users and analogous classifiers based on the step-wise linear discriminant analysis. More importantly, the MDM-multi outperforms the performance of the best player within the pair.

Computer code for estimating installed performance of aircraft gas turbine engines. Volume 2: Users manual

NASA Technical Reports Server (NTRS)

Kowalski, E. J.

1979-01-01

A computerized method which utilizes the engine performance data and estimates the installed performance of aircraft gas turbine engines is presented. This installation includes: engine weight and dimensions, inlet and nozzle internal performance and drag, inlet and nacelle weight, and nacelle drag. A user oriented description of the program input requirements, program output, deck setup, and operating instructions is presented.

Security enhanced multi-factor biometric authentication scheme using bio-hash function.

PubMed

Choi, Younsung; Lee, Youngsook; Moon, Jongho; Won, Dongho

2017-01-01

With the rapid development of personal information and wireless communication technology, user authentication schemes have been crucial to ensure that wireless communications are secure. As such, various authentication schemes with multi-factor authentication have been proposed to improve the security of electronic communications. Multi-factor authentication involves the use of passwords, smart cards, and various biometrics to provide users with the utmost privacy and data protection. Cao and Ge analyzed various authentication schemes and found that Younghwa An's scheme was susceptible to a replay attack where an adversary masquerades as a legal server and a user masquerading attack where user anonymity is not provided, allowing an adversary to execute a password change process by intercepting the user's ID during login. Cao and Ge improved upon Younghwa An's scheme, but various security problems remained. This study demonstrates that Cao and Ge's scheme is susceptible to a biometric recognition error, slow wrong password detection, off-line password attack, user impersonation attack, ID guessing attack, a DoS attack, and that their scheme cannot provide session key agreement. Then, to address all weaknesses identified in Cao and Ge's scheme, this study proposes a security enhanced multi-factor biometric authentication scheme and provides a security analysis and formal analysis using Burrows-Abadi-Needham logic. Finally, the efficiency analysis reveals that the proposed scheme can protect against several possible types of attacks with only a slightly high computational cost.

Orbitofrontal and caudate volumes in cannabis users: a multi-site mega-analysis comparing dependent versus non-dependent users.

PubMed

Chye, Yann; Solowij, Nadia; Suo, Chao; Batalla, Albert; Cousijn, Janna; Goudriaan, Anna E; Martin-Santos, Rocio; Whittle, Sarah; Lorenzetti, Valentina; Yücel, Murat

2017-07-01

Cannabis (CB) use and dependence are associated with regionally specific alterations to brain circuitry and substantial psychosocial impairment. The objective of this study was to investigate the association between CB use and dependence, and the volumes of brain regions critically involved in goal-directed learning and behaviour-the orbitofrontal cortex (OFC) and caudate. In the largest multi-site structural imaging study of CB users vs healthy controls (HC), 140 CB users and 121 HC were recruited from four research sites. Group differences in OFC and caudate volumes were investigated between HC and CB users and between 70 dependent (CB-dep) and 50 non-dependent (CB-nondep) users. The relationship between quantity of CB use and age of onset of use and caudate and OFC volumes was explored. CB users (consisting of CB-dep and CB-nondep) did not significantly differ from HC in OFC or caudate volume. CB-dep compared to CB-nondep users exhibited significantly smaller volume in the medial and the lateral OFC. Lateral OFC volume was particularly smaller in CB-dep females, and reduced volume in the CB-dep group was associated with higher monthly cannabis dosage. Smaller medial OFC volume may be driven by CB dependence-related mechanisms, while smaller lateral OFC volume may be due to ongoing exposure to cannabinoid compounds. The results highlight a distinction between cannabis use and dependence and warrant examination of gender-specific effects in studies of CB dependence.

Textual emotion recognition for enhancing enterprise computing

NASA Astrophysics Data System (ADS)

Quan, Changqin; Ren, Fuji

2016-05-01

The growing interest in affective computing (AC) brings a lot of valuable research topics that can meet different application demands in enterprise systems. The present study explores a sub area of AC techniques - textual emotion recognition for enhancing enterprise computing. Multi-label emotion recognition in text is able to provide a more comprehensive understanding of emotions than single label emotion recognition. A representation of 'emotion state in text' is proposed to encompass the multidimensional emotions in text. It ensures the description in a formal way of the configurations of basic emotions as well as of the relations between them. Our method allows recognition of the emotions for the words bear indirect emotions, emotion ambiguity and multiple emotions. We further investigate the effect of word order for emotional expression by comparing the performances of bag-of-words model and sequence model for multi-label sentence emotion recognition. The experiments show that the classification results under sequence model are better than under bag-of-words model. And homogeneous Markov model showed promising results of multi-label sentence emotion recognition. This emotion recognition system is able to provide a convenient way to acquire valuable emotion information and to improve enterprise competitive ability in many aspects.

A joint swarm intelligence algorithm for multi-user detection in MIMO-OFDM system

NASA Astrophysics Data System (ADS)

Hu, Fengye; Du, Dakun; Zhang, Peng; Wang, Zhijun

2014-11-01

In the multi-input multi-output orthogonal frequency division multiplexing (MIMO-OFDM) system, traditional multi-user detection (MUD) algorithms that usually used to suppress multiple access interference are difficult to balance system detection performance and the complexity of the algorithm. To solve this problem, this paper proposes a joint swarm intelligence algorithm called Ant Colony and Particle Swarm Optimisation (AC-PSO) by integrating particle swarm optimisation (PSO) and ant colony optimisation (ACO) algorithms. According to simulation results, it has been shown that, with low computational complexity, the MUD for the MIMO-OFDM system based on AC-PSO algorithm gains comparable MUD performance with maximum likelihood algorithm. Thus, the proposed AC-PSO algorithm provides a satisfactory trade-off between computational complexity and detection performance.

ICT-Aided Engineering Courses: A Multi-Campus Course Management

ERIC Educational Resources Information Center

Dana-Picard, Thierry; Kidron, Ivy; Komar, Meir; Steiner, Joseph

2006-01-01

Jerusalem College of Technology (JCT) is a multi-campus institution with identical syllabi for courses in every campus. Moreover, learning at JCT requires at the same time synchronous and asynchronous learning and teaching. For some introductory courses in Mathematics for Engineering students, websites have been built and now upgraded in order to…

Muecas: A Multi-Sensor Robotic Head for Affective Human Robot Interaction and Imitation

PubMed Central

Cid, Felipe; Moreno, Jose; Bustos, Pablo; Núñez, Pedro

2014-01-01

This paper presents a multi-sensor humanoid robotic head for human robot interaction. The design of the robotic head, Muecas, is based on ongoing research on the mechanisms of perception and imitation of human expressions and emotions. These mechanisms allow direct interaction between the robot and its human companion through the different natural language modalities: speech, body language and facial expressions. The robotic head has 12 degrees of freedom, in a human-like configuration, including eyes, eyebrows, mouth and neck, and has been designed and built entirely by IADeX (Engineering, Automation and Design of Extremadura) and RoboLab. A detailed description of its kinematics is provided along with the design of the most complex controllers. Muecas can be directly controlled by FACS (Facial Action Coding System), the de facto standard for facial expression recognition and synthesis. This feature facilitates its use by third party platforms and encourages the development of imitation and of goal-based systems. Imitation systems learn from the user, while goal-based ones use planning techniques to drive the user towards a final desired state. To show the flexibility and reliability of the robotic head, the paper presents a software architecture that is able to detect, recognize, classify and generate facial expressions in real time using FACS. This system has been implemented using the robotics framework, RoboComp, which provides hardware-independent access to the sensors in the head. Finally, the paper presents experimental results showing the real-time functioning of the whole system, including recognition and imitation of human facial expressions. PMID:24787636

Indoor navigation by image recognition

NASA Astrophysics Data System (ADS)

Choi, Io Teng; Leong, Chi Chong; Hong, Ka Wo; Pun, Chi-Man

2017-07-01

With the progress of smartphones hardware, it is simple on smartphone using image recognition technique such as face detection. In addition, indoor navigation system development is much slower than outdoor navigation system. Hence, this research proves a usage of image recognition technique for navigation in indoor environment. In this paper, we introduced an indoor navigation application that uses the indoor environment features to locate user's location and a route calculating algorithm to generate an appropriate path for user. The application is implemented on Android smartphone rather than iPhone. Yet, the application design can also be applied on iOS because the design is implemented without using special features only for Android. We found that digital navigation system provides better and clearer location information than paper map. Also, the indoor environment is ideal for Image recognition processing. Hence, the results motivate us to design an indoor navigation system using image recognition.

Civic Participation among Seventh-Grade Social Studies Students in Multi-User Virtual Environments

ERIC Educational Resources Information Center

Zieger, Laura; Farber, Matthew

2012-01-01

Technological advances on the Internet now enable students to develop participation skills in virtual worlds. Similar to controlling a character in a video game, multi-user virtual environments, or MUVEs, allow participants to interact with others in synchronous, online settings. The authors of this study created a link between MUVEs and…

Advancing MEMS Technology Usage through the MUMPS (Multi-User MEMS Processes) Program

NASA Technical Reports Server (NTRS)

Koester, D. A.; Markus, K. W.; Dhuler, V.; Mahadevan, R.; Cowen, A.

1995-01-01

In order to help provide access to advanced micro-electro-mechanical systems (MEMS) technologies and lower the barriers for both industry and academia, the Microelectronic Center of North Carolina (MCNC) and ARPA have developed a program which provides users with access to both MEMS processes and advanced electronic integration techniques. The four distinct aspects of this program, the multi-user MEMS processes (MUMP's), the consolidated micro-mechanical element library, smart MEMS, and the MEMS technology network are described in this paper. MUMP's is an ARPA-supported program created to provide inexpensive access to MEMS technology in a multi-user environment. It is both a proof-of-concept and educational tool that aids in the development of MEMS in the domestic community. MUMP's technologies currently include a 3-layer poly-silicon surface micromachining process and LIGA (lithography, electroforming, and injection molding) processes that provide reasonable design flexibility within set guidelines. The consolidated micromechanical element library (CaMEL) is a library of active and passive MEMS structures that can be downloaded by the MEMS community via the internet. Smart MEMS is the development of advanced electronics integration techniques for MEMS through the application of flip chip technology. The MEMS technology network (TechNet) is a menu of standard substrates and MEMS fabrication processes that can be purchased and combined to create unique process flows. TechNet provides the MEMS community greater flexibility and enhanced technology accessibility.

A preliminary analysis of human factors affecting the recognition accuracy of a discrete word recognizer for C3 systems

NASA Astrophysics Data System (ADS)

Yellen, H. W.

1983-03-01

Literature pertaining to Voice Recognition abounds with information relevant to the assessment of transitory speech recognition devices. In the past, engineering requirements have dictated the path this technology followed. But, other factors do exist that influence recognition accuracy. This thesis explores the impact of Human Factors on the successful recognition of speech, principally addressing the differences or variability among users. A Threshold Technology T-600 was used for a 100 utterance vocubalary to test 44 subjects. A statistical analysis was conducted on 5 generic categories of Human Factors: Occupational, Operational, Psychological, Physiological and Personal. How the equipment is trained and the experience level of the speaker were found to be key characteristics influencing recognition accuracy. To a lesser extent computer experience, time or week, accent, vital capacity and rate of air flow, speaker cooperativeness and anxiety were found to affect overall error rates.

Software Engineering Laboratory (SEL). Data base organization and user's guide, revision 1

NASA Technical Reports Server (NTRS)

Lo, P. S.; Wyckoff, D.; Page, J.; Mcgarry, F. E.

1983-01-01

The structure of the Software Engineering Laboratory (SEL) data base is described. It defines each data base file in detail and provides information about how to access and use the data for programmers and other users. Several data base reporting programs are described also.

A Robotic Coach Architecture for Elder Care (ROCARE) Based on Multi-User Engagement Models.

PubMed

Fan, Jing; Bian, Dayi; Zheng, Zhi; Beuscher, Linda; Newhouse, Paul A; Mion, Lorraine C; Sarkar, Nilanjan

2017-08-01

The aging population with its concomitant medical conditions, physical and cognitive impairments, at a time of strained resources, establishes the urgent need to explore advanced technologies that may enhance function and quality of life. Recently, robotic technology, especially socially assistive robotics has been investigated to address the physical, cognitive, and social needs of older adults. Most system to date have predominantly focused on one-on-one human robot interaction (HRI). In this paper, we present a multi-user engagement-based robotic coach system architecture (ROCARE). ROCARE is capable of administering both one-on-one and multi-user HRI, providing implicit and explicit channels of communication, and individualized activity management for long-term engagement. Two preliminary feasibility studies, a one-on-one interaction and a triadic interaction with two humans and a robot, were conducted and the results indicated potential usefulness and acceptance by older adults, with and without cognitive impairment.

Soil engineering in vivo: harnessing natural biogeochemical systems for sustainable, multi-functional engineering solutions

PubMed Central

DeJong, Jason T.; Soga, Kenichi; Banwart, Steven A.; Whalley, W. Richard; Ginn, Timothy R.; Nelson, Douglas C.; Mortensen, Brina M.; Martinez, Brian C.; Barkouki, Tammer

2011-01-01

Carbon sequestration, infrastructure rehabilitation, brownfields clean-up, hazardous waste disposal, water resources protection and global warming—these twenty-first century challenges can neither be solved by the high-energy consumptive practices that hallmark industry today, nor by minor tweaking or optimization of these processes. A more radical, holistic approach is required to develop the sustainable solutions society needs. Most of the above challenges occur within, are supported on, are enabled by or grown from soil. Soil, contrary to conventional civil engineering thought, is a living system host to multiple simultaneous processes. It is proposed herein that ‘soil engineering in vivo’, wherein the natural capacity of soil as a living ecosystem is used to provide multiple solutions simultaneously, may provide new, innovative, sustainable solutions to some of these great challenges of the twenty-first century. This requires a multi-disciplinary perspective that embraces the science of biology, chemistry and physics and applies this knowledge to provide multi-functional civil and environmental engineering designs for the soil environment. For example, can native soil bacterial species moderate the carbonate cycle in soils to simultaneously solidify liquefiable soil, immobilize reactive heavy metals and sequester carbon—effectively providing civil engineering functionality while clarifying the ground water and removing carbon from the atmosphere? Exploration of these ideas has begun in earnest in recent years. This paper explores the potential, challenges and opportunities of this new field, and highlights one biogeochemical function of soil that has shown promise and is developing rapidly as a new technology. The example is used to propose a generalized approach in which the potential of this new field can be fully realized. PMID:20829246

Soil engineering in vivo: harnessing natural biogeochemical systems for sustainable, multi-functional engineering solutions.

PubMed

DeJong, Jason T; Soga, Kenichi; Banwart, Steven A; Whalley, W Richard; Ginn, Timothy R; Nelson, Douglas C; Mortensen, Brina M; Martinez, Brian C; Barkouki, Tammer

2011-01-06

Carbon sequestration, infrastructure rehabilitation, brownfields clean-up, hazardous waste disposal, water resources protection and global warming-these twenty-first century challenges can neither be solved by the high-energy consumptive practices that hallmark industry today, nor by minor tweaking or optimization of these processes. A more radical, holistic approach is required to develop the sustainable solutions society needs. Most of the above challenges occur within, are supported on, are enabled by or grown from soil. Soil, contrary to conventional civil engineering thought, is a living system host to multiple simultaneous processes. It is proposed herein that 'soil engineering in vivo', wherein the natural capacity of soil as a living ecosystem is used to provide multiple solutions simultaneously, may provide new, innovative, sustainable solutions to some of these great challenges of the twenty-first century. This requires a multi-disciplinary perspective that embraces the science of biology, chemistry and physics and applies this knowledge to provide multi-functional civil and environmental engineering designs for the soil environment. For example, can native soil bacterial species moderate the carbonate cycle in soils to simultaneously solidify liquefiable soil, immobilize reactive heavy metals and sequester carbon-effectively providing civil engineering functionality while clarifying the ground water and removing carbon from the atmosphere? Exploration of these ideas has begun in earnest in recent years. This paper explores the potential, challenges and opportunities of this new field, and highlights one biogeochemical function of soil that has shown promise and is developing rapidly as a new technology. The example is used to propose a generalized approach in which the potential of this new field can be fully realized.

EPANET Multi-Species Extension Software and User's Manual ...

EPA Pesticide Factsheets

Software and User's Manual EPANET is used in homeland security research to model contamination threats to water systems. Historically, EPANET has been limited to tracking the dynamics of a single chemical transported through a network of pipes and storage tanks, such as a fluoride used in a tracer study or free chlorine used in a disinfection decay study. Recently, the NHSRC released a new extension to EPANET called EPANET-MSX (Multi-Species eXtension) that allows for the consideration of multiple interacting species in the bulk flow and on the pipe walls. This capability has been incorporated into both a stand-alone executable program as well as a toolkit library of functions that programmers can use to build customized applications.

Multi-User Spaceport Update News Conference

NASA Image and Video Library

2014-01-23

CAPE CANAVERAL, Fla. – Larry Price, Lockheed Martin Space Systems deputy program manager for NASA's Orion spacecraft, joins Sierra Nevada Corporation, or SNC, Space Systems, as the company announces the steps it will take to prepare for a November 2016 orbital flight of its Dream Chaser spacecraft from Florida’s Space Coast. The steps are considered substantial for SNC and important to plans by NASA and Space Florida for Kennedy Space Center’s transformation into a multi-user spaceport for both commercial and government customers. SNC said it plans to work with United Launch Alliance, or ULA, to launch the Dream Chaser spacecraft into orbit atop an Atlas V rocket from Space Launch Complex 41 at Cape Canaveral Air Force Station intends to land the winged spacecraft at Kennedy’s 3.5-mile long runway at the Shuttle Landing Facility lease office space at Exploration Park, right outside Kennedy’s gates and process the spacecraft in the high bay of the Operations and Checkout Building at Kennedy, with Lockheed Martin performing the work. Photo credit: NASA/Kim Shiflett

Multi-User Spaceport Update News Conference

NASA Image and Video Library

2014-01-23

CAPE CANAVERAL, Fla. – Frank DiBello, right, president and CEO of Space Florida, joins Sierra Nevada Corporation, or SNC, Space Systems, as the company announces the steps it will take to prepare for a November 2016 orbital flight of its Dream Chaser spacecraft from Florida’s Space Coast. The steps are considered substantial for SNC and important to plans by NASA and Space Florida for Kennedy Space Center’s transformation into a multi-user spaceport for both commercial and government customers. SNC said it plans to work with United Launch Alliance, or ULA, to launch the Dream Chaser spacecraft into orbit atop an Atlas V rocket from Space Launch Complex 41 at Cape Canaveral Air Force Station intends to land the winged spacecraft at Kennedy’s 3.5-mile long runway at the Shuttle Landing Facility lease office space at Exploration Park, right outside Kennedy’s gates and process the spacecraft in the high bay of the Operations and Checkout Building at Kennedy, with Lockheed Martin performing the work. Photo credit: NASA/Kim Shiflett

The Relationship between Binaural Benefit and Difference in Unilateral Speech Recognition Performance for Bilateral Cochlear Implant Users

PubMed Central

Yoon, Yang-soo; Li, Yongxin; Kang, Hou-Yong; Fu, Qian-Jie

2011-01-01

Objective The full benefit of bilateral cochlear implants may depend on the unilateral performance with each device, the speech materials, processing ability of the user, and/or the listening environment. In this study, bilateral and unilateral speech performances were evaluated in terms of recognition of phonemes and sentences presented in quiet or in noise. Design Speech recognition was measured for unilateral left, unilateral right, and bilateral listening conditions; speech and noise were presented at 0° azimuth. The “binaural benefit” was defined as the difference between bilateral performance and unilateral performance with the better ear. Study Sample 9 adults with bilateral cochlear implants participated. Results On average, results showed a greater binaural benefit in noise than in quiet for all speech tests. More importantly, the binaural benefit was greater when unilateral performance was similar across ears. As the difference in unilateral performance between ears increased, the binaural advantage decreased; this functional relationship was observed across the different speech materials and noise levels even though there was substantial intra- and inter-subject variability. Conclusions The results indicate that subjects who show symmetry in speech recognition performance between implanted ears in general show a large binaural benefit. PMID:21696329

Managing Cognitive Load in Educational Multi-User Virtual Environments: Reflection on Design Practice

ERIC Educational Resources Information Center

Nelson, Brian C.; Erlandson, Benjamin E.

2008-01-01

In this paper, we explore how the application of multimedia design principles may inform the development of educational multi-user virtual environments (MUVEs). We look at design principles that have been shown to help learners manage cognitive load within multimedia environments and conduct a conjectural analysis of the extent to which such…

Inspiring Equal Contribution and Opportunity in a 3D Multi-User Virtual Environment: Bringing Together Men Gamers and Women Non-Gamers in Second Life[R

ERIC Educational Resources Information Center

deNoyelles, Aimee; Seo, Kay Kyeong-Ju

2012-01-01

A 3D multi-user virtual environment holds promise to support and enhance student online learning communities due to its ability to promote global synchronous interaction and collaboration, rich multisensory experience and expression, and elaborate design capabilities. Second Life[R], a multi-user virtual environment intended for adult users 18 and…

Advanced graphical user interface for multi-physics simulations using AMST

NASA Astrophysics Data System (ADS)

Hoffmann, Florian; Vogel, Frank

2017-07-01

Numerical modelling of particulate matter has gained much popularity in recent decades. Advanced Multi-physics Simulation Technology (AMST) is a state-of-the-art three dimensional numerical modelling technique combining the eX-tended Discrete Element Method (XDEM) with Computational Fluid Dynamics (CFD) and Finite Element Analysis (FEA) [1]. One major limitation of this code is the lack of a graphical user interface (GUI) meaning that all pre-processing has to be made directly in a HDF5-file. This contribution presents the first graphical pre-processor developed for AMST.

Engineering of a multi-station shoulder simulator.

PubMed

Smith, Simon L; Li, Lisa; Joyce, Thomas J

2016-05-01

This work aimed to engineer a multi-station shoulder simulator in order to wear test shoulder prostheses using recognized shoulder activities of daily living. A bespoke simulator was designed, built and subject to commissioning trials before a first wear test was conducted. Five JRI Orthopaedics Reverse Shoulder VAIOS 42 mm prostheses were tested for 2.0 million cycles and a mean wear rate and standard deviation of 14.2 ± 2.1 mm(3)/10(6) cycles measured for the polymeric glenoid components. This result when adjusted for prostheses diameters and test conditions showed excellent agreement with results from hip simulator studies of similar materials in a lubricant of bovine serum. The Newcastle Shoulder Simulator is the first multi-station shoulder simulator capable of applying physiological motion and loading for typical activities of daily living. © IMechE 2016.

Memorandum : feasibility assessment, bicycle or bicycle/pedestrian (multi-user) facility at Appomattox Court house NHP

DOT National Transportation Integrated Search

2007-10-12

This memorandum provides a preliminary feasibility assessment for a bicycle or combined bicycle/pedestrian (i.e., multi-user) facility at Appomattox Court House National Historical Park (NHP). This assessment is based on discussions with park staff, ...

Human fatigue expression recognition through image-based dynamic multi-information and bimodal deep learning

NASA Astrophysics Data System (ADS)

Zhao, Lei; Wang, Zengcai; Wang, Xiaojin; Qi, Yazhou; Liu, Qing; Zhang, Guoxin

2016-09-01

Human fatigue is an important cause of traffic accidents. To improve the safety of transportation, we propose, in this paper, a framework for fatigue expression recognition using image-based facial dynamic multi-information and a bimodal deep neural network. First, the landmark of face region and the texture of eye region, which complement each other in fatigue expression recognition, are extracted from facial image sequences captured by a single camera. Then, two stacked autoencoder neural networks are trained for landmark and texture, respectively. Finally, the two trained neural networks are combined by learning a joint layer on top of them to construct a bimodal deep neural network. The model can be used to extract a unified representation that fuses landmark and texture modalities together and classify fatigue expressions accurately. The proposed system is tested on a human fatigue dataset obtained from an actual driving environment. The experimental results demonstrate that the proposed method performs stably and robustly, and that the average accuracy achieves 96.2%.

Cochlear Implants Special Issue Article: Vocal Emotion Recognition by Normal-Hearing Listeners and Cochlear Implant Users

PubMed Central

Luo, Xin; Fu, Qian-Jie; Galvin, John J.

2007-01-01

The present study investigated the ability of normal-hearing listeners and cochlear implant users to recognize vocal emotions. Sentences were produced by 1 male and 1 female talker according to 5 target emotions: angry, anxious, happy, sad, and neutral. Overall amplitude differences between the stimuli were either preserved or normalized. In experiment 1, vocal emotion recognition was measured in normal-hearing and cochlear implant listeners; cochlear implant subjects were tested using their clinically assigned processors. When overall amplitude cues were preserved, normal-hearing listeners achieved near-perfect performance, whereas listeners with cochlear implant recognized less than half of the target emotions. Removing the overall amplitude cues significantly worsened mean normal-hearing and cochlear implant performance. In experiment 2, vocal emotion recognition was measured in listeners with cochlear implant as a function of the number of channels (from 1 to 8) and envelope filter cutoff frequency (50 vs 400 Hz) in experimental speech processors. In experiment 3, vocal emotion recognition was measured in normal-hearing listeners as a function of the number of channels (from 1 to 16) and envelope filter cutoff frequency (50 vs 500 Hz) in acoustic cochlear implant simulations. Results from experiments 2 and 3 showed that both cochlear implant and normal-hearing performance significantly improved as the number of channels or the envelope filter cutoff frequency was increased. The results suggest that spectral, temporal, and overall amplitude cues each contribute to vocal emotion recognition. The poorer cochlear implant performance is most likely attributable to the lack of salient pitch cues and the limited functional spectral resolution. PMID:18003871

Toward visual user interfaces supporting collaborative multimedia content management

NASA Astrophysics Data System (ADS)

Husein, Fathi; Leissler, Martin; Hemmje, Matthias

2000-12-01

Supporting collaborative multimedia content management activities, as e.g., image and video acquisition, exploration, and access dialogues between naive users and multi media information systems is a non-trivial task. Although a wide variety of experimental and prototypical multimedia storage technologies as well as corresponding indexing and retrieval engines are available, most of them lack appropriate support for collaborative end-user oriented user interface front ends. The development of advanced user adaptable interfaces is necessary for building collaborative multimedia information- space presentations based upon advanced tools for information browsing, searching, filtering, and brokering to be applied on potentially very large and highly dynamic multimedia collections with a large number of users and user groups. Therefore, the development of advanced and at the same time adaptable and collaborative computer graphical information presentation schemes that allow to easily apply adequate visual metaphors for defined target user stereotypes has to become a key focus within ongoing research activities trying to support collaborative information work with multimedia collections.

Kernel-aligned multi-view canonical correlation analysis for image recognition

NASA Astrophysics Data System (ADS)

Su, Shuzhi; Ge, Hongwei; Yuan, Yun-Hao

2016-09-01

Existing kernel-based correlation analysis methods mainly adopt a single kernel in each view. However, only a single kernel is usually insufficient to characterize nonlinear distribution information of a view. To solve the problem, we transform each original feature vector into a 2-dimensional feature matrix by means of kernel alignment, and then propose a novel kernel-aligned multi-view canonical correlation analysis (KAMCCA) method on the basis of the feature matrices. Our proposed method can simultaneously employ multiple kernels to better capture the nonlinear distribution information of each view, so that correlation features learned by KAMCCA can have well discriminating power in real-world image recognition. Extensive experiments are designed on five real-world image datasets, including NIR face images, thermal face images, visible face images, handwritten digit images, and object images. Promising experimental results on the datasets have manifested the effectiveness of our proposed method.

Security enhanced multi-factor biometric authentication scheme using bio-hash function

PubMed Central

Lee, Youngsook; Moon, Jongho

2017-01-01

With the rapid development of personal information and wireless communication technology, user authentication schemes have been crucial to ensure that wireless communications are secure. As such, various authentication schemes with multi-factor authentication have been proposed to improve the security of electronic communications. Multi-factor authentication involves the use of passwords, smart cards, and various biometrics to provide users with the utmost privacy and data protection. Cao and Ge analyzed various authentication schemes and found that Younghwa An’s scheme was susceptible to a replay attack where an adversary masquerades as a legal server and a user masquerading attack where user anonymity is not provided, allowing an adversary to execute a password change process by intercepting the user’s ID during login. Cao and Ge improved upon Younghwa An’s scheme, but various security problems remained. This study demonstrates that Cao and Ge’s scheme is susceptible to a biometric recognition error, slow wrong password detection, off-line password attack, user impersonation attack, ID guessing attack, a DoS attack, and that their scheme cannot provide session key agreement. Then, to address all weaknesses identified in Cao and Ge’s scheme, this study proposes a security enhanced multi-factor biometric authentication scheme and provides a security analysis and formal analysis using Burrows-Abadi-Needham logic. Finally, the efficiency analysis reveals that the proposed scheme can protect against several possible types of attacks with only a slightly high computational cost. PMID:28459867

A Robotic Coach Architecture for Elder Care (ROCARE) Based on Multi-user Engagement Models

PubMed Central

Fan, Jing; Bian, Dayi; Zheng, Zhi; Beuscher, Linda; Newhouse, Paul A.; Mion, Lorraine C.; Sarkar, Nilanjan

2017-01-01

The aging population with its concomitant medical conditions, physical and cognitive impairments, at a time of strained resources, establishes the urgent need to explore advanced technologies that may enhance function and quality of life. Recently, robotic technology, especially socially assistive robotics has been investigated to address the physical, cognitive, and social needs of older adults. Most system to date have predominantly focused on one-on-one human robot interaction (HRI). In this paper, we present a multi-user engagement-based robotic coach system architecture (ROCARE). ROCARE is capable of administering both one-on-one and multi-user HRI, providing implicit and explicit channels of communication, and individualized activity management for long-term engagement. Two preliminary feasibility studies, a one-on-one interaction and a triadic interaction with two humans and a robot, were conducted and the results indicated potential usefulness and acceptance by older adults, with and without cognitive impairment. PMID:28113672

Proceedings of the Workshop on Software Engineering Foundations for End-User Programming (SEEUP 2009)

DTIC Science & Technology

2009-11-01

interest of scientific and technical information exchange. This work is sponsored by the U.S. Department of Defense. The Software Engineering Institute is a...an interesting conti- nuum between how many different requirements a program must satisfy: the more complex and diverse the requirements, the more... Gender differences in approaches to end-user software development have also been reported in debugging feature usage [1] and in end-user web programming

A Flexible Approach for Human Activity Recognition Using Artificial Hydrocarbon Networks

PubMed Central

Ponce, Hiram; Miralles-Pechuán, Luis; Martínez-Villaseñor, María de Lourdes

2016-01-01

Physical activity recognition based on sensors is a growing area of interest given the great advances in wearable sensors. Applications in various domains are taking advantage of the ease of obtaining data to monitor personal activities and behavior in order to deliver proactive and personalized services. Although many activity recognition systems have been developed for more than two decades, there are still open issues to be tackled with new techniques. We address in this paper one of the main challenges of human activity recognition: Flexibility. Our goal in this work is to present artificial hydrocarbon networks as a novel flexible approach in a human activity recognition system. In order to evaluate the performance of artificial hydrocarbon networks based classifier, experimentation was designed for user-independent, and also for user-dependent case scenarios. Our results demonstrate that artificial hydrocarbon networks classifier is flexible enough to be used when building a human activity recognition system with either user-dependent or user-independent approaches. PMID:27792136

A Flexible Approach for Human Activity Recognition Using Artificial Hydrocarbon Networks.

PubMed

Ponce, Hiram; Miralles-Pechuán, Luis; Martínez-Villaseñor, María de Lourdes

2016-10-25

Physical activity recognition based on sensors is a growing area of interest given the great advances in wearable sensors. Applications in various domains are taking advantage of the ease of obtaining data to monitor personal activities and behavior in order to deliver proactive and personalized services. Although many activity recognition systems have been developed for more than two decades, there are still open issues to be tackled with new techniques. We address in this paper one of the main challenges of human activity recognition: Flexibility. Our goal in this work is to present artificial hydrocarbon networks as a novel flexible approach in a human activity recognition system. In order to evaluate the performance of artificial hydrocarbon networks based classifier, experimentation was designed for user-independent, and also for user-dependent case scenarios. Our results demonstrate that artificial hydrocarbon networks classifier is flexible enough to be used when building a human activity recognition system with either user-dependent or user-independent approaches.

Recognition of rotated images using the multi-valued neuron and rotation-invariant 2D Fourier descriptors

NASA Astrophysics Data System (ADS)

Aizenberg, Evgeni; Bigio, Irving J.; Rodriguez-Diaz, Eladio

2012-03-01

The Fourier descriptors paradigm is a well-established approach for affine-invariant characterization of shape contours. In the work presented here, we extend this method to images, and obtain a 2D Fourier representation that is invariant to image rotation. The proposed technique retains phase uniqueness, and therefore structural image information is not lost. Rotation-invariant phase coefficients were used to train a single multi-valued neuron (MVN) to recognize satellite and human face images rotated by a wide range of angles. Experiments yielded 100% and 96.43% classification rate for each data set, respectively. Recognition performance was additionally evaluated under effects of lossy JPEG compression and additive Gaussian noise. Preliminary results show that the derived rotation-invariant features combined with the MVN provide a promising scheme for efficient recognition of rotated images.

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

PubMed

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

2017-01-01

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

Automatic threshold selection for multi-class open set recognition

NASA Astrophysics Data System (ADS)

Scherreik, Matthew; Rigling, Brian

2017-05-01

Multi-class open set recognition is the problem of supervised classification with additional unknown classes encountered after a model has been trained. An open set classifer often has two core components. The first component is a base classifier which estimates the most likely class of a given example. The second component consists of open set logic which estimates if the example is truly a member of the candidate class. Such a system is operated in a feed-forward fashion. That is, a candidate label is first estimated by the base classifier, and the true membership of the example to the candidate class is estimated afterward. Previous works have developed an iterative threshold selection algorithm for rejecting examples from classes which were not present at training time. In those studies, a Platt-calibrated SVM was used as the base classifier, and the thresholds were applied to class posterior probabilities for rejection. In this work, we investigate the effectiveness of other base classifiers when paired with the threshold selection algorithm and compare their performance with the original SVM solution.

Smartphone based face recognition tool for the blind.

PubMed

Kramer, K M; Hedin, D S; Rolkosky, D J

2010-01-01

The inability to identify people during group meetings is a disadvantage for blind people in many professional and educational situations. To explore the efficacy of face recognition using smartphones in these settings, we have prototyped and tested a face recognition tool for blind users. The tool utilizes Smartphone technology in conjunction with a wireless network to provide audio feedback of the people in front of the blind user. Testing indicated that the face recognition technology can tolerate up to a 40 degree angle between the direction a person is looking and the camera's axis and a 96% success rate with no false positives. Future work will be done to further develop the technology for local face recognition on the smartphone in addition to remote server based face recognition.

Multi-EMR Structured Data Entry Form: User-Acceptance Testing of a Prototype.

PubMed

Zavar, Abbas; Keshavjee, Karim

2017-01-01

Capturing standardized data from multiple EMRs at the point of care is highly desirable for a variety of uses, including quality improvement programs, multi-centered clinical trials and clinical decision support. In this paper, we describe the design, development and user acceptance testing of a prototype web-based form (the Form) that can integrate with multiple EMRs. We used the validated UTAUT questionnaire to assess the likelihood of uptake of the Form into clinical practice. The Form was found to be easy to use, elicits low anxiety, supports productivity and is perceived to have good support. Users would benefit from training and from better social signaling about the importance of using the Form in their practice. Making the Form more fun and interesting could help increase uptake.

A multi-user real time inventorying system for radioactive materials: a networking approach.

PubMed

Mehta, S; Bandyopadhyay, D; Hoory, S

1998-01-01

A computerized system for radioisotope management and real time inventory coordinated across a large organization is reported. It handles hundreds of individual users and their separate inventory records. Use of highly efficient computer network and database technologies makes it possible to accept, maintain, and furnish all records related to receipt, usage, and disposal of the radioactive materials for the users separately and collectively. The system's central processor is an HP-9000/800 G60 RISC server and users from across the organization use their personal computers to login to this server using the TCP/IP networking protocol, which makes distributed use of the system possible. Radioisotope decay is automatically calculated by the program, so that it can make the up-to-date radioisotope inventory data of an entire institution available immediately. The system is specifically designed to allow use by large numbers of users (about 300) and accommodates high volumes of data input and retrieval without compromising simplicity and accuracy. Overall, it is an example of a true multi-user, on-line, relational database information system that makes the functioning of a radiation safety department efficient.

A Multi-Modal Face Recognition Method Using Complete Local Derivative Patterns and Depth Maps

PubMed Central

Yin, Shouyi; Dai, Xu; Ouyang, Peng; Liu, Leibo; Wei, Shaojun

2014-01-01

In this paper, we propose a multi-modal 2D + 3D face recognition method for a smart city application based on a Wireless Sensor Network (WSN) and various kinds of sensors. Depth maps are exploited for the 3D face representation. As for feature extraction, we propose a new feature called Complete Local Derivative Pattern (CLDP). It adopts the idea of layering and has four layers. In the whole system, we apply CLDP separately on Gabor features extracted from a 2D image and depth map. Then, we obtain two features: CLDP-Gabor and CLDP-Depth. The two features weighted by the corresponding coefficients are combined together in the decision level to compute the total classification distance. At last, the probe face is assigned the identity with the smallest classification distance. Extensive experiments are conducted on three different databases. The results demonstrate the robustness and superiority of the new approach. The experimental results also prove that the proposed multi-modal 2D + 3D method is superior to other multi-modal ones and CLDP performs better than other Local Binary Pattern (LBP) based features. PMID:25333290

Interface Prostheses With Classifier-Feedback-Based User Training.

PubMed

Fang, Yinfeng; Zhou, Dalin; Li, Kairu; Liu, Honghai

2017-11-01

It is evident that user training significantly affects performance of pattern-recognition-based myoelectric prosthetic device control. Despite plausible classification accuracy on offline datasets, online accuracy usually suffers from the changes in physiological conditions and electrode displacement. The user ability in generating consistent electromyographic (EMG) patterns can be enhanced via proper user training strategies in order to improve online performance. This study proposes a clustering-feedback strategy that provides real-time feedback to users by means of a visualized online EMG signal input as well as the centroids of the training samples, whose dimensionality is reduced to minimal number by dimension reduction. Clustering feedback provides a criterion that guides users to adjust motion gestures and muscle contraction forces intentionally. The experiment results have demonstrated that hand motion recognition accuracy increases steadily along the progress of the clustering-feedback-based user training, while conventional classifier-feedback methods, i.e., label feedback, hardly achieve any improvement. The result concludes that the use of proper classifier feedback can accelerate the process of user training, and implies prosperous future for the amputees with limited or no experience in pattern-recognition-based prosthetic device manipulation.It is evident that user training significantly affects performance of pattern-recognition-based myoelectric prosthetic device control. Despite plausible classification accuracy on offline datasets, online accuracy usually suffers from the changes in physiological conditions and electrode displacement. The user ability in generating consistent electromyographic (EMG) patterns can be enhanced via proper user training strategies in order to improve online performance. This study proposes a clustering-feedback strategy that provides real-time feedback to users by means of a visualized online EMG signal input as well

Socialisation for Learning at a Distance in a 3-D Multi-User Virtual Environment

ERIC Educational Resources Information Center

Edirisingha, Palitha; Nie, Ming; Pluciennik, Mark; Young, Ruth

2009-01-01

This paper reports findings of a pilot study that examined the pedagogical potential of "Second Life" (SL), a popular three-dimensional multi-user virtual environment (3-D MUVE) developed by the Linden Lab. The study is part of a 1-year research and development project titled "Modelling of Secondlife Environments"…

A neural network based artificial vision system for licence plate recognition.

PubMed

Draghici, S

1997-02-01

This paper presents a neural network based artificial vision system able to analyze the image of a car given by a camera, locate the registration plate and recognize the registration number of the car. The paper describes in detail various practical problems encountered in implementing this particular application and the solutions used to solve them. The main features of the system presented are: controlled stability-plasticity behavior, controlled reliability threshold, both off-line and on-line learning, self assessment of the output reliability and high reliability based on high level multiple feedback. The system has been designed using a modular approach. Sub-modules can be upgraded and/or substituted independently, thus making the system potentially suitable in a large variety of vision applications. The OCR engine was designed as an interchangeable plug-in module. This allows the user to choose an OCR engine which is suited to the particular application and to upgrade it easily in the future. At present, there are several versions of this OCR engine. One of them is based on a fully connected feedforward artificial neural network with sigmoidal activation functions. This network can be trained with various training algorithms such as error backpropagation. An alternative OCR engine is based on the constraint based decomposition (CBD) training architecture. The system has showed the following performances (on average) on real-world data: successful plate location and segmentation about 99%, successful character recognition about 98% and successful recognition of complete registration plates about 80%.

Marky: a tool supporting annotation consistency in multi-user and iterative document annotation projects.

PubMed

Pérez-Pérez, Martín; Glez-Peña, Daniel; Fdez-Riverola, Florentino; Lourenço, Anália

2015-02-01

Document annotation is a key task in the development of Text Mining methods and applications. High quality annotated corpora are invaluable, but their preparation requires a considerable amount of resources and time. Although the existing annotation tools offer good user interaction interfaces to domain experts, project management and quality control abilities are still limited. Therefore, the current work introduces Marky, a new Web-based document annotation tool equipped to manage multi-user and iterative projects, and to evaluate annotation quality throughout the project life cycle. At the core, Marky is a Web application based on the open source CakePHP framework. User interface relies on HTML5 and CSS3 technologies. Rangy library assists in browser-independent implementation of common DOM range and selection tasks, and Ajax and JQuery technologies are used to enhance user-system interaction. Marky grants solid management of inter- and intra-annotator work. Most notably, its annotation tracking system supports systematic and on-demand agreement analysis and annotation amendment. Each annotator may work over documents as usual, but all the annotations made are saved by the tracking system and may be further compared. So, the project administrator is able to evaluate annotation consistency among annotators and across rounds of annotation, while annotators are able to reject or amend subsets of annotations made in previous rounds. As a side effect, the tracking system minimises resource and time consumption. Marky is a novel environment for managing multi-user and iterative document annotation projects. Compared to other tools, Marky offers a similar visually intuitive annotation experience while providing unique means to minimise annotation effort and enforce annotation quality, and therefore corpus consistency. Marky is freely available for non-commercial use at http://sing.ei.uvigo.es/marky. Copyright © 2014 Elsevier Ireland Ltd. All rights reserved.

ISS Expedition 18 Multi-User Droplet Combustion Apparatus (MDCA) Chamber Insert Assembly (CIA) in Node 2

NASA Image and Video Library

2008-12-06

ISS018-E-010645 (6 Dec. 2008) --- Astronaut Michael Fincke, Expedition 18 commander, works on the Multi-User Droplet Combustion Apparatus (MDCA) Chamber Insert Assembly (CIA) in the Harmony node of the International Space Station.

Performing speech recognition research with hypercard

NASA Technical Reports Server (NTRS)

Shepherd, Chip

1993-01-01

The purpose of this paper is to describe a HyperCard-based system for performing speech recognition research and to instruct Human Factors professionals on how to use the system to obtain detailed data about the user interface of a prototype speech recognition application.

mlCAF: Multi-Level Cross-Domain Semantic Context Fusioning for Behavior Identification.

PubMed

Razzaq, Muhammad Asif; Villalonga, Claudia; Lee, Sungyoung; Akhtar, Usman; Ali, Maqbool; Kim, Eun-Soo; Khattak, Asad Masood; Seung, Hyonwoo; Hur, Taeho; Bang, Jaehun; Kim, Dohyeong; Ali Khan, Wajahat

2017-10-24

The emerging research on automatic identification of user's contexts from the cross-domain environment in ubiquitous and pervasive computing systems has proved to be successful. Monitoring the diversified user's contexts and behaviors can help in controlling lifestyle associated to chronic diseases using context-aware applications. However, availability of cross-domain heterogeneous contexts provides a challenging opportunity for their fusion to obtain abstract information for further analysis. This work demonstrates extension of our previous work from a single domain (i.e., physical activity) to multiple domains (physical activity, nutrition and clinical) for context-awareness. We propose multi-level Context-aware Framework (mlCAF), which fuses the multi-level cross-domain contexts in order to arbitrate richer behavioral contexts. This work explicitly focuses on key challenges linked to multi-level context modeling, reasoning and fusioning based on the mlCAF open-source ontology. More specifically, it addresses the interpretation of contexts from three different domains, their fusioning conforming to richer contextual information. This paper contributes in terms of ontology evolution with additional domains, context definitions, rules and inclusion of semantic queries. For the framework evaluation, multi-level cross-domain contexts collected from 20 users were used to ascertain abstract contexts, which served as basis for behavior modeling and lifestyle identification. The experimental results indicate a context recognition average accuracy of around 92.65% for the collected cross-domain contexts.

Energy Logic (EL): a novel fusion engine of multi-modality multi-agent data/information fusion for intelligent surveillance systems

NASA Astrophysics Data System (ADS)

Rababaah, Haroun; Shirkhodaie, Amir

2009-04-01

The rapidly advancing hardware technology, smart sensors and sensor networks are advancing environment sensing. One major potential of this technology is Large-Scale Surveillance Systems (LS3) especially for, homeland security, battlefield intelligence, facility guarding and other civilian applications. The efficient and effective deployment of LS3 requires addressing number of aspects impacting the scalability of such systems. The scalability factors are related to: computation and memory utilization efficiency, communication bandwidth utilization, network topology (e.g., centralized, ad-hoc, hierarchical or hybrid), network communication protocol and data routing schemes; and local and global data/information fusion scheme for situational awareness. Although, many models have been proposed to address one aspect or another of these issues but, few have addressed the need for a multi-modality multi-agent data/information fusion that has characteristics satisfying the requirements of current and future intelligent sensors and sensor networks. In this paper, we have presented a novel scalable fusion engine for multi-modality multi-agent information fusion for LS3. The new fusion engine is based on a concept we call: Energy Logic. Experimental results of this work as compared to a Fuzzy logic model strongly supported the validity of the new model and inspired future directions for different levels of fusion and different applications.

Activity recognition from minimal distinguishing subsequence mining

NASA Astrophysics Data System (ADS)

Iqbal, Mohammad; Pao, Hsing-Kuo

2017-08-01

Human activity recognition is one of the most important research topics in the era of Internet of Things. To separate different activities given sensory data, we utilize a Minimal Distinguishing Subsequence (MDS) mining approach to efficiently find distinguishing patterns among different activities. We first transform the sensory data into a series of sensor triggering events and operate the MDS mining procedure afterwards. The gap constraints are also considered in the MDS mining. Given the multi-class nature of most activity recognition tasks, we modify the MDS mining approach from a binary case to a multi-class one to fit the need for multiple activity recognition. We also study how to select the best parameter set including the minimal and the maximal support thresholds in finding the MDSs for effective activity recognition. Overall, the prediction accuracy is 86.59% on the van Kasteren dataset which consists of four different activities for recognition.

Fuzzy Logic-Based Audio Pattern Recognition

NASA Astrophysics Data System (ADS)

Malcangi, M.

2008-11-01

Audio and audio-pattern recognition is becoming one of the most important technologies to automatically control embedded systems. Fuzzy logic may be the most important enabling methodology due to its ability to rapidly and economically model such application. An audio and audio-pattern recognition engine based on fuzzy logic has been developed for use in very low-cost and deeply embedded systems to automate human-to-machine and machine-to-machine interaction. This engine consists of simple digital signal-processing algorithms for feature extraction and normalization, and a set of pattern-recognition rules manually tuned or automatically tuned by a self-learning process.

The FORTRAN static source code analyzer program (SAP) user's guide, revision 1

NASA Technical Reports Server (NTRS)

Decker, W.; Taylor, W.; Eslinger, S.

1982-01-01

The FORTRAN Static Source Code Analyzer Program (SAP) User's Guide (Revision 1) is presented. SAP is a software tool designed to assist Software Engineering Laboratory (SEL) personnel in conducting studies of FORTRAN programs. SAP scans FORTRAN source code and produces reports that present statistics and measures of statements and structures that make up a module. This document is a revision of the previous SAP user's guide, Computer Sciences Corporation document CSC/TM-78/6045. SAP Revision 1 is the result of program modifications to provide several new reports, additional complexity analysis, and recognition of all statements described in the FORTRAN 77 standard. This document provides instructions for operating SAP and contains information useful in interpreting SAP output.

Multi-template image matching using alpha-rooted biquaternion phase correlation with application to logo recognition

NASA Astrophysics Data System (ADS)

DelMarco, Stephen

2011-06-01

Hypercomplex approaches are seeing increased application to signal and image processing problems. The use of multicomponent hypercomplex numbers, such as quaternions, enables the simultaneous co-processing of multiple signal or image components. This joint processing capability can provide improved exploitation of the information contained in the data, thereby leading to improved performance in detection and recognition problems. In this paper, we apply hypercomplex processing techniques to the logo image recognition problem. Specifically, we develop an image matcher by generalizing classical phase correlation to the biquaternion case. We further incorporate biquaternion Fourier domain alpha-rooting enhancement to create Alpha-Rooted Biquaternion Phase Correlation (ARBPC). We present the mathematical properties which justify use of ARBPC as an image matcher. We present numerical performance results of a logo verification problem using real-world logo data, demonstrating the performance improvement obtained using the hypercomplex approach. We compare results of the hypercomplex approach to standard multi-template matching approaches.

Impact of multi-focused images on recognition of soft biometric traits

NASA Astrophysics Data System (ADS)

Chiesa, V.; Dugelay, J. L.

2016-09-01

In video surveillance semantic traits estimation as gender and age has always been debated topic because of the uncontrolled environment: while light or pose variations have been largely studied, defocused images are still rarely investigated. Recently the emergence of new technologies, as plenoptic cameras, yields to deal with these problems analyzing multi-focus images. Thanks to a microlens array arranged between the sensor and the main lens, light field cameras are able to record not only the RGB values but also the information related to the direction of light rays: the additional data make possible rendering the image with different focal plane after the acquisition. For our experiments, we use the GUC Light Field Face Database that includes pictures from the First Generation Lytro camera. Taking advantage of light field images, we explore the influence of defocusing on gender recognition and age estimation problems. Evaluations are computed on up-to-date and competitive technologies based on deep learning algorithms. After studying the relationship between focus and gender recognition and focus and age estimation, we compare the results obtained by images defocused by Lytro software with images blurred by more standard filters in order to explore the difference between defocusing and blurring effects. In addition we investigate the impact of deblurring on defocused images with the goal to better understand the different impacts of defocusing and standard blurring on gender and age estimation.

Supporting Multi-view User Ontology to Understand Company Value Chains

NASA Astrophysics Data System (ADS)

Zuo, Landong; Salvadores, Manuel; Imtiaz, Sm Hazzaz; Darlington, John; Gibbins, Nicholas; Shadbolt, Nigel R.; Dobree, James

The objective of the Market Blended Insight (MBI) project is to develop web based techniques to improve the performance of UK Business to Business (B2B) marketing activities. The analysis of company value chains is a fundamental task within MBI because it is an important model for understanding the market place and the company interactions within it. The project has aggregated rich data profiles of 3.7 million companies that form the active UK business community. The profiles are augmented by Web extractions from heterogeneous sources to provide unparalleled business insight. Advances by the Semantic Web in knowledge representation and logic reasoning allow flexible integration of data from heterogeneous sources, transformation between different representations and reasoning about their meaning. The MBI project has identified that the market insight and analysis interests of different types of users are difficult to maintain using a single domain ontology. Therefore, the project has developed a technique to undertake a plurality of analyses of value chains by deploying a distributed multi-view ontology to capture different user views over the classification of companies and their various relationships.

Probabilistic Open Set Recognition

NASA Astrophysics Data System (ADS)

Jain, Lalit Prithviraj

Real-world tasks in computer vision, pattern recognition and machine learning often touch upon the open set recognition problem: multi-class recognition with incomplete knowledge of the world and many unknown inputs. An obvious way to approach such problems is to develop a recognition system that thresholds probabilities to reject unknown classes. Traditional rejection techniques are not about the unknown; they are about the uncertain boundary and rejection around that boundary. Thus traditional techniques only represent the "known unknowns". However, a proper open set recognition algorithm is needed to reduce the risk from the "unknown unknowns". This dissertation examines this concept and finds existing probabilistic multi-class recognition approaches are ineffective for true open set recognition. We hypothesize the cause is due to weak adhoc assumptions combined with closed-world assumptions made by existing calibration techniques. Intuitively, if we could accurately model just the positive data for any known class without overfitting, we could reject the large set of unknown classes even under this assumption of incomplete class knowledge. For this, we formulate the problem as one of modeling positive training data by invoking statistical extreme value theory (EVT) near the decision boundary of positive data with respect to negative data. We provide a new algorithm called the PI-SVM for estimating the unnormalized posterior probability of class inclusion. This dissertation also introduces a new open set recognition model called Compact Abating Probability (CAP), where the probability of class membership decreases in value (abates) as points move from known data toward open space. We show that CAP models improve open set recognition for multiple algorithms. Leveraging the CAP formulation, we go on to describe the novel Weibull-calibrated SVM (W-SVM) algorithm, which combines the useful properties of statistical EVT for score calibration with one-class and binary

Discrimination of geographical origin and detection of adulteration of kudzu root by fluorescence spectroscopy coupled with multi-way pattern recognition

NASA Astrophysics Data System (ADS)

Hu, Leqian; Ma, Shuai; Yin, Chunling

2018-03-01

In this work, fluorescence spectroscopy combined with multi-way pattern recognition techniques were developed for determining the geographical origin of kudzu root and detection and quantification of adulterants in kudzu root. Excitation-emission (EEM) spectra were obtained for 150 pure kudzu root samples of different geographical origins and 150 fake kudzu roots with different adulteration proportions by recording emission from 330 to 570 nm with excitation in the range of 320-480 nm, respectively. Multi-way principal components analysis (M-PCA) and multilinear partial least squares discriminant analysis (N-PLS-DA) methods were used to decompose the excitation-emission matrices datasets. 150 pure kudzu root samples could be differentiated exactly from each other according to their geographical origins by M-PCA and N-PLS-DA models. For the adulteration kudzu root samples, N-PLS-DA got better and more reliable classification result comparing with the M-PCA model. The results obtained in this study indicated that EEM spectroscopy coupling with multi-way pattern recognition could be used as an easy, rapid and novel tool to distinguish the geographical origin of kudzu root and detect adulterated kudzu root. Besides, this method was also suitable for determining the geographic origin and detection the adulteration of the other foodstuffs which can produce fluorescence.

Noise Robust Speech Recognition Applied to Voice-Driven Wheelchair

NASA Astrophysics Data System (ADS)

Sasou, Akira; Kojima, Hiroaki

2009-12-01

Conventional voice-driven wheelchairs usually employ headset microphones that are capable of achieving sufficient recognition accuracy, even in the presence of surrounding noise. However, such interfaces require users to wear sensors such as a headset microphone, which can be an impediment, especially for the hand disabled. Conversely, it is also well known that the speech recognition accuracy drastically degrades when the microphone is placed far from the user. In this paper, we develop a noise robust speech recognition system for a voice-driven wheelchair. This system can achieve almost the same recognition accuracy as the headset microphone without wearing sensors. We verified the effectiveness of our system in experiments in different environments, and confirmed that our system can achieve almost the same recognition accuracy as the headset microphone without wearing sensors.

Single-user MIMO versus multi-user MIMO in distributed antenna systems with limited feedback

NASA Astrophysics Data System (ADS)

Schwarz, Stefan; Heath, Robert W.; Rupp, Markus

2013-12-01

This article investigates the performance of cellular networks employing distributed antennas in addition to the central antennas of the base station. Distributed antennas are likely to be implemented using remote radio units, which is enabled by a low latency and high bandwidth dedicated link to the base station. This facilitates coherent transmission from potentially all available antennas at the same time. Such distributed antenna system (DAS) is an effective way to deal with path loss and large-scale fading in cellular systems. DAS can apply precoding across multiple transmission points to implement single-user MIMO (SU-MIMO) and multi-user MIMO (MU-MIMO) transmission. The throughput performance of various SU-MIMO and MU-MIMO transmission strategies is investigated in this article, employing a Long-Term evolution (LTE) standard compliant simulation framework. The previously theoretically established cell-capacity improvement of MU-MIMO in comparison to SU-MIMO in DASs is confirmed under the practical constraints imposed by the LTE standard, even under the assumption of imperfect channel state information (CSI) at the base station. Because practical systems will use quantized feedback, the performance of different CSI feedback algorithms for DASs is investigated. It is shown that significant gains in the CSI quantization accuracy and in the throughput of especially MU-MIMO systems can be achieved with relatively simple quantization codebook constructions that exploit the available temporal correlation and channel gain differences.

Comparison of two underwater acoustic communications techniques for multi-user access

NASA Astrophysics Data System (ADS)

Hursky, Paul; Siderius, T. Martin; Kauaiex Group

2004-05-01

Frequency hopped frequency shift keying (FHFSK) and code division multiple access (CDMA) are two different modulation techniques for multiple users to communicate with a single receiver simultaneously. In July 2003, these two techniques were tested alongside each other in a shallow water coastal environment off the coast of Kauai. A variety of instruments were used to measure the prevailing oceanography, enabling detailed modeling of the channel. The channel was acoustically probed using LFM waveforms and m-sequences as well. We will present the results of demodulating the FHFSK and CDMA waveforms and discuss modeling the channel for the purpose of predicting multi-user communications performance. a)Michael B. Porter, Paul Hursky, Martin Siderius (SAIC), Mohsen Badiey (UD), Jerald Caruthers (USM), William S. Hodgkiss, Kaustubha Raghukumar (SIO), Dan Rouseff, Warren Fox (APL-UW), Christian de Moustier, Brian Calder, Barbara J. Kraft (UNH), Keyko McDonald (SPAWARSSC), Peter Stein, James K. Lewis, and Subramaniam Rajan (SSI).

Developing Recognition Programs for Units within Student Affairs.

ERIC Educational Resources Information Center

Avery, Cynthia M.

2001-01-01

According to many psychologists, the connections between motivation and rewards and recognition are crucial to employee satisfaction. A plan for developing a multi-layered recognition program within a division of student affairs is described. These recognitions programs are designed taking into account the differences in perceptions of awards by…

Probing binding hot spots at protein-RNA recognition sites.

PubMed

Barik, Amita; Nithin, Chandran; Karampudi, Naga Bhushana Rao; Mukherjee, Sunandan; Bahadur, Ranjit Prasad

2016-01-29

We use evolutionary conservation derived from structure alignment of polypeptide sequences along with structural and physicochemical attributes of protein-RNA interfaces to probe the binding hot spots at protein-RNA recognition sites. We find that the degree of conservation varies across the RNA binding proteins; some evolve rapidly compared to others. Additionally, irrespective of the structural class of the complexes, residues at the RNA binding sites are evolutionary better conserved than those at the solvent exposed surfaces. For recognitions involving duplex RNA, residues interacting with the major groove are better conserved than those interacting with the minor groove. We identify multi-interface residues participating simultaneously in protein-protein and protein-RNA interfaces in complexes where more than one polypeptide is involved in RNA recognition, and show that they are better conserved compared to any other RNA binding residues. We find that the residues at water preservation site are better conserved than those at hydrated or at dehydrated sites. Finally, we develop a Random Forests model using structural and physicochemical attributes for predicting binding hot spots. The model accurately predicts 80% of the instances of experimental ΔΔG values in a particular class, and provides a stepping-stone towards the engineering of protein-RNA recognition sites with desired affinity. © The Author(s) 2015. Published by Oxford University Press on behalf of Nucleic Acids Research.

Robust and Effective Component-based Banknote Recognition for the Blind

PubMed Central

Hasanuzzaman, Faiz M.; Yang, Xiaodong; Tian, YingLi

2012-01-01

We develop a novel camera-based computer vision technology to automatically recognize banknotes for assisting visually impaired people. Our banknote recognition system is robust and effective with the following features: 1) high accuracy: high true recognition rate and low false recognition rate, 2) robustness: handles a variety of currency designs and bills in various conditions, 3) high efficiency: recognizes banknotes quickly, and 4) ease of use: helps blind users to aim the target for image capture. To make the system robust to a variety of conditions including occlusion, rotation, scaling, cluttered background, illumination change, viewpoint variation, and worn or wrinkled bills, we propose a component-based framework by using Speeded Up Robust Features (SURF). Furthermore, we employ the spatial relationship of matched SURF features to detect if there is a bill in the camera view. This process largely alleviates false recognition and can guide the user to correctly aim at the bill to be recognized. The robustness and generalizability of the proposed system is evaluated on a dataset including both positive images (with U.S. banknotes) and negative images (no U.S. banknotes) collected under a variety of conditions. The proposed algorithm, achieves 100% true recognition rate and 0% false recognition rate. Our banknote recognition system is also tested by blind users. PMID:22661884

Human Factors Engineering and testing for a wearable, long duration ultrasound system self-applied by an end user.

PubMed

Taggart, Rebecca; Langer, Matthew D; Lewis, George K

2014-01-01

One of the major challenges in the design of a new class of medical device is ensuring that the device will have a safe and effective user interface for the intended users. Human Factors Engineering addresses these concerns through direct study of how a user interacts with newly designed devices with unique features. In this study, a novel long duration, low intensity therapeutic ultrasound device is tested by 20 end users representative of the intended user population. Over 90% of users were able to operate the device successfully. The therapeutic ultrasound device was found to be reasonably safe and effective for the intended users, uses, and use environments.

Handwritten Word Recognition Using Multi-view Analysis

NASA Astrophysics Data System (ADS)

de Oliveira, J. J.; de A. Freitas, C. O.; de Carvalho, J. M.; Sabourin, R.

This paper brings a contribution to the problem of efficiently recognizing handwritten words from a limited size lexicon. For that, a multiple classifier system has been developed that analyzes the words from three different approximation levels, in order to get a computational approach inspired on the human reading process. For each approximation level a three-module architecture composed of a zoning mechanism (pseudo-segmenter), a feature extractor and a classifier is defined. The proposed application is the recognition of the Portuguese handwritten names of the months, for which a best recognition rate of 97.7% was obtained, using classifier combination.

Supporting Dictation Speech Recognition Error Correction: The Impact of External Information

ERIC Educational Resources Information Center

Shi, Yongmei; Zhou, Lina

2011-01-01

Although speech recognition technology has made remarkable progress, its wide adoption is still restricted by notable effort made and frustration experienced by users while correcting speech recognition errors. One of the promising ways to improve error correction is by providing user support. Although support mechanisms have been proposed for…

Multi-bottle, no compressor, mean pressure control system for a Stirling engine

DOEpatents

Corey, John A.

1990-01-01

The invention relates to an apparatus for mean pressure control of a Stirling engine without the need for a compressor. The invention includes a multi-tank system in which there is at least one high pressure level tank and one low pressure level tank wherein gas flows through a maximum pressure and supply line from the engine to the high pressure tank when a first valve is opened until the maximum pressure of the engine drops below that of the high pressure tank opening an inlet regulator to permit gas flow from the engine to the low pressure tank. When gas flows toward the engine it flows through the minimum pressure supply line 2 when a second valve is opened from the low pressure tank until the tank reaches the engine's minimum pressure level at which time the outlet regulator opens permitting gas to be supplied from the high pressure tank to the engine. Check valves between the two tanks prevent any backflow of gas from occurring.

A Multi-User Virtual Environment for Building and Assessing Higher Order Inquiry Skills in Science

ERIC Educational Resources Information Center

Ketelhut, Diane Jass; Nelson, Brian C.; Clarke, Jody; Dede, Chris

2010-01-01

This study investigated novel pedagogies for helping teachers infuse inquiry into a standards-based science curriculum. Using a multi-user virtual environment (MUVE) as a pedagogical vehicle, teams of middle-school students collaboratively solved problems around disease in a virtual town called River City. The students interacted with "avatars" of…

Material recognition based on thermal cues: Mechanisms and applications

PubMed Central

Ho, Hsin-Ni

2018-01-01

ABSTRACT Some materials feel colder to the touch than others, and we can use this difference in perceived coldness for material recognition. This review focuses on the mechanisms underlying material recognition based on thermal cues. It provides an overview of the physical, perceptual, and cognitive processes involved in material recognition. It also describes engineering domains in which material recognition based on thermal cues have been applied. This includes haptic interfaces that seek to reproduce the sensations associated with contact in virtual environments and tactile sensors aim for automatic material recognition. The review concludes by considering the contributions of this line of research in both science and engineering. PMID:29687043

Material recognition based on thermal cues: Mechanisms and applications.

PubMed

Ho, Hsin-Ni

2018-01-01

Some materials feel colder to the touch than others, and we can use this difference in perceived coldness for material recognition. This review focuses on the mechanisms underlying material recognition based on thermal cues. It provides an overview of the physical, perceptual, and cognitive processes involved in material recognition. It also describes engineering domains in which material recognition based on thermal cues have been applied. This includes haptic interfaces that seek to reproduce the sensations associated with contact in virtual environments and tactile sensors aim for automatic material recognition. The review concludes by considering the contributions of this line of research in both science and engineering.

Pupil dilation during recognition memory: Isolating unexpected recognition from judgment uncertainty.

PubMed

Mill, Ravi D; O'Connor, Akira R; Dobbins, Ian G

2016-09-01

Optimally discriminating familiar from novel stimuli demands a decision-making process informed by prior expectations. Here we demonstrate that pupillary dilation (PD) responses during recognition memory decisions are modulated by expectations, and more specifically, that pupil dilation increases for unexpected compared to expected recognition. Furthermore, multi-level modeling demonstrated that the time course of the dilation during each individual trial contains separable early and late dilation components, with the early amplitude capturing unexpected recognition, and the later trailing slope reflecting general judgment uncertainty or effort. This is the first demonstration that the early dilation response during recognition is dependent upon observer expectations and that separate recognition expectation and judgment uncertainty components are present in the dilation time course of every trial. The findings provide novel insights into adaptive memory-linked orienting mechanisms as well as the general cognitive underpinnings of the pupillary index of autonomic nervous system activity. Copyright © 2016 Elsevier B.V. All rights reserved.

Design and Implementation of a 3D Multi-User Virtual World for Language Learning

ERIC Educational Resources Information Center

Ibanez, Maria Blanca; Garcia, Jose Jesus; Galan, Sergio; Maroto, David; Morillo, Diego; Kloos, Carlos Delgado

2011-01-01

The best way to learn is by having a good teacher and the best language learning takes place when the learner is immersed in an environment where the language is natively spoken. 3D multi-user virtual worlds have been claimed to be useful for learning, and the field of exploiting them for education is becoming more and more active thanks to the…

Generic Learning-Based Ensemble Framework for Small Sample Size Face Recognition in Multi-Camera Networks.

PubMed

Zhang, Cuicui; Liang, Xuefeng; Matsuyama, Takashi

2014-12-08

Multi-camera networks have gained great interest in video-based surveillance systems for security monitoring, access control, etc. Person re-identification is an essential and challenging task in multi-camera networks, which aims to determine if a given individual has already appeared over the camera network. Individual recognition often uses faces as a trial and requires a large number of samples during the training phrase. This is difficult to fulfill due to the limitation of the camera hardware system and the unconstrained image capturing conditions. Conventional face recognition algorithms often encounter the "small sample size" (SSS) problem arising from the small number of training samples compared to the high dimensionality of the sample space. To overcome this problem, interest in the combination of multiple base classifiers has sparked research efforts in ensemble methods. However, existing ensemble methods still open two questions: (1) how to define diverse base classifiers from the small data; (2) how to avoid the diversity/accuracy dilemma occurring during ensemble. To address these problems, this paper proposes a novel generic learning-based ensemble framework, which augments the small data by generating new samples based on a generic distribution and introduces a tailored 0-1 knapsack algorithm to alleviate the diversity/accuracy dilemma. More diverse base classifiers can be generated from the expanded face space, and more appropriate base classifiers are selected for ensemble. Extensive experimental results on four benchmarks demonstrate the higher ability of our system to cope with the SSS problem compared to the state-of-the-art system.

Generic Learning-Based Ensemble Framework for Small Sample Size Face Recognition in Multi-Camera Networks

PubMed Central

Zhang, Cuicui; Liang, Xuefeng; Matsuyama, Takashi

2014-01-01

Multi-camera networks have gained great interest in video-based surveillance systems for security monitoring, access control, etc. Person re-identification is an essential and challenging task in multi-camera networks, which aims to determine if a given individual has already appeared over the camera network. Individual recognition often uses faces as a trial and requires a large number of samples during the training phrase. This is difficult to fulfill due to the limitation of the camera hardware system and the unconstrained image capturing conditions. Conventional face recognition algorithms often encounter the “small sample size” (SSS) problem arising from the small number of training samples compared to the high dimensionality of the sample space. To overcome this problem, interest in the combination of multiple base classifiers has sparked research efforts in ensemble methods. However, existing ensemble methods still open two questions: (1) how to define diverse base classifiers from the small data; (2) how to avoid the diversity/accuracy dilemma occurring during ensemble. To address these problems, this paper proposes a novel generic learning-based ensemble framework, which augments the small data by generating new samples based on a generic distribution and introduces a tailored 0–1 knapsack algorithm to alleviate the diversity/accuracy dilemma. More diverse base classifiers can be generated from the expanded face space, and more appropriate base classifiers are selected for ensemble. Extensive experimental results on four benchmarks demonstrate the higher ability of our system to cope with the SSS problem compared to the state-of-the-art system. PMID:25494350

Automated recognition and extraction of tabular fields for the indexing of census records

NASA Astrophysics Data System (ADS)

Clawson, Robert; Bauer, Kevin; Chidester, Glen; Pohontsch, Milan; Kennard, Douglas; Ryu, Jongha; Barrett, William

2013-01-01

We describe a system for indexing of census records in tabular documents with the goal of recognizing the content of each cell, including both headers and handwritten entries. Each document is automatically rectified, registered and scaled to a known template following which lines and fields are detected and delimited as cells in a tabular form. Whole-word or whole-phrase recognition of noisy machine-printed text is performed using a glyph library, providing greatly increased efficiency and accuracy (approaching 100%), while avoiding the problems inherent with traditional OCR approaches. Constrained handwriting recognition results for a single author reach as high as 98% and 94.5% for the Gender field and Birthplace respectively. Multi-author accuracy (currently 82%) can be improved through an increased training set. Active integration of user feedback in the system will accelerate the indexing of records while providing a tightly coupled learning mechanism for system improvement.

Development and implementation of an integrated, multi-modality, user-centered interactive dietary change program

PubMed Central

Glasgow, Russell E.; Christiansen, Steve; Smith, K. Sabina; Stevens, Victor J.; Toobert, Deborah J.

2009-01-01

Computer-tailored behavior change programs offer the potential for reaching large populations at a much lower cost than individual or group-based programs. However, few of these programs to date appear to integrate behavioral theory with user choice, or combine different electronic modalities. We describe the development of an integrated CD-ROM and interactive voice response dietary change intervention that combines behavioral problem-solving theory with a high degree of user choice. The program, WISE CHOICES, is being evaluated as part of an ongoing trial. This paper describes the program development, emphasizing how user preferences are accommodated, and presents implementation and user satisfaction data. The program was successfully implemented; the linkages among the central database, the CD-ROM and the automated telephone components were robust, and participants liked the program almost as well as a counselor-delivered dietary change condition. Multi-modality programs that emphasize the strengths of each approach appear to be feasible. Future research is needed to determine the program impact and cost-effectiveness compared with counselor-delivered intervention. PMID:18711204

Hybrid Augmented Reality for Participatory Learning: The Hidden Efficacy of Multi-User Game-Based Simulation

ERIC Educational Resources Information Center

Oh, Seungjae; So, Hyo-Jeong; Gaydos, Matthew

2018-01-01

The goal for this research is to articulate and test a new hybrid Augmented Reality (AR) environment for conceptual understanding. From the theoretical lens of embodied interaction, we have designed a multi-user participatory simulation called ARfract where visitors in a science museum can learn about complex scientific concepts on the refraction…

Robots, multi-user virtual environments and healthcare: synergies for future directions.

PubMed

Moon, Ajung; Grajales, Francisco J; Van der Loos, H F Machiel

2011-01-01

The adoption of technology in healthcare over the last twenty years has steadily increased, particularly as it relates to medical robotics and Multi-User Virtual Environments (MUVEs) such as Second Life. Both disciplines have been shown to improve the quality of care and have evolved, for the most part, in isolation from each other. In this paper, we present four synergies between medical robotics and MUVEs that have the potential to decrease resource utilization and improve the quality of healthcare delivery. We conclude with some foreseeable barriers and future research directions for researchers in these fields.

A DFT-Based Method of Feature Extraction for Palmprint Recognition

NASA Astrophysics Data System (ADS)

Choge, H. Kipsang; Karungaru, Stephen G.; Tsuge, Satoru; Fukumi, Minoru

Over the last quarter century, research in biometric systems has developed at a breathtaking pace and what started with the focus on the fingerprint has now expanded to include face, voice, iris, and behavioral characteristics such as gait. Palmprint is one of the most recent additions, and is currently the subject of great research interest due to its inherent uniqueness, stability, user-friendliness and ease of acquisition. This paper describes an effective and procedurally simple method of palmprint feature extraction specifically for palmprint recognition, although verification experiments are also conducted. This method takes advantage of the correspondences that exist between prominent palmprint features or objects in the spatial domain with those in the frequency or Fourier domain. Multi-dimensional feature vectors are formed by extracting a GA-optimized set of points from the 2-D Fourier spectrum of the palmprint images. The feature vectors are then used for palmprint recognition, before and after dimensionality reduction via the Karhunen-Loeve Transform (KLT). Experiments performed using palmprint images from the ‘PolyU Palmprint Database’ indicate that using a compact set of DFT coefficients, combined with KLT and data preprocessing, produces a recognition accuracy of more than 98% and can provide a fast and effective technique for personal identification.

A sensor and video based ontology for activity recognition in smart environments.

PubMed

Mitchell, D; Morrow, Philip J; Nugent, Chris D

2014-01-01

Activity recognition is used in a wide range of applications including healthcare and security. In a smart environment activity recognition can be used to monitor and support the activities of a user. There have been a range of methods used in activity recognition including sensor-based approaches, vision-based approaches and ontological approaches. This paper presents a novel approach to activity recognition in a smart home environment which combines sensor and video data through an ontological framework. The ontology describes the relationships and interactions between activities, the user, objects, sensors and video data.

Action Recognition Using 3D Histograms of Texture and A Multi-Class Boosting Classifier.

PubMed

Zhang, Baochang; Yang, Yun; Chen, Chen; Yang, Linlin; Han, Jungong; Shao, Ling

2017-10-01

Human action recognition is an important yet challenging task. This paper presents a low-cost descriptor called 3D histograms of texture (3DHoTs) to extract discriminant features from a sequence of depth maps. 3DHoTs are derived from projecting depth frames onto three orthogonal Cartesian planes, i.e., the frontal, side, and top planes, and thus compactly characterize the salient information of a specific action, on which texture features are calculated to represent the action. Besides this fast feature descriptor, a new multi-class boosting classifier (MBC) is also proposed to efficiently exploit different kinds of features in a unified framework for action classification. Compared with the existing boosting frameworks, we add a new multi-class constraint into the objective function, which helps to maintain a better margin distribution by maximizing the mean of margin, whereas still minimizing the variance of margin. Experiments on the MSRAction3D, MSRGesture3D, MSRActivity3D, and UTD-MHAD data sets demonstrate that the proposed system combining 3DHoTs and MBC is superior to the state of the art.

Vibration detecting apparatus for multi-rotor rotary piston engines

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

Fujimoto, Y.; Sasaki, H.; Karimata, Y.

1987-04-28

A multi-rotor rotary piston engine is described comprising rotor housings each having an inner wall surface of a two-lobe trochoidal form, an intermediate housing having opposite side surfaces and located between two adjacent ones of the rotor housings, a pair of side housings attached to outer surfaces of outermost rotor housings, rotors which are disposed in respective ones of the rotor housings and carried by an eccentric shaft, adjacent rotors each having a side surface adapted for sliding engagement with the side surfaces of the intermediate housing, vibration detecting means mounted on the intermediate housing for detecting engine vibrations, themore » vibration detecting means being oriented so that it has a sensitivity in the direction substantially perpendicular to the side surface of the intermediate housing.« less

Resource Quantity and Quality Determine the Inter-Specific Associations between Ecosystem Engineers and Resource Users in a Cavity-Nest Web

PubMed Central

Robles, Hugo; Martin, Kathy

2013-01-01

While ecosystem engineering is a widespread structural force of ecological communities, the mechanisms underlying the inter-specific associations between ecosystem engineers and resource users are poorly understood. A proper knowledge of these mechanisms is, however, essential to understand how communities are structured. Previous studies suggest that increasing the quantity of resources provided by ecosystem engineers enhances populations of resource users. In a long-term study (1995-2011), we show that the quality of the resources (i.e. tree cavities) provided by ecosystem engineers is also a key feature that explains the inter-specific associations in a tree cavity-nest web. Red-naped sapsuckers ( Sphyrapicus nuchalis ) provided the most abundant cavities (52% of cavities, 0.49 cavities/ha). These cavities were less likely to be used than other cavity types by mountain bluebirds ( Sialia currucoides ), but provided numerous nest-sites (41% of nesting cavities) to tree swallows ( Tachycineta bicolour ). Swallows experienced low reproductive outputs in northern flicker ( Colaptes auratus ) cavities compared to those in sapsucker cavities (1.1 vs. 2.1 fledglings/nest), but the highly abundant flickers (33% of cavities, 0.25 cavities/ha) provided numerous suitable nest-sites for bluebirds (58%). The relative shortage of cavities supplied by hairy woodpeckers ( Picoides villosus ) and fungal/insect decay (<10% of cavities each, <0.09 cavities/ha) provided fewer breeding opportunities (<15% of nests), but represented high quality nest-sites for both bluebirds and swallows. Because both the quantity and quality of resources supplied by different ecosystem engineers may explain the amount of resources used by each resource user, conservation strategies may require different management actions to be implemented for the key ecosystem engineer of each resource user. We, therefore, urge the incorporation of both resource quantity and quality into models that assess

Resource quantity and quality determine the inter-specific associations between ecosystem engineers and resource users in a cavity-nest web.

PubMed

Robles, Hugo; Martin, Kathy

2013-01-01

While ecosystem engineering is a widespread structural force of ecological communities, the mechanisms underlying the inter-specific associations between ecosystem engineers and resource users are poorly understood. A proper knowledge of these mechanisms is, however, essential to understand how communities are structured. Previous studies suggest that increasing the quantity of resources provided by ecosystem engineers enhances populations of resource users. In a long-term study (1995-2011), we show that the quality of the resources (i.e. tree cavities) provided by ecosystem engineers is also a key feature that explains the inter-specific associations in a tree cavity-nest web. Red-naped sapsuckers (Sphyrapicusnuchalis) provided the most abundant cavities (52% of cavities, 0.49 cavities/ha). These cavities were less likely to be used than other cavity types by mountain bluebirds (Sialiacurrucoides), but provided numerous nest-sites (41% of nesting cavities) to tree swallows (Tachycinetabicolour). Swallows experienced low reproductive outputs in northern flicker (Colaptesauratus) cavities compared to those in sapsucker cavities (1.1 vs. 2.1 fledglings/nest), but the highly abundant flickers (33% of cavities, 0.25 cavities/ha) provided numerous suitable nest-sites for bluebirds (58%). The relative shortage of cavities supplied by hairy woodpeckers (Picoidesvillosus) and fungal/insect decay (<10% of cavities each, <0.09 cavities/ha) provided fewer breeding opportunities (<15% of nests), but represented high quality nest-sites for both bluebirds and swallows. Because both the quantity and quality of resources supplied by different ecosystem engineers may explain the amount of resources used by each resource user, conservation strategies may require different management actions to be implemented for the key ecosystem engineer of each resource user. We, therefore, urge the incorporation of both resource quantity and quality into models that assess community dynamics to

The Pandora multi-algorithm approach to automated pattern recognition of cosmic-ray muon and neutrino events in the MicroBooNE detector

NASA Astrophysics Data System (ADS)

Acciarri, R.; Adams, C.; An, R.; Anthony, J.; Asaadi, J.; Auger, M.; Bagby, L.; Balasubramanian, S.; Baller, B.; Barnes, C.; Barr, G.; Bass, M.; Bay, F.; Bishai, M.; Blake, A.; Bolton, T.; Camilleri, L.; Caratelli, D.; Carls, B.; Castillo Fernandez, R.; Cavanna, F.; Chen, H.; Church, E.; Cianci, D.; Cohen, E.; Collin, G. H.; Conrad, J. M.; Convery, M.; Crespo-Anadón, J. I.; Del Tutto, M.; Devitt, D.; Dytman, S.; Eberly, B.; Ereditato, A.; Escudero Sanchez, L.; Esquivel, J.; Fadeeva, A. A.; Fleming, B. T.; Foreman, W.; Furmanski, A. P.; Garcia-Gamez, D.; Garvey, G. T.; Genty, V.; Goeldi, D.; Gollapinni, S.; Graf, N.; Gramellini, E.; Greenlee, H.; Grosso, R.; Guenette, R.; Hackenburg, A.; Hamilton, P.; Hen, O.; Hewes, J.; Hill, C.; Ho, J.; Horton-Smith, G.; Hourlier, A.; Huang, E.-C.; James, C.; Jan de Vries, J.; Jen, C.-M.; Jiang, L.; Johnson, R. A.; Joshi, J.; Jostlein, H.; Kaleko, D.; Karagiorgi, G.; Ketchum, W.; Kirby, B.; Kirby, M.; Kobilarcik, T.; Kreslo, I.; Laube, A.; Li, Y.; Lister, A.; Littlejohn, B. R.; Lockwitz, S.; Lorca, D.; Louis, W. C.; Luethi, M.; Lundberg, B.; Luo, X.; Marchionni, A.; Mariani, C.; Marshall, J.; Martinez Caicedo, D. A.; Meddage, V.; Miceli, T.; Mills, G. B.; Moon, J.; Mooney, M.; Moore, C. D.; Mousseau, J.; Murrells, R.; Naples, D.; Nienaber, P.; Nowak, J.; Palamara, O.; Paolone, V.; Papavassiliou, V.; Pate, S. F.; Pavlovic, Z.; Piasetzky, E.; Porzio, D.; Pulliam, G.; Qian, X.; Raaf, J. L.; Rafique, A.; Rochester, L.; Rudolf von Rohr, C.; Russell, B.; Schmitz, D. W.; Schukraft, A.; Seligman, W.; Shaevitz, M. H.; Sinclair, J.; Smith, A.; Snider, E. L.; Soderberg, M.; Söldner-Rembold, S.; Soleti, S. R.; Spentzouris, P.; Spitz, J.; St. John, J.; Strauss, T.; Szelc, A. M.; Tagg, N.; Terao, K.; Thomson, M.; Toups, M.; Tsai, Y.-T.; Tufanli, S.; Usher, T.; Van De Pontseele, W.; Van de Water, R. G.; Viren, B.; Weber, M.; Wickremasinghe, D. A.; Wolbers, S.; Wongjirad, T.; Woodruff, K.; Yang, T.; Yates, L.; Zeller, G. P.; Zennamo, J.; Zhang, C.

2018-01-01

The development and operation of liquid-argon time-projection chambers for neutrino physics has created a need for new approaches to pattern recognition in order to fully exploit the imaging capabilities offered by this technology. Whereas the human brain can excel at identifying features in the recorded events, it is a significant challenge to develop an automated, algorithmic solution. The Pandora Software Development Kit provides functionality to aid the design and implementation of pattern-recognition algorithms. It promotes the use of a multi-algorithm approach to pattern recognition, in which individual algorithms each address a specific task in a particular topology. Many tens of algorithms then carefully build up a picture of the event and, together, provide a robust automated pattern-recognition solution. This paper describes details of the chain of over one hundred Pandora algorithms and tools used to reconstruct cosmic-ray muon and neutrino events in the MicroBooNE detector. Metrics that assess the current pattern-recognition performance are presented for simulated MicroBooNE events, using a selection of final-state event topologies.

The Pandora multi-algorithm approach to automated pattern recognition of cosmic-ray muon and neutrino events in the MicroBooNE detector

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

Acciarri, R.; Adams, C.; An, R.

The development and operation of Liquid-Argon Time-Projection Chambers for neutrino physics has created a need for new approaches to pattern recognition in order to fully exploit the imaging capabilities offered by this technology. Whereas the human brain can excel at identifying features in the recorded events, it is a significant challenge to develop an automated, algorithmic solution. The Pandora Software Development Kit provides functionality to aid the design and implementation of pattern-recognition algorithms. It promotes the use of a multi-algorithm approach to pattern recognition, in which individual algorithms each address a specific task in a particular topology. Many tens ofmore » algorithms then carefully build up a picture of the event and, together, provide a robust automated pattern-recognition solution. This paper describes details of the chain of over one hundred Pandora algorithms and tools used to reconstruct cosmic-ray muon and neutrino events in the MicroBooNE detector. Metrics that assess the current pattern-recognition performance are presented for simulated MicroBooNE events, using a selection of final-state event topologies.« less

The Pandora multi-algorithm approach to automated pattern recognition of cosmic-ray muon and neutrino events in the MicroBooNE detector

DOE PAGES

Acciarri, R.; Adams, C.; An, R.; ...

2018-01-29

The development and operation of Liquid-Argon Time-Projection Chambers for neutrino physics has created a need for new approaches to pattern recognition in order to fully exploit the imaging capabilities offered by this technology. Whereas the human brain can excel at identifying features in the recorded events, it is a significant challenge to develop an automated, algorithmic solution. The Pandora Software Development Kit provides functionality to aid the design and implementation of pattern-recognition algorithms. It promotes the use of a multi-algorithm approach to pattern recognition, in which individual algorithms each address a specific task in a particular topology. Many tens ofmore » algorithms then carefully build up a picture of the event and, together, provide a robust automated pattern-recognition solution. This paper describes details of the chain of over one hundred Pandora algorithms and tools used to reconstruct cosmic-ray muon and neutrino events in the MicroBooNE detector. Metrics that assess the current pattern-recognition performance are presented for simulated MicroBooNE events, using a selection of final-state event topologies.« less

Integrating care for frequent users of emergency departments: implementation evaluation of a brief multi-organizational intensive case management intervention.

PubMed

Kahan, Deborah; Leszcz, Molyn; O'Campo, Patricia; Hwang, Stephen W; Wasylenki, Donald A; Kurdyak, Paul; Wise Harris, Deborah; Gozdzik, Agnes; Stergiopoulos, Vicky

2016-04-27

Addressing the needs of frequent users of emergency departments (EDs) is a health system priority in many jurisdictions. This study describes stakeholder perspectives on the implementation of a multi-organizational brief intervention designed to support integration and continuity of care for frequent ED users with mental health and addictions problems, focusing on perceived barriers and facilitators to early implementation in a large urban centre. Coordinating Access to Care from Hospital Emergency Departments (CATCH-ED) is a brief case management intervention bridging hospital, primary and community care for frequent ED users experiencing mental illness and addictions. To examine barriers and facilitators to early implementation of this multi-organizational intervention, between July and October 2012, 47 stakeholders, including direct service providers, managers and administrators participated in 32 semi-structured qualitative interviews and one focus group exploring their experience with the intervention and factors that helped or hindered successful early implementation. Qualitative data were analyzed using thematic analysis. Stakeholders valued the intervention and its potential to support continuity of care for this population. Service delivery system factors, including organizational capacity and a history of collaborative relationships across the healthcare continuum, and support system factors, such as training and supervision, emerged as key facilitators of program implementation. Operational challenges included early low program referral rates, management of a multi-organizational initiative, variable adherence to the model among participating organizations, and scant access to specialty psychiatric resources. Factors contributing to these challenges included lack of dedicated staff in the ED and limited local system capacity to support this population, and insufficient training and technical assistance available to participating organizations. A multi

Multi-User Domain Object Oriented (MOO) as a High School Procedure for Foreign Language Acquisition.

ERIC Educational Resources Information Center

Backer, James A.

Foreign language students experience added difficulty when they are isolated from native speakers and from the culture of the target language. It has been posited that MOO (Multi-User Domain Object Oriented) may help overcome the geographical isolation of these students. MOOs are Internet-based virtual worlds in which people from all over the real…

Continuous Speech Recognition for Clinicians

PubMed Central

Zafar, Atif; Overhage, J. Marc; McDonald, Clement J.

1999-01-01

The current generation of continuous speech recognition systems claims to offer high accuracy (greater than 95 percent) speech recognition at natural speech rates (150 words per minute) on low-cost (under $2000) platforms. This paper presents a state-of-the-technology summary, along with insights the authors have gained through testing one such product extensively and other products superficially. The authors have identified a number of issues that are important in managing accuracy and usability. First, for efficient recognition users must start with a dictionary containing the phonetic spellings of all words they anticipate using. The authors dictated 50 discharge summaries using one inexpensive internal medicine dictionary ($30) and found that they needed to add an additional 400 terms to get recognition rates of 98 percent. However, if they used either of two more expensive and extensive commercial medical vocabularies ($349 and $695), they did not need to add terms to get a 98 percent recognition rate. Second, users must speak clearly and continuously, distinctly pronouncing all syllables. Users must also correct errors as they occur, because accuracy improves with error correction by at least 5 percent over two weeks. Users may find it difficult to train the system to recognize certain terms, regardless of the amount of training, and appropriate substitutions must be created. For example, the authors had to substitute “twice a day” for “bid” when using the less expensive dictionary, but not when using the other two dictionaries. From trials they conducted in settings ranging from an emergency room to hospital wards and clinicians' offices, they learned that ambient noise has minimal effect. Finally, they found that a minimal “usable” hardware configuration (which keeps up with dictation) comprises a 300-MHz Pentium processor with 128 MB of RAM and a “speech quality” sound card (e.g., SoundBlaster, $99). Anything less powerful will result in

Understanding user needs for carbon monitoring information

NASA Astrophysics Data System (ADS)

Duren, R. M.; Macauley, M.; Gurney, K. R.; Saatchi, S. S.; Woodall, C. W.; Larsen, K.; Reidmiller, D.; Hockstad, L.; Weitz, M.; Croes, B.; Down, A.; West, T.; Mercury, M.

2015-12-01

The objectives of the Understanding User Needs project for NASA's Carbon Monitoring System (CMS) program are to: 1) engage the user community and identify needs for policy-relevant carbon monitoring information, 2) evaluate current and planned CMS data products with regard to their value for decision making, and 3) explore alternative methods for visualizing and communicating carbon monitoring information and associated uncertainties to decision makers and other stakeholders. To meet these objectives and help establish a sustained link between science and decision-making we have established a multi-disciplinary team that combines expertise in carbon-cycle science, engineering, economics, and carbon management and policy. We will present preliminary findings regarding emerging themes and needs for carbon information that may warrant increased attention by the science community. We will also demonstrate a new web-based tool that offers a common framework for facilitating user evaluation of carbon data products from multiple CMS projects.

Cross-modal face recognition using multi-matcher face scores

NASA Astrophysics Data System (ADS)

Zheng, Yufeng; Blasch, Erik

2015-05-01

The performance of face recognition can be improved using information fusion of multimodal images and/or multiple algorithms. When multimodal face images are available, cross-modal recognition is meaningful for security and surveillance applications. For example, a probe face is a thermal image (especially at nighttime), while only visible face images are available in the gallery database. Matching a thermal probe face onto the visible gallery faces requires crossmodal matching approaches. A few such studies were implemented in facial feature space with medium recognition performance. In this paper, we propose a cross-modal recognition approach, where multimodal faces are cross-matched in feature space and the recognition performance is enhanced with stereo fusion at image, feature and/or score level. In the proposed scenario, there are two cameras for stereo imaging, two face imagers (visible and thermal images) in each camera, and three recognition algorithms (circular Gaussian filter, face pattern byte, linear discriminant analysis). A score vector is formed with three cross-matched face scores from the aforementioned three algorithms. A classifier (e.g., k-nearest neighbor, support vector machine, binomial logical regression [BLR]) is trained then tested with the score vectors by using 10-fold cross validations. The proposed approach was validated with a multispectral stereo face dataset from 105 subjects. Our experiments show very promising results: ACR (accuracy rate) = 97.84%, FAR (false accept rate) = 0.84% when cross-matching the fused thermal faces onto the fused visible faces by using three face scores and the BLR classifier.

Multi-Scale/Multi-Functional Probabilistic Composite Fatigue

NASA Technical Reports Server (NTRS)

Chamis, Christos C.

2008-01-01

A multi-level (multi-scale/multi-functional) evaluation is demonstrated by applying it to three different sample problems. These problems include the probabilistic evaluation of a space shuttle main engine blade, an engine rotor and an aircraft wing. The results demonstrate that the blade will fail at the highest probability path, the engine two-stage rotor will fail by fracture at the rim and the aircraft wing will fail at 109 fatigue cycles with a probability of 0.9967.

Towards a Usability and Error "Safety Net": A Multi-Phased Multi-Method Approach to Ensuring System Usability and Safety.

PubMed

Kushniruk, Andre; Senathirajah, Yalini; Borycki, Elizabeth

2017-01-01

The usability and safety of health information systems have become major issues in the design and implementation of useful healthcare IT. In this paper we describe a multi-phased multi-method approach to integrating usability engineering methods into system testing to ensure both usability and safety of healthcare IT upon widespread deployment. The approach involves usability testing followed by clinical simulation (conducted in-situ) and "near-live" recording of user interactions with systems. At key stages in this process, usability problems are identified and rectified forming a usability and technology-induced error "safety net" that catches different types of usability and safety problems prior to releasing systems widely in healthcare settings.

Exploring the Integration of Technology into Jewish Education: Multi-User Virtual Environments and Supplementary School Settings

ERIC Educational Resources Information Center

Sohn, Johannah Eve

2014-01-01

This descriptive case study explores the implementation of a multi-user virtual environment (MUVE) in a Jewish supplemental school setting. The research was conducted to present the recollections and reflections of three constituent populations of a new technology exploring constructivist education in the context of supplemental and online…

From user needs to system specifications: multi-disciplinary thematic seminars as a collaborative design method for development of health information systems.

PubMed

Scandurra, I; Hägglund, M; Koch, S

2008-08-01

This paper presents a new multi-disciplinary method for user needs analysis and requirements specification in the context of health information systems based on established theories from the fields of participatory design and computer supported cooperative work (CSCW). Whereas conventional methods imply a separate, sequential needs analysis for each profession, the "multi-disciplinary thematic seminar" (MdTS) method uses a collaborative design process. Application of the method in elderly homecare resulted in prototypes that were well adapted to the intended user groups. Vital information in the points of intersection between different care professions was elicited and a holistic view of the entire care process was obtained. Health informatics-usability specialists and clinical domain experts are necessary to apply the method. Although user needs acquisition can be time-consuming, MdTS was perceived to efficiently identify in-context user needs, and transformed these directly into requirements specifications. Consequently the method was perceived to expedite the entire ICT implementation process.

Orbital Maneuvering Engine Feed System Coupled Stability Investigation, Computer User's Manual

NASA Technical Reports Server (NTRS)

Schuman, M. D.; Fertig, K. W.; Hunting, J. K.; Kahn, D. R.

1975-01-01

An operating manual for the feed system coupled stability model was given, in partial fulfillment of a program designed to develop, verify, and document a digital computer model that can be used to analyze and predict engine/feed system coupled instabilities in pressure-fed storable propellant propulsion systems over a frequency range of 10 to 1,000 Hz. The first section describes the analytical approach to modelling the feed system hydrodynamics, combustion dynamics, chamber dynamics, and overall engineering model structure, and presents the governing equations in each of the technical areas. This is followed by the program user's guide, which is a complete description of the structure and operation of the computerized model. Last, appendices provide an alphabetized FORTRAN symbol table, detailed program logic diagrams, computer code listings, and sample case input and output data listings.

A power-efficient ZF precoding scheme for multi-user indoor visible light communication systems

NASA Astrophysics Data System (ADS)

Zhao, Qiong; Fan, Yangyu; Deng, Lijun; Kang, Bochao

2017-02-01

In this study, we propose a power-efficient ZF precoding scheme for visible light communication (VLC) downlink multi-user multiple-input-single-output (MU-MISO) systems, which incorporates the zero-forcing (ZF) and the characteristics of VLC systems. The main idea of this scheme is that the channel matrix used to perform pseudoinverse comes from the set of optical Access Points (APs) shared by more than one user, instead of the set of all involved serving APs as the existing ZF precoding schemes often used. By doing this, the waste of power, which is caused by the transmission of one user's data in the un-serving APs, can be avoided. In addition, the size of the channel matrix needs to perform pseudoinverse becomes smaller, which helps to reduce the computation complexity. Simulation results in two scenarios show that the proposed ZF precoding scheme has higher power efficiency, better bit error rate (BER) performance and lower computation complexity compared with traditional ZF precoding schemes.

Relating hearing loss and executive functions to hearing aid users' preference for, and speech recognition with, different combinations of binaural noise reduction and microphone directionality

PubMed Central

Neher, Tobias

2014-01-01

Knowledge of how executive functions relate to preferred hearing aid (HA) processing is sparse and seemingly inconsistent with related knowledge for speech recognition outcomes. This study thus aimed to find out if (1) performance on a measure of reading span (RS) is related to preferred binaural noise reduction (NR) strength, (2) similar relations exist for two different, non-verbal measures of executive function, (3) pure-tone average hearing loss (PTA), signal-to-noise ratio (SNR), and microphone directionality (DIR) also influence preferred NR strength, and (4) preference and speech recognition outcomes are similar. Sixty elderly HA users took part. Six HA conditions consisting of omnidirectional or cardioid microphones followed by inactive, moderate, or strong binaural NR as well as linear amplification were tested. Outcome was assessed at fixed SNRs using headphone simulations of a frontal target talker in a busy cafeteria. Analyses showed positive effects of active NR and DIR on preference, and negative and positive effects of, respectively, strong NR and DIR on speech recognition. Also, while moderate NR was the most preferred NR setting overall, preference for strong NR increased with SNR. No relation between RS and preference was found. However, larger PTA was related to weaker preference for inactive NR and stronger preference for strong NR for both microphone modes. Equivalent (but weaker) relations between worse performance on one non-verbal measure of executive function and the HA conditions without DIR were found. For speech recognition, there were relations between HA condition, PTA, and RS, but their pattern differed from that for preference. Altogether, these results indicate that, while moderate NR works well in general, a notable proportion of HA users prefer stronger NR. Furthermore, PTA and executive functions can account for some of the variability in preference for, and speech recognition with, different binaural NR and DIR settings. PMID

Exhibits Recognition System for Combining Online Services and Offline Services

NASA Astrophysics Data System (ADS)

Ma, He; Liu, Jianbo; Zhang, Yuan; Wu, Xiaoyu

2017-10-01

In order to achieve a more convenient and accurate digital museum navigation, we have developed a real-time and online-to-offline museum exhibits recognition system using image recognition method based on deep learning. In this paper, the client and server of the system are separated and connected through the HTTP. Firstly, by using the client app in the Android mobile phone, the user can take pictures and upload them to the server. Secondly, the features of the picture are extracted using the deep learning network in the server. With the help of the features, the pictures user uploaded are classified with a well-trained SVM. Finally, the classification results are sent to the client and the detailed exhibition’s introduction corresponding to the classification results are shown in the client app. Experimental results demonstrate that the recognition accuracy is close to 100% and the computing time from the image uploading to the exhibit information show is less than 1S. By means of exhibition image recognition algorithm, our implemented exhibits recognition system can combine online detailed exhibition information to the user in the offline exhibition hall so as to achieve better digital navigation.

Multi-factor authentication using quantum communication

DOEpatents

Hughes, Richard John; Peterson, Charles Glen; Thrasher, James T.; Nordholt, Jane E.; Yard, Jon T.; Newell, Raymond Thorson; Somma, Rolando D.

2018-02-06

Multi-factor authentication using quantum communication ("QC") includes stages for enrollment and identification. For example, a user enrolls for multi-factor authentication that uses QC with a trusted authority. The trusted authority transmits device factor information associated with a user device (such as a hash function) and user factor information associated with the user (such as an encrypted version of a user password). The user device receives and stores the device factor information and user factor information. For multi-factor authentication that uses QC, the user device retrieves its stored device factor information and user factor information, then transmits the user factor information to the trusted authority, which also retrieves its stored device factor information. The user device and trusted authority use the device factor information and user factor information (more specifically, information such as a user password that is the basis of the user factor information) in multi-factor authentication that uses QC.

A Lightweight Hierarchical Activity Recognition Framework Using Smartphone Sensors

PubMed Central

Han, Manhyung; Bang, Jae Hun; Nugent, Chris; McClean, Sally; Lee, Sungyoung

2014-01-01

Activity recognition for the purposes of recognizing a user's intentions using multimodal sensors is becoming a widely researched topic largely based on the prevalence of the smartphone. Previous studies have reported the difficulty in recognizing life-logs by only using a smartphone due to the challenges with activity modeling and real-time recognition. In addition, recognizing life-logs is difficult due to the absence of an established framework which enables the use of different sources of sensor data. In this paper, we propose a smartphone-based Hierarchical Activity Recognition Framework which extends the Naïve Bayes approach for the processing of activity modeling and real-time activity recognition. The proposed algorithm demonstrates higher accuracy than the Naïve Bayes approach and also enables the recognition of a user's activities within a mobile environment. The proposed algorithm has the ability to classify fifteen activities with an average classification accuracy of 92.96%. PMID:25184486

"Less Clicking, More Watching": Results from the User-Centered Design of a Multi-Institutional Web Site for Art and Culture.

ERIC Educational Resources Information Center

Vergo, John; Karat, Clare-Marie; Karat, John; Pinhanez, Claudio; Arora, Renee; Cofino, Thomas; Riecken, Doug; Podlaseck, Mark

This paper summarizes a 10-month long research project conducted at the IBM T.J. Watson Research Center aimed at developing the design concept of a multi-institutional art and culture web site. The work followed a user-centered design (UCD) approach, where interaction with prototypes and feedback from potential users of the web site were sought…

Novel Intersection Type Recognition for Autonomous Vehicles Using a Multi-Layer Laser Scanner.

PubMed

An, Jhonghyun; Choi, Baehoon; Sim, Kwee-Bo; Kim, Euntai

2016-07-20

There are several types of intersections such as merge-roads, diverge-roads, plus-shape intersections and two types of T-shape junctions in urban roads. When an autonomous vehicle encounters new intersections, it is crucial to recognize the types of intersections for safe navigation. In this paper, a novel intersection type recognition method is proposed for an autonomous vehicle using a multi-layer laser scanner. The proposed method consists of two steps: (1) static local coordinate occupancy grid map (SLOGM) building and (2) intersection classification. In the first step, the SLOGM is built relative to the local coordinate using the dynamic binary Bayes filter. In the second step, the SLOGM is used as an attribute for the classification. The proposed method is applied to a real-world environment and its validity is demonstrated through experimentation.

Geomorphometric multi-scale analysis for the recognition of Moon surface features using multi-resolution DTMs

NASA Astrophysics Data System (ADS)

Li, Ke; Chen, Jianping; Sofia, Giulia; Tarolli, Paolo

2014-05-01

Moon surface features have great significance in understanding and reconstructing the lunar geological evolution. Linear structures like rilles and ridges are closely related to the internal forced tectonic movement. The craters widely distributed on the moon are also the key research targets for external forced geological evolution. The extremely rare availability of samples and the difficulty for field works make remote sensing the most important approach for planetary studies. New and advanced lunar probes launched by China, U.S., Japan and India provide nowadays a lot of high-quality data, especially in the form of high-resolution Digital Terrain Models (DTMs), bringing new opportunities and challenges for feature extraction on the moon. The aim of this study is to recognize and extract lunar features using geomorphometric analysis based on multi-scale parameters and multi-resolution DTMs. The considered digital datasets include CE1-LAM (Chang'E One, Laser AltiMeter) data with resolution of 500m/pix, LRO-WAC (Lunar Reconnaissance Orbiter, Wide Angle Camera) data with resolution of 100m/pix, LRO-LOLA (Lunar Reconnaissance Orbiter, Lunar Orbiter Laser Altimeter) data with resolution of 60m/pix, and LRO-NAC (Lunar Reconnaissance Orbiter, Narrow Angle Camera) data with resolution of 2-5m/pix. We considered surface derivatives to recognize the linear structures including Rilles and Ridges. Different window scales and thresholds for are considered for feature extraction. We also calculated the roughness index to identify the erosion/deposits area within craters. The results underline the suitability of the adopted methods for feature recognition on the moon surface. The roughness index is found to be a useful tool to distinguish new craters, with higher roughness, from the old craters, which present a smooth and less rough surface.

Robust Indoor Human Activity Recognition Using Wireless Signals.

PubMed

Wang, Yi; Jiang, Xinli; Cao, Rongyu; Wang, Xiyang

2015-07-15

Wireless signals-based activity detection and recognition technology may be complementary to the existing vision-based methods, especially under the circumstance of occlusions, viewpoint change, complex background, lighting condition change, and so on. This paper explores the properties of the channel state information (CSI) of Wi-Fi signals, and presents a robust indoor daily human activity recognition framework with only one pair of transmission points (TP) and access points (AP). First of all, some indoor human actions are selected as primitive actions forming a training set. Then, an online filtering method is designed to make actions' CSI curves smooth and allow them to contain enough pattern information. Each primitive action pattern can be segmented from the outliers of its multi-input multi-output (MIMO) signals by a proposed segmentation method. Lastly, in online activities recognition, by selecting proper features and Support Vector Machine (SVM) based multi-classification, activities constituted by primitive actions can be recognized insensitive to the locations, orientations, and speeds.

Software for Partly Automated Recognition of Targets

NASA Technical Reports Server (NTRS)

Opitz, David; Blundell, Stuart; Bain, William; Morris, Matthew; Carlson, Ian; Mangrich, Mark; Selinsky, T.

2002-01-01

The Feature Analyst is a computer program for assisted (partially automated) recognition of targets in images. This program was developed to accelerate the processing of high-resolution satellite image data for incorporation into geographic information systems (GIS). This program creates an advanced user interface that embeds proprietary machine-learning algorithms in commercial image-processing and GIS software. A human analyst provides samples of target features from multiple sets of data, then the software develops a data-fusion model that automatically extracts the remaining features from selected sets of data. The program thus leverages the natural ability of humans to recognize objects in complex scenes, without requiring the user to explain the human visual recognition process by means of lengthy software. Two major subprograms are the reactive agent and the thinking agent. The reactive agent strives to quickly learn the user's tendencies while the user is selecting targets and to increase the user's productivity by immediately suggesting the next set of pixels that the user may wish to select. The thinking agent utilizes all available resources, taking as much time as needed, to produce the most accurate autonomous feature-extraction model possible.

Software for Partly Automated Recognition of Targets

NASA Technical Reports Server (NTRS)

Opitz, David; Blundell, Stuart; Bain, William; Morris, Matthew; Carlson, Ian; Mangrich, Mark

2003-01-01

The Feature Analyst is a computer program for assisted (partially automated) recognition of targets in images. This program was developed to accelerate the processing of high-resolution satellite image data for incorporation into geographic information systems (GIS). This program creates an advanced user interface that embeds proprietary machine-learning algorithms in commercial image-processing and GIS software. A human analyst provides samples of target features from multiple sets of data, then the software develops a data-fusion model that automatically extracts the remaining features from selected sets of data. The program thus leverages the natural ability of humans to recognize objects in complex scenes, without requiring the user to explain the human visual recognition process by means of lengthy software. Two major subprograms are the reactive agent and the thinking agent. The reactive agent strives to quickly learn the user s tendencies while the user is selecting targets and to increase the user s productivity by immediately suggesting the next set of pixels that the user may wish to select. The thinking agent utilizes all available resources, taking as much time as needed, to produce the most accurate autonomous feature-extraction model possible.

Finessing filter scarcity problem in face recognition via multi-fold filter convolution

NASA Astrophysics Data System (ADS)

Low, Cheng-Yaw; Teoh, Andrew Beng-Jin

2017-06-01

The deep convolutional neural networks for face recognition, from DeepFace to the recent FaceNet, demand a sufficiently large volume of filters for feature extraction, in addition to being deep. The shallow filter-bank approaches, e.g., principal component analysis network (PCANet), binarized statistical image features (BSIF), and other analogous variants, endure the filter scarcity problem that not all PCA and ICA filters available are discriminative to abstract noise-free features. This paper extends our previous work on multi-fold filter convolution (ℳ-FFC), where the pre-learned PCA and ICA filter sets are exponentially diversified by ℳ folds to instantiate PCA, ICA, and PCA-ICA offspring. The experimental results unveil that the 2-FFC operation solves the filter scarcity state. The 2-FFC descriptors are also evidenced to be superior to that of PCANet, BSIF, and other face descriptors, in terms of rank-1 identification rate (%).

A Review of Multi-Sensory Technologies in a Science, Technology, Engineering, Arts and Mathematics (STEAM) Classroom

ERIC Educational Resources Information Center

Taljaard, Johann

2016-01-01

This article reviews the literature on multi-sensory technology and, in particular, looks at answering the question: "What multi-sensory technologies are available to use in a science, technology, engineering, arts and mathematics (STEAM) classroom, and do they affect student engagement and learning outcomes?" Here engagement is defined…

Multi-temporal Land Use Mapping of Coastal Wetlands Area using Machine Learning in Google Earth Engine

NASA Astrophysics Data System (ADS)

Farda, N. M.

2017-12-01

Coastal wetlands provide ecosystem services essential to people and the environment. Changes in coastal wetlands, especially on land use, are important to monitor by utilizing multi-temporal imagery. The Google Earth Engine (GEE) provides many machine learning algorithms (10 algorithms) that are very useful for extracting land use from imagery. The research objective is to explore machine learning in Google Earth Engine and its accuracy for multi-temporal land use mapping of coastal wetland area. Landsat 3 MSS (1978), Landsat 5 TM (1991), Landsat 7 ETM+ (2001), and Landsat 8 OLI (2014) images located in Segara Anakan lagoon are selected to represent multi temporal images. The input for machine learning are visible and near infrared bands, PCA band, invers PCA bands, bare soil index, vegetation index, wetness index, elevation from ASTER GDEM, and GLCM (Harralick) texture, and also polygon samples in 140 locations. There are 10 machine learning algorithms applied to extract coastal wetlands land use from Landsat imagery. The algorithms are Fast Naive Bayes, CART (Classification and Regression Tree), Random Forests, GMO Max Entropy, Perceptron (Multi Class Perceptron), Winnow, Voting SVM, Margin SVM, Pegasos (Primal Estimated sub-GrAdient SOlver for Svm), IKPamir (Intersection Kernel Passive Aggressive Method for Information Retrieval, SVM). Machine learning in Google Earth Engine are very helpful in multi-temporal land use mapping, the highest accuracy for land use mapping of coastal wetland is CART with 96.98 % Overall Accuracy using K-Fold Cross Validation (K = 10). GEE is particularly useful for multi-temporal land use mapping with ready used image and classification algorithms, and also very challenging for other applications.

MultiSpec: A Desktop and Online Geospatial Image Data Processing Tool

NASA Astrophysics Data System (ADS)

Biehl, L. L.; Hsu, W. K.; Maud, A. R. M.; Yeh, T. T.

2017-12-01

Spec (engineering.purdue.edu/ biehl/MultiSpec/) including a reference manual and several tutorials allowing young high-school students through research faculty to learn the basic functions in MultiSpec. Some of the tutorials have been translated to other languages by MultiSpec users.

Action recognition using mined hierarchical compound features.

PubMed

Gilbert, Andrew; Illingworth, John; Bowden, Richard

2011-05-01

The field of Action Recognition has seen a large increase in activity in recent years. Much of the progress has been through incorporating ideas from single-frame object recognition and adapting them for temporal-based action recognition. Inspired by the success of interest points in the 2D spatial domain, their 3D (space-time) counterparts typically form the basic components used to describe actions, and in action recognition the features used are often engineered to fire sparsely. This is to ensure that the problem is tractable; however, this can sacrifice recognition accuracy as it cannot be assumed that the optimum features in terms of class discrimination are obtained from this approach. In contrast, we propose to initially use an overcomplete set of simple 2D corners in both space and time. These are grouped spatially and temporally using a hierarchical process, with an increasing search area. At each stage of the hierarchy, the most distinctive and descriptive features are learned efficiently through data mining. This allows large amounts of data to be searched for frequently reoccurring patterns of features. At each level of the hierarchy, the mined compound features become more complex, discriminative, and sparse. This results in fast, accurate recognition with real-time performance on high-resolution video. As the compound features are constructed and selected based upon their ability to discriminate, their speed and accuracy increase at each level of the hierarchy. The approach is tested on four state-of-the-art data sets, the popular KTH data set to provide a comparison with other state-of-the-art approaches, the Multi-KTH data set to illustrate performance at simultaneous multiaction classification, despite no explicit localization information provided during training. Finally, the recent Hollywood and Hollywood2 data sets provide challenging complex actions taken from commercial movie sequences. For all four data sets, the proposed hierarchical

A new method for incoherent combining of far-field laser beams based on multiple faculae recognition

NASA Astrophysics Data System (ADS)

Ye, Demao; Li, Sichao; Yan, Zhihui; Zhang, Zenan; Liu, Yuan

2018-03-01

Compared to coherent beam combining, incoherent beam combining can complete the output of high power laser beam with high efficiency, simple structure, low cost and high thermal damage resistance, and it is easy to realize in engineering. Higher target power is achieved by incoherent beam combination which using technology of multi-channel optical path correction. However, each channel forms a spot in the far field respectively, which cannot form higher laser power density with low overlap ratio of faculae. In order to improve the combat effectiveness of the system, it is necessary to overlap different faculae that improve the target energy density. Hence, a novel method for incoherent combining of far-field laser beams is present. The method compromises piezoelectric ceramic technology and evaluation algorithm of faculae coincidence degree which based on high precision multi-channel optical path correction. The results show that the faculae recognition algorithm is low-latency(less than 10ms), which can meet the needs of practical engineering. Furthermore, the real time focusing ability of far field faculae is improved which was beneficial to the engineering of high-energy laser weapon or other laser jamming systems.

A new FOD recognition algorithm based on multi-source information fusion and experiment analysis

NASA Astrophysics Data System (ADS)

Li, Yu; Xiao, Gang

2011-08-01

Foreign Object Debris (FOD) is a kind of substance, debris or article alien to an aircraft or system, which would potentially cause huge damage when it appears on the airport runway. Due to the airport's complex circumstance, quick and precise detection of FOD target on the runway is one of the important protections for airplane's safety. A multi-sensor system including millimeter-wave radar and Infrared image sensors is introduced and a developed new FOD detection and recognition algorithm based on inherent feature of FOD is proposed in this paper. Firstly, the FOD's location and coordinate can be accurately obtained by millimeter-wave radar, and then according to the coordinate IR camera will take target images and background images. Secondly, in IR image the runway's edges which are straight lines can be extracted by using Hough transformation method. The potential target region, that is, runway region, can be segmented from the whole image. Thirdly, background subtraction is utilized to localize the FOD target in runway region. Finally, in the detailed small images of FOD target, a new characteristic is discussed and used in target classification. The experiment results show that this algorithm can effectively reduce the computational complexity, satisfy the real-time requirement and possess of high detection and recognition probability.

Web-based multi-channel analyzer

DOEpatents

Gritzo, Russ E.

2003-12-23

The present invention provides an improved multi-channel analyzer designed to conveniently gather, process, and distribute spectrographic pulse data. The multi-channel analyzer may operate on a computer system having memory, a processor, and the capability to connect to a network and to receive digitized spectrographic pulses. The multi-channel analyzer may have a software module integrated with a general-purpose operating system that may receive digitized spectrographic pulses for at least 10,000 pulses per second. The multi-channel analyzer may further have a user-level software module that may receive user-specified controls dictating the operation of the multi-channel analyzer, making the multi-channel analyzer customizable by the end-user. The user-level software may further categorize and conveniently distribute spectrographic pulse data employing non-proprietary, standard communication protocols and formats.

Model-Driven Useware Engineering

NASA Astrophysics Data System (ADS)

Meixner, Gerrit; Seissler, Marc; Breiner, Kai

User-oriented hardware and software development relies on a systematic development process based on a comprehensive analysis focusing on the users' requirements and preferences. Such a development process calls for the integration of numerous disciplines, from psychology and ergonomics to computer sciences and mechanical engineering. Hence, a correspondingly interdisciplinary team must be equipped with suitable software tools to allow it to handle the complexity of a multimodal and multi-device user interface development approach. An abstract, model-based development approach seems to be adequate for handling this complexity. This approach comprises different levels of abstraction requiring adequate tool support. Thus, in this chapter, we present the current state of our model-based software tool chain. We introduce the use model as the core model of our model-based process, transformation processes, and a model-based architecture, and we present different software tools that provide support for creating and maintaining the models or performing the necessary model transformations.

A multi-approach feature extractions for iris recognition

NASA Astrophysics Data System (ADS)

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.

Multi-source Geospatial Data Analysis with Google Earth Engine

NASA Astrophysics Data System (ADS)

Erickson, T.

2014-12-01

The Google Earth Engine platform is a cloud computing environment for data analysis that combines a public data catalog with a large-scale computational facility optimized for parallel processing of geospatial data. The data catalog is a multi-petabyte archive of georeferenced datasets that include images from Earth observing satellite and airborne sensors (examples: USGS Landsat, NASA MODIS, USDA NAIP), weather and climate datasets, and digital elevation models. Earth Engine supports both a just-in-time computation model that enables real-time preview and debugging during algorithm development for open-ended data exploration, and a batch computation mode for applying algorithms over large spatial and temporal extents. The platform automatically handles many traditionally-onerous data management tasks, such as data format conversion, reprojection, and resampling, which facilitates writing algorithms that combine data from multiple sensors and/or models. Although the primary use of Earth Engine, to date, has been the analysis of large Earth observing satellite datasets, the computational platform is generally applicable to a wide variety of use cases that require large-scale geospatial data analyses. This presentation will focus on how Earth Engine facilitates the analysis of geospatial data streams that originate from multiple separate sources (and often communities) and how it enables collaboration during algorithm development and data exploration. The talk will highlight current projects/analyses that are enabled by this functionality.https://earthengine.google.org

Multi-resolution analysis for ear recognition using wavelet features

NASA Astrophysics Data System (ADS)

Shoaib, M.; Basit, A.; Faye, I.

2016-11-01

Security is very important and in order to avoid any physical contact, identification of human when they are moving is necessary. Ear biometric is one of the methods by which a person can be identified using surveillance cameras. Various techniques have been proposed to increase the ear based recognition systems. In this work, a feature extraction method for human ear recognition based on wavelet transforms is proposed. The proposed features are approximation coefficients and specific details of level two after applying various types of wavelet transforms. Different wavelet transforms are applied to find the suitable wavelet. Minimum Euclidean distance is used as a matching criterion. Results achieved by the proposed method are promising and can be used in real time ear recognition system.

The Spatial Vision Tree: A Generic Pattern Recognition Engine- Scientific Foundations, Design Principles, and Preliminary Tree Design

NASA Technical Reports Server (NTRS)

Rahman, Zia-ur; Jobson, Daniel J.; Woodell, Glenn A.

2010-01-01

New foundational ideas are used to define a novel approach to generic visual pattern recognition. These ideas proceed from the starting point of the intrinsic equivalence of noise reduction and pattern recognition when noise reduction is taken to its theoretical limit of explicit matched filtering. This led us to think of the logical extension of sparse coding using basis function transforms for both de-noising and pattern recognition to the full pattern specificity of a lexicon of matched filter pattern templates. A key hypothesis is that such a lexicon can be constructed and is, in fact, a generic visual alphabet of spatial vision. Hence it provides a tractable solution for the design of a generic pattern recognition engine. Here we present the key scientific ideas, the basic design principles which emerge from these ideas, and a preliminary design of the Spatial Vision Tree (SVT). The latter is based upon a cryptographic approach whereby we measure a large aggregate estimate of the frequency of occurrence (FOO) for each pattern. These distributions are employed together with Hamming distance criteria to design a two-tier tree. Then using information theory, these same FOO distributions are used to define a precise method for pattern representation. Finally the experimental performance of the preliminary SVT on computer generated test images and complex natural images is assessed.

A free-piston Stirling engine/linear alternator controls and load interaction test facility

NASA Technical Reports Server (NTRS)

Rauch, Jeffrey S.; Kankam, M. David; Santiago, Walter; Madi, Frank J.

1992-01-01

A test facility at LeRC was assembled for evaluating free-piston Stirling engine/linear alternator control options, and interaction with various electrical loads. This facility is based on a 'SPIKE' engine/alternator. The engine/alternator, a multi-purpose load system, a digital computer based load and facility control, and a data acquisition system with both steady-periodic and transient capability are described. Preliminary steady-periodic results are included for several operating modes of a digital AC parasitic load control. Preliminary results on the transient response to switching a resistive AC user load are discussed.

Multi-User Spaceport Update News Conference

NASA Image and Video Library

2014-01-23

CAPE CANAVERAL, Fla. – Sierra Nevada Corporation, or SNC, Space Systems, announces the steps the company will take to prepare for a November 2016 orbital flight of its Dream Chaser spacecraft from Florida’s Space Coast during a news conference at NASA’s Kennedy Space Center in Florida. Participants are, from left, Michael Curie, NASA spokesman, Bob Cabana, director of Kennedy, Michael Gass, president and CEO of United Launch Alliance, or ULA, Frank DiBello, president and CEO of Space Florida, Mark Sirangelo, corporate vice president and head of SNC Space Systems, Larry Price, Lockheed Martin Space Systems deputy program manager for NASA's Orion spacecraft, and Steve Lindsey, Dream Chaser program manager for SNC Space Systems. The steps are considered substantial for SNC and important to plans by NASA and Space Florida for Kennedy’s transformation into a multi-user spaceport for both commercial and government customers. SNC said it plans to work with ULA to launch the Dream Chaser spacecraft into orbit atop an Atlas V rocket from Space Launch Complex 41 at Cape Canaveral Air Force Station intends to land the winged spacecraft at Kennedy’s 3.5-mile long runway at the Shuttle Landing Facility lease office space at Exploration Park, right outside Kennedy’s gates and process the spacecraft in the high bay of the Operations and Checkout Building at Kennedy, with Lockheed Martin performing the work. Photo credit: NASA/Kim Shiflett

NHERI: Advancing the Research Infrastructure of the Multi-Hazard Community

NASA Astrophysics Data System (ADS)

Blain, C. A.; Ramirez, J. A.; Bobet, A.; Browning, J.; Edge, B.; Holmes, W.; Johnson, D.; Robertson, I.; Smith, T.; Zuo, D.

2017-12-01

The Natural Hazards Engineering Research Infrastructure (NHERI), supported by the National Science Foundation (NSF), is a distributed, multi-user national facility that provides the natural hazards research community with access to an advanced research infrastructure. Components of NHERI are comprised of a Network Coordination Office (NCO), a cloud-based cyberinfrastructure (DesignSafe-CI), a computational modeling and simulation center (SimCenter), and eight Experimental Facilities (EFs), including a post-disaster, rapid response research facility (RAPID). Utimately NHERI enables researchers to explore and test ground-breaking concepts to protect homes, businesses and infrastructure lifelines from earthquakes, windstorms, tsunamis, and surge enabling innovations to help prevent natural hazards from becoming societal disasters. When coupled with education and community outreach, NHERI will facilitate research and educational advances that contribute knowledge and innovation toward improving the resiliency of the nation's civil infrastructure to withstand natural hazards. The unique capabilities and coordinating activities over Year 1 between NHERI's DesignSafe-CI, the SimCenter, and individual EFs will be presented. Basic descriptions of each component are also found at https://www.designsafe-ci.org/facilities/. Additionally to be discussed are the various roles of the NCO in leading development of a 5-year multi-hazard science plan, coordinating facility scheduling and fostering the sharing of technical knowledge and best practices, leading education and outreach programs such as the recent Summer Institute and multi-facility REU program, ensuring a platform for technology transfer to practicing engineers, and developing strategic national and international partnerships to support a diverse multi-hazard research and user community.

A Weld Position Recognition Method Based on Directional and Structured Light Information Fusion in Multi-Layer/Multi-Pass Welding.

PubMed

Zeng, Jinle; Chang, Baohua; Du, Dong; Wang, Li; Chang, Shuhe; Peng, Guodong; Wang, Wenzhu

2018-01-05

Multi-layer/multi-pass welding (MLMPW) technology is widely used in the energy industry to join thick components. During automatic welding using robots or other actuators, it is very important to recognize the actual weld pass position using visual methods, which can then be used not only to perform reasonable path planning for actuators, but also to correct any deviations between the welding torch and the weld pass position in real time. However, due to the small geometrical differences between adjacent weld passes, existing weld position recognition technologies such as structured light methods are not suitable for weld position detection in MLMPW. This paper proposes a novel method for weld position detection, which fuses various kinds of information in MLMPW. First, a synchronous acquisition method is developed to obtain various kinds of visual information when directional light and structured light sources are on, respectively. Then, interferences are eliminated by fusing adjacent images. Finally, the information from directional and structured light images is fused to obtain the 3D positions of the weld passes. Experiment results show that each process can be done in 30 ms and the deviation is less than 0.6 mm. The proposed method can be used for automatic path planning and seam tracking in the robotic MLMPW process as well as electron beam freeform fabrication process.

Using Dynamic Multi-Task Non-Negative Matrix Factorization to Detect the Evolution of User Preferences in Collaborative Filtering

PubMed Central

Ju, Bin; Qian, Yuntao; Ye, Minchao; Ni, Rong; Zhu, Chenxi

2015-01-01

Predicting what items will be selected by a target user in the future is an important function for recommendation systems. Matrix factorization techniques have been shown to achieve good performance on temporal rating-type data, but little is known about temporal item selection data. In this paper, we developed a unified model that combines Multi-task Non-negative Matrix Factorization and Linear Dynamical Systems to capture the evolution of user preferences. Specifically, user and item features are projected into latent factor space by factoring co-occurrence matrices into a common basis item-factor matrix and multiple factor-user matrices. Moreover, we represented both within and between relationships of multiple factor-user matrices using a state transition matrix to capture the changes in user preferences over time. The experiments show that our proposed algorithm outperforms the other algorithms on two real datasets, which were extracted from Netflix movies and Last.fm music. Furthermore, our model provides a novel dynamic topic model for tracking the evolution of the behavior of a user over time. PMID:26270539

Using Dynamic Multi-Task Non-Negative Matrix Factorization to Detect the Evolution of User Preferences in Collaborative Filtering.

PubMed

Ju, Bin; Qian, Yuntao; Ye, Minchao; Ni, Rong; Zhu, Chenxi

2015-01-01

Predicting what items will be selected by a target user in the future is an important function for recommendation systems. Matrix factorization techniques have been shown to achieve good performance on temporal rating-type data, but little is known about temporal item selection data. In this paper, we developed a unified model that combines Multi-task Non-negative Matrix Factorization and Linear Dynamical Systems to capture the evolution of user preferences. Specifically, user and item features are projected into latent factor space by factoring co-occurrence matrices into a common basis item-factor matrix and multiple factor-user matrices. Moreover, we represented both within and between relationships of multiple factor-user matrices using a state transition matrix to capture the changes in user preferences over time. The experiments show that our proposed algorithm outperforms the other algorithms on two real datasets, which were extracted from Netflix movies and Last.fm music. Furthermore, our model provides a novel dynamic topic model for tracking the evolution of the behavior of a user over time.

Three dimensional multi-cellular muscle-like tissue engineering in perfusion-based bioreactors.

PubMed

Cerino, Giulia; Gaudiello, Emanuele; Grussenmeyer, Thomas; Melly, Ludovic; Massai, Diana; Banfi, Andrea; Martin, Ivan; Eckstein, Friedrich; Grapow, Martin; Marsano, Anna

2016-01-01

Conventional tissue engineering strategies often rely on the use of a single progenitor cell source to engineer in vitro biological models; however, multi-cellular environments can better resemble the complexity of native tissues. Previous described co-culture models used skeletal myoblasts, as parenchymal cell source, and mesenchymal or endothelial cells, as stromal component. Here, we propose instead the use of adipose tissue-derived stromal vascular fraction cells, which include both mesenchymal and endothelial cells, to better resemble the native stroma. Percentage of serum supplementation is one of the crucial parameters to steer skeletal myoblasts toward either proliferation (20%) or differentiation (5%) in two-dimensional culture conditions. On the contrary, three-dimensional (3D) skeletal myoblast culture often simply adopts the serum content used in monolayer, without taking into account the new cell environment. When considering 3D cultures of mm-thick engineered tissues, homogeneous and sufficient oxygen supply is paramount to avoid formation of necrotic cores. Perfusion-based bioreactor culture can significantly improve the oxygen access to the cells, enhancing the viability and the contractility of the engineered tissues. In this study, we first investigated the influence of different serum supplementations on the skeletal myoblast ability to proliferate and differentiate during 3D perfusion-based culture. We tested percentages of serum promoting monolayer skeletal myoblast-proliferation (20%) and differentiation (5%) and suitable for stromal cell culture (10%) with a view to identify the most suitable condition for the subsequent co-culture. The 10% serum medium composition resulted in the highest number of mature myotubes and construct functionality. Co-culture with stromal vascular fraction cells at 10% serum also supported the skeletal myoblast differentiation and maturation, hence providing a functional engineered 3D muscle model that resembles

Google Earth Engine

NASA Astrophysics Data System (ADS)

Gorelick, Noel

2013-04-01

The Google Earth Engine platform is a system designed to enable petabyte-scale, scientific analysis and visualization of geospatial datasets. Earth Engine provides a consolidated environment including a massive data catalog co-located with thousands of computers for analysis. The user-friendly front-end provides a workbench environment to allow interactive data and algorithm development and exploration and provides a convenient mechanism for scientists to share data, visualizations and analytic algorithms via URLs. The Earth Engine data catalog contains a wide variety of popular, curated datasets, including the world's largest online collection of Landsat scenes (> 2.0M), numerous MODIS collections, and many vector-based data sets. The platform provides a uniform access mechanism to a variety of data types, independent of their bands, projection, bit-depth, resolution, etc..., facilitating easy multi-sensor analysis. Additionally, a user is able to add and curate their own data and collections. Using a just-in-time, distributed computation model, Earth Engine can rapidly process enormous quantities of geo-spatial data. All computation is performed lazily; nothing is computed until it's required either for output or as input to another step. This model allows real-time feedback and preview during algorithm development, supporting a rapid algorithm development, test, and improvement cycle that scales seamlessly to large-scale production data processing. Through integration with a variety of other services, Earth Engine is able to bring to bear considerable analytic and technical firepower in a transparent fashion, including: AI-based classification via integration with Google's machine learning infrastructure, publishing and distribution at Google scale through integration with the Google Maps API, Maps Engine and Google Earth, and support for in-the-field activities such as validation, ground-truthing, crowd-sourcing and citizen science though the Android Open Data

Google Earth Engine

NASA Astrophysics Data System (ADS)

Gorelick, N.

2012-12-01

The Google Earth Engine platform is a system designed to enable petabyte-scale, scientific analysis and visualization of geospatial datasets. Earth Engine provides a consolidated environment including a massive data catalog co-located with thousands of computers for analysis. The user-friendly front-end provides a workbench environment to allow interactive data and algorithm development and exploration and provides a convenient mechanism for scientists to share data, visualizations and analytic algorithms via URLs. The Earth Engine data catalog contains a wide variety of popular, curated datasets, including the world's largest online collection of Landsat scenes (> 2.0M), numerous MODIS collections, and many vector-based data sets. The platform provides a uniform access mechanism to a variety of data types, independent of their bands, projection, bit-depth, resolution, etc..., facilitating easy multi-sensor analysis. Additionally, a user is able to add and curate their own data and collections. Using a just-in-time, distributed computation model, Earth Engine can rapidly process enormous quantities of geo-spatial data. All computation is performed lazily; nothing is computed until it's required either for output or as input to another step. This model allows real-time feedback and preview during algorithm development, supporting a rapid algorithm development, test, and improvement cycle that scales seamlessly to large-scale production data processing. Through integration with a variety of other services, Earth Engine is able to bring to bear considerable analytic and technical firepower in a transparent fashion, including: AI-based classification via integration with Google's machine learning infrastructure, publishing and distribution at Google scale through integration with the Google Maps API, Maps Engine and Google Earth, and support for in-the-field activities such as validation, ground-truthing, crowd-sourcing and citizen science though the Android Open Data

User-centered design of multi-gene sequencing panel reports for clinicians.

PubMed

Cutting, Elizabeth; Banchero, Meghan; Beitelshees, Amber L; Cimino, James J; Fiol, Guilherme Del; Gurses, Ayse P; Hoffman, Mark A; Jeng, Linda Jo Bone; Kawamoto, Kensaku; Kelemen, Mark; Pincus, Harold Alan; Shuldiner, Alan R; Williams, Marc S; Pollin, Toni I; Overby, Casey Lynnette

2016-10-01

The objective of this study was to develop a high-fidelity prototype for delivering multi-gene sequencing panel (GS) reports to clinicians that simulates the user experience of a final application. The delivery and use of GS reports can occur within complex and high-paced healthcare environments. We employ a user-centered software design approach in a focus group setting in order to facilitate gathering rich contextual information from a diverse group of stakeholders potentially impacted by the delivery of GS reports relevant to two precision medicine programs at the University of Maryland Medical Center. Responses from focus group sessions were transcribed, coded and analyzed by two team members. Notification mechanisms and information resources preferred by participants from our first phase of focus groups were incorporated into scenarios and the design of a software prototype for delivering GS reports. The goal of our second phase of focus group, to gain input on the prototype software design, was accomplished through conducting task walkthroughs with GS reporting scenarios. Preferences for notification, content and consultation from genetics specialists appeared to depend upon familiarity with scenarios for ordering and delivering GS reports. Despite familiarity with some aspects of the scenarios we proposed, many of our participants agreed that they would likely seek consultation from a genetics specialist after viewing the test reports. In addition, participants offered design and content recommendations. Findings illustrated a need to support customized notification approaches, user-specific information, and access to genetics specialists with GS reports. These design principles can be incorporated into software applications that deliver GS reports. Our user-centered approach to conduct this assessment and the specific input we received from clinicians may also be relevant to others working on similar projects. Copyright © 2016 Elsevier Inc. All rights

Novel Intersection Type Recognition for Autonomous Vehicles Using a Multi-Layer Laser Scanner

PubMed Central

An, Jhonghyun; Choi, Baehoon; Sim, Kwee-Bo; Kim, Euntai

2016-01-01

There are several types of intersections such as merge-roads, diverge-roads, plus-shape intersections and two types of T-shape junctions in urban roads. When an autonomous vehicle encounters new intersections, it is crucial to recognize the types of intersections for safe navigation. In this paper, a novel intersection type recognition method is proposed for an autonomous vehicle using a multi-layer laser scanner. The proposed method consists of two steps: (1) static local coordinate occupancy grid map (SLOGM) building and (2) intersection classification. In the first step, the SLOGM is built relative to the local coordinate using the dynamic binary Bayes filter. In the second step, the SLOGM is used as an attribute for the classification. The proposed method is applied to a real-world environment and its validity is demonstrated through experimentation. PMID:27447640

Efficient Parallel Engineering Computing on Linux Workstations

NASA Technical Reports Server (NTRS)

Lou, John Z.

2010-01-01

A C software module has been developed that creates lightweight processes (LWPs) dynamically to achieve parallel computing performance in a variety of engineering simulation and analysis applications to support NASA and DoD project tasks. The required interface between the module and the application it supports is simple, minimal and almost completely transparent to the user applications, and it can achieve nearly ideal computing speed-up on multi-CPU engineering workstations of all operating system platforms. The module can be integrated into an existing application (C, C++, Fortran and others) either as part of a compiled module or as a dynamically linked library (DLL).

An Improved Iris Recognition Algorithm Based on Hybrid Feature and ELM

NASA Astrophysics Data System (ADS)

Wang, Juan

2018-03-01

The iris image is easily polluted by noise and uneven light. This paper proposed an improved extreme learning machine (ELM) based iris recognition algorithm with hybrid feature. 2D-Gabor filters and GLCM is employed to generate a multi-granularity hybrid feature vector. 2D-Gabor filter and GLCM feature work for capturing low-intermediate frequency and high frequency texture information, respectively. Finally, we utilize extreme learning machine for iris recognition. Experimental results reveal our proposed ELM based multi-granularity iris recognition algorithm (ELM-MGIR) has higher accuracy of 99.86%, and lower EER of 0.12% under the premise of real-time performance. The proposed ELM-MGIR algorithm outperforms other mainstream iris recognition algorithms.

An integrated approach to engineering curricula improvement with multi-objective decision modeling and linear programming

NASA Astrophysics Data System (ADS)

Shea, John E.

The structure of engineering curricula currently in place at most colleges and universities has existed since the early 1950's, and reflects an historical emphasis on a solid foundation in math, science, and engineering science. However, there is often not a close match between elements of the traditional engineering education, and the skill sets that graduates need to possess for success in the industrial environment. Considerable progress has been made to restructure engineering courses and curricula. What is lacking, however, are tools and methodologies that incorporate the many dimensions of college courses, and how they are structured to form a curriculum. If curriculum changes are to be made, the first objective must be to determine what knowledge and skills engineering graduates need to possess. To accomplish this, a set of engineering competencies was developed from existing literature, and used in the development of a comprehensive mail survey of alumni, employers, students and faculty. Respondents proposed some changes to the topics in the curriculum and recommended that work to improve the curriculum be focused on communication, problem solving and people skills. The process of designing a curriculum is similar to engineering design, with requirements that must be met, and objectives that must be optimized. From this similarity came the idea for developing a linear, additive, multi-objective model that identifies the objectives that must be considered when designing a curriculum, and contains the mathematical relationships necessary to quantify the value of a specific alternative. The model incorporates the three primary objectives of engineering topics, skills, and curriculum design principles and uses data from the survey. It was used to design new courses, to evaluate various curricula alternatives, and to conduct sensitivity analysis to better understand their differences. Using the multi-objective model to identify the highest scoring curriculum

Speech Recognition in Adults With Cochlear Implants: The Effects of Working Memory, Phonological Sensitivity, and Aging.

PubMed

Moberly, Aaron C; Harris, Michael S; Boyce, Lauren; Nittrouer, Susan

2017-04-14

Models of speech recognition suggest that "top-down" linguistic and cognitive functions, such as use of phonotactic constraints and working memory, facilitate recognition under conditions of degradation, such as in noise. The question addressed in this study was what happens to these functions when a listener who has experienced years of hearing loss obtains a cochlear implant. Thirty adults with cochlear implants and 30 age-matched controls with age-normal hearing underwent testing of verbal working memory using digit span and serial recall of words. Phonological capacities were assessed using a lexical decision task and nonword repetition. Recognition of words in sentences in speech-shaped noise was measured. Implant users had only slightly poorer working memory accuracy than did controls and only on serial recall of words; however, phonological sensitivity was highly impaired. Working memory did not facilitate speech recognition in noise for either group. Phonological sensitivity predicted sentence recognition for implant users but not for listeners with normal hearing. Clinical speech recognition outcomes for adult implant users relate to the ability of these users to process phonological information. Results suggest that phonological capacities may serve as potential clinical targets through rehabilitative training. Such novel interventions may be particularly helpful for older adult implant users.

Speech Recognition in Adults With Cochlear Implants: The Effects of Working Memory, Phonological Sensitivity, and Aging

PubMed Central

Harris, Michael S.; Boyce, Lauren; Nittrouer, Susan

2017-01-01

Purpose Models of speech recognition suggest that “top-down” linguistic and cognitive functions, such as use of phonotactic constraints and working memory, facilitate recognition under conditions of degradation, such as in noise. The question addressed in this study was what happens to these functions when a listener who has experienced years of hearing loss obtains a cochlear implant. Method Thirty adults with cochlear implants and 30 age-matched controls with age-normal hearing underwent testing of verbal working memory using digit span and serial recall of words. Phonological capacities were assessed using a lexical decision task and nonword repetition. Recognition of words in sentences in speech-shaped noise was measured. Results Implant users had only slightly poorer working memory accuracy than did controls and only on serial recall of words; however, phonological sensitivity was highly impaired. Working memory did not facilitate speech recognition in noise for either group. Phonological sensitivity predicted sentence recognition for implant users but not for listeners with normal hearing. Conclusion Clinical speech recognition outcomes for adult implant users relate to the ability of these users to process phonological information. Results suggest that phonological capacities may serve as potential clinical targets through rehabilitative training. Such novel interventions may be particularly helpful for older adult implant users. PMID:28384805

Direct Numerical Simulation of Turbulent Multi-Stage Autoignition Relevant to Engine Conditions

NASA Astrophysics Data System (ADS)

Chen, Jacqueline

2017-11-01

Due to the unrivaled energy density of liquid hydrocarbon fuels combustion will continue to provide over 80% of the world's energy for at least the next fifty years. Hence, combustion needs to be understood and controlled to optimize combustion systems for efficiency to prevent further climate change, to reduce emissions and to ensure U.S. energy security. In this talk I will discuss recent progress in direct numerical simulations of turbulent combustion focused on providing fundamental insights into key `turbulence-chemistry' interactions that underpin the development of next generation fuel efficient, fuel flexible engines for transportation and power generation. Petascale direct numerical simulation (DNS) of multi-stage mixed-mode turbulent combustion in canonical configurations have elucidated key physics that govern autoignition and flame stabilization in engines and provide benchmark data for combustion model development under the conditions of advanced engines which operate near combustion limits to maximize efficiency and minimize emissions. Mixed-mode combustion refers to premixed or partially-premixed flames propagating into stratified autoignitive mixtures. Multi-stage ignition refers to hydrocarbon fuels with negative temperature coefficient behavior that undergo sequential low- and high-temperature autoignition. Key issues that will be discussed include: 1) the role of mixing in shear driven turbulence on the dynamics of multi-stage autoignition and cool flame propagation in diesel environments, 2) the role of thermal and composition stratification on the evolution of the balance of mixed combustion modes - flame propagation versus spontaneous ignition - which determines the overall combustion rate in autoignition processes, and 3) the role of cool flames on lifted flame stabilization. Finally prospects for DNS of turbulent combustion at the exascale will be discussed in the context of anticipated heterogeneous machine architectures. sponsored by DOE

A Multi-Institution Study of Student Demographics and Outcomes in Chemical Engineering

ERIC Educational Resources Information Center

Lord, Susan M.; Layton, Richard A.; Ohland, Matthew W.; Brawner, Catherine E.; Long, Russell A.

2014-01-01

Using a large multi-institutional dataset, we describe demographics and outcomes for students starting in and transferring into chemical engineering (ChE). In this dataset, men outnumber women in ChE except among black students. While ChE starters graduate in ChE at rates comparable to or above their racial/ethnic population average for…

Informationally Efficient Multi-User Communication

DTIC Science & Technology

2010-01-01

DSM algorithms, the Op- timal Spectrum Balancing ( OSB ) algorithm and the Iterative Spectrum Balanc- ing (ISB) algorithm, were proposed to solve the...problem of maximization of a weighted rate-sum across all users [CYM06, YL06]. OSB has an exponential complexity in the number of users. ISB only has a...the duality gap min λ1,λ2 D (λ1, λ2) − max P1,P2 f (P1,P2) is not zero. Fig. 3.3 summarizes the three key steps of a dual method, the OSB algorithm

A new predictive multi-zone model for HCCI engine combustion

DOE PAGES

Bissoli, Mattia; Frassoldati, Alessio; Cuoci, Alberto; ...

2016-06-30

Here, this work introduces a new predictive multi-zone model for the description of combustion in Homogeneous Charge Compression Ignition (HCCI) engines. The model exploits the existing OpenSMOKE++ computational suite to handle detailed kinetic mechanisms, providing reliable predictions of the in-cylinder auto-ignition processes. All the elements with a significant impact on the combustion performances and emissions, like turbulence, heat and mass exchanges, crevices, residual burned gases, thermal and feed stratification are taken into account. Compared to other computational approaches, this model improves the description of mixture stratification phenomena by coupling a wall heat transfer model derived from CFD application with amore » proper turbulence model. Furthermore, the calibration of this multi-zone model requires only three parameters, which can be derived from a non-reactive CFD simulation: these adaptive variables depend only on the engine geometry and remain fixed across a wide range of operating conditions, allowing the prediction of auto-ignition, pressure traces and pollutants. This computational framework enables the use of detail kinetic mechanisms, as well as Rate of Production Analysis (RoPA) and Sensitivity Analysis (SA) to investigate the complex chemistry involved in the auto-ignition and the pollutants formation processes. In the final sections of the paper, these capabilities are demonstrated through the comparison with experimental data.« less

Post processing for offline Chinese handwritten character string recognition

NASA Astrophysics Data System (ADS)

Wang, YanWei; Ding, XiaoQing; Liu, ChangSong

2012-01-01

Offline Chinese handwritten character string recognition is one of the most important research fields in pattern recognition. Due to the free writing style, large variability in character shapes and different geometric characteristics, Chinese handwritten character string recognition is a challenging problem to deal with. However, among the current methods over-segmentation and merging method which integrates geometric information, character recognition information and contextual information, shows a promising result. It is found experimentally that a large part of errors are segmentation error and mainly occur around non-Chinese characters. In a Chinese character string, there are not only wide characters namely Chinese characters, but also narrow characters like digits and letters of the alphabet. The segmentation error is mainly caused by uniform geometric model imposed on all segmented candidate characters. To solve this problem, post processing is employed to improve recognition accuracy of narrow characters. On one hand, multi-geometric models are established for wide characters and narrow characters respectively. Under multi-geometric models narrow characters are not prone to be merged. On the other hand, top rank recognition results of candidate paths are integrated to boost final recognition of narrow characters. The post processing method is investigated on two datasets, in total 1405 handwritten address strings. The wide character recognition accuracy has been improved lightly and narrow character recognition accuracy has been increased up by 10.41% and 10.03% respectively. It indicates that the post processing method is effective to improve recognition accuracy of narrow characters.

Enhancing Learning Management Systems Utility for Blind Students: A Task-Oriented, User-Centered, Multi-Method Evaluation Technique

ERIC Educational Resources Information Center

Babu, Rakesh; Singh, Rahul

2013-01-01

This paper presents a novel task-oriented, user-centered, multi-method evaluation (TUME) technique and shows how it is useful in providing a more complete, practical and solution-oriented assessment of the accessibility and usability of Learning Management Systems (LMS) for blind and visually impaired (BVI) students. Novel components of TUME…

Identification of an internal combustion engine model by nonlinear multi-input multi-output system identification. Ph.D. Thesis

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

Luh, G.C.

1994-01-01

This thesis presents the application of advanced modeling techniques to construct nonlinear forward and inverse models of internal combustion engines for the detection and isolation of incipient faults. The NARMAX (Nonlinear Auto-Regressive Moving Average modeling with eXogenous inputs) technique of system identification proposed by Leontaritis and Billings was used to derive the nonlinear model of a internal combustion engine, over operating conditions corresponding to the I/M240 cycle. The I/M240 cycle is a standard proposed by the United States Environmental Protection Agency to measure tailpipe emissions in inspection and maintenance programs and consists of a driving schedule developed for the purposemore » of testing compliance with federal vehicle emission standards for carbon monoxide, unburned hydrocarbons, and nitrogen oxides. The experimental work for model identification and validation was performed on a 3.0 liter V6 engine installed in an engine test cell at the Center for Automotive Research at The Ohio State University. In this thesis, different types of model structures were proposed to obtain multi-input multi-output (MIMO) nonlinear NARX models. A modification of the algorithm proposed by He and Asada was used to estimate the robust orders of the derived MIMO nonlinear models. A methodology for the analysis of inverse NARX model was developed. Two methods were proposed to derive the inverse NARX model: (1) inversion from the forward NARX model; and (2) direct identification of inverse model from the output-input data set. In this thesis, invertibility, minimum-phase characteristic of zero dynamics, and stability analysis of NARX forward model are also discussed. Stability in the sense of Lyapunov is also investigated to check the stability of the identified forward and inverse models. This application of inverse problem leads to the estimation of unknown inputs and to actuator fault diagnosis.« less

The physical work environment and end-user requirements: Investigating marine engineering officers' operational demands and ship design.

PubMed

Mallam, Steven C; Lundh, Monica

2016-08-12

Physical environments influence how individuals perceive a space and behave within it. Previous research has revealed deficiencies in ship engine department work environments, and their impact on crew productivity, health and wellbeing. Connect operational task demands to pragmatic physical design and layout solutions by implementing a user-centric perspective. Three focus groups, each consisting of three marine engineers participated in this study. Focus groups were divided into two sessions: first, to investigate the end-user's operational requirements and their relationship with ship physical design and layout. Second, criteria formulated from group discussions were applied to a ship design case study. All focus group sessions were audio recorded and transcribed verbatim. The data were analyzed using Grounded Theory. Design choices made in a ships general arrangement were described to inherently influence how individuals and teams are able to function within the system. Participants detailed logistical relationships between key areas, stressing that the work environment and physical linkages must allow for flexibility of work organization and task execution. Traditional engine control paradigms do not allow effective mitigation of traditional engine department challenges. The influence of technology and modernization of ship systems can facilitate improvement of physical environments and work organization if effectively utilized.

A Limited-Vocabulary, Multi-Speaker Automatic Isolated Word Recognition System.

ERIC Educational Resources Information Center

Paul, James E., Jr.

Techniques for automatic recognition of isolated words are investigated, and a computer simulation of a word recognition system is effected. Considered in detail are data acquisition and digitizing, word detection, amplitude and time normalization, short-time spectral estimation including spectral windowing, spectral envelope approximation,…

A Support System for the Electric Appliance Control Using Pose Recognition

NASA Astrophysics Data System (ADS)

Kawano, Takuya; Yamamoto, Kazuhiko; Kato, Kunihito; Hongo, Hitoshi

In this paper, we propose an electric appliance control support system for aged and bedridden people using pose recognition. We proposed a pose recognition system that distinguishes between seven poses of the user on the bed. First, the face and arm regions of the user are detected by using the skin color. Our system focuses a recognition region surrounding the face region. Next, the higher order local autocorrelation features within the region are extracted. The linear discriminant analysis creates the coefficient matrix that can optimally distinguish among training data from the seven poses. Our algorithm can recognize the seven poses even if the subject wears different clothes and slightly shifts or slants on the bed. From the experimental results, our system achieved an accuracy rate of over 99 %. Then, we show that it possibles to construct one of a user-friendly system.

Impact of Internet Search Engines on OPAC Users: A Study of Punjabi University, Patiala (India)

ERIC Educational Resources Information Center

Kumar, Shiv

2012-01-01

Purpose: The aim of this paper is to study the impact of internet search engine usage with special reference to OPAC searches in the Punjabi University Library, Patiala, Punjab (India). Design/methodology/approach: The primary data were collected from 352 users comprising faculty, research scholars and postgraduate students of the university. A…

Professional perspectives on service user and carer involvement in mental health care planning: a qualitative study.

PubMed

Bee, Penny; Brooks, Helen; Fraser, Claire; Lovell, Karina

2015-12-01

Involving users/carers in mental health care-planning is central to international policy initiatives yet users frequently report feeling excluded from the care planning process. Rigorous explorations of mental health professionals' experiences of care planning are lacking, limiting our understanding of this important translational gap. To explore professional perceptions of delivering collaborative mental health care-planning and involving service users and carers in their care. Qualitative interviews and focus groups with data combined and subjected to framework analysis. UK secondary care mental health services. 51 multi-disciplinary professionals involved in care planning and recruited via study advertisements. Emergent themes identified care-planning as a meaningful platform for user/carer involvement but revealed philosophical tensions between user involvement and professional accountability. Professionals emphasised their individual, relational skills as a core facilitator of involvement, highlighting some important deficiencies in conventional staff training programmes. Although internationally accepted on philosophical grounds, user-involved care-planning is poorly defined and lacks effective implementation support. Its full realisation demands greater recognition of both the historical and contemporary contexts in which statutory mental healthcare occurs. Copyright © 2015 The Authors. Published by Elsevier Ltd.. All rights reserved.

A digital memories based user authentication scheme with privacy preservation.

PubMed

Liu, JunLiang; Lyu, Qiuyun; Wang, Qiuhua; Yu, Xiangxiang

2017-01-01

The traditional username/password or PIN based authentication scheme, which still remains the most popular form of authentication, has been proved insecure, unmemorable and vulnerable to guessing, dictionary attack, key-logger, shoulder-surfing and social engineering. Based on this, a large number of new alternative methods have recently been proposed. However, most of them rely on users being able to accurately recall complex and unmemorable information or using extra hardware (such as a USB Key), which makes authentication more difficult and confusing. In this paper, we propose a Digital Memories based user authentication scheme adopting homomorphic encryption and a public key encryption design which can protect users' privacy effectively, prevent tracking and provide multi-level security in an Internet & IoT environment. Also, we prove the superior reliability and security of our scheme compared to other schemes and present a performance analysis and promising evaluation results.

Development of coffee maker service robot using speech and face recognition systems using POMDP

NASA Astrophysics Data System (ADS)

Budiharto, Widodo; Meiliana; Santoso Gunawan, Alexander Agung

2016-07-01

There are many development of intelligent service robot in order to interact with user naturally. This purpose can be done by embedding speech and face recognition ability on specific tasks to the robot. In this research, we would like to propose Intelligent Coffee Maker Robot which the speech recognition is based on Indonesian language and powered by statistical dialogue systems. This kind of robot can be used in the office, supermarket or restaurant. In our scenario, robot will recognize user's face and then accept commands from the user to do an action, specifically in making a coffee. Based on our previous work, the accuracy for speech recognition is about 86% and face recognition is about 93% in laboratory experiments. The main problem in here is to know the intention of user about how sweetness of the coffee. The intelligent coffee maker robot should conclude the user intention through conversation under unreliable automatic speech in noisy environment. In this paper, this spoken dialog problem is treated as a partially observable Markov decision process (POMDP). We describe how this formulation establish a promising framework by empirical results. The dialog simulations are presented which demonstrate significant quantitative outcome.

The effect of product characteristic familiarity on product recognition

NASA Astrophysics Data System (ADS)

Yang, Cheng; An, Fang; Chen, Chen; Zhu, Bin

2017-09-01

In order to explore the effect of product appearance characteristic familiarity on product recognition, both EEG experiment and questionnaire evaluation are used in this research. The objective feedback of user is obtained through the EEG experiment and the subjective opinions are collected through the questionnaires. The EEG experiment is combined with the classical learning-recognition paradigm, and the old-new effect of recognition experiment is used as a metric of recognition degree. Experimental results show that the difference of characteristic familiarity does have a significant effect on product recognition. The conclusion can be used in innovation design.

Method and system for providing work machine multi-functional user interface

DOEpatents

Hoff, Brian D [Peoria, IL; Akasam, Sivaprasad [Peoria, IL; Baker, Thomas M [Peoria, IL

2007-07-10

A method is performed to provide a multi-functional user interface on a work machine for displaying suggested corrective action. The process includes receiving status information associated with the work machine and analyzing the status information to determine an abnormal condition. The process also includes displaying a warning message on the display device indicating the abnormal condition and determining one or more corrective actions to handle the abnormal condition. Further, the process includes determining an appropriate corrective action among the one or more corrective actions and displaying a recommendation message on the display device reflecting the appropriate corrective action. The process may also include displaying a list including the remaining one or more corrective actions on the display device to provide alternative actions to an operator.

Combined Dynamic Time Warping with Multiple Sensors for 3D Gesture Recognition.

PubMed

Choi, Hyo-Rim; Kim, TaeYong

2017-08-17

Cyber-physical systems, which closely integrate physical systems and humans, can be applied to a wider range of applications through user movement analysis. In three-dimensional (3D) gesture recognition, multiple sensors are required to recognize various natural gestures. Several studies have been undertaken in the field of gesture recognition; however, gesture recognition was conducted based on data captured from various independent sensors, which rendered the capture and combination of real-time data complicated. In this study, a 3D gesture recognition method using combined information obtained from multiple sensors is proposed. The proposed method can robustly perform gesture recognition regardless of a user's location and movement directions by providing viewpoint-weighted values and/or motion-weighted values. In the proposed method, the viewpoint-weighted dynamic time warping with multiple sensors has enhanced performance by preventing joint measurement errors and noise due to sensor measurement tolerance, which has resulted in the enhancement of recognition performance by comparing multiple joint sequences effectively.

Alteration and Implementation of the CP/M-86 Operating System for a Multi-User Environment.

DTIC Science & Technology

1982-12-01

THE CP/M-86 OPERATING SYSTEM FOR A MULTI-USER ENVIRONMENT by Thomas V. Almquist and David S. Stevens C-, December 1982 ,LU Thesis Advisor : U. R. Kodres...tool$ 044, robo O0eA 6^900091 Approved for public release; distribution unlimited Alteration and Implementation of the CP/M-86 Operating System for a...SCIENCE IN COMPUTER SCIENCE from the NAVAL POSTGRADUATE SCHOOL December 1982 Authors: Approved by: ..... .. . . . . . . . . Thesis Advisor Second

Method and apparatus for obtaining complete speech signals for speech recognition applications

NASA Technical Reports Server (NTRS)

Abrash, Victor (Inventor); Cesari, Federico (Inventor); Franco, Horacio (Inventor); George, Christopher (Inventor); Zheng, Jing (Inventor)

2009-01-01

The present invention relates to a method and apparatus for obtaining complete speech signals for speech recognition applications. In one embodiment, the method continuously records an audio stream comprising a sequence of frames to a circular buffer. When a user command to commence or terminate speech recognition is received, the method obtains a number of frames of the audio stream occurring before or after the user command in order to identify an augmented audio signal for speech recognition processing. In further embodiments, the method analyzes the augmented audio signal in order to locate starting and ending speech endpoints that bound at least a portion of speech to be processed for recognition. At least one of the speech endpoints is located using a Hidden Markov Model.

Engine structures modeling software system: Computer code. User's manual

NASA Technical Reports Server (NTRS)

1992-01-01

ESMOSS is a specialized software system for the construction of geometric descriptive and discrete analytical models of engine parts, components and substructures which can be transferred to finite element analysis programs such as NASTRAN. The software architecture of ESMOSS is designed in modular form with a central executive module through which the user controls and directs the development of the analytical model. Modules consist of a geometric shape generator, a library of discretization procedures, interfacing modules to join both geometric and discrete models, a deck generator to produce input for NASTRAN and a 'recipe' processor which generates geometric models from parametric definitions. ESMOSS can be executed both in interactive and batch modes. Interactive mode is considered to be the default mode and that mode will be assumed in the discussion in this document unless stated otherwise.

Activity Recognition for Personal Time Management

NASA Astrophysics Data System (ADS)

Prekopcsák, Zoltán; Soha, Sugárka; Henk, Tamás; Gáspár-Papanek, Csaba

We describe an accelerometer based activity recognition system for mobile phones with a special focus on personal time management. We compare several data mining algorithms for the automatic recognition task in the case of single user and multiuser scenario, and improve accuracy with heuristics and advanced data mining methods. The results show that daily activities can be recognized with high accuracy and the integration with the RescueTime software can give good insights for personal time management.

Multi-user quantum key distribution with entangled photons from an AlGaAs chip

NASA Astrophysics Data System (ADS)

Autebert, C.; Trapateau, J.; Orieux, A.; Lemaître, A.; Gomez-Carbonell, C.; Diamanti, E.; Zaquine, I.; Ducci, S.

2016-12-01

In view of real-world applications of quantum information technologies, the combination of miniature quantum resources with existing fibre networks is a crucial issue. Among such resources, on-chip entangled photon sources play a central role for applications spanning quantum communications, computing and metrology. Here, we use a semiconductor source of entangled photons operating at room temperature in conjunction with standard telecom components to demonstrate multi-user quantum key distribution, a core protocol for securing communications in quantum networks. The source consists of an AlGaAs chip-emitting polarisation entangled photon pairs over a large bandwidth in the main telecom band around 1550 nm without the use of any off-chip compensation or interferometric scheme; the photon pairs are directly launched into a dense wavelength division multiplexer (DWDM) and secret keys are distributed between several pairs of users communicating through different channels. We achieve a visibility measured after the DWDM of 87% and show long-distance key distribution using a 50-km standard telecom fibre link between two network users. These results illustrate a promising route to practical, resource-efficient implementations adapted to quantum network infrastructures.

Information theoretic partitioning and confidence based weight assignment for multi-classifier decision level fusion in hyperspectral target recognition applications

NASA Astrophysics Data System (ADS)

Prasad, S.; Bruce, L. M.

2007-04-01

There is a growing interest in using multiple sources for automatic target recognition (ATR) applications. One approach is to take multiple, independent observations of a phenomenon and perform a feature level or a decision level fusion for ATR. This paper proposes a method to utilize these types of multi-source fusion techniques to exploit hyperspectral data when only a small number of training pixels are available. Conventional hyperspectral image based ATR techniques project the high dimensional reflectance signature onto a lower dimensional subspace using techniques such as Principal Components Analysis (PCA), Fisher's linear discriminant analysis (LDA), subspace LDA and stepwise LDA. While some of these techniques attempt to solve the curse of dimensionality, or small sample size problem, these are not necessarily optimal projections. In this paper, we present a divide and conquer approach to address the small sample size problem. The hyperspectral space is partitioned into contiguous subspaces such that the discriminative information within each subspace is maximized, and the statistical dependence between subspaces is minimized. We then treat each subspace as a separate source in a multi-source multi-classifier setup and test various decision fusion schemes to determine their efficacy. Unlike previous approaches which use correlation between variables for band grouping, we study the efficacy of higher order statistical information (using average mutual information) for a bottom up band grouping. We also propose a confidence measure based decision fusion technique, where the weights associated with various classifiers are based on their confidence in recognizing the training data. To this end, training accuracies of all classifiers are used for weight assignment in the fusion process of test pixels. The proposed methods are tested using hyperspectral data with known ground truth, such that the efficacy can be quantitatively measured in terms of target

The AstroVR Collaboratory, an On-line Multi-User Environment for Research in Astrophysics

NASA Astrophysics Data System (ADS)

van Buren, D.; Curtis, P.; Nichols, D. A.; Brundage, M.

We describe our experiment with an on-line collaborative environment where users share the execution of programs and communicate via audio, video, and typed text. Collaborative environments represent the next step in computer-mediated conferencing, combining powerful compute engines, data persistence, shared applications, and teleconferencing tools. As proof of concept, we have implemented a shared image analysis tool, allowing geographically distinct users to analyze FITS images together. We anticipate that \\htmllink{AstroVR}{http://astrovr.ipac.caltech.edu:8888} and similar systems will become an important part of collaborative work in the next decade, including with applications in remote observing, spacecraft operations, on-line meetings, as well as and day-to-day research activities. The technology is generic and promises to find uses in business, medicine, government, and education.

SHARP User Manual

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

Yu, Y. Q.; Shemon, E. R.; Thomas, J. W.

SHARP is an advanced modeling and simulation toolkit for the analysis of nuclear reactors. It is comprised of several components including physical modeling tools, tools to integrate the physics codes for multi-physics analyses, and a set of tools to couple the codes within the MOAB framework. Physics modules currently include the neutronics code PROTEUS, the thermal-hydraulics code Nek5000, and the structural mechanics code Diablo. This manual focuses on performing multi-physics calculations with the SHARP ToolKit. Manuals for the three individual physics modules are available with the SHARP distribution to help the user to either carry out the primary multi-physics calculationmore » with basic knowledge or perform further advanced development with in-depth knowledge of these codes. This manual provides step-by-step instructions on employing SHARP, including how to download and install the code, how to build the drivers for a test case, how to perform a calculation and how to visualize the results. Since SHARP has some specific library and environment dependencies, it is highly recommended that the user read this manual prior to installing SHARP. Verification tests cases are included to check proper installation of each module. It is suggested that the new user should first follow the step-by-step instructions provided for a test problem in this manual to understand the basic procedure of using SHARP before using SHARP for his/her own analysis. Both reference output and scripts are provided along with the test cases in order to verify correct installation and execution of the SHARP package. At the end of this manual, detailed instructions are provided on how to create a new test case so that user can perform novel multi-physics calculations with SHARP. Frequently asked questions are listed at the end of this manual to help the user to troubleshoot issues.« less

The A2iA French handwriting recognition system at the Rimes-ICDAR2011 competition

NASA Astrophysics Data System (ADS)

Menasri, Farès; Louradour, Jérôme; Bianne-Bernard, Anne-Laure; Kermorvant, Christopher

2012-01-01

This paper describes the system for the recognition of French handwriting submitted by A2iA to the competition organized at ICDAR2011 using the Rimes database. This system is composed of several recognizers based on three different recognition technologies, combined using a novel combination method. A framework multi-word recognition based on weighted finite state transducers is presented, using an explicit word segmentation, a combination of isolated word recognizers and a language model. The system was tested both for isolated word recognition and for multi-word line recognition and submitted to the RIMES-ICDAR2011 competition. This system outperformed all previously proposed systems on these tasks.

Improving User Access to the Integrated Multi-Satellite Retrievals for GPM (IMERG) Products

NASA Astrophysics Data System (ADS)

Huffman, George; Bolvin, David; Nelkin, Eric; Kidd, Christopher

2016-04-01

The U.S. Global Precipitation Measurement mission (GPM) team has developed the Integrated Multi-satellitE Retrievals for GPM (IMERG) algorithm to take advantage of the international constellation of precipitation-relevant satellites and the Global Precipitation Climatology Centre surface precipitation gauge analysis. The goal is to provide a long record of homogeneous, high-resolution quasi-global estimates of precipitation. While expert scientific researchers are major users of the IMERG products, it is clear that many other user communities and disciplines also desire access to the data for wide-ranging applications. Lessons learned during the Tropical Rainfall Measuring Mission, the predecessor to GPM, led to some basic design choices that provided the framework for supporting multiple user bases. For example, two near-real-time "runs" are computed, the Early and Late (currently 5 and 15 hours after observation time, respectively), then the Final Run about 3 months later. The datasets contain multiple fields that provide insight into the computation of the complete precipitation data field, as well as diagnostic (currently) estimates of the precipitation's phase. In parallel with this, the archive sites are working to provide the IMERG data in a variety of formats, and with subsetting and simple interactive analysis to make the data more easily available to non-expert users. The various options for accessing the data are summarized under the pmm.nasa.gov data access page. The talk will end by considering the feasibility of major user requests, including polar coverage, a simplified Data Quality Index, and reduced data latency for the Early Run. In brief, the first two are challenging, but under the team's control. The last requires significant action by some of the satellite data providers.

Installation for the catalytic afterburning of exhaust gases of a multi-cylinder internal combustion engine

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

Lange, K.

1974-04-24

An installation for the catalytic afterburning of exhaust gases of a multi-cylinder internal combustion engine has two cylinder rows with two exhaust gas lines, each of which includes at least one catalyst. A temperature-responsive control is operable during engine start-up to conduct substantially the entire exhaust gas flow from the internal combustion engine during warmup for a predetermined time by way of only one of the two catalyst and then, after a short period of time, to conduct the exhaust gas flow from each row of cylinders by way of its associated gas line and catalyst.

The coevolution of recognition and social behavior.

PubMed

Smead, Rory; Forber, Patrick

2016-05-26

Recognition of behavioral types can facilitate the evolution of cooperation by enabling altruistic behavior to be directed at other cooperators and withheld from defectors. While much is known about the tendency for recognition to promote cooperation, relatively little is known about whether such a capacity can coevolve with the social behavior it supports. Here we use evolutionary game theory and multi-population dynamics to model the coevolution of social behavior and recognition. We show that conditional harming behavior enables the evolution and stability of social recognition, whereas conditional helping leads to a deterioration of recognition ability. Expanding the model to include a complex game where both helping and harming interactions are possible, we find that conditional harming behavior can stabilize recognition, and thereby lead to the evolution of conditional helping. Our model identifies a novel hypothesis for the evolution of cooperation: conditional harm may have coevolved with recognition first, thereby helping to establish the mechanisms necessary for the evolution of cooperation.

The coevolution of recognition and social behavior

PubMed Central

Smead, Rory; Forber, Patrick

2016-01-01

Recognition of behavioral types can facilitate the evolution of cooperation by enabling altruistic behavior to be directed at other cooperators and withheld from defectors. While much is known about the tendency for recognition to promote cooperation, relatively little is known about whether such a capacity can coevolve with the social behavior it supports. Here we use evolutionary game theory and multi-population dynamics to model the coevolution of social behavior and recognition. We show that conditional harming behavior enables the evolution and stability of social recognition, whereas conditional helping leads to a deterioration of recognition ability. Expanding the model to include a complex game where both helping and harming interactions are possible, we find that conditional harming behavior can stabilize recognition, and thereby lead to the evolution of conditional helping. Our model identifies a novel hypothesis for the evolution of cooperation: conditional harm may have coevolved with recognition first, thereby helping to establish the mechanisms necessary for the evolution of cooperation. PMID:27225673

Multi-Sensor Information Fusion for Optimizing Electric Bicycle Routes Using a Swarm Intelligence Algorithm

PubMed Central

Villarubia, Gabriel; De Paz, Juan F.; Bajo, Javier

2017-01-01

The use of electric bikes (e-bikes) has grown in popularity, especially in large cities where overcrowding and traffic congestion are common. This paper proposes an intelligent engine management system for e-bikes which uses the information collected from sensors to optimize battery energy and time. The intelligent engine management system consists of a built-in network of sensors in the e-bike, which is used for multi-sensor data fusion; the collected data is analysed and fused and on the basis of this information the system can provide the user with optimal and personalized assistance. The user is given recommendations related to battery consumption, sensors, and other parameters associated with the route travelled, such as duration, speed, or variation in altitude. To provide a user with these recommendations, artificial neural networks are used to estimate speed and consumption for each of the segments of a route. These estimates are incorporated into evolutionary algorithms in order to make the optimizations. A comparative analysis of the results obtained has been conducted for when routes were travelled with and without the optimization system. From the experiments, it is evident that the use of an engine management system results in significant energy and time savings. Moreover, user satisfaction increases as the level of assistance adapts to user behavior and the characteristics of the route. PMID:29088087

Multi-Sensor Information Fusion for Optimizing Electric Bicycle Routes Using a Swarm Intelligence Algorithm.

PubMed

De La Iglesia, Daniel H; Villarrubia, Gabriel; De Paz, Juan F; Bajo, Javier

2017-10-31

The use of electric bikes (e-bikes) has grown in popularity, especially in large cities where overcrowding and traffic congestion are common. This paper proposes an intelligent engine management system for e-bikes which uses the information collected from sensors to optimize battery energy and time. The intelligent engine management system consists of a built-in network of sensors in the e-bike, which is used for multi-sensor data fusion; the collected data is analysed and fused and on the basis of this information the system can provide the user with optimal and personalized assistance. The user is given recommendations related to battery consumption, sensors, and other parameters associated with the route travelled, such as duration, speed, or variation in altitude. To provide a user with these recommendations, artificial neural networks are used to estimate speed and consumption for each of the segments of a route. These estimates are incorporated into evolutionary algorithms in order to make the optimizations. A comparative analysis of the results obtained has been conducted for when routes were travelled with and without the optimization system. From the experiments, it is evident that the use of an engine management system results in significant energy and time savings. Moreover, user satisfaction increases as the level of assistance adapts to user behavior and the characteristics of the route.

Event identification by acoustic signature recognition

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

Dress, W.B.; Kercel, S.W.

1995-07-01

Many events of interest to the security commnnity produce acoustic emissions that are, in principle, identifiable as to cause. Some obvious examples are gunshots, breaking glass, takeoffs and landings of small aircraft, vehicular engine noises, footsteps (high frequencies when on gravel, very low frequencies. when on soil), and voices (whispers to shouts). We are investigating wavelet-based methods to extract unique features of such events for classification and identification. We also discuss methods of classification and pattern recognition specifically tailored for acoustic signatures obtained by wavelet analysis. The paper is divided into three parts: completed work, work in progress, and futuremore » applications. The completed phase has led to the successful recognition of aircraft types on landing and takeoff. Both small aircraft (twin-engine turboprop) and large (commercial airliners) were included in the study. The project considered the design of a small, field-deployable, inexpensive device. The techniques developed during the aircraft identification phase were then adapted to a multispectral electromagnetic interference monitoring device now deployed in a nuclear power plant. This is a general-purpose wavelet analysis engine, spanning 14 octaves, and can be adapted for other specific tasks. Work in progress is focused on applying the methods previously developed to speaker identification. Some of the problems to be overcome include recognition of sounds as voice patterns and as distinct from possible background noises (e.g., music), as well as identification of the speaker from a short-duration voice sample. A generalization of the completed work and the work in progress is a device capable of classifying any number of acoustic events-particularly quasi-stationary events such as engine noises and voices and singular events such as gunshots and breaking glass. We will show examples of both kinds of events and discuss their recognition likelihood.« less

Intrasystem Electromagnetic Compatibility Analysis Program. Volume 1. User’s Manual Engineering Section

DTIC Science & Technology

1974-12-01

AD-A008 526 INTRASYSTEM ELECTROMAGNETIC COMPATI- BILITY ANALYSIS PROGRAM. VOLUME I. USER’S MANUAL ENGINEERING SECTION J. L. Bogdanor , et al McDonnell...e) 8 CONTRACT OR GRANT NUMBERfs) J.L. Bogdanor F30602-72-C-0277 R.A. Pearlman M.D. Siegel PERFORMING ORGANIZATION NAME AND ADDRESS I0 PROGRAM ELEMENT...June 1968. 14. J. L. Bogdanor , M. D. Siegel, G. L. Weinstock, "Intra-Vehicle Electromagnetic Compatibility Analysis," AFAL-TR-71-155, July 1971. 15

Engineering multi-functional bacterial outer membrane vesicles as modular nanodevices for biosensing and bioimaging.

PubMed

Chen, Qi; Rozovsky, Sharon; Chen, Wilfred

2017-07-04

Outer membrane vesicles (OMVs) are proteoliposomes derived from the outer membrane and periplasmic space of many Gram-negative bacteria including E. coli as part of their natural growth cycle. Inspired by the natural ability of E. coli to sort proteins to both the exterior and interior of OMVs, we reported here a one-pot synthesis approach to engineer multi-functionalized OMV-based sensors for both antigen binding and signal generation. SlyB, a native lipoprotein, was used a fusion partner to package nanoluciferase (Nluc) within OMVs, while a previously developed INP-Scaf3 surface scaffold was fused to the Z-domain for antibody recruiting. The multi-functionalized OMVs were used for thrombin detection with a detection limit of 0.5 nM, comparable to other detection methods. Using the cohesin domains inserted between the Z-domain and INP, these engineered OMVs were further functionalized with a dockerin-tagged GFP for cancer cell imaging.

Activity recognition of assembly tasks using body-worn microphones and accelerometers.

PubMed

Ward, Jamie A; Lukowicz, Paul; Tröster, Gerhard; Starner, Thad E

2006-10-01

In order to provide relevant information to mobile users, such as workers engaging in the manual tasks of maintenance and assembly, a wearable computer requires information about the user's specific activities. This work focuses on the recognition of activities that are characterized by a hand motion and an accompanying sound. Suitable activities can be found in assembly and maintenance work. Here, we provide an initial exploration into the problem domain of continuous activity recognition using on-body sensing. We use a mock "wood workshop" assembly task to ground our investigation. We describe a method for the continuous recognition of activities (sawing, hammering, filing, drilling, grinding, sanding, opening a drawer, tightening a vise, and turning a screwdriver) using microphones and three-axis accelerometers mounted at two positions on the user's arms. Potentially "interesting" activities are segmented from continuous streams of data using an analysis of the sound intensity detected at the two different locations. Activity classification is then performed on these detected segments using linear discriminant analysis (LDA) on the sound channel and hidden Markov models (HMMs) on the acceleration data. Four different methods at classifier fusion are compared for improving these classifications. Using user-dependent training, we obtain continuous average recall and precision rates (for positive activities) of 78 percent and 74 percent, respectively. Using user-independent training (leave-one-out across five users), we obtain recall rates of 66 percent and precision rates of 63 percent. In isolation, these activities were recognized with accuracies of 98 percent, 87 percent, and 95 percent for the user-dependent, user-independent, and user-adapted cases, respectively.

Dynamic Gesture Recognition with a Terahertz Radar Based on Range Profile Sequences and Doppler Signatures

PubMed Central

Pi, Yiming

2017-01-01

The frequency of terahertz radar ranges from 0.1 THz to 10 THz, which is higher than that of microwaves. Multi-modal signals, including high-resolution range profile (HRRP) and Doppler signatures, can be acquired by the terahertz radar system. These two kinds of information are commonly used in automatic target recognition; however, dynamic gesture recognition is rarely discussed in the terahertz regime. In this paper, a dynamic gesture recognition system using a terahertz radar is proposed, based on multi-modal signals. The HRRP sequences and Doppler signatures were first achieved from the radar echoes. Considering the electromagnetic scattering characteristics, a feature extraction model is designed using location parameter estimation of scattering centers. Dynamic Time Warping (DTW) extended to multi-modal signals is used to accomplish the classifications. Ten types of gesture signals, collected from a terahertz radar, are applied to validate the analysis and the recognition system. The results of the experiment indicate that the recognition rate reaches more than 91%. This research verifies the potential applications of dynamic gesture recognition using a terahertz radar. PMID:29267249

Dynamic Gesture Recognition with a Terahertz Radar Based on Range Profile Sequences and Doppler Signatures.

PubMed

Zhou, Zhi; Cao, Zongjie; Pi, Yiming

2017-12-21

The frequency of terahertz radar ranges from 0.1 THz to 10 THz, which is higher than that of microwaves. Multi-modal signals, including high-resolution range profile (HRRP) and Doppler signatures, can be acquired by the terahertz radar system. These two kinds of information are commonly used in automatic target recognition; however, dynamic gesture recognition is rarely discussed in the terahertz regime. In this paper, a dynamic gesture recognition system using a terahertz radar is proposed, based on multi-modal signals. The HRRP sequences and Doppler signatures were first achieved from the radar echoes. Considering the electromagnetic scattering characteristics, a feature extraction model is designed using location parameter estimation of scattering centers. Dynamic Time Warping (DTW) extended to multi-modal signals is used to accomplish the classifications. Ten types of gesture signals, collected from a terahertz radar, are applied to validate the analysis and the recognition system. The results of the experiment indicate that the recognition rate reaches more than 91%. This research verifies the potential applications of dynamic gesture recognition using a terahertz radar.

Automatic detection and recognition of multiple macular lesions in retinal optical coherence tomography images with multi-instance multilabel learning

NASA Astrophysics Data System (ADS)

Fang, Leyuan; Yang, Liumao; Li, Shutao; Rabbani, Hossein; Liu, Zhimin; Peng, Qinghua; Chen, Xiangdong

2017-06-01

Detection and recognition of macular lesions in optical coherence tomography (OCT) are very important for retinal diseases diagnosis and treatment. As one kind of retinal disease (e.g., diabetic retinopathy) may contain multiple lesions (e.g., edema, exudates, and microaneurysms) and eye patients may suffer from multiple retinal diseases, multiple lesions often coexist within one retinal image. Therefore, one single-lesion-based detector may not support the diagnosis of clinical eye diseases. To address this issue, we propose a multi-instance multilabel-based lesions recognition (MIML-LR) method for the simultaneous detection and recognition of multiple lesions. The proposed MIML-LR method consists of the following steps: (1) segment the regions of interest (ROIs) for different lesions, (2) compute descriptive instances (features) for each lesion region, (3) construct multilabel detectors, and (4) recognize each ROI with the detectors. The proposed MIML-LR method was tested on 823 clinically labeled OCT images with normal macular and macular with three common lesions: epiretinal membrane, edema, and drusen. For each input OCT image, our MIML-LR method can automatically identify the number of lesions and assign the class labels, achieving the average accuracy of 88.72% for the cases with multiple lesions, which better assists macular disease diagnosis and treatment.

Painting recognition with smartphones equipped with inertial measurement unit

NASA Astrophysics Data System (ADS)

Masiero, Andrea; Guarnieri, Alberto; Pirotti, Francesco; Vettore, Antonio

2015-06-01

Recently, several works have been proposed in the literature to take advantage of the diffusion of smartphones to improve people experience during museum visits. The rationale is that of substituting traditional written/audio guides with interactive electronic guides usable on a mobile phone. Augmented reality systems are usually considered to make the use of such electronic guides more effective for the user. The main goal of such augmented reality system (i.e. providing the user with the information of his/her interest) is usually achieved by properly executing the following three tasks: recognizing the object of interest to the user, retrieving the most relevant information about it, properly presenting the retrieved information. This paper focuses on the first task: we consider the problem of painting recognition by means of measure- ments provided by a smartphone. We assume that the user acquires one image of the painting of interest with the standard camera of the device. This image is compared with a set of reference images of the museum objects in order to recognize the object of interest to the user. Since comparing images taken in different conditions can lead to unsatisfactory recognition results, the acquired image is typically properly transformed in order to improve the results of the recognition system: first, the system estimates the homography between properly matched features in the two images. Then, the user image is transformed accordingly to the estimated homography. Finally, it is compared with the reference one. This work proposes a novel method to exploit inertial measurement unit (IMU) measurements to improve the system performance, in particular in terms of computational load reduction: IMU measurements are exploited to reduce both the computational burden required to estimate the transformation to be applied to the user image, and the number of reference images to be compared with it.

A grid generation system for multi-disciplinary design optimization

NASA Technical Reports Server (NTRS)

Jones, William T.; Samareh-Abolhassani, Jamshid

1995-01-01

A general multi-block three-dimensional volume grid generator is presented which is suitable for Multi-Disciplinary Design Optimization. The code is timely, robust, highly automated, and written in ANSI 'C' for platform independence. Algebraic techniques are used to generate and/or modify block face and volume grids to reflect geometric changes resulting from design optimization. Volume grids are generated/modified in a batch environment and controlled via an ASCII user input deck. This allows the code to be incorporated directly into the design loop. Generated volume grids are presented for a High Speed Civil Transport (HSCT) Wing/Body geometry as well a complex HSCT configuration including horizontal and vertical tails, engine nacelles and pylons, and canard surfaces.

User compliance with documenting on a track and trigger-based observation and response chart: a two-phase multi-site audit study.

PubMed

Elliott, Doug; Allen, Emily; McKinley, Sharon; Perry, Lin; Duffield, Christine; Fry, Margaret; Gallagher, Robyn; Iedema, Rick; Roche, Michael

2017-12-01

To examine user compliance and completeness of documentation with a newly designed observation and response chart and whether a rapid response system call was triggered when clinically indicated. Timely recognition and responses to patient deterioration in hospital general wards remain a challenge for healthcare systems globally. Evaluating practice initiatives to improve recognition and response are required. Two-phase audit. Following introduction of the charts in ten health service sites in Australia, an audit of chart completion was conducted during a short trial for initial usability (Phase 1; 2011). After chart adoption as routine use in practice, retrospective and prospective chart audits were conducted (Phase 2; 2012). Overall, 818 and 1,058 charts were audited during the two phases respectively. Compliance was mixed but improved with the new chart (4%-14%). Contrary to chart guidelines, numbers rather than dots were written in the graphing section in 60% of cases. Rates of recognition of abnormal vital signs improved slightly with new charts in use, particularly for higher levels of surveillance and clinical review. Based on local calling criteria, an emergency call was initiated in 33% of cases during the retrospective audit and in 41% of cases with the new chart. User compliance was less than optimal, limiting full function of the chart sections and compliance with local calling criteria. Overcoming apparent behavioural and work culture barriers may improve chart completion, aiding identification of abnormal vital signs and triggering a rapid response system activation when clinical deterioration is detected. © 2017 The Authors. Journal of Advanced Nursing Published by John Wiley & Sons Ltd.

Heavy-tailed distribution of the SSH Brute-force attack duration in a multi-user environment

NASA Astrophysics Data System (ADS)

Lee, Jae-Kook; Kim, Sung-Jun; Park, Chan Yeol; Hong, Taeyoung; Chae, Huiseung

2016-07-01

Quite a number of cyber-attacks to be place against supercomputers that provide highperformance computing (HPC) services to public researcher. Particularly, although the secure shell protocol (SSH) brute-force attack is one of the traditional attack methods, it is still being used. Because stealth attacks that feign regular access may occur, they are even harder to detect. In this paper, we introduce methods to detect SSH brute-force attacks by analyzing the server's unsuccessful access logs and the firewall's drop events in a multi-user environment. Then, we analyze the durations of the SSH brute-force attacks that are detected by applying these methods. The results of an analysis of about 10 thousands attack source IP addresses show that the behaviors of abnormal users using SSH brute-force attacks are based on human dynamic characteristics of a typical heavy-tailed distribution.

Interaction Design and Usability of Learning Spaces in 3D Multi-user Virtual Worlds

NASA Astrophysics Data System (ADS)

Minocha, Shailey; Reeves, Ahmad John

Three-dimensional virtual worlds are multimedia, simulated environments, often managed over the Web, which users can 'inhabit' and interact via their own graphical, self-representations known as 'avatars'. 3D virtual worlds are being used in many applications: education/training, gaming, social networking, marketing and commerce. Second Life is the most widely used 3D virtual world in education. However, problems associated with usability, navigation and way finding in 3D virtual worlds may impact on student learning and engagement. Based on empirical investigations of learning spaces in Second Life, this paper presents design guidelines to improve the usability and ease of navigation in 3D spaces. Methods of data collection include semi-structured interviews with Second Life students, educators and designers. The findings have revealed that design principles from the fields of urban planning, Human- Computer Interaction, Web usability, geography and psychology can influence the design of spaces in 3D multi-user virtual environments.

Context-aware tunable office lighting application and user response

NASA Astrophysics Data System (ADS)

Chen, Nancy H.; Nawyn, Jason; Thompson, Maria; Gibbs, Julie; Larson, Kent

2013-09-01

LED light sources having multiple independently controllable color channels allow tuning of both the intensity and color output. Consequently, highly tailored lighting can be applied according to instantaneous user needs and preferences. Besides improving lighting performance, energy use can also be reduced since the brightest illumination is applied only when necessary. In an example application, low activity or vacant areas of a multi-zone office are lit by low power illumination, including colored light options, which can reduce energy consumption to 20-45% of typical full-time, fullbrightness, office-wide illumination. The availability of color also allows communication functions and additional aesthetic design possibilities. To reduce user burden in frequent switching between various illumination settings, an activity recognition sensor network is used to identify selected office activities. The illumination is then adjusted automatically to satisfy the needs of the occupants. A handheld mobile device provides an interactive interface for gathering user feedback regarding impressions and illumination preferences. The activity-triggered queries collect contemporaneous feedback that reduces reliance on memory; immediate previews of illumination options are also provided. Through mobile queries and post-experience interviews, user feedback was gathered regarding automation, colored lighting, and illumination preferences. Overall reaction was indicated by a range of response words such as fun, stimulating, very cool, very pleasant, enjoyed, good, comfortable, satisfactory, fine, energy saving, interesting, curious, dim, cave, isolated, distracting, and unfamiliar. Positive reaction from a meaningful, though not universal, fraction of users indicates reasonable application potential, particularly as personal preferences and control are accommodated.

Atmospheric lidar multi-user instrument system definition study

NASA Technical Reports Server (NTRS)

Greco, R. V. (Editor)

1980-01-01

A spaceborne lidar system for atmospheric studies was defined. The primary input was the Science Objectives Experiment Description and Evolutionary Flow Document. The first task of the study was to perform an experiment evolutionary analysis of the SEED. The second task was the system definition effort of the instrument system. The third task was the generation of a program plan for the hardware phase. The fourth task was the supporting studies which included a Shuttle deficiency analysis, a preliminary safety hazard analysis, the identification of long lead items, and development studies required. As a result of the study an evolutionary Lidar Multi-User Instrument System (MUIS) was defined. The MUIS occupies a full Spacelab pallet and has a weight of 1300 kg. The Lidar MUIS laser provides a 2 joule frequency doubled Nd:YAG laser that can also pump a tuneable dye laser wide frequency range and bandwidth. The MUIS includes a 1.25 meter diameter aperture Cassegrain receiver, with a moveable secondary mirror to provide precise alignment with the laser. The receiver can transmit the return signal to three single and multiple photomultiple tube detectors by use of a rotating fold mirror. It is concluded that the Lidar MUIS proceed to program implementation.

Automation of Shuttle Tile Inspection - Engineering methodology for Space Station

NASA Technical Reports Server (NTRS)

Wiskerchen, M. J.; Mollakarimi, C.

1987-01-01

The Space Systems Integration and Operations Research Applications (SIORA) Program was initiated in late 1986 as a cooperative applications research effort between Stanford University, NASA Kennedy Space Center, and Lockheed Space Operations Company. One of the major initial SIORA tasks was the application of automation and robotics technology to all aspects of the Shuttle tile processing and inspection system. This effort has adopted a systems engineering approach consisting of an integrated set of rapid prototyping testbeds in which a government/university/industry team of users, technologists, and engineers test and evaluate new concepts and technologies within the operational world of Shuttle. These integrated testbeds include speech recognition and synthesis, laser imaging inspection systems, distributed Ada programming environments, distributed relational database architectures, distributed computer network architectures, multimedia workbenches, and human factors considerations.

Generating and Visualizing Climate Indices using Google Earth Engine

NASA Astrophysics Data System (ADS)

Erickson, T. A.; Guentchev, G.; Rood, R. B.

2017-12-01

Climate change is expected to have largest impacts on regional and local scales. Relevant and credible climate information is needed to support the planning and adaptation efforts in our communities. The volume of climate projections of temperature and precipitation is steadily increasing, as datasets are being generated on finer spatial and temporal grids with an increasing number of ensembles to characterize uncertainty. Despite advancements in tools for querying and retrieving subsets of these large, multi-dimensional datasets, ease of access remains a barrier for many existing and potential users who want to derive useful information from these data, particularly for those outside of the climate modelling research community. Climate indices, that can be derived from daily temperature and precipitation data, such as annual number of frost days or growing season length, can provide useful information to practitioners and stakeholders. For this work the NASA Earth Exchange Global Daily Downscaled Projections (NEX-GDDP) dataset was loaded into Google Earth Engine, a cloud-based geospatial processing platform. Algorithms that use the Earth Engine API to generate several climate indices were written. The indices were chosen from the set developed by the joint CCl/CLIVAR/JCOMM Expert Team on Climate Change Detection and Indices (ETCCDI). Simple user interfaces were created that allow users to query, produce maps and graphs of the indices, as well as download results for additional analyses. These browser-based interfaces could allow users in low-bandwidth environments to access climate information. This research shows that calculating climate indices from global downscaled climate projection datasets and sharing them widely using cloud computing technologies is feasible. Further development will focus on exposing the climate indices to existing applications via the Earth Engine API, and building custom user interfaces for presenting climate indices to a diverse set of

Speech-recognition interfaces for music information retrieval

NASA Astrophysics Data System (ADS)

Goto, Masataka

2005-09-01

This paper describes two hands-free music information retrieval (MIR) systems that enable a user to retrieve and play back a musical piece by saying its title or the artist's name. Although various interfaces for MIR have been proposed, speech-recognition interfaces suitable for retrieving musical pieces have not been studied. Our MIR-based jukebox systems employ two different speech-recognition interfaces for MIR, speech completion and speech spotter, which exploit intentionally controlled nonverbal speech information in original ways. The first is a music retrieval system with the speech-completion interface that is suitable for music stores and car-driving situations. When a user only remembers part of the name of a musical piece or an artist and utters only a remembered fragment, the system helps the user recall and enter the name by completing the fragment. The second is a background-music playback system with the speech-spotter interface that can enrich human-human conversation. When a user is talking to another person, the system allows the user to enter voice commands for music playback control by spotting a special voice-command utterance in face-to-face or telephone conversations. Experimental results from use of these systems have demonstrated the effectiveness of the speech-completion and speech-spotter interfaces. (Video clips: http://staff.aist.go.jp/m.goto/MIR/speech-if.html)

Engineering responsive polymer building blocks with host-guest molecular recognition for functional applications.

PubMed

Hu, Jinming; Liu, Shiyong

2014-07-15

CONSPECTUS: All living organisms and soft matter are intrinsically responsive and adaptive to external stimuli. Inspired by this fact, tremendous effort aiming to emulate subtle responsive features exhibited by nature has spurred the invention of a diverse range of responsive polymeric materials. Conventional stimuli-responsive polymers are constructed via covalent bonds and can undergo reversible or irreversible changes in chemical structures, physicochemical properties, or both in response to a variety of external stimuli. They have been imparted with a variety of emerging applications including drug and gene delivery, optical sensing and imaging, diagnostics and therapies, smart coatings and textiles, and tissue engineering. On the other hand, in comparison with molecular chemistry held by covalent bonds, supramolecular chemistry built on weak and reversible noncovalent interactions has emerged as a powerful and versatile strategy for materials fabrication due to its facile accessibility, extraordinary reversibility and adaptivity, and potent applications in diverse fields. Typically involving more than one type of noncovalent interactions (e.g., hydrogen bonding, metal coordination, hydrophobic association, electrostatic interactions, van der Waals forces, and π-π stacking), host-guest recognition refers to the formation of supramolecular inclusion complexes between two or more entities connected together in a highly controlled and cooperative manner. The inherently reversible and adaptive nature of host-guest molecular recognition chemistry, stemming from multiple noncovalent interactions, has opened up a new platform to construct novel types of stimuli-responsive materials. The introduction of host-guest chemistry not only enriches the realm of responsive materials but also confers them with promising new applications. Most intriguingly, the integration of responsive polymer building blocks with host-guest recognition motifs will endow the former with

Children with a cochlear implant: characteristics and determinants of speech recognition, speech-recognition growth rate, and speech production.

PubMed

Wie, Ona Bø; Falkenberg, Eva-Signe; Tvete, Ole; Tomblin, Bruce

2007-05-01

The objectives of the study were to describe the characteristics of the first 79 prelingually deaf cochlear implant users in Norway and to investigate to what degree the variation in speech recognition, speech- recognition growth rate, and speech production could be explained by the characteristics of the child, the cochlear implant, the family, and the educational setting. Data gathered longitudinally were analysed using descriptive statistics, multiple regression, and growth-curve analysis. The results show that more than 50% of the variation could be explained by these characteristics. Daily user-time, non-verbal intelligence, mode of communication, length of CI experience, and educational placement had the highest effect on the outcome. The results also indicate that children educated in a bilingual approach to education have better speech perception and faster speech perception growth rate with increased focus on spoken language.

Object recognition through a multi-mode fiber

NASA Astrophysics Data System (ADS)

Takagi, Ryosuke; Horisaki, Ryoichi; Tanida, Jun

2017-04-01

We present a method of recognizing an object through a multi-mode fiber. A number of speckle patterns transmitted through a multi-mode fiber are provided to a classifier based on machine learning. We experimentally demonstrated binary classification of face and non-face targets based on the method. The measurement process of the experimental setup was random and nonlinear because a multi-mode fiber is a typical strongly scattering medium and any reference light was not used in our setup. Comparisons between three supervised learning methods, support vector machine, adaptive boosting, and neural network, are also provided. All of those learning methods achieved high accuracy rates at about 90% for the classification. The approach presented here can realize a compact and smart optical sensor. It is practically useful for medical applications, such as endoscopy. Also our study indicated a promising utilization of artificial intelligence, which has rapidly progressed, for reducing optical and computational costs in optical sensing systems.

Sonic IR crack detection of aircraft turbine engine blades with multi-frequency ultrasound excitations

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

Zhang, Ding; Han, Xiaoyan; Newaz, Golam

Effectively and accurately detecting cracks or defects in critical engine components, such as turbine engine blades, is very important for aircraft safety. Sonic Infrared (IR) Imaging is such a technology with great potential for these applications. This technology combines ultrasound excitation and IR imaging to identify cracks and flaws in targets. In general, failure of engine components, such as blades, begins with tiny cracks. Since the attenuation of the ultrasound wave propagation in turbine engine blades is small, the efficiency of crack detection in turbine engine blades can be quite high. The authors at Wayne State University have been developingmore » the technology as a reliable tool for the future field use in aircraft engines and engine parts. One part of the development is to use finite element modeling to assist our understanding of effects of different parameters on crack heating while experimentally hard to achieve. The development has been focused with single frequency ultrasound excitation and some results have been presented in a previous conference. We are currently working on multi-frequency excitation models. The study will provide results and insights of the efficiency of different frequency excitation sources to foster the development of the technology for crack detection in aircraft engine components.« less

Executive function deficits in short-term abstinent cannabis users.

PubMed

McHale, Sue; Hunt, Nigel

2008-07-01

Few cognitive tasks are adequately sensitive to show the small decrements in performance in abstinent chronic cannabis users. In this series of three experiments we set out to demonstrate a variety of tasks that are sufficiently sensitive to show differences in visual memory, verbal memory, everyday memory and executive function between controls and cannabis users. A series of three studies explored cognitive function deficits in cannabis users (phonemic verbal fluency, visual recognition and immediate and delayed recall, and prospective memory) in short-term abstinent cannabis users. Participants were selected using snowball sampling, with cannabis users being compared to a standard control group and a tobacco-use control group. The cannabis users, compared to both control groups, had deficits on verbal fluency, visual recognition, delayed visual recall, and short- and long-interval prospective memory. There were no differences for immediate visual recall. These findings suggest that cannabis use leads to impaired executive function. Further research needs to explore the longer term impact of cannabis use. Copyright 2008 John Wiley & Sons, Ltd.

ReliefF-Based EEG Sensor Selection Methods for Emotion Recognition.

PubMed

Zhang, Jianhai; Chen, Ming; Zhao, Shaokai; Hu, Sanqing; Shi, Zhiguo; Cao, Yu

2016-09-22

Electroencephalogram (EEG) signals recorded from sensor electrodes on the scalp can directly detect the brain dynamics in response to different emotional states. Emotion recognition from EEG signals has attracted broad attention, partly due to the rapid development of wearable computing and the needs of a more immersive human-computer interface (HCI) environment. To improve the recognition performance, multi-channel EEG signals are usually used. A large set of EEG sensor channels will add to the computational complexity and cause users inconvenience. ReliefF-based channel selection methods were systematically investigated for EEG-based emotion recognition on a database for emotion analysis using physiological signals (DEAP). Three strategies were employed to select the best channels in classifying four emotional states (joy, fear, sadness and relaxation). Furthermore, support vector machine (SVM) was used as a classifier to validate the performance of the channel selection results. The experimental results showed the effectiveness of our methods and the comparison with the similar strategies, based on the F-score, was given. Strategies to evaluate a channel as a unity gave better performance in channel reduction with an acceptable loss of accuracy. In the third strategy, after adjusting channels' weights according to their contribution to the classification accuracy, the number of channels was reduced to eight with a slight loss of accuracy (58.51% ± 10.05% versus the best classification accuracy 59.13% ± 11.00% using 19 channels). In addition, the study of selecting subject-independent channels, related to emotion processing, was also implemented. The sensors, selected subject-independently from frontal, parietal lobes, have been identified to provide more discriminative information associated with emotion processing, and are distributed symmetrically over the scalp, which is consistent with the existing literature. The results will make a contribution to the

Selective recognition of N4-methylcytosine in DNA by engineered transcription-activator-like effectors.

PubMed

Rathi, Preeti; Maurer, Sara; Summerer, Daniel

2018-06-05

The epigenetic DNA nucleobases 5-methylcytosine (5mC) and N 4-methylcytosine (4mC) coexist in bacterial genomes and have important functions in host defence and transcription regulation. To better understand the individual biological roles of both methylated nucleobases, analytical strategies for distinguishing unmodified cytosine (C) from 4mC and 5mC are required. Transcription-activator-like effectors (TALEs) are programmable DNA-binding repeat proteins, which can be re-engineered for the direct detection of epigenetic nucleobases in user-defined DNA sequences. We here report the natural, cytosine-binding TALE repeat to not strongly differentiate between 5mC and 4mC. To engineer repeats with selectivity in the context of C, 5mC and 4mC, we developed a homogeneous fluorescence assay and screened a library of size-reduced TALE repeats for binding to all three nucleobases. This provided insights into the requirements of size-reduced TALE repeats for 4mC binding and revealed a single mutant repeat as a selective binder of 4mC. Employment of a TALE with this repeat in affinity enrichment enabled the isolation of a user-defined DNA sequence containing a single 4mC but not C or 5mC from the background of a bacterial genome. Comparative enrichments with TALEs bearing this or the natural C-binding repeat provides an approach for the complete, programmable decoding of all cytosine nucleobases found in bacterial genomes.This article is part of a discussion meeting issue 'Frontiers in epigenetic chemical biology'. © 2018 The Author(s).

Protecting the patient by promoting end-user competence in health informatics systems-moves towards a generic health computer user "driving license".

PubMed

Rigby, Michael

2004-03-18

The effectiveness and quality of health informatics systems' support to healthcare delivery are largely determined by two factors-the suitability of the system installed, and the competence of the users. However, the profile of users of large-scale clinical health systems is significantly different from the profile of end-users in other enterprises such as the finance sector, insurance, travel or retail sales. Work with a mental health provider in Ireland, who was introducing a customized electronic patient record (EPR) system, identified the strong legal and ethical importance of adequately skills for the health professionals and others, who would be the system users. The experience identified the need for a clear and comprehensive generic user qualification at a basic but robust level. The European computer driving license (ECDL) has gained wide recognition as a basic generic qualification for users of computer systems. However, health systems and data have a series of characteristics that differentiate them from other data systems. The logical conclusion was the recognition of a need for an additional domain-specific qualification-an "ECDL Health Supplement". Development of this is now being progressed.

Multi-Mission Power Analysis Tool (MMPAT) Version 3

NASA Technical Reports Server (NTRS)

Wood, Eric G.; Chang, George W.; Chen, Fannie C.

2012-01-01

The Multi-Mission Power Analysis Tool (MMPAT) simulates a spacecraft power subsystem including the power source (solar array and/or radioisotope thermoelectric generator), bus-voltage control, secondary battery (lithium-ion or nickel-hydrogen), thermostatic heaters, and power-consuming equipment. It handles multiple mission types including heliocentric orbiters, planetary orbiters, and surface operations. Being parametrically driven along with its user-programmable features can reduce or even eliminate any need for software modifications when configuring it for a particular spacecraft. It provides multiple levels of fidelity, thereby fulfilling the vast majority of a project s power simulation needs throughout the lifecycle. It can operate in a stand-alone mode with a graphical user interface, in batch mode, or as a library linked with other tools. This software can simulate all major aspects of a spacecraft power subsystem. It is parametrically driven to reduce or eliminate the need for a programmer. Added flexibility is provided through user-designed state models and table-driven parameters. MMPAT is designed to be used by a variety of users, such as power subsystem engineers for sizing power subsystem components; mission planners for adjusting mission scenarios using power profiles generated by the model; system engineers for performing system- level trade studies using the results of the model during the early design phases of a spacecraft; and operations personnel for high-fidelity modeling of the essential power aspect of the planning picture.

Voice gender discrimination provides a measure of more than pitch-related perception in cochlear implant users.

PubMed

Li, Tianhao; Fu, Qian-Jie

2011-08-01

(1) To investigate whether voice gender discrimination (VGD) could be a useful indicator of the spectral and temporal processing abilities of individual cochlear implant (CI) users; (2) To examine the relationship between VGD and speech recognition with CI when comparable acoustic cues are used for both perception processes. VGD was measured using two talker sets with different inter-gender fundamental frequencies (F(0)), as well as different acoustic CI simulations. Vowel and consonant recognition in quiet and noise were also measured and compared with VGD performance. Eleven postlingually deaf CI users. The results showed that (1) mean VGD performance differed for different stimulus sets, (2) VGD and speech recognition performance varied among individual CI users, and (3) individual VGD performance was significantly correlated with speech recognition performance under certain conditions. VGD measured with selected stimulus sets might be useful for assessing not only pitch-related perception, but also spectral and temporal processing by individual CI users. In addition to improvements in spectral resolution and modulation detection, the improvement in higher modulation frequency discrimination might be particularly important for CI users in noisy environments.

Development of a Multi-Target Contingency Management Intervention for HIV Positive Substance Users.

PubMed

Stitzer, Maxine; Calsyn, Donald; Matheson, Timothy; Sorensen, James; Gooden, Lauren; Metsch, Lisa

2017-01-01

Contingency management (CM) interventions generally target a single behavior such as attendance or drug use. However, disease outcomes are mediated by complex chains of both healthy and interfering behaviors enacted over extended periods of time. This paper describes a novel multi-target contingency management (CM) program developed for use with HIV positive substance users enrolled in a CTN multi-site study (0049 Project HOPE). Participants were randomly assigned to usual care (referral to health care and SUD treatment) or 6-months strength-based patient navigation interventions with (PN+CM) or without (PN only) the CM program. Primary outcome of the trial was viral load suppression at 12-months post-randomization. Up to $1160 could be earned over 6 months under escalating schedules of reinforcement. Earnings were divided among eight CM targets; two PN-related (PN visits; paperwork completion; 26% of possible earnings), four health-related (HIV care visits, lab blood draw visits, medication check, viral load suppression; 47% of possible earnings) and two drug-use abatement (treatment entry; submission of drug negative UAs; 27% of earnings). The paper describes rationale for selection of targets, pay amounts and pay schedules. The CM program was compatible with and fully integrated into the PN intervention. The study design will allow comparison of behavioral and health outcomes for participants receiving PN with and without CM; results will inform future multi-target CM development. Copyright © 2016 Elsevier Inc. All rights reserved.

Contribution of auditory working memory to speech understanding in mandarin-speaking cochlear implant users.

PubMed

Tao, Duoduo; Deng, Rui; Jiang, Ye; Galvin, John J; Fu, Qian-Jie; Chen, Bing

2014-01-01

To investigate how auditory working memory relates to speech perception performance by Mandarin-speaking cochlear implant (CI) users. Auditory working memory and speech perception was measured in Mandarin-speaking CI and normal-hearing (NH) participants. Working memory capacity was measured using forward digit span and backward digit span; working memory efficiency was measured using articulation rate. Speech perception was assessed with: (a) word-in-sentence recognition in quiet, (b) word-in-sentence recognition in speech-shaped steady noise at +5 dB signal-to-noise ratio, (c) Chinese disyllable recognition in quiet, (d) Chinese lexical tone recognition in quiet. Self-reported school rank was also collected regarding performance in schoolwork. There was large inter-subject variability in auditory working memory and speech performance for CI participants. Working memory and speech performance were significantly poorer for CI than for NH participants. All three working memory measures were strongly correlated with each other for both CI and NH participants. Partial correlation analyses were performed on the CI data while controlling for demographic variables. Working memory efficiency was significantly correlated only with sentence recognition in quiet when working memory capacity was partialled out. Working memory capacity was correlated with disyllable recognition and school rank when efficiency was partialled out. There was no correlation between working memory and lexical tone recognition in the present CI participants. Mandarin-speaking CI users experience significant deficits in auditory working memory and speech performance compared with NH listeners. The present data suggest that auditory working memory may contribute to CI users' difficulties in speech understanding. The present pattern of results with Mandarin-speaking CI users is consistent with previous auditory working memory studies with English-speaking CI users, suggesting that the lexical importance

Is White Light the Best Illumination for Palmprint Recognition?

NASA Astrophysics Data System (ADS)

Guo, Zhenhua; Zhang, David; Zhang, Lei

Palmprint as a new biometric has received great research attention in the past decades. It owns many merits, such as robustness, low cost, user friendliness, and high accuracy. Most of the current palmprint recognition systems use an active light to acquire clear palmprint images. Thus, light source is a key component in the system to capture enough of discriminant information for palmprint recognition. To the best of our knowledge, white light is the most widely used light source. However, little work has been done on investigating whether white light is the best illumination for palmprint recognition. In this study, we empirically compared palmprint recognition accuracy using white light and other six different color lights. The experiments on a large database show that white light is not the optimal illumination for palmprint recognition. This finding will be useful to future palmprint recognition system design.

Multimodal approaches for emotion recognition: a survey

NASA Astrophysics Data System (ADS)

Sebe, Nicu; Cohen, Ira; Gevers, Theo; Huang, Thomas S.

2004-12-01

Recent technological advances have enabled human users to interact with computers in ways previously unimaginable. Beyond the confines of the keyboard and mouse, new modalities for human-computer interaction such as voice, gesture, and force-feedback are emerging. Despite important advances, one necessary ingredient for natural interaction is still missing-emotions. Emotions play an important role in human-to-human communication and interaction, allowing people to express themselves beyond the verbal domain. The ability to understand human emotions is desirable for the computer in several applications. This paper explores new ways of human-computer interaction that enable the computer to be more aware of the user's emotional and attentional expressions. We present the basic research in the field and the recent advances into the emotion recognition from facial, voice, and physiological signals, where the different modalities are treated independently. We then describe the challenging problem of multimodal emotion recognition and we advocate the use of probabilistic graphical models when fusing the different modalities. We also discuss the difficult issues of obtaining reliable affective data, obtaining ground truth for emotion recognition, and the use of unlabeled data.

Multimodal approaches for emotion recognition: a survey

NASA Astrophysics Data System (ADS)

Sebe, Nicu; Cohen, Ira; Gevers, Theo; Huang, Thomas S.

2005-01-01

Recent technological advances have enabled human users to interact with computers in ways previously unimaginable. Beyond the confines of the keyboard and mouse, new modalities for human-computer interaction such as voice, gesture, and force-feedback are emerging. Despite important advances, one necessary ingredient for natural interaction is still missing-emotions. Emotions play an important role in human-to-human communication and interaction, allowing people to express themselves beyond the verbal domain. The ability to understand human emotions is desirable for the computer in several applications. This paper explores new ways of human-computer interaction that enable the computer to be more aware of the user's emotional and attentional expressions. We present the basic research in the field and the recent advances into the emotion recognition from facial, voice, and physiological signals, where the different modalities are treated independently. We then describe the challenging problem of multimodal emotion recognition and we advocate the use of probabilistic graphical models when fusing the different modalities. We also discuss the difficult issues of obtaining reliable affective data, obtaining ground truth for emotion recognition, and the use of unlabeled data.

Cross-sensor iris recognition through kernel learning.

PubMed

Pillai, Jaishanker K; Puertas, Maria; Chellappa, Rama

2014-01-01

Due to the increasing popularity of iris biometrics, new sensors are being developed for acquiring iris images and existing ones are being continuously upgraded. Re-enrolling users every time a new sensor is deployed is expensive and time-consuming, especially in applications with a large number of enrolled users. However, recent studies show that cross-sensor matching, where the test samples are verified using data enrolled with a different sensor, often lead to reduced performance. In this paper, we propose a machine learning technique to mitigate the cross-sensor performance degradation by adapting the iris samples from one sensor to another. We first present a novel optimization framework for learning transformations on iris biometrics. We then utilize this framework for sensor adaptation, by reducing the distance between samples of the same class, and increasing it between samples of different classes, irrespective of the sensors acquiring them. Extensive evaluations on iris data from multiple sensors demonstrate that the proposed method leads to improvement in cross-sensor recognition accuracy. Furthermore, since the proposed technique requires minimal changes to the iris recognition pipeline, it can easily be incorporated into existing iris recognition systems.

Adaptive strategy for multi-user robotic rehabilitation games.

PubMed

Caurin, Glauco A P; Siqueira, Adriano A G; Andrade, Kleber O; Joaquim, Ricardo C; Krebs, Hermano I

2011-01-01

In this paper, we discuss a strategy for the adaptation of the "difficulty level" in games intended to include motor planning during robotic rehabilitation. We consider concurrently the motivation of the user and his/her performance in a Pong game. User motivation is classified in three levels (not motivated, well motivated and overloaded). User performance is measured as a combination of knowledge of results--achieved goals and score points in the game--and knowledge of performance--joint displacement, speed, aiming, user work, etc. Initial results of a pilot test with unimpaired healthy young volunteers are also presented showing a tendency for individualization of the parameter values.

Effects of Talker Variability on Vowel Recognition in Cochlear Implants

ERIC Educational Resources Information Center

Chang, Yi-ping; Fu, Qian-Jie

2006-01-01

Purpose: To investigate the effects of talker variability on vowel recognition by cochlear implant (CI) users and by normal-hearing (NH) participants listening to 4-channel acoustic CI simulations. Method: CI users were tested with their clinically assigned speech processors. For NH participants, 3 CI processors were simulated, using different…

Learning curve of speech recognition.

PubMed

Kauppinen, Tomi A; Kaipio, Johanna; Koivikko, Mika P

2013-12-01

Speech recognition (SR) speeds patient care processes by reducing report turnaround times. However, concerns have emerged about prolonged training and an added secretarial burden for radiologists. We assessed how much proofing radiologists who have years of experience with SR and radiologists new to SR must perform, and estimated how quickly the new users become as skilled as the experienced users. We studied SR log entries for 0.25 million reports from 154 radiologists and after careful exclusions, defined a group of 11 experienced radiologists and 71 radiologists new to SR (24,833 and 122,093 reports, respectively). Data were analyzed for sound file and report lengths, character-based error rates, and words unknown to the SR's dictionary. Experienced radiologists corrected 6 characters for each report and for new users, 11. Some users presented a very unfavorable learning curve, with error rates not declining as expected. New users' reports were longer, and data for the experienced users indicates that their reports, initially equally lengthy, shortened over a period of several years. For most radiologists, only minor corrections of dictated reports were necessary. While new users adopted SR quickly, with a subset outperforming experienced users from the start, identification of users struggling with SR will help facilitate troubleshooting and support.

Exercise recognition for Kinect-based telerehabilitation.

PubMed

Antón, D; Goñi, A; Illarramendi, A

2015-01-01

An aging population and people's higher survival to diseases and traumas that leave physical consequences are challenging aspects in the context of an efficient health management. This is why telerehabilitation systems are being developed, to allow monitoring and support of physiotherapy sessions at home, which could reduce healthcare costs while also improving the quality of life of the users. Our goal is the development of a Kinect-based algorithm that provides a very accurate real-time monitoring of physical rehabilitation exercises and that also provides a friendly interface oriented both to users and physiotherapists. The two main constituents of our algorithm are the posture classification method and the exercises recognition method. The exercises consist of series of movements. Each movement is composed of an initial posture, a final posture and the angular trajectories of the limbs involved in the movement. The algorithm was designed and tested with datasets of real movements performed by volunteers. We also explain in the paper how we obtained the optimal values for the trade-off values for posture and trajectory recognition. Two relevant aspects of the algorithm were evaluated in our tests, classification accuracy and real-time data processing. We achieved 91.9% accuracy in posture classification and 93.75% accuracy in trajectory recognition. We also checked whether the algorithm was able to process the data in real-time. We found that our algorithm could process more than 20,000 postures per second and all the required trajectory data-series in real-time, which in practice guarantees no perceptible delays. Later on, we carried out two clinical trials with real patients that suffered shoulder disorders. We obtained an exercise monitoring accuracy of 95.16%. We present an exercise recognition algorithm that handles the data provided by Kinect efficiently. The algorithm has been validated in a real scenario where we have verified its suitability. Moreover

Design for robustness of unique, multi-component engineering systems

NASA Astrophysics Data System (ADS)

Shelton, Kenneth A.

2007-12-01

The purpose of this research is to advance the science of conceptual designing for robustness in unique, multi-component engineering systems. Robustness is herein defined as the ability of an engineering system to operate within a desired performance range even if the actual configuration has differences from specifications within specified tolerances. These differences are caused by three sources, namely manufacturing errors, system degradation (operational wear and tear), and parts availability. Unique, multi-component engineering systems are defined as systems produced in unique or very small production numbers. They typically have design and manufacturing costs on the order of billions of dollars, and have multiple, competing performance objectives. Design time for these systems must be minimized due to competition, high manpower costs, long manufacturing times, technology obsolescence, and limited available manpower expertise. Most importantly, design mistakes cannot be easily corrected after the systems are operational. For all these reasons, robustness of these systems is absolutely critical. This research examines the space satellite industry in particular. Although inherent robustness assurance is absolutely critical, it is difficult to achieve in practice. The current state of the art for robustness in the industry is to overdesign components and subsystems with redundancy and margin. The shortfall is that it is not known if the added margins were either necessary or sufficient given the risk management preferences of the designer or engineering system customer. To address this shortcoming, new assessment criteria to evaluate robustness in design concepts have been developed. The criteria are comprised of the "Value Distance", addressing manufacturing errors and system degradation, and "Component Distance", addressing parts availability. They are based on an evolutionary computation format that uses a string of alleles to describe the components in the

Face Recognition in Humans and Machines

NASA Astrophysics Data System (ADS)

O'Toole, Alice; Tistarelli, Massimo

The study of human face recognition by psychologists and neuroscientists has run parallel to the development of automatic face recognition technologies by computer scientists and engineers. In both cases, there are analogous steps of data acquisition, image processing, and the formation of representations that can support the complex and diverse tasks we accomplish with faces. These processes can be understood and compared in the context of their neural and computational implementations. In this chapter, we present the essential elements of face recognition by humans and machines, taking a perspective that spans psychological, neural, and computational approaches. From the human side, we overview the methods and techniques used in the neurobiology of face recognition, the underlying neural architecture of the system, the role of visual attention, and the nature of the representations that emerges. From the computational side, we discuss face recognition technologies and the strategies they use to overcome challenges to robust operation over viewing parameters. Finally, we conclude the chapter with a look at some recent studies that compare human and machine performances at face recognition.

Gimli: open source and high-performance biomedical name recognition

PubMed Central

2013-01-01

Background Automatic recognition of biomedical names is an essential task in biomedical information extraction, presenting several complex and unsolved challenges. In recent years, various solutions have been implemented to tackle this problem. However, limitations regarding system characteristics, customization and usability still hinder their wider application outside text mining research. Results We present Gimli, an open-source, state-of-the-art tool for automatic recognition of biomedical names. Gimli includes an extended set of implemented and user-selectable features, such as orthographic, morphological, linguistic-based, conjunctions and dictionary-based. A simple and fast method to combine different trained models is also provided. Gimli achieves an F-measure of 87.17% on GENETAG and 72.23% on JNLPBA corpus, significantly outperforming existing open-source solutions. Conclusions Gimli is an off-the-shelf, ready to use tool for named-entity recognition, providing trained and optimized models for recognition of biomedical entities from scientific text. It can be used as a command line tool, offering full functionality, including training of new models and customization of the feature set and model parameters through a configuration file. Advanced users can integrate Gimli in their text mining workflows through the provided library, and extend or adapt its functionalities. Based on the underlying system characteristics and functionality, both for final users and developers, and on the reported performance results, we believe that Gimli is a state-of-the-art solution for biomedical NER, contributing to faster and better research in the field. Gimli is freely available at http://bioinformatics.ua.pt/gimli. PMID:23413997

Fast cat-eye effect target recognition based on saliency extraction

NASA Astrophysics Data System (ADS)

Li, Li; Ren, Jianlin; Wang, Xingbin

2015-09-01

Background complexity is a main reason that results in false detection in cat-eye target recognition. Human vision has selective attention property which can help search the salient target from complex unknown scenes quickly and precisely. In the paper, we propose a novel cat-eye effect target recognition method named Multi-channel Saliency Processing before Fusion (MSPF). This method combines traditional cat-eye target recognition with the selective characters of visual attention. Furthermore, parallel processing enables it to achieve fast recognition. Experimental results show that the proposed method performs better in accuracy, robustness and speed compared to other methods.

Molecular Cloning Designer Simulator (MCDS): All-in-one molecular cloning and genetic engineering design, simulation and management software for complex synthetic biology and metabolic engineering projects.

PubMed

Shi, Zhenyu; Vickers, Claudia E

2016-12-01

Molecular Cloning Designer Simulator (MCDS) is a powerful new all-in-one cloning and genetic engineering design, simulation and management software platform developed for complex synthetic biology and metabolic engineering projects. In addition to standard functions, it has a number of features that are either unique, or are not found in combination in any one software package: (1) it has a novel interactive flow-chart user interface for complex multi-step processes, allowing an integrated overview of the whole project; (2) it can perform a user-defined workflow of cloning steps in a single execution of the software; (3) it can handle multiple types of genetic recombineering, a technique that is rapidly replacing classical cloning for many applications; (4) it includes experimental information to conveniently guide wet lab work; and (5) it can store results and comments to allow the tracking and management of the whole project in one platform. MCDS is freely available from https://mcds.codeplex.com.