A field programmable gate array-based reconfigurable smart-sensor network for wireless monitoring of new generation computer numerically controlled machines.

PubMed

Moreno-Tapia, Sandra Veronica; Vera-Salas, Luis Alberto; Osornio-Rios, Roque Alfredo; Dominguez-Gonzalez, Aurelio; Stiharu, Ion; Romero-Troncoso, Rene de Jesus

2010-01-01

Computer numerically controlled (CNC) machines have evolved to adapt to increasing technological and industrial requirements. To cover these needs, new generation machines have to perform monitoring strategies by incorporating multiple sensors. Since in most of applications the online Processing of the variables is essential, the use of smart sensors is necessary. The contribution of this work is the development of a wireless network platform of reconfigurable smart sensors for CNC machine applications complying with the measurement requirements of new generation CNC machines. Four different smart sensors are put under test in the network and their corresponding signal processing techniques are implemented in a Field Programmable Gate Array (FPGA)-based sensor node.

Insect-machine interface based neurocybernetics.

PubMed

Bozkurt, Alper; Gilmour, Robert F; Sinha, Ayesa; Stern, David; Lal, Amit

2009-06-01

We present details of a novel bioelectric interface formed by placing microfabricated probes into insect during metamorphic growth cycles. The inserted microprobes emerge with the insect where the development of tissue around the electronics during the pupal development allows mechanically stable and electrically reliable structures coupled to the insect. Remarkably, the insects do not react adversely or otherwise to the inserted electronics in the pupae stage, as is true when the electrodes are inserted in adult stages. We report on the electrical and mechanical characteristics of this novel bioelectronic interface, which we believe would be adopted by many investigators trying to investigate biological behavior in insects with negligible or minimal traumatic effect encountered when probes are inserted in adult stages. This novel insect-machine interface also allows for hybrid insect-machine platforms for further studies. As an application, we demonstrate our first results toward navigation of flight in moths. When instrumented with equipment to gather information for environmental sensing, such insects potentially can assist man to monitor the ecosystems that we share with them for sustainability. The simplicity of the optimized surgical procedure we invented allows for batch insertions to the insect for automatic and mass production of such hybrid insect-machine platforms. Therefore, our bioelectronic interface and hybrid insect-machine platform enables multidisciplinary scientific and engineering studies not only to investigate the details of insect behavioral physiology but also to control it.

Developing an Intelligent Diagnosis and Assessment E-Learning Tool for Introductory Programming

ERIC Educational Resources Information Center

Huang, Chenn-Jung; Chen, Chun-Hua; Luo, Yun-Cheng; Chen, Hong-Xin; Chuang, Yi-Ta

2008-01-01

Recently, a lot of open source e-learning platforms have been offered for free in the Internet. We thus incorporate the intelligent diagnosis and assessment tool into an open software e-learning platform developed for programming language courses, wherein the proposed learning diagnosis assessment tools based on text mining and machine learning…

An intelligent CNC machine control system architecture

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

Miller, D.J.; Loucks, C.S.

1996-10-01

Intelligent, agile manufacturing relies on automated programming of digitally controlled processes. Currently, processes such as Computer Numerically Controlled (CNC) machining are difficult to automate because of highly restrictive controllers and poor software environments. It is also difficult to utilize sensors and process models for adaptive control, or to integrate machining processes with other tasks within a factory floor setting. As part of a Laboratory Directed Research and Development (LDRD) program, a CNC machine control system architecture based on object-oriented design and graphical programming has been developed to address some of these problems and to demonstrate automated agile machining applications usingmore » platform-independent software.« less

Versatile and Programmable DNA Logic Gates on Universal and Label-Free Homogeneous Electrochemical Platform.

PubMed

Ge, Lei; Wang, Wenxiao; Sun, Ximei; Hou, Ting; Li, Feng

2016-10-04

Herein, a novel universal and label-free homogeneous electrochemical platform is demonstrated, on which a complete set of DNA-based two-input Boolean logic gates (OR, NAND, AND, NOR, INHIBIT, IMPLICATION, XOR, and XNOR) is constructed by simply and rationally deploying the designed DNA polymerization/nicking machines without complicated sequence modulation. Single-stranded DNA is employed as the proof-of-concept target/input to initiate or prevent the DNA polymerization/nicking cyclic reactions on these DNA machines to synthesize numerous intact G-quadruplex sequences or binary G-quadruplex subunits as the output. The generated output strands then self-assemble into G-quadruplexes that render remarkable decrease to the diffusion current response of methylene blue and, thus, provide the amplified homogeneous electrochemical readout signal not only for the logic gate operations but also for the ultrasensitive detection of the target/input. This system represents the first example of homogeneous electrochemical logic operation. Importantly, the proposed homogeneous electrochemical logic gates possess the input/output homogeneity and share a constant output threshold value. Moreover, the modular design of DNA polymerization/nicking machines enables the adaptation of these homogeneous electrochemical logic gates to various input and output sequences. The results of this study demonstrate the versatility and universality of the label-free homogeneous electrochemical platform in the design of biomolecular logic gates and provide a potential platform for the further development of large-scale DNA-based biocomputing circuits and advanced biosensors for multiple molecular targets.

An automated ranking platform for machine learning regression models for meat spoilage prediction using multi-spectral imaging and metabolic profiling.

PubMed

Estelles-Lopez, Lucia; Ropodi, Athina; Pavlidis, Dimitris; Fotopoulou, Jenny; Gkousari, Christina; Peyrodie, Audrey; Panagou, Efstathios; Nychas, George-John; Mohareb, Fady

2017-09-01

Over the past decade, analytical approaches based on vibrational spectroscopy, hyperspectral/multispectral imagining and biomimetic sensors started gaining popularity as rapid and efficient methods for assessing food quality, safety and authentication; as a sensible alternative to the expensive and time-consuming conventional microbiological techniques. Due to the multi-dimensional nature of the data generated from such analyses, the output needs to be coupled with a suitable statistical approach or machine-learning algorithms before the results can be interpreted. Choosing the optimum pattern recognition or machine learning approach for a given analytical platform is often challenging and involves a comparative analysis between various algorithms in order to achieve the best possible prediction accuracy. In this work, "MeatReg", a web-based application is presented, able to automate the procedure of identifying the best machine learning method for comparing data from several analytical techniques, to predict the counts of microorganisms responsible of meat spoilage regardless of the packaging system applied. In particularly up to 7 regression methods were applied and these are ordinary least squares regression, stepwise linear regression, partial least square regression, principal component regression, support vector regression, random forest and k-nearest neighbours. MeatReg" was tested with minced beef samples stored under aerobic and modified atmosphere packaging and analysed with electronic nose, HPLC, FT-IR, GC-MS and Multispectral imaging instrument. Population of total viable count, lactic acid bacteria, pseudomonads, Enterobacteriaceae and B. thermosphacta, were predicted. As a result, recommendations of which analytical platforms are suitable to predict each type of bacteria and which machine learning methods to use in each case were obtained. The developed system is accessible via the link: www.sorfml.com. Copyright © 2017 Elsevier Ltd. All rights reserved.

Clustering and Candidate Motif Detection in Exosomal miRNAs by Application of Machine Learning Algorithms.

PubMed

Gaur, Pallavi; Chaturvedi, Anoop

2017-07-22

The clustering pattern and motifs give immense information about any biological data. An application of machine learning algorithms for clustering and candidate motif detection in miRNAs derived from exosomes is depicted in this paper. Recent progress in the field of exosome research and more particularly regarding exosomal miRNAs has led much bioinformatic-based research to come into existence. The information on clustering pattern and candidate motifs in miRNAs of exosomal origin would help in analyzing existing, as well as newly discovered miRNAs within exosomes. Along with obtaining clustering pattern and candidate motifs in exosomal miRNAs, this work also elaborates the usefulness of the machine learning algorithms that can be efficiently used and executed on various programming languages/platforms. Data were clustered and sequence candidate motifs were detected successfully. The results were compared and validated with some available web tools such as 'BLASTN' and 'MEME suite'. The machine learning algorithms for aforementioned objectives were applied successfully. This work elaborated utility of machine learning algorithms and language platforms to achieve the tasks of clustering and candidate motif detection in exosomal miRNAs. With the information on mentioned objectives, deeper insight would be gained for analyses of newly discovered miRNAs in exosomes which are considered to be circulating biomarkers. In addition, the execution of machine learning algorithms on various language platforms gives more flexibility to users to try multiple iterations according to their requirements. This approach can be applied to other biological data-mining tasks as well.

Measuring FLOPS Using Hardware Performance Counter Technologies on LC systems

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

Ahn, D H

2008-09-05

FLOPS (FLoating-point Operations Per Second) is a commonly used performance metric for scientific programs that rely heavily on floating-point (FP) calculations. The metric is based on the number of FP operations rather than instructions, thereby facilitating a fair comparison between different machines. A well-known use of this metric is the LINPACK benchmark that is used to generate the Top500 list. It measures how fast a computer solves a dense N by N system of linear equations Ax=b, which requires a known number of FP operations, and reports the result in millions of FP operations per second (MFLOPS). While running amore » benchmark with known FP workloads can provide insightful information about the efficiency of a machine's FP pipelines in relation to other machines, measuring FLOPS of an arbitrary scientific application in a platform-independent manner is nontrivial. The goal of this paper is twofold. First, we explore the FP microarchitectures of key processors that are underpinning the LC machines. Second, we present the hardware performance monitoring counter-based measurement techniques that a user can use to get the native FLOPS of his or her program, which are practical solutions readily available on LC platforms. By nature, however, these native FLOPS metrics are not directly comparable across different machines mainly because FP operations are not consistent across microarchitectures. Thus, the first goal of this paper represents the base reference by which a user can interpret the measured FLOPS more judiciously.« less

Scaling Support Vector Machines On Modern HPC Platforms

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

You, Yang; Fu, Haohuan; Song, Shuaiwen

2015-02-01

We designed and implemented MIC-SVM, a highly efficient parallel SVM for x86 based multicore and many-core architectures, such as the Intel Ivy Bridge CPUs and Intel Xeon Phi co-processor (MIC). We propose various novel analysis methods and optimization techniques to fully utilize the multilevel parallelism provided by these architectures and serve as general optimization methods for other machine learning tools.

Machine Learning for Flood Prediction in Google Earth Engine

NASA Astrophysics Data System (ADS)

Kuhn, C.; Tellman, B.; Max, S. A.; Schwarz, B.

2015-12-01

With the increasing availability of high-resolution satellite imagery, dynamic flood mapping in near real time is becoming a reachable goal for decision-makers. This talk describes a newly developed framework for predicting biophysical flood vulnerability using public data, cloud computing and machine learning. Our objective is to define an approach to flood inundation modeling using statistical learning methods deployed in a cloud-based computing platform. Traditionally, static flood extent maps grounded in physically based hydrologic models can require hours of human expertise to construct at significant financial cost. In addition, desktop modeling software and limited local server storage can impose restraints on the size and resolution of input datasets. Data-driven, cloud-based processing holds promise for predictive watershed modeling at a wide range of spatio-temporal scales. However, these benefits come with constraints. In particular, parallel computing limits a modeler's ability to simulate the flow of water across a landscape, rendering traditional routing algorithms unusable in this platform. Our project pushes these limits by testing the performance of two machine learning algorithms, Support Vector Machine (SVM) and Random Forests, at predicting flood extent. Constructed in Google Earth Engine, the model mines a suite of publicly available satellite imagery layers to use as algorithm inputs. Results are cross-validated using MODIS-based flood maps created using the Dartmouth Flood Observatory detection algorithm. Model uncertainty highlights the difficulty of deploying unbalanced training data sets based on rare extreme events.

An Android malware detection system based on machine learning

NASA Astrophysics Data System (ADS)

Wen, Long; Yu, Haiyang

2017-08-01

The Android smartphone, with its open source character and excellent performance, has attracted many users. However, the convenience of the Android platform also has motivated the development of malware. The traditional method which detects the malware based on the signature is unable to detect unknown applications. The article proposes a machine learning-based lightweight system that is capable of identifying malware on Android devices. In this system we extract features based on the static analysis and the dynamitic analysis, then a new feature selection approach based on principle component analysis (PCA) and relief are presented in the article to decrease the dimensions of the features. After that, a model will be constructed with support vector machine (SVM) for classification. Experimental results show that our system provides an effective method in Android malware detection.

Hardware-Assisted Large-Scale Neuroevolution for Multiagent Learning

DTIC Science & Technology

2014-12-30

SECURITY CLASSIFICATION OF: This DURIP equipment award was used to purchase, install, and bring on-line two Berkeley Emulation Engines ( BEEs ) and two...mini- BEE machines to establish an FPGA-based high-performance multiagent training platform and its associated software. This acquisition of BEE4-W...Platform; Probabilistic Domain Transformation; Hardware-Assisted; FPGA; BEE ; Hive Brain; Multiagent. REPORT DOCUMENTATION PAGE 11. SPONSOR/MONITOR’S

Build platform that provides mechanical engagement with additive manufacturing prints

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

Elliott, Amelia M.

A build platform and methods of fabricating an article with such a platform in an extrusion-type additive manufacturing machine are provided. A platform body 202 includes features 204 that extend outward from the body 202. The features 204 define protrusive areas 206 and recessive areas 208 that cooperate to mechanically engage the extruded material that forms the initial layers 220 of an article when the article is being fabricated by a nozzle 12 of the additive manufacturing machine 10.

Machine vision for digital microfluidics

NASA Astrophysics Data System (ADS)

Shin, Yong-Jun; Lee, Jeong-Bong

2010-01-01

Machine vision is widely used in an industrial environment today. It can perform various tasks, such as inspecting and controlling production processes, that may require humanlike intelligence. The importance of imaging technology for biological research or medical diagnosis is greater than ever. For example, fluorescent reporter imaging enables scientists to study the dynamics of gene networks with high spatial and temporal resolution. Such high-throughput imaging is increasingly demanding the use of machine vision for real-time analysis and control. Digital microfluidics is a relatively new technology with expectations of becoming a true lab-on-a-chip platform. Utilizing digital microfluidics, only small amounts of biological samples are required and the experimental procedures can be automatically controlled. There is a strong need for the development of a digital microfluidics system integrated with machine vision for innovative biological research today. In this paper, we show how machine vision can be applied to digital microfluidics by demonstrating two applications: machine vision-based measurement of the kinetics of biomolecular interactions and machine vision-based droplet motion control. It is expected that digital microfluidics-based machine vision system will add intelligence and automation to high-throughput biological imaging in the future.

Initial planetary base construction techniques and machine implementation

NASA Technical Reports Server (NTRS)

Crockford, William W.

1987-01-01

Conceptual designs of (1) initial planetary base structures, and (2) an unmanned machine to perform the construction of these structures using materials local to the planet are presented. Rock melting is suggested as a possible technique to be used by the machine in fabricating roads, platforms, and interlocking bricks. Identification of problem areas in machine design and materials processing is accomplished. The feasibility of the designs is contingent upon favorable results of an analysis of the engineering behavior of the product materials. The analysis requires knowledge of several parameters for solution of the constitutive equations of the theory of elasticity. An initial collection of these parameters is presented which helps to define research needed to perform a realistic feasibility study. A qualitative approach to estimating power and mass lift requirements for the proposed machine is used which employs specifications of currently available equipment. An initial, unmanned mission scenario is discussed with emphasis on identifying uncompleted tasks and suggesting design considerations for vehicles and primitive structures which use the products of the machine processing.

Neurosurgery and the dawning age of Brain-Machine Interfaces

PubMed Central

Rowland, Nathan C.; Breshears, Jonathan; Chang, Edward F.

2013-01-01

Brain–machine interfaces (BMIs) are on the horizon for clinical neurosurgery. Electrocorticography-based platforms are less invasive than implanted microelectrodes, however, the latter are unmatched in their ability to achieve fine motor control of a robotic prosthesis capable of natural human behaviors. These technologies will be crucial to restoring neural function to a large population of patients with severe neurologic impairment – including those with spinal cord injury, stroke, limb amputation, and disabling neuromuscular disorders such as amyotrophic lateral sclerosis. On the opposite end of the spectrum are neural enhancement technologies for specialized applications such as combat. An ongoing ethical dialogue is imminent as we prepare for BMI platforms to enter the neurosurgical realm of clinical management. PMID:23653884

The HARNESS Workbench: Unified and Adaptive Access to Diverse HPC Platforms

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

Sunderam, Vaidy S.

2012-03-20

The primary goal of the Harness WorkBench (HWB) project is to investigate innovative software environments that will help enhance the overall productivity of applications science on diverse HPC platforms. Two complementary frameworks were designed: one, a virtualized command toolkit for application building, deployment, and execution, that provides a common view across diverse HPC systems, in particular the DOE leadership computing platforms (Cray, IBM, SGI, and clusters); and two, a unified runtime environment that consolidates access to runtime services via an adaptive framework for execution-time and post processing activities. A prototype of the first was developed based on the concept ofmore » a 'system-call virtual machine' (SCVM), to enhance portability of the HPC application deployment process across heterogeneous high-end machines. The SCVM approach to portable builds is based on the insertion of toolkit-interpretable directives into original application build scripts. Modifications resulting from these directives preserve the semantics of the original build instruction flow. The execution of the build script is controlled by our toolkit that intercepts build script commands in a manner transparent to the end-user. We have applied this approach to a scientific production code (Gamess-US) on the Cray-XT5 machine. The second facet, termed Unibus, aims to facilitate provisioning and aggregation of multifaceted resources from resource providers and end-users perspectives. To achieve that, Unibus proposes a Capability Model and mediators (resource drivers) to virtualize access to diverse resources, and soft and successive conditioning to enable automatic and user-transparent resource provisioning. A proof of concept implementation has demonstrated the viability of this approach on high end machines, grid systems and computing clouds.« less

Use of IT platform in determination of efficiency of mining machines

NASA Astrophysics Data System (ADS)

Brodny, Jarosław; Tutak, Magdalena

2018-01-01

Determination of effective use of mining devices has very significant meaning for mining enterprises. High costs of their purchase and tenancy cause that these enterprises tend to the best use of possessed technical potential. However, specifics of mining production causes that this process not always proceeds without interferences. Practical experiences show that determination of objective measure of utilization of machine in mining enterprise is not simple. In the paper a proposition for solution of this problem is presented. For this purpose an IT platform and overall efficiency model OEE were used. This model enables to evaluate the machine in a range of its availability performance and quality of product, and constitutes a quantitative tool of TPM strategy. Adapted to the specificity of mining branch the OEE model together with acquired data from industrial automatic system enabled to determine the partial indicators and overall efficiency of tested machines. Studies were performed for a set of machines directly use in coal exploitation process. They were: longwall-shearer and armoured face conveyor, and beam stage loader. Obtained results clearly indicate that degree of use of machines by mining enterprises are unsatisfactory. Use of IT platforms will significantly facilitate the process of registration, archiving and analytical processing of the acquired data. In the paper there is presented methodology of determination of partial indices and total OEE together with a practical example of its application for investigated machines set. Also IT platform was characterized for its construction, function and application.

Simulation platform of LEO satellite communication system based on OPNET

NASA Astrophysics Data System (ADS)

Zhang, Yu; Zhang, Yong; Li, Xiaozhuo; Wang, Chuqiao; Li, Haihao

2018-02-01

For the purpose of verifying communication protocol in the low earth orbit (LEO) satellite communication system, an Optimized Network Engineering Tool (OPNET) based simulation platform is built. Using the three-layer modeling mechanism, the network model, the node model and the process model of the satellite communication system are built respectively from top to bottom, and the protocol will be implemented by finite state machine and Proto-C language. According to satellite orbit parameters, orbit files are generated via Satellite Tool Kit (STK) and imported into OPNET, and the satellite nodes move along their orbits. The simulation platform adopts time-slot-driven mode, divides simulation time into continuous time slots, and allocates slot number for each time slot. A resource allocation strategy is simulated on this platform, and the simulation results such as resource utilization rate, system throughput and packet delay are analyzed, which indicate that this simulation platform has outstanding versatility.

Preliminary Analysis of a Trusted Platform Module (TPM) Initialization Process

DTIC Science & Technology

2007-06-01

during system startup. For a laptop, extra precaution must be taken to prevent the machine from transitioning into a Sleep or Hibernate mode, since... hibernate mode [81]. D. TEST AND AUDIT After the system has gone through the predefined initialization and configuration processes, it needs to go...Conference on Computer and Communications Security, 2004, pp. 308-317. [45] L. Sarmenta, “TPM/J java -based API for the Trusted Platform Module (TPM

Metabolic Profiling and Classification of Propolis Samples from Southern Brazil: An NMR-Based Platform Coupled with Machine Learning.

PubMed

Maraschin, Marcelo; Somensi-Zeggio, Amélia; Oliveira, Simone K; Kuhnen, Shirley; Tomazzoli, Maíra M; Raguzzoni, Josiane C; Zeri, Ana C M; Carreira, Rafael; Correia, Sara; Costa, Christopher; Rocha, Miguel

2016-01-22

The chemical composition of propolis is affected by environmental factors and harvest season, making it difficult to standardize its extracts for medicinal usage. By detecting a typical chemical profile associated with propolis from a specific production region or season, certain types of propolis may be used to obtain a specific pharmacological activity. In this study, propolis from three agroecological regions (plain, plateau, and highlands) from southern Brazil, collected over the four seasons of 2010, were investigated through a novel NMR-based metabolomics data analysis workflow. Chemometrics and machine learning algorithms (PLS-DA and RF), including methods to estimate variable importance in classification, were used in this study. The machine learning and feature selection methods permitted construction of models for propolis sample classification with high accuracy (>75%, reaching ∼90% in the best case), better discriminating samples regarding their collection seasons comparatively to the harvest regions. PLS-DA and RF allowed the identification of biomarkers for sample discrimination, expanding the set of discriminating features and adding relevant information for the identification of the class-determining metabolites. The NMR-based metabolomics analytical platform, coupled to bioinformatic tools, allowed characterization and classification of Brazilian propolis samples regarding the metabolite signature of important compounds, i.e., chemical fingerprint, harvest seasons, and production regions.

A wearable smartphone-based platform for real-time cardiovascular disease detection via electrocardiogram processing.

PubMed

Oresko, Joseph J; Duschl, Heather; Cheng, Allen C

2010-05-01

Cardiovascular disease (CVD) is the single leading cause of global mortality and is projected to remain so. Cardiac arrhythmia is a very common type of CVD and may indicate an increased risk of stroke or sudden cardiac death. The ECG is the most widely adopted clinical tool to diagnose and assess the risk of arrhythmia. ECGs measure and display the electrical activity of the heart from the body surface. During patients' hospital visits, however, arrhythmias may not be detected on standard resting ECG machines, since the condition may not be present at that moment in time. While Holter-based portable monitoring solutions offer 24-48 h ECG recording, they lack the capability of providing any real-time feedback for the thousands of heart beats they record, which must be tediously analyzed offline. In this paper, we seek to unite the portability of Holter monitors and the real-time processing capability of state-of-the-art resting ECG machines to provide an assistive diagnosis solution using smartphones. Specifically, we developed two smartphone-based wearable CVD-detection platforms capable of performing real-time ECG acquisition and display, feature extraction, and beat classification. Furthermore, the same statistical summaries available on resting ECG machines are provided.

A survey of supervised machine learning models for mobile-phone based pathogen identification and classification

NASA Astrophysics Data System (ADS)

Ceylan Koydemir, Hatice; Feng, Steve; Liang, Kyle; Nadkarni, Rohan; Tseng, Derek; Benien, Parul; Ozcan, Aydogan

2017-03-01

Giardia lamblia causes a disease known as giardiasis, which results in diarrhea, abdominal cramps, and bloating. Although conventional pathogen detection methods used in water analysis laboratories offer high sensitivity and specificity, they are time consuming, and need experts to operate bulky equipment and analyze the samples. Here we present a field-portable and cost-effective smartphone-based waterborne pathogen detection platform that can automatically classify Giardia cysts using machine learning. Our platform enables the detection and quantification of Giardia cysts in one hour, including sample collection, labeling, filtration, and automated counting steps. We evaluated the performance of three prototypes using Giardia-spiked water samples from different sources (e.g., reagent-grade, tap, non-potable, and pond water samples). We populated a training database with >30,000 cysts and estimated our detection sensitivity and specificity using 20 different classifier models, including decision trees, nearest neighbor classifiers, support vector machines (SVMs), and ensemble classifiers, and compared their speed of training and classification, as well as predicted accuracies. Among them, cubic SVM, medium Gaussian SVM, and bagged-trees were the most promising classifier types with accuracies of 94.1%, 94.2%, and 95%, respectively; we selected the latter as our preferred classifier for the detection and enumeration of Giardia cysts that are imaged using our mobile-phone fluorescence microscope. Without the need for any experts or microbiologists, this field-portable pathogen detection platform can present a useful tool for water quality monitoring in resource-limited-settings.

Dynamic high-speed acquisition system design of transmission error with USB based on LabVIEW and FPGA

NASA Astrophysics Data System (ADS)

Zheng, Yong; Chen, Yan

2013-10-01

To realize the design of dynamic acquisition system for real-time detection of transmission chain error is very important to improve the machining accuracy of machine tool. In this paper, the USB controller and FPGA is used for hardware platform design, combined with LabVIEW to design user applications, NI-VISA is taken for develop USB drivers, and ultimately achieve the dynamic acquisition system design of transmission error

Software architecture for time-constrained machine vision applications

NASA Astrophysics Data System (ADS)

Usamentiaga, Rubén; Molleda, Julio; García, Daniel F.; Bulnes, Francisco G.

2013-01-01

Real-time image and video processing applications require skilled architects, and recent trends in the hardware platform make the design and implementation of these applications increasingly complex. Many frameworks and libraries have been proposed or commercialized to simplify the design and tuning of real-time image processing applications. However, they tend to lack flexibility, because they are normally oriented toward particular types of applications, or they impose specific data processing models such as the pipeline. Other issues include large memory footprints, difficulty for reuse, and inefficient execution on multicore processors. We present a novel software architecture for time-constrained machine vision applications that addresses these issues. The architecture is divided into three layers. The platform abstraction layer provides a high-level application programming interface for the rest of the architecture. The messaging layer provides a message-passing interface based on a dynamic publish/subscribe pattern. A topic-based filtering in which messages are published to topics is used to route the messages from the publishers to the subscribers interested in a particular type of message. The application layer provides a repository for reusable application modules designed for machine vision applications. These modules, which include acquisition, visualization, communication, user interface, and data processing, take advantage of the power of well-known libraries such as OpenCV, Intel IPP, or CUDA. Finally, the proposed architecture is applied to a real machine vision application: a jam detector for steel pickling lines.

Energy-Efficient Hosting Rich Content from Mobile Platforms with Relative Proximity Sensing.

PubMed

Park, Ki-Woong; Lee, Younho; Baek, Sung Hoon

2017-08-08

In this paper, we present a tiny networked mobile platform, termed Tiny-Web-Thing ( T-Wing ), which allows the sharing of data-intensive content among objects in cyber physical systems. The object includes mobile platforms like a smartphone, and Internet of Things (IoT) platforms for Human-to-Human (H2H), Human-to-Machine (H2M), Machine-to-Human (M2H), and Machine-to-Machine (M2M) communications. T-Wing makes it possible to host rich web content directly on their objects, which nearby objects can access instantaneously. Using a new mechanism that allows the Wi-Fi interface of the object to be turned on purely on-demand, T-Wing achieves very high energy efficiency. We have implemented T-Wing on an embedded board, and present evaluation results from our testbed. From the evaluation result of T-Wing , we compare our system against alternative approaches to implement this functionality using only the cellular or Wi-Fi (but not both), and show that in typical usage, T-Wing consumes less than 15× the energy and is faster by an order of magnitude.

Quantum Monte Carlo for large chemical systems: implementing efficient strategies for petascale platforms and beyond.

PubMed

Scemama, Anthony; Caffarel, Michel; Oseret, Emmanuel; Jalby, William

2013-04-30

Various strategies to implement efficiently quantum Monte Carlo (QMC) simulations for large chemical systems are presented. These include: (i) the introduction of an efficient algorithm to calculate the computationally expensive Slater matrices. This novel scheme is based on the use of the highly localized character of atomic Gaussian basis functions (not the molecular orbitals as usually done), (ii) the possibility of keeping the memory footprint minimal, (iii) the important enhancement of single-core performance when efficient optimization tools are used, and (iv) the definition of a universal, dynamic, fault-tolerant, and load-balanced framework adapted to all kinds of computational platforms (massively parallel machines, clusters, or distributed grids). These strategies have been implemented in the QMC=Chem code developed at Toulouse and illustrated with numerical applications on small peptides of increasing sizes (158, 434, 1056, and 1731 electrons). Using 10-80 k computing cores of the Curie machine (GENCI-TGCC-CEA, France), QMC=Chem has been shown to be capable of running at the petascale level, thus demonstrating that for this machine a large part of the peak performance can be achieved. Implementation of large-scale QMC simulations for future exascale platforms with a comparable level of efficiency is expected to be feasible. Copyright © 2013 Wiley Periodicals, Inc.

GeNets: a unified web platform for network-based genomic analyses.

PubMed

Li, Taibo; Kim, April; Rosenbluh, Joseph; Horn, Heiko; Greenfeld, Liraz; An, David; Zimmer, Andrew; Liberzon, Arthur; Bistline, Jon; Natoli, Ted; Li, Yang; Tsherniak, Aviad; Narayan, Rajiv; Subramanian, Aravind; Liefeld, Ted; Wong, Bang; Thompson, Dawn; Calvo, Sarah; Carr, Steve; Boehm, Jesse; Jaffe, Jake; Mesirov, Jill; Hacohen, Nir; Regev, Aviv; Lage, Kasper

2018-06-18

Functional genomics networks are widely used to identify unexpected pathway relationships in large genomic datasets. However, it is challenging to compare the signal-to-noise ratios of different networks and to identify the optimal network with which to interpret a particular genetic dataset. We present GeNets, a platform in which users can train a machine-learning model (Quack) to carry out these comparisons and execute, store, and share analyses of genetic and RNA-sequencing datasets.

Zooniverse - Web scale citizen science with people and machines. (Invited)

NASA Astrophysics Data System (ADS)

Smith, A.; Lynn, S.; Lintott, C.; Simpson, R.

2013-12-01

The Zooniverse (zooniverse.org) began in 2007 with the launch of Galaxy Zoo, a project in which more than 175,000 people provided shape analyses of more than 1 million galaxy images sourced from the Sloan Digital Sky Survey. These galaxy 'classifications', some 60 million in total, have since been used to produce more than 50 peer-reviewed publications based not only on the original research goals of the project but also because of serendipitous discoveries made by the volunteer community. Based upon the success of Galaxy Zoo the team have gone on to develop more than 25 web-based citizen science projects, all with a strong research focus in a range of subjects from astronomy to zoology where human-based analysis still exceeds that of machine intelligence. Over the past 6 years Zooniverse projects have collected more than 300 million data analyses from over 1 million volunteers providing fantastically rich datasets for not only the individuals working to produce research from their project but also the machine learning and computer vision research communities. The Zooniverse platform has always been developed to be the 'simplest thing that works' implementing only the most rudimentary algorithms for functionality such as task allocation and user-performance metrics - simplifications necessary to scale the Zooniverse such that the core team of developers and data scientists can remain small and the cost of running the computing infrastructure relatively modest. To date these simplifications have been appropriate for the data volumes and analysis tasks being addressed. This situation however is changing: next generation telescopes such as the Large Synoptic Sky Telescope (LSST) will produce data volumes dwarfing those previously analyzed. If citizen science is to have a part to play in analyzing these next-generation datasets then the Zooniverse will need to evolve into a smarter system capable for example of modeling the abilities of users and the complexities of the data being classified in real time. In this session I will outline the current architecture of the Zooniverse platform and introduce new functionality being developed to enable the development of a true 'social machines'. Our platform is evolving into a system capable of integrating human and machine intelligence in a live environment thus capable of addressing some of the biggest challenges in big-data science.

Centre of mass determination based on an optical weighing machine using fiber Bragg gratings

NASA Astrophysics Data System (ADS)

Oliveira, Rui; Roriz, Paulo; Marques, Manuel B.; Frazão, Orlando

2015-09-01

The purpose of the present work was to construct a weighing machine based on fiber Bragg gratings (FBGs) for the location of the 2D coordinates of the center of gravity (COG) of objects with complex geometry and density distribution. The apparatus consisted of a rigid equilateral triangular platform mounted on three supports at its vertices, two of them having cantilevers instrumented with FBGs. As an example, two femur bone models, one with and one without a hip stem prosthesis, are used to discuss the changing of the COM caused by the implementation of the prosthesis.

minimega v. 3.0

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

Crussell, Jonathan; Erickson, Jeremy; Fritz, David

minimega is an emulytics platform for creating testbeds of networked devices. The platoform consists of easily deployable tools to facilitate bringing up large networks of virtual machines including Windows, Linux, and Android. minimega allows experiments to be brought up quickly with almost no configuration. minimega also includes tools for simple cluster, management, as well as tools for creating Linux-based virtual machines. This release of minimega includes new emulated sensors for Android devices to improve the fidelity of testbeds that include mobile devices. Emulated sensors include GPS and

A System for Supporting Development and Update of the International Classification of Health Interventions (ICHI).

PubMed

Donada, Marc; Della Mea, Vincenzo; Cumerlato, Megan; Rankin, Nicole; Madden, Richard

2018-01-01

The International Classification of Health Interventions (ICHI) is a member of the WHO Family of International Classifications, being developed to provide a common tool for reporting and analysing health interventions for statistical purposes. A web-based platform for classification development and update has been specifically developed to support the initial development step and then, after final approval, the continuous revision and update of the classification. The platform provides features for classification editing, versioning, comment management and URI identifiers. During the last 12 months it has been used for developing the ICHI Beta version, replacing the previous process based on the exchange of Excel files. At November 2017, 90 users have provided input to the development of the classification, which has resulted in 2913 comments and 2971 changes in the classification, since June 2017. Further work includes the development of an URI API for machine to machine communication, following the model established for ICD-11.

Optical RISC computer

NASA Astrophysics Data System (ADS)

Guilfoyle, Peter S.; Stone, Richard V.; Hessenbruch, John M.; Zeise, Frederick F.

1993-07-01

A second generation digital optical computer (DOC II) has been developed which utilizes a RISC based operating system as its host. This 32 bit, high performance (12.8 GByte/sec), computing platform demonstrates a number of basic principals that are inherent to parallel free space optical interconnects such as speed (up to 1012 bit operations per second) and low power 1.2 fJ per bit). Although DOC II is a general purpose machine, special purpose applications have been developed and are currently being evaluated on the optical platform.

Energy-Efficient Hosting Rich Content from Mobile Platforms with Relative Proximity Sensing

PubMed Central

Baek, Sung Hoon

2017-01-01

In this paper, we present a tiny networked mobile platform, termed Tiny-Web-Thing (T-Wing), which allows the sharing of data-intensive content among objects in cyber physical systems. The object includes mobile platforms like a smartphone, and Internet of Things (IoT) platforms for Human-to-Human (H2H), Human-to-Machine (H2M), Machine-to-Human (M2H), and Machine-to-Machine (M2M) communications. T-Wing makes it possible to host rich web content directly on their objects, which nearby objects can access instantaneously. Using a new mechanism that allows the Wi-Fi interface of the object to be turned on purely on-demand, T-Wing achieves very high energy efficiency. We have implemented T-Wing on an embedded board, and present evaluation results from our testbed. From the evaluation result of T-Wing, we compare our system against alternative approaches to implement this functionality using only the cellular or Wi-Fi (but not both), and show that in typical usage, T-Wing consumes less than 15× the energy and is faster by an order of magnitude. PMID:28786942

Virtual Distances Methodology as Verification Technique for AACMMs with a Capacitive Sensor Based Indexed Metrology Platform.

PubMed

Acero, Raquel; Santolaria, Jorge; Brau, Agustin; Pueo, Marcos

2016-11-18

This paper presents a new verification procedure for articulated arm coordinate measuring machines (AACMMs) together with a capacitive sensor-based indexed metrology platform (IMP) based on the generation of virtual reference distances. The novelty of this procedure lays on the possibility of creating virtual points, virtual gauges and virtual distances through the indexed metrology platform's mathematical model taking as a reference the measurements of a ball bar gauge located in a fixed position of the instrument's working volume. The measurements are carried out with the AACMM assembled on the IMP from the six rotating positions of the platform. In this way, an unlimited number and types of reference distances could be created without the need of using a physical gauge, therefore optimizing the testing time, the number of gauge positions and the space needed in the calibration and verification procedures. Four evaluation methods are presented to assess the volumetric performance of the AACMM. The results obtained proved the suitability of the virtual distances methodology as an alternative procedure for verification of AACMMs using the indexed metrology platform.

Performance of an MPI-only semiconductor device simulator on a quad socket/quad core InfiniBand platform.

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

Shadid, John Nicolas; Lin, Paul Tinphone

2009-01-01

This preliminary study considers the scaling and performance of a finite element (FE) semiconductor device simulator on a capacity cluster with 272 compute nodes based on a homogeneous multicore node architecture utilizing 16 cores. The inter-node communication backbone for this Tri-Lab Linux Capacity Cluster (TLCC) machine is comprised of an InfiniBand interconnect. The nonuniform memory access (NUMA) nodes consist of 2.2 GHz quad socket/quad core AMD Opteron processors. The performance results for this study are obtained with a FE semiconductor device simulation code (Charon) that is based on a fully-coupled Newton-Krylov solver with domain decomposition and multilevel preconditioners. Scaling andmore » multicore performance results are presented for large-scale problems of 100+ million unknowns on up to 4096 cores. A parallel scaling comparison is also presented with the Cray XT3/4 Red Storm capability platform. The results indicate that an MPI-only programming model for utilizing the multicore nodes is reasonably efficient on all 16 cores per compute node. However, the results also indicated that the multilevel preconditioner, which is critical for large-scale capability type simulations, scales better on the Red Storm machine than the TLCC machine.« less

A Low-Cost Hand Trainer Device Based On Microcontroller Platform

NASA Astrophysics Data System (ADS)

Sabor, Muhammad Akmal Mohammad; Thamrin, Norashikin M.

2018-03-01

Conventionally, the rehabilitation equipment used in the hospital or recovery center to treat and train the muscle of the stroke patient is implementing the pneumatic or compressed air machine. The main problem caused by this equipment is that the arrangement of the machine is quite complex and the position of it has been locked and fixed, which can cause uncomfortable feeling to the patients throughout the recovery session. Furthermore, the harsh movement from the machine could harm the patient as it does not allow flexibility movement and the use of pneumatic actuator has increased the gripping force towards the patient which could hurt them. Therefore, the main aim of this paper is to propose the development of the Bionic Hand Trainer based on Arduino platform, for a low-cost solution for rehabilitation machine as well as allows flexibility and smooth hand movement for the patients during the healing process. The scope of this work is to replicate the structure of the hand only at the fingers structure that is the phalanges part, which inclusive the proximal, intermediate and distal of the fingers. In order to do this, a hand glove is designed by equipping with flex sensors at every finger and connected them to the Arduino platform. The movement of the hand will motorize the movement of the dummy hand that has been controlled by the servo motors, which have been equipped along the phalanges part. As a result, the bending flex sensors due to the movement of the fingers has doubled up the rotation of the servo motors to mimic this movement at the dummy hand. The voltage output from the bending sensors are ranging from 0 volt to 5 volts, which are suitable for low-cost hand trainer device implementation. Through this system, the patient will have the power to control their gripping operation slowly without having a painful force from the external actuators throughout the rehabilitation process.

[Porting Radiotherapy Software of Varian to Cloud Platform].

PubMed

Zou, Lian; Zhang, Weisha; Liu, Xiangxiang; Xie, Zhao; Xie, Yaoqin

2017-09-30

To develop a low-cost private cloud platform of radiotherapy software. First, a private cloud platform which was based on OpenStack and the virtual GPU hardware was builded. Then on the private cloud platform, all the Varian radiotherapy software modules were installed to the virtual machine, and the corresponding function configuration was completed. Finally the software on the cloud was able to be accessed by virtual desktop client. The function test results of the cloud workstation show that a cloud workstation is equivalent to an isolated physical workstation, and any clients on the LAN can use the cloud workstation smoothly. The cloud platform transplantation in this study is economical and practical. The project not only improves the utilization rates of radiotherapy software, but also makes it possible that the cloud computing technology can expand its applications to the field of radiation oncology.

Ultra-Compact Transputer-Based Controller for High-Level, Multi-Axis Coordination

NASA Technical Reports Server (NTRS)

Zenowich, Brian; Crowell, Adam; Townsend, William T.

2013-01-01

The design of machines that rely on arrays of servomotors such as robotic arms, orbital platforms, and combinations of both, imposes a heavy computational burden to coordinate their actions to perform coherent tasks. For example, the robotic equivalent of a person tracing a straight line in space requires enormously complex kinematics calculations, and complexity increases with the number of servo nodes. A new high-level architecture for coordinated servo-machine control enables a practical, distributed transputer alternative to conventional central processor electronics. The solution is inherently scalable, dramatically reduces bulkiness and number of conductor runs throughout the machine, requires only a fraction of the power, and is designed for cooling in a vacuum.

Repurposing mainstream CNC machine tools for laser-based additive manufacturing

NASA Astrophysics Data System (ADS)

Jones, Jason B.

2016-04-01

The advent of laser technology has been a key enabler for industrial 3D printing, known as Additive Manufacturing (AM). Despite its commercial success and unique technical capabilities, laser-based AM systems are not yet able to produce parts with the same accuracy and surface finish as CNC machining. To enable the geometry and material freedoms afforded by AM, yet achieve the precision and productivity of CNC machining, hybrid combinations of these two processes have started to gain traction. To achieve the benefits of combined processing, laser technology has been integrated into mainstream CNC machines - effectively repurposing them as hybrid manufacturing platforms. This paper reviews how this engineering challenge has prompted beam delivery innovations to allow automated changeover between laser processing and machining, using standard CNC tool changers. Handling laser-processing heads using the tool changer also enables automated change over between different types of laser processing heads, further expanding the breadth of laser processing flexibility in a hybrid CNC. This paper highlights the development, challenges and future impact of hybrid CNCs on laser processing.

Cloud Based Resource for Data Hosting, Visualization and Analysis Using UCSC Cancer Genomics Browser | Informatics Technology for Cancer Research (ITCR)

Cancer.gov

The Cancer Analysis Virtual Machine (CAVM) project will leverage cloud technology, the UCSC Cancer Genomics Browser, and the Galaxy analysis workflow system to provide investigators with a flexible, scalable platform for hosting, visualizing and analyzing their own genomic data.

A Model-Driven Approach to e-Course Management

ERIC Educational Resources Information Center

Savic, Goran; Segedinac, Milan; Milenkovic, Dušica; Hrin, Tamara; Segedinac, Mirjana

2018-01-01

This paper presents research on using a model-driven approach to the development and management of electronic courses. We propose a course management system which stores a course model represented as distinct machine-readable components containing domain knowledge of different course aspects. Based on this formally defined platform-independent…

Considerations on the construction of a Powder Bed Fusion platform for Additive Manufacturing

NASA Astrophysics Data System (ADS)

Andersen, Sebastian Aagaard; Nielsen, Karl-Emil; Pedersen, David Bue; Nielsen, Jakob Skov

As the demand for moulds and other tools becomes increasingly specific and complex, an additive manufacturing approach to production is making its way to the industry through laser based consolidation of metal powder particles by a method known as powder bed fusion. This paper concerns a variety of design choices facilitating the development of an experimental powder bed fusion machine tool, capable of manufacturing metal parts with strength matching that of conventional manufactured parts and a complexity surpassing that of subtractive processes. To understand the different mechanisms acting within such an experimental machine tool, a fully open and customizable rig is constructed. Emphasizing modularity in the rig, allows alternation of lasers, scanner systems, optical elements, powder deposition, layer height, temperature, atmosphere, and powder type. Through a custom-made software platform, control of the process is achieved, which extends into a graphical user interface, easing adjustment of process parameters and the job file generation.

A wearable computing platform for developing cloud-based machine learning models for health monitoring applications.

PubMed

Patel, Shyamal; McGinnis, Ryan S; Silva, Ikaro; DiCristofaro, Steve; Mahadevan, Nikhil; Jortberg, Elise; Franco, Jaime; Martin, Albert; Lust, Joseph; Raj, Milan; McGrane, Bryan; DePetrillo, Paolo; Aranyosi, A J; Ceruolo, Melissa; Pindado, Jesus; Ghaffari, Roozbeh

2016-08-01

Wearable sensors have the potential to enable clinical-grade ambulatory health monitoring outside the clinic. Technological advances have enabled development of devices that can measure vital signs with great precision and significant progress has been made towards extracting clinically meaningful information from these devices in research studies. However, translating measurement accuracies achieved in the controlled settings such as the lab and clinic to unconstrained environments such as the home remains a challenge. In this paper, we present a novel wearable computing platform for unobtrusive collection of labeled datasets and a new paradigm for continuous development, deployment and evaluation of machine learning models to ensure robust model performance as we transition from the lab to home. Using this system, we train activity classification models across two studies and track changes in model performance as we go from constrained to unconstrained settings.

Technological choices for mobile clinical applications.

PubMed

Ehrler, Frederic; Issom, David; Lovis, Christian

2011-01-01

The rise of cheaper and more powerful mobile devices make them a new and attractive platform for clinical applications. The interaction paradigm and portability of the device facilitates bedside human-machine interactions. The better accessibility to information and decision-support anywhere in the hospital improves the efficiency and the safety of care processes. In this study, we attempt to find out what are the most appropriate Operating System (OS) and Software Development Kit (SDK) to support the development of clinical applications on mobile devices. The Android platform is a Linux-based, open source platform that has many advantages. Two main SDKs are available on this platform: the native Android and the Adobe Flex SDK. Both of them have interesting features, but the latter has been preferred due its portability at comparable performance and ease of development.

Pile Driving

NASA Technical Reports Server (NTRS)

1987-01-01

Machine-oriented structural engineering firm TERA, Inc. is engaged in a project to evaluate the reliability of offshore pile driving prediction methods to eventually predict the best pile driving technique for each new offshore oil platform. Phase I Pile driving records of 48 offshore platforms including such information as blow counts, soil composition and pertinent construction details were digitized. In Phase II, pile driving records were statistically compared with current methods of prediction. Result was development of modular software, the CRIPS80 Software Design Analyzer System, that companies can use to evaluate other prediction procedures or other data bases.

Application verification research of cloud computing technology in the field of real time aerospace experiment

NASA Astrophysics Data System (ADS)

Wan, Junwei; Chen, Hongyan; Zhao, Jing

2017-08-01

According to the requirements of real-time, reliability and safety for aerospace experiment, the single center cloud computing technology application verification platform is constructed. At the IAAS level, the feasibility of the cloud computing technology be applied to the field of aerospace experiment is tested and verified. Based on the analysis of the test results, a preliminary conclusion is obtained: Cloud computing platform can be applied to the aerospace experiment computing intensive business. For I/O intensive business, it is recommended to use the traditional physical machine.

The backend design of an environmental monitoring system upon real-time prediction of groundwater level fluctuation under the hillslope.

PubMed

Lin, Hsueh-Chun; Hong, Yao-Ming; Kan, Yao-Chiang

2012-01-01

The groundwater level represents a critical factor to evaluate hillside landslides. A monitoring system upon the real-time prediction platform with online analytical functions is important to forecast the groundwater level due to instantaneously monitored data when the heavy precipitation raises the groundwater level under the hillslope and causes instability. This study is to design the backend of an environmental monitoring system with efficient algorithms for machine learning and knowledge bank for the groundwater level fluctuation prediction. A Web-based platform upon the model-view controller-based architecture is established with technology of Web services and engineering data warehouse to support online analytical process and feedback risk assessment parameters for real-time prediction. The proposed system incorporates models of hydrological computation, machine learning, Web services, and online prediction to satisfy varieties of risk assessment requirements and approaches of hazard prevention. The rainfall data monitored from the potential landslide area at Lu-Shan, Nantou and Li-Shan, Taichung, in Taiwan, are applied to examine the system design.

Implementing Audio-CASI on Windows’ Platforms

PubMed Central

Cooley, Philip C.; Turner, Charles F.

2011-01-01

Audio computer-assisted self interviewing (Audio-CASI) technologies have recently been shown to provide important and sometimes dramatic improvements in the quality of survey measurements. This is particularly true for measurements requiring respondents to divulge highly sensitive information such as their sexual, drug use, or other sensitive behaviors. However, DOS-based Audio-CASI systems that were designed and adopted in the early 1990s have important limitations. Most salient is the poor control they provide for manipulating the video presentation of survey questions. This article reports our experiences adapting Audio-CASI to Microsoft Windows 3.1 and Windows 95 platforms. Overall, our Windows-based system provided the desired control over video presentation and afforded other advantages including compatibility with a much wider array of audio devices than our DOS-based Audio-CASI technologies. These advantages came at the cost of increased system requirements --including the need for both more RAM and larger hard disks. While these costs will be an issue for organizations converting large inventories of PCS to Windows Audio-CASI today, this will not be a serious constraint for organizations and individuals with small inventories of machines to upgrade or those purchasing new machines today. PMID:22081743

Ubiquitous and ambient-assisted living eHealth platforms for Down's syndrome and palliative care in the Republic of Panama: A systematic review.

PubMed

Saldaña Barrios, Juan Jose; Mendoza, Luis; Pitti, Edgardo; Vargas, Miguel

2016-10-21

In this work, the authors present two eHealth platforms that are examples of how health systems are migrating from client-server architecture to the web-based and ubiquitous paradigm. These two platforms were modeled, designed, developed and implemented with positive results. First, using ambient-assisted living and ubiquitous computing, the authors enhance how palliative care is being provided to the elderly patients and patients with terminal illness, making the work of doctors, nurses and other health actors easier. Second, applying machine learning methods and a data-centered, ubiquitous, patient's results' repository, the authors intent to improve the Down's syndrome risk estimation process with more accurate predictions based on local woman patients' parameters. These two eHealth platforms can improve the quality of life, not only physically but also psychologically, of the patients and their families in the country of Panama. © The Author(s) 2016.

PepArML: A Meta-Search Peptide Identification Platform

PubMed Central

Edwards, Nathan J.

2014-01-01

The PepArML meta-search peptide identification platform provides a unified search interface to seven search engines; a robust cluster, grid, and cloud computing scheduler for large-scale searches; and an unsupervised, model-free, machine-learning-based result combiner, which selects the best peptide identification for each spectrum, estimates false-discovery rates, and outputs pepXML format identifications. The meta-search platform supports Mascot; Tandem with native, k-score, and s-score scoring; OMSSA; MyriMatch; and InsPecT with MS-GF spectral probability scores — reformatting spectral data and constructing search configurations for each search engine on the fly. The combiner selects the best peptide identification for each spectrum based on search engine results and features that model enzymatic digestion, retention time, precursor isotope clusters, mass accuracy, and proteotypic peptide properties, requiring no prior knowledge of feature utility or weighting. The PepArML meta-search peptide identification platform often identifies 2–3 times more spectra than individual search engines at 10% FDR. PMID:25663956

Virtual Distances Methodology as Verification Technique for AACMMs with a Capacitive Sensor Based Indexed Metrology Platform

PubMed Central

Acero, Raquel; Santolaria, Jorge; Brau, Agustin; Pueo, Marcos

2016-01-01

This paper presents a new verification procedure for articulated arm coordinate measuring machines (AACMMs) together with a capacitive sensor-based indexed metrology platform (IMP) based on the generation of virtual reference distances. The novelty of this procedure lays on the possibility of creating virtual points, virtual gauges and virtual distances through the indexed metrology platform’s mathematical model taking as a reference the measurements of a ball bar gauge located in a fixed position of the instrument’s working volume. The measurements are carried out with the AACMM assembled on the IMP from the six rotating positions of the platform. In this way, an unlimited number and types of reference distances could be created without the need of using a physical gauge, therefore optimizing the testing time, the number of gauge positions and the space needed in the calibration and verification procedures. Four evaluation methods are presented to assess the volumetric performance of the AACMM. The results obtained proved the suitability of the virtual distances methodology as an alternative procedure for verification of AACMMs using the indexed metrology platform. PMID:27869722

Staghorn: An Automated Large-Scale Distributed System Analysis Platform

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

Gabert, Kasimir; Burns, Ian; Elliott, Steven

2016-09-01

Conducting experiments on large-scale distributed computing systems is becoming significantly easier with the assistance of emulation. Researchers can now create a model of a distributed computing environment and then generate a virtual, laboratory copy of the entire system composed of potentially thousands of virtual machines, switches, and software. The use of real software, running at clock rate in full virtual machines, allows experiments to produce meaningful results without necessitating a full understanding of all model components. However, the ability to inspect and modify elements within these models is bound by the limitation that such modifications must compete with the model,more » either running in or alongside it. This inhibits entire classes of analyses from being conducted upon these models. We developed a mechanism to snapshot an entire emulation-based model as it is running. This allows us to \\freeze time" and subsequently fork execution, replay execution, modify arbitrary parts of the model, or deeply explore the model. This snapshot includes capturing packets in transit and other input/output state along with the running virtual machines. We were able to build this system in Linux using Open vSwitch and Kernel Virtual Machines on top of Sandia's emulation platform Firewheel. This primitive opens the door to numerous subsequent analyses on models, including state space exploration, debugging distributed systems, performance optimizations, improved training environments, and improved experiment repeatability.« less

Graphene-based bimorphs for micron-sized, autonomous origami machines.

PubMed

Miskin, Marc Z; Dorsey, Kyle J; Bircan, Baris; Han, Yimo; Muller, David A; McEuen, Paul L; Cohen, Itai

2018-01-16

Origami-inspired fabrication presents an attractive platform for miniaturizing machines: thinner layers of folding material lead to smaller devices, provided that key functional aspects, such as conductivity, stiffness, and flexibility, are persevered. Here, we show origami fabrication at its ultimate limit by using 2D atomic membranes as a folding material. As a prototype, we bond graphene sheets to nanometer-thick layers of glass to make ultrathin bimorph actuators that bend to micrometer radii of curvature in response to small strain differentials. These strains are two orders of magnitude lower than the fracture threshold for the device, thus maintaining conductivity across the structure. By patterning 2-[Formula: see text]m-thick rigid panels on top of bimorphs, we localize bending to the unpatterned regions to produce folds. Although the graphene bimorphs are only nanometers thick, they can lift these panels, the weight equivalent of a 500-nm-thick silicon chip. Using panels and bimorphs, we can scale down existing origami patterns to produce a wide range of machines. These machines change shape in fractions of a second when crossing a tunable pH threshold, showing that they sense their environments, respond, and perform useful functions on time and length scales comparable with microscale biological organisms. With the incorporation of electronic, photonic, and chemical payloads, these basic elements will become a powerful platform for robotics at the micrometer scale.

Successful fabrication of a convex platform PMMA cell-counting slide using a high-precision perpendicular dual-spindle CNC machine tool

NASA Astrophysics Data System (ADS)

Chen, Shun-Tong; Chang, Chih-Hsien

2013-12-01

This study presents a novel approach to the fabrication of a biomedical-mold for producing convex platform PMMA (poly-methyl-meth-acrylate) slides for counting cells. These slides allow for the microscopic examination of urine sediment cells. Manufacturing of such slides incorporates three important procedures: (1) the development of a tabletop high-precision dual-spindle CNC (computerized numerical control) machine tool; (2) the formation of a boron-doped polycrystalline composite diamond (BD-PCD) wheel-tool on the machine tool developed in procedure (1); and (3) the cutting of a multi-groove-biomedical-mold array using the formed diamond wheel-tool in situ on the developed machine. The machine incorporates a hybrid working platform providing wheel-tool thinning using spark erosion to cut, polish, and deburr microgrooves on NAK80 steel directly. With consideration given for the electrical conductive properties of BD-PCD, the diamond wheel-tool is thinned to a thickness of 5 µm by rotary wire electrical discharge machining. The thinned wheel-tool can grind microgrooves 10 µm wide. An embedded design, which inserts a close fitting precision core into the biomedical-mold to create step-difference (concave inward) of 50 µm in height between the core and the mold, is also proposed and realized. The perpendicular dual-spindles and precision rotary stage are features that allow for biomedical-mold machining without the necessity of uploading and repositioning materials until all tasks are completed. A PMMA biomedical-slide with a plurality of juxtaposed counting chambers is formed and its usefulness verified.

GATECloud.net: a platform for large-scale, open-source text processing on the cloud.

PubMed

Tablan, Valentin; Roberts, Ian; Cunningham, Hamish; Bontcheva, Kalina

2013-01-28

Cloud computing is increasingly being regarded as a key enabler of the 'democratization of science', because on-demand, highly scalable cloud computing facilities enable researchers anywhere to carry out data-intensive experiments. In the context of natural language processing (NLP), algorithms tend to be complex, which makes their parallelization and deployment on cloud platforms a non-trivial task. This study presents a new, unique, cloud-based platform for large-scale NLP research--GATECloud. net. It enables researchers to carry out data-intensive NLP experiments by harnessing the vast, on-demand compute power of the Amazon cloud. Important infrastructural issues are dealt with by the platform, completely transparently for the researcher: load balancing, efficient data upload and storage, deployment on the virtual machines, security and fault tolerance. We also include a cost-benefit analysis and usage evaluation.

ERDDAP: Reducing Data Friction with an Open Source Data Platform

NASA Astrophysics Data System (ADS)

O'Brien, K.

2017-12-01

Data friction is not just an issue facing interdisciplinary research. Often times, even within disciplines, significant data friction can exist. Issues of differing formats, limited metadata and non-existent machine-to-machine data access are all issues that exist within disciplines and make it that much harder for successful interdisciplinary cooperation. Therefore, reducing data friction within disciplines is crucial first step in providing better overall collaboration. ERDDAP, an open source data platform developed at NOAA's Southwest Fisheries Center, is well poised to improve data useability and understanding and reduce data friction, both in single and multi-disciplinary research. By virtue of its ability to integrate data of varying formats and provide RESTful-based user access to data and metadata, use of ERDDAP has grown substantially throughout the ocean data community. ERDDAP also supports standards such as the DAP data protocol, the Climate and Forecast (CF) metadata conventions and the Bagit document standard for data archival. In this presentation, we will discuss the advantages of using ERDDAP as a data platform. We will also show specific use cases where utilizing ERDDAP has reduced friction within a single discipline (physical oceanography) and improved interdisciplinary collaboration as well.

GOTree Machine (GOTM): a web-based platform for interpreting sets of interesting genes using Gene Ontology hierarchies

PubMed Central

Zhang, Bing; Schmoyer, Denise; Kirov, Stefan; Snoddy, Jay

2004-01-01

Background Microarray and other high-throughput technologies are producing large sets of interesting genes that are difficult to analyze directly. Bioinformatics tools are needed to interpret the functional information in the gene sets. Results We have created a web-based tool for data analysis and data visualization for sets of genes called GOTree Machine (GOTM). This tool was originally intended to analyze sets of co-regulated genes identified from microarray analysis but is adaptable for use with other gene sets from other high-throughput analyses. GOTree Machine generates a GOTree, a tree-like structure to navigate the Gene Ontology Directed Acyclic Graph for input gene sets. This system provides user friendly data navigation and visualization. Statistical analysis helps users to identify the most important Gene Ontology categories for the input gene sets and suggests biological areas that warrant further study. GOTree Machine is available online at . Conclusion GOTree Machine has a broad application in functional genomic, proteomic and other high-throughput methods that generate large sets of interesting genes; its primary purpose is to help users sort for interesting patterns in gene sets. PMID:14975175

Three-point compound sine plate offers cost and weight savings

NASA Technical Reports Server (NTRS)

Barras, A. P.

1972-01-01

Work piece adjustment fixture reduces size, weight and set-up complexity of alignment platforms used in metal blank machining. Design benefits designers and manufacturers of machine tools and measuring equipment.

Design and FPGA Implementation of a Universal Chaotic Signal Generator Based on the Verilog HDL Fixed-Point Algorithm and State Machine Control

NASA Astrophysics Data System (ADS)

Qiu, Mo; Yu, Simin; Wen, Yuqiong; Lü, Jinhu; He, Jianbin; Lin, Zhuosheng

In this paper, a novel design methodology and its FPGA hardware implementation for a universal chaotic signal generator is proposed via the Verilog HDL fixed-point algorithm and state machine control. According to continuous-time or discrete-time chaotic equations, a Verilog HDL fixed-point algorithm and its corresponding digital system are first designed. In the FPGA hardware platform, each operation step of Verilog HDL fixed-point algorithm is then controlled by a state machine. The generality of this method is that, for any given chaotic equation, it can be decomposed into four basic operation procedures, i.e. nonlinear function calculation, iterative sequence operation, iterative values right shifting and ceiling, and chaotic iterative sequences output, each of which corresponds to only a state via state machine control. Compared with the Verilog HDL floating-point algorithm, the Verilog HDL fixed-point algorithm can save the FPGA hardware resources and improve the operation efficiency. FPGA-based hardware experimental results validate the feasibility and reliability of the proposed approach.

Kinematics and dynamics of robotic systems with multiple closed loops

NASA Astrophysics Data System (ADS)

Zhang, Chang-De

The kinematics and dynamics of robotic systems with multiple closed loops, such as Stewart platforms, walking machines, and hybrid manipulators, are studied. In the study of kinematics, focus is on the closed-form solutions of the forward position analysis of different parallel systems. A closed-form solution means that the solution is expressed as a polynomial in one variable. If the order of the polynomial is less than or equal to four, the solution has analytical closed-form. First, the conditions of obtaining analytical closed-form solutions are studied. For a Stewart platform, the condition is found to be that one rotational degree of freedom of the output link is decoupled from the other five. Based on this condition, a class of Stewart platforms which has analytical closed-form solution is formulated. Conditions of analytical closed-form solution for other parallel systems are also studied. Closed-form solutions of forward kinematics for walking machines and multi-fingered grippers are then studied. For a parallel system with three three-degree-of-freedom subchains, there are 84 possible ways to select six independent joints among nine joints. These 84 ways can be classified into three categories: Category 3:3:0, Category 3:2:1, and Category 2:2:2. It is shown that the first category has no solutions; the solutions of the second category have analytical closed-form; and the solutions of the last category are higher order polynomials. The study is then extended to a nearly general Stewart platform. The solution is a 20th order polynomial and the Stewart platform has a maximum of 40 possible configurations. Also, the study is extended to a new class of hybrid manipulators which consists of two serially connected parallel mechanisms. In the study of dynamics, a computationally efficient method for inverse dynamics of manipulators based on the virtual work principle is developed. Although this method is comparable with the recursive Newton-Euler method for serial manipulators, its advantage is more noteworthy when applied to parallel systems. An approach of inverse dynamics of a walking machine is also developed, which includes inverse dynamic modeling, foot force distribution, and joint force/torque allocation.

Whole-machine calibration approach for phased array radar with self-test

NASA Astrophysics Data System (ADS)

Shen, Kai; Yao, Zhi-Cheng; Zhang, Jin-Chang; Yang, Jian

2017-06-01

The performance of the missile-borne phased array radar is greatly influenced by the inter-channel amplitude and phase inconsistencies. In order to ensure its performance, the amplitude and the phase characteristics of radar should be calibrated. Commonly used methods mainly focus on antenna calibration, such as FFT, REV, etc. However, the radar channel also contains T / R components, channels, ADC and messenger. In order to achieve on-based phased array radar amplitude information for rapid machine calibration and compensation, we adopt a high-precision plane scanning test platform for phase amplitude test. A calibration approach for the whole channel system based on the radar frequency source test is proposed. Finally, the advantages and the application prospect of this approach are analysed.

A high performance scientific cloud computing environment for materials simulations

NASA Astrophysics Data System (ADS)

Jorissen, K.; Vila, F. D.; Rehr, J. J.

2012-09-01

We describe the development of a scientific cloud computing (SCC) platform that offers high performance computation capability. The platform consists of a scientific virtual machine prototype containing a UNIX operating system and several materials science codes, together with essential interface tools (an SCC toolset) that offers functionality comparable to local compute clusters. In particular, our SCC toolset provides automatic creation of virtual clusters for parallel computing, including tools for execution and monitoring performance, as well as efficient I/O utilities that enable seamless connections to and from the cloud. Our SCC platform is optimized for the Amazon Elastic Compute Cloud (EC2). We present benchmarks for prototypical scientific applications and demonstrate performance comparable to local compute clusters. To facilitate code execution and provide user-friendly access, we have also integrated cloud computing capability in a JAVA-based GUI. Our SCC platform may be an alternative to traditional HPC resources for materials science or quantum chemistry applications.

A Platform for Scalable Satellite and Geospatial Data Analysis

NASA Astrophysics Data System (ADS)

Beneke, C. M.; Skillman, S.; Warren, M. S.; Kelton, T.; Brumby, S. P.; Chartrand, R.; Mathis, M.

2017-12-01

At Descartes Labs, we use the commercial cloud to run global-scale machine learning applications over satellite imagery. We have processed over 5 Petabytes of public and commercial satellite imagery, including the full Landsat and Sentinel archives. By combining open-source tools with a FUSE-based filesystem for cloud storage, we have enabled a scalable compute platform that has demonstrated reading over 200 GB/s of satellite imagery into cloud compute nodes. In one application, we generated global 15m Landsat-8, 20m Sentinel-1, and 10m Sentinel-2 composites from 15 trillion pixels, using over 10,000 CPUs. We recently created a public open-source Python client library that can be used to query and access preprocessed public satellite imagery from within our platform, and made this platform available to researchers for non-commercial projects. In this session, we will describe how you can use the Descartes Labs Platform for rapid prototyping and scaling of geospatial analyses and demonstrate examples in land cover classification.

The community FabLab platform: applications and implications in biomedical engineering.

PubMed

Stephenson, Makeda K; Dow, Douglas E

2014-01-01

Skill development in science, technology, engineering and math (STEM) education present one of the most formidable challenges of modern society. The Community FabLab platform presents a viable solution. Each FabLab contains a suite of modern computer numerical control (CNC) equipment, electronics and computing hardware and design, programming, computer aided design (CAD) and computer aided machining (CAM) software. FabLabs are community and educational resources and open to the public. Development of STEM based workforce skills such as digital fabrication and advanced manufacturing can be enhanced using this platform. Particularly notable is the potential of the FabLab platform in STEM education. The active learning environment engages and supports a diversity of learners, while the iterative learning that is supported by the FabLab rapid prototyping platform facilitates depth of understanding, creativity, innovation and mastery. The product and project based learning that occurs in FabLabs develops in the student a personal sense of accomplishment, self-awareness, command of the material and technology. This helps build the interest and confidence necessary to excel in STEM and throughout life. Finally the introduction and use of relevant technologies at every stage of the education process ensures technical familiarity and a broad knowledge base needed for work in STEM based fields. Biomedical engineering education strives to cultivate broad technical adeptness, creativity, interdisciplinary thought, and an ability to form deep conceptual understanding of complex systems. The FabLab platform is well designed to enhance biomedical engineering education.

A machine learning approach for viral genome classification.

PubMed

Remita, Mohamed Amine; Halioui, Ahmed; Malick Diouara, Abou Abdallah; Daigle, Bruno; Kiani, Golrokh; Diallo, Abdoulaye Baniré

2017-04-11

Advances in cloning and sequencing technology are yielding a massive number of viral genomes. The classification and annotation of these genomes constitute important assets in the discovery of genomic variability, taxonomic characteristics and disease mechanisms. Existing classification methods are often designed for specific well-studied family of viruses. Thus, the viral comparative genomic studies could benefit from more generic, fast and accurate tools for classifying and typing newly sequenced strains of diverse virus families. Here, we introduce a virus classification platform, CASTOR, based on machine learning methods. CASTOR is inspired by a well-known technique in molecular biology: restriction fragment length polymorphism (RFLP). It simulates, in silico, the restriction digestion of genomic material by different enzymes into fragments. It uses two metrics to construct feature vectors for machine learning algorithms in the classification step. We benchmark CASTOR for the classification of distinct datasets of human papillomaviruses (HPV), hepatitis B viruses (HBV) and human immunodeficiency viruses type 1 (HIV-1). Results reveal true positive rates of 99%, 99% and 98% for HPV Alpha species, HBV genotyping and HIV-1 M subtyping, respectively. Furthermore, CASTOR shows a competitive performance compared to well-known HIV-1 specific classifiers (REGA and COMET) on whole genomes and pol fragments. The performance of CASTOR, its genericity and robustness could permit to perform novel and accurate large scale virus studies. The CASTOR web platform provides an open access, collaborative and reproducible machine learning classifiers. CASTOR can be accessed at http://castor.bioinfo.uqam.ca .

DCL System Using Deep Learning Approaches for Land-based or Ship-based Real-Time Recognition and Localization of Marine Mammals

DTIC Science & Technology

2012-09-30

platform (HPC) was developed, called the HPC-Acoustic Data Accelerator, or HPC-ADA for short. The HPC-ADA was designed based on fielded systems [1-4...software (Detection cLassificaiton for MAchine learning - High Peformance Computing). The software package was designed to utilize parallel and...Sedna [7] and is designed using a parallel architecture2, allowing existing algorithms to distribute to the various processing nodes with minimal changes

A Framework for Finding and Summarizing Product Defects, and Ranking Helpful Threads from Online Customer Forums through Machine Learning

ERIC Educational Resources Information Center

Jiao, Jian

2013-01-01

The Internet has revolutionized the way users share and acquire knowledge. As important and popular Web-based applications, online discussion forums provide interactive platforms for users to exchange information and report problems. With the rapid growth of social networks and an ever increasing number of Internet users, online forums have…

A noninvasive technique for real-time detection of bruises in apple surface based on machine vision

NASA Astrophysics Data System (ADS)

Zhao, Juan; Peng, Yankun; Dhakal, Sagar; Zhang, Leilei; Sasao, Akira

2013-05-01

Apple is one of the highly consumed fruit item in daily life. However, due to its high damage potential and massive influence on taste and export, the quality of apple has to be detected before it reaches the consumer's hand. This study was aimed to develop a hardware and software unit for real-time detection of apple bruises based on machine vision technology. The hardware unit consisted of a light shield installed two monochrome cameras at different angles, LED light source to illuminate the sample, and sensors at the entrance of box to signal the positioning of sample. Graphical Users Interface (GUI) was developed in VS2010 platform to control the overall hardware and display the image processing result. The hardware-software system was developed to acquire the images of 3 samples from each camera and display the image processing result in real time basis. An image processing algorithm was developed in Opencv and C++ platform. The software is able to control the hardware system to classify the apple into two grades based on presence/absence of surface bruises with the size of 5mm. The experimental result is promising and the system with further modification can be applicable for industrial production in near future.

Automatic energy expenditure measurement for health science.

PubMed

Catal, Cagatay; Akbulut, Akhan

2018-04-01

It is crucial to predict the human energy expenditure in any sports activity and health science application accurately to investigate the impact of the activity. However, measurement of the real energy expenditure is not a trivial task and involves complex steps. The objective of this work is to improve the performance of existing estimation models of energy expenditure by using machine learning algorithms and several data from different sensors and provide this estimation service in a cloud-based platform. In this study, we used input data such as breathe rate, and hearth rate from three sensors. Inputs are received from a web form and sent to the web service which applies a regression model on Azure cloud platform. During the experiments, we assessed several machine learning models based on regression methods. Our experimental results showed that our novel model which applies Boosted Decision Tree Regression in conjunction with the median aggregation technique provides the best result among other five regression algorithms. This cloud-based energy expenditure system which uses a web service showed that cloud computing technology is a great opportunity to develop estimation systems and the new model which applies Boosted Decision Tree Regression with the median aggregation provides remarkable results. Copyright © 2018 Elsevier B.V. All rights reserved.

Design and Mechanical Evaluation of a Capacitive Sensor-Based Indexed Platform for Verification of Portable Coordinate Measuring Instruments

PubMed Central

Avila, Agustín Brau; Mazo, Jorge Santolaria; Martín, Juan José Aguilar

2014-01-01

During the last years, the use of Portable Coordinate Measuring Machines (PCMMs) in industry has increased considerably, mostly due to their flexibility for accomplishing in-line measuring tasks as well as their reduced costs and operational advantages as compared to traditional coordinate measuring machines (CMMs). However, their operation has a significant drawback derived from the techniques applied in the verification and optimization procedures of their kinematic parameters. These techniques are based on the capture of data with the measuring instrument from a calibrated gauge object, fixed successively in various positions so that most of the instrument measuring volume is covered, which results in time-consuming, tedious and expensive verification procedures. In this work the mechanical design of an indexed metrology platform (IMP) is presented. The aim of the IMP is to increase the final accuracy and to radically simplify the calibration, identification and verification of geometrical parameter procedures of PCMMs. The IMP allows us to fix the calibrated gauge object and move the measuring instrument in such a way that it is possible to cover most of the instrument working volume, reducing the time and operator fatigue to carry out these types of procedures. PMID:24451458

Design and mechanical evaluation of a capacitive sensor-based indexed platform for verification of portable coordinate measuring instruments.

PubMed

Avila, Agustín Brau; Mazo, Jorge Santolaria; Martín, Juan José Aguilar

2014-01-02

During the last years, the use of Portable Coordinate Measuring Machines (PCMMs) in industry has increased considerably, mostly due to their flexibility for accomplishing in-line measuring tasks as well as their reduced costs and operational advantages as compared to traditional coordinate measuring machines (CMMs). However, their operation has a significant drawback derived from the techniques applied in the verification and optimization procedures of their kinematic parameters. These techniques are based on the capture of data with the measuring instrument from a calibrated gauge object, fixed successively in various positions so that most of the instrument measuring volume is covered, which results in time-consuming, tedious and expensive verification procedures. In this work the mechanical design of an indexed metrology platform (IMP) is presented. The aim of the IMP is to increase the final accuracy and to radically simplify the calibration, identification and verification of geometrical parameter procedures of PCMMs. The IMP allows us to fix the calibrated gauge object and move the measuring instrument in such a way that it is possible to cover most of the instrument working volume, reducing the time and operator fatigue to carry out these types of procedures.

Developing Ubiquitous Sensor Network Platform Using Internet of Things: Application in Precision Agriculture.

PubMed

Ferrández-Pastor, Francisco Javier; García-Chamizo, Juan Manuel; Nieto-Hidalgo, Mario; Mora-Pascual, Jerónimo; Mora-Martínez, José

2016-07-22

The application of Information Technologies into Precision Agriculture methods has clear benefits. Precision Agriculture optimises production efficiency, increases quality, minimises environmental impact and reduces the use of resources (energy, water); however, there are different barriers that have delayed its wide development. Some of these main barriers are expensive equipment, the difficulty to operate and maintain and the standard for sensor networks are still under development. Nowadays, new technological development in embedded devices (hardware and communication protocols), the evolution of Internet technologies (Internet of Things) and ubiquitous computing (Ubiquitous Sensor Networks) allow developing less expensive systems, easier to control, install and maintain, using standard protocols with low-power consumption. This work develops and test a low-cost sensor/actuator network platform, based in Internet of Things, integrating machine-to-machine and human-machine-interface protocols. Edge computing uses this multi-protocol approach to develop control processes on Precision Agriculture scenarios. A greenhouse with hydroponic crop production was developed and tested using Ubiquitous Sensor Network monitoring and edge control on Internet of Things paradigm. The experimental results showed that the Internet technologies and Smart Object Communication Patterns can be combined to encourage development of Precision Agriculture. They demonstrated added benefits (cost, energy, smart developing, acceptance by agricultural specialists) when a project is launched.

Developing Ubiquitous Sensor Network Platform Using Internet of Things: Application in Precision Agriculture

PubMed Central

Ferrández-Pastor, Francisco Javier; García-Chamizo, Juan Manuel; Nieto-Hidalgo, Mario; Mora-Pascual, Jerónimo; Mora-Martínez, José

2016-01-01

The application of Information Technologies into Precision Agriculture methods has clear benefits. Precision Agriculture optimises production efficiency, increases quality, minimises environmental impact and reduces the use of resources (energy, water); however, there are different barriers that have delayed its wide development. Some of these main barriers are expensive equipment, the difficulty to operate and maintain and the standard for sensor networks are still under development. Nowadays, new technological development in embedded devices (hardware and communication protocols), the evolution of Internet technologies (Internet of Things) and ubiquitous computing (Ubiquitous Sensor Networks) allow developing less expensive systems, easier to control, install and maintain, using standard protocols with low-power consumption. This work develops and test a low-cost sensor/actuator network platform, based in Internet of Things, integrating machine-to-machine and human-machine-interface protocols. Edge computing uses this multi-protocol approach to develop control processes on Precision Agriculture scenarios. A greenhouse with hydroponic crop production was developed and tested using Ubiquitous Sensor Network monitoring and edge control on Internet of Things paradigm. The experimental results showed that the Internet technologies and Smart Object Communication Patterns can be combined to encourage development of Precision Agriculture. They demonstrated added benefits (cost, energy, smart developing, acceptance by agricultural specialists) when a project is launched. PMID:27455265

Ground Simulation of an Autonomous Satellite Rendezvous and Tracking System Using Dual Robotic Systems

NASA Technical Reports Server (NTRS)

Trube, Matthew J.; Hyslop, Andrew M.; Carignan, Craig R.; Easley, Joseph W.

2012-01-01

A hardware-in-the-loop ground system was developed for simulating a robotic servicer spacecraft tracking a target satellite at short range. A relative navigation sensor package "Argon" is mounted on the end-effector of a Fanuc 430 manipulator, which functions as the base platform of the robotic spacecraft servicer. Machine vision algorithms estimate the pose of the target spacecraft, mounted on a Rotopod R-2000 platform, relay the solution to a simulation of the servicer spacecraft running in "Freespace", which performs guidance, navigation and control functions, integrates dynamics, and issues motion commands to a Fanuc platform controller so that it tracks the simulated servicer spacecraft. Results will be reviewed for several satellite motion scenarios at different ranges. Key words: robotics, satellite, servicing, guidance, navigation, tracking, control, docking.

Agents Technology Research

DTIC Science & Technology

2010-02-01

multi-agent reputation management. State abstraction is a technique used to allow machine learning technologies to cope with problems that have large...state abstrac- tion process to enable reinforcement learning in domains with large state spaces. State abstraction is vital to machine learning ...across a collective of independent platforms. These individual elements, often referred to as agents in the machine learning community, should exhibit both

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

Bates, Robert; McConnell, Elizabeth

Machining methods across many industries generally require multiple operations to machine and process advanced materials, features with micron precision, and complex shapes. The resulting multiple machining platforms can significantly affect manufacturing cycle time and the precision of the final parts, with a resultant increase in cost and energy consumption. Ultrafast lasers represent a transformative and disruptive technology that removes material with micron precision and in a single step manufacturing process. Such precision results from athermal ablation without modification or damage to the remaining material which is the key differentiator between ultrafast laser technologies and traditional laser technologies or mechanical processes.more » Athermal ablation without modification or damage to the material eliminates post-processing or multiple manufacturing steps. Combined with the appropriate technology to control the motion of the work piece, ultrafast lasers are excellent candidates to provide breakthrough machining capability for difficult-to-machine materials. At the project onset in early 2012, the project team recognized that substantial effort was necessary to improve the application of ultrafast laser and precise motion control technologies (for micromachining difficult-to-machine materials) to further the aggregate throughput and yield improvements over conventional machining methods. The project described in this report advanced these leading-edge technologies thru the development and verification of two platforms: a hybrid enhanced laser chassis and a multi-application testbed.« less

Development of testing machine for tunnel inspection using multi-rotor UAV

NASA Astrophysics Data System (ADS)

Iwamoto, Tatsuya; Enaka, Tomoya; Tada, Keijirou

2017-05-01

Many concrete structures are deteriorating to dangerous levels throughout Japan. These concrete structures need to be inspected regularly to be sure that they are safe enough to be used. The inspection method for these concrete structures is typically the impact acoustic method. In the impact acoustic method, the worker taps the surface of the concrete with a hammer. Thus, it is necessary to set up scaffolding to access tunnel walls for inspection. Alternatively, aerial work platforms can be used. However, setting up scaffolding and aerial work platforms is not economical with regard to time or money. Therefore, we developed a testing machine using a multirotor UAV for tunnel inspection. This test machine flies by a plurality of rotors, and it is pushed along a concrete wall and moved by using rubber crawlers. The impact acoustic method is used in this testing machine. This testing machine has a hammer to make an impact, and a microphone to acquire the impact sound. The impact sound is converted into an electrical signal and is wirelessly transmitted to the computer. At the same time, the position of the testing machine is measured by image processing using a camera. The weight and dimensions of the testing machine are approximately 1.25 kg and 500 mm by 500 mm by 250 mm, respectively.

Predicting Droplet Formation on Centrifugal Microfluidic Platforms

NASA Astrophysics Data System (ADS)

Moebius, Jacob Alfred

Centrifugal microfluidics is a widely known research tool for biological sample and water quality analysis. Currently, the standard equipment used for such diagnostic applications include slow, bulky machines controlled by multiple operators. These machines can be condensed into a smaller, faster benchtop sample-to-answer system. Sample processing is an important step taken to extract, isolate, and convert biological factors, such as nucleic acids or proteins, from a raw sample to an analyzable solution. Volume definition is one such step. The focus of this thesis is the development of a model predicting monodispersed droplet formation and the application of droplets as a technique for volume definition. First, a background of droplet microfluidic platforms is presented, along with current biological analysis technologies and the advantages of integrating such technologies onto microfluidic platforms. Second, background and theories of centrifugal microfluidics is given, followed by theories relevant to droplet emulsions. Third, fabrication techniques for centrifugal microfluidic designs are discussed. Finally, the development of a model for predicting droplet formation on the centrifugal microfluidic platform are presented for the rest of the thesis. Predicting droplet formation analytically based on the volumetric flow rates of the continuous and dispersed phases, the ratios of these two flow rates, and the interfacial tension between the continuous and dispersed phases presented many challenges, which will be discussed in this work. Experimental validation was completed using continuous phase solutions of different interfacial tensions. To conclude, prospective applications are discussed with expected challenges.

Creating Web-Based Scientific Applications Using Java Servlets

NASA Technical Reports Server (NTRS)

Palmer, Grant; Arnold, James O. (Technical Monitor)

2001-01-01

There are many advantages to developing web-based scientific applications. Any number of people can access the application concurrently. The application can be accessed from a remote location. The application becomes essentially platform-independent because it can be run from any machine that has internet access and can run a web browser. Maintenance and upgrades to the application are simplified since only one copy of the application exists in a centralized location. This paper details the creation of web-based applications using Java servlets. Java is a powerful, versatile programming language that is well suited to developing web-based programs. A Java servlet provides the interface between the central server and the remote client machines. The servlet accepts input data from the client, runs the application on the server, and sends the output back to the client machine. The type of servlet that supports the HTTP protocol will be discussed in depth. Among the topics the paper will discuss are how to write an http servlet, how the servlet can run applications written in Java and other languages, and how to set up a Java web server. The entire process will be demonstrated by building a web-based application to compute stagnation point heat transfer.

Graphene-based bimorphs for the fabrication of micron-sized, autonomous origami machines.

NASA Astrophysics Data System (ADS)

Miskin, Marc; Dorsey, Kyle; Bircan, Baris; Reynolds, Michael; Rose, Peter; Cohen, Itai; McEuen, Paul

We present a new platform for the construction of micron sized origami machines that change shape in fractions of a second in response to environmental stimuli. The enabling technology behind our machines is the graphene-glass bimorph. We show that graphene sheets bound to nanometer thick layers of glass are ultrathin actuators that bend in response to small strain differentials. These bimorphs can bend to micron radii of curvature using strains that are two orders of magnitude lower than the fracture strain of graphene. By patterning thick rigid panels on top of bimorphs, we localize bending to the unpatterned regions to produce folds. Using panels and bimorphs, we can scale down existing origami patterns to produce a wide range of machines. These machines can sense their environments, respond, and perform useful functions on time and length scales comparable to microscale biological organisms. this work was supported by NSF Grants DMR-1435829 and DMR-1120296, and performed at Cornell NanoScale Facility, a member of the National Nanotechnology Infrastructure Network (NSF Grant ECCS-0335765).

Security Aspects of Smart Cards vs. Embedded Security in Machine-to-Machine (M2M) Advanced Mobile Network Applications

NASA Astrophysics Data System (ADS)

Meyerstein, Mike; Cha, Inhyok; Shah, Yogendra

The Third Generation Partnership Project (3GPP) standardisation group currently discusses advanced applications of mobile networks such as Machine-to-Machine (M2M) communication. Several security issues arise in these contexts which warrant a fresh look at mobile networks’ security foundations, resting on smart cards. This paper contributes a security/efficiency analysis to this discussion and highlights the role of trusted platform technology to approach these issues.

Hybrid polylingual object model: an efficient and seamless integration of Java and native components on the Dalvik virtual machine.

PubMed

Huang, Yukun; Chen, Rong; Wei, Jingbo; Pei, Xilong; Cao, Jing; Prakash Jayaraman, Prem; Ranjan, Rajiv

2014-01-01

JNI in the Android platform is often observed with low efficiency and high coding complexity. Although many researchers have investigated the JNI mechanism, few of them solve the efficiency and the complexity problems of JNI in the Android platform simultaneously. In this paper, a hybrid polylingual object (HPO) model is proposed to allow a CAR object being accessed as a Java object and as vice in the Dalvik virtual machine. It is an acceptable substitute for JNI to reuse the CAR-compliant components in Android applications in a seamless and efficient way. The metadata injection mechanism is designed to support the automatic mapping and reflection between CAR objects and Java objects. A prototype virtual machine, called HPO-Dalvik, is implemented by extending the Dalvik virtual machine to support the HPO model. Lifespan management, garbage collection, and data type transformation of HPO objects are also handled in the HPO-Dalvik virtual machine automatically. The experimental result shows that the HPO model outweighs the standard JNI in lower overhead on native side, better executing performance with no JNI bridging code being demanded.

Ion beam figuring of highly steep mirrors with a 5-axis hybrid machine tool

NASA Astrophysics Data System (ADS)

Yin, Xiaolin; Tang, Wa; Hu, Haixiang; Zeng, Xuefeng; Wang, Dekang; Xue, Donglin; Zhang, Feng; Deng, Weijie; Zhang, Xuejun

2018-02-01

Ion beam figuring (IBF) is an advanced and deterministic method for optical mirror surface processing. The removal function of IBF varies with the different incident angles of ion beam. Therefore, for the curved surface especially the highly steep one, the Ion Beam Source (IBS) should be equipped with 5-axis machining capability to remove the material along the normal direction of the mirror surface, so as to ensure the stability of the removal function. Based on the 3-RPS parallel mechanism and two dimensional displacement platform, a new type of 5-axis hybrid machine tool for IBF is presented. With the hybrid machine tool, the figuring process of a highly steep fused silica spherical mirror is introduced. The R/# of the mirror is 0.96 and the aperture is 104mm. The figuring result shows that, PV value of the mirror surface error is converged from 121.1nm to32.3nm, and RMS value 23.6nm to 3.4nm.

An open platform for personal health record apps with platform-level privacy protection.

PubMed

Van Gorp, P; Comuzzi, M; Jahnen, A; Kaymak, U; Middleton, B

2014-08-01

One of the main barriers to the adoption of Personal Health Records (PHR) systems is their closed nature. It has been argued in the literature that this barrier can be overcome by introducing an open market of substitutable PHR apps. The requirements introduced by such an open market on the underlying platform have also been derived. In this paper, we argue that MyPHRMachines, a cloud-based PHR platform recently developed by the authors, satisfies these requirements better than its alternatives. The MyPHRMachines platform leverages Virtual Machines as flexible and secure execution sandboxes for health apps. MyPHRMachines does not prevent pushing hospital- or patient-generated data to one of its instances, nor does it prevent patients from sharing data with their trusted caregivers. External software developers have minimal barriers to contribute innovative apps to the platform, since apps are only required to avoid pushing patient data outside a MyPHRMachines cloud. We demonstrate the potential of MyPHRMachines by presenting two externally contributed apps. Both apps provide functionality going beyond the state-of-the-art in their application domain, while they did not require any specific MyPHRMachines platform extension. Copyright © 2014 Elsevier Ltd. All rights reserved.

AZOrange - High performance open source machine learning for QSAR modeling in a graphical programming environment

PubMed Central

2011-01-01

Background Machine learning has a vast range of applications. In particular, advanced machine learning methods are routinely and increasingly used in quantitative structure activity relationship (QSAR) modeling. QSAR data sets often encompass tens of thousands of compounds and the size of proprietary, as well as public data sets, is rapidly growing. Hence, there is a demand for computationally efficient machine learning algorithms, easily available to researchers without extensive machine learning knowledge. In granting the scientific principles of transparency and reproducibility, Open Source solutions are increasingly acknowledged by regulatory authorities. Thus, an Open Source state-of-the-art high performance machine learning platform, interfacing multiple, customized machine learning algorithms for both graphical programming and scripting, to be used for large scale development of QSAR models of regulatory quality, is of great value to the QSAR community. Results This paper describes the implementation of the Open Source machine learning package AZOrange. AZOrange is specially developed to support batch generation of QSAR models in providing the full work flow of QSAR modeling, from descriptor calculation to automated model building, validation and selection. The automated work flow relies upon the customization of the machine learning algorithms and a generalized, automated model hyper-parameter selection process. Several high performance machine learning algorithms are interfaced for efficient data set specific selection of the statistical method, promoting model accuracy. Using the high performance machine learning algorithms of AZOrange does not require programming knowledge as flexible applications can be created, not only at a scripting level, but also in a graphical programming environment. Conclusions AZOrange is a step towards meeting the needs for an Open Source high performance machine learning platform, supporting the efficient development of highly accurate QSAR models fulfilling regulatory requirements. PMID:21798025

AZOrange - High performance open source machine learning for QSAR modeling in a graphical programming environment.

PubMed

Stålring, Jonna C; Carlsson, Lars A; Almeida, Pedro; Boyer, Scott

2011-07-28

Machine learning has a vast range of applications. In particular, advanced machine learning methods are routinely and increasingly used in quantitative structure activity relationship (QSAR) modeling. QSAR data sets often encompass tens of thousands of compounds and the size of proprietary, as well as public data sets, is rapidly growing. Hence, there is a demand for computationally efficient machine learning algorithms, easily available to researchers without extensive machine learning knowledge. In granting the scientific principles of transparency and reproducibility, Open Source solutions are increasingly acknowledged by regulatory authorities. Thus, an Open Source state-of-the-art high performance machine learning platform, interfacing multiple, customized machine learning algorithms for both graphical programming and scripting, to be used for large scale development of QSAR models of regulatory quality, is of great value to the QSAR community. This paper describes the implementation of the Open Source machine learning package AZOrange. AZOrange is specially developed to support batch generation of QSAR models in providing the full work flow of QSAR modeling, from descriptor calculation to automated model building, validation and selection. The automated work flow relies upon the customization of the machine learning algorithms and a generalized, automated model hyper-parameter selection process. Several high performance machine learning algorithms are interfaced for efficient data set specific selection of the statistical method, promoting model accuracy. Using the high performance machine learning algorithms of AZOrange does not require programming knowledge as flexible applications can be created, not only at a scripting level, but also in a graphical programming environment. AZOrange is a step towards meeting the needs for an Open Source high performance machine learning platform, supporting the efficient development of highly accurate QSAR models fulfilling regulatory requirements.

Parallelization of an Object-Oriented Unstructured Aeroacoustics Solver

NASA Technical Reports Server (NTRS)

Baggag, Abdelkader; Atkins, Harold; Oezturan, Can; Keyes, David

1999-01-01

A computational aeroacoustics code based on the discontinuous Galerkin method is ported to several parallel platforms using MPI. The discontinuous Galerkin method is a compact high-order method that retains its accuracy and robustness on non-smooth unstructured meshes. In its semi-discrete form, the discontinuous Galerkin method can be combined with explicit time marching methods making it well suited to time accurate computations. The compact nature of the discontinuous Galerkin method also makes it well suited for distributed memory parallel platforms. The original serial code was written using an object-oriented approach and was previously optimized for cache-based machines. The port to parallel platforms was achieved simply by treating partition boundaries as a type of boundary condition. Code modifications were minimal because boundary conditions were abstractions in the original program. Scalability results are presented for the SCI Origin, IBM SP2, and clusters of SGI and Sun workstations. Slightly superlinear speedup is achieved on a fixed-size problem on the Origin, due to cache effects.

Onsite Fabrication of Trusses and Structures

NASA Technical Reports Server (NTRS)

Bodle, J. G.; Browning, D. L.; Fisher, J. G.; Hujsak, E. J.; Kleidon, E. H.; Siden, L. E.; Tremblay, G. A.

1982-01-01

Tribeam truss that is strong and light made at site where used. Reinforced plastic members are fabricated by beam-making machine and assembled by assembly and welding machines. Although proposed for space-platform assembly, concept may be useful in terrestrial applications in remote or inaccessible places.

Cross-platform normalization of microarray and RNA-seq data for machine learning applications

PubMed Central

Thompson, Jeffrey A.; Tan, Jie

2016-01-01

Large, publicly available gene expression datasets are often analyzed with the aid of machine learning algorithms. Although RNA-seq is increasingly the technology of choice, a wealth of expression data already exist in the form of microarray data. If machine learning models built from legacy data can be applied to RNA-seq data, larger, more diverse training datasets can be created and validation can be performed on newly generated data. We developed Training Distribution Matching (TDM), which transforms RNA-seq data for use with models constructed from legacy platforms. We evaluated TDM, as well as quantile normalization, nonparanormal transformation, and a simple log2 transformation, on both simulated and biological datasets of gene expression. Our evaluation included both supervised and unsupervised machine learning approaches. We found that TDM exhibited consistently strong performance across settings and that quantile normalization also performed well in many circumstances. We also provide a TDM package for the R programming language. PMID:26844019

Design and Simulation of Material-Integrated Distributed Sensor Processing with a Code-Based Agent Platform and Mobile Multi-Agent Systems

PubMed Central

Bosse, Stefan

2015-01-01

Multi-agent systems (MAS) can be used for decentralized and self-organizing data processing in a distributed system, like a resource-constrained sensor network, enabling distributed information extraction, for example, based on pattern recognition and self-organization, by decomposing complex tasks in simpler cooperative agents. Reliable MAS-based data processing approaches can aid the material-integration of structural-monitoring applications, with agent processing platforms scaled to the microchip level. The agent behavior, based on a dynamic activity-transition graph (ATG) model, is implemented with program code storing the control and the data state of an agent, which is novel. The program code can be modified by the agent itself using code morphing techniques and is capable of migrating in the network between nodes. The program code is a self-contained unit (a container) and embeds the agent data, the initialization instructions and the ATG behavior implementation. The microchip agent processing platform used for the execution of the agent code is a standalone multi-core stack machine with a zero-operand instruction format, leading to a small-sized agent program code, low system complexity and high system performance. The agent processing is token-queue-based, similar to Petri-nets. The agent platform can be implemented in software, too, offering compatibility at the operational and code level, supporting agent processing in strong heterogeneous networks. In this work, the agent platform embedded in a large-scale distributed sensor network is simulated at the architectural level by using agent-based simulation techniques. PMID:25690550

Design and simulation of material-integrated distributed sensor processing with a code-based agent platform and mobile multi-agent systems.

PubMed

Bosse, Stefan

2015-02-16

Multi-agent systems (MAS) can be used for decentralized and self-organizing data processing in a distributed system, like a resource-constrained sensor network, enabling distributed information extraction, for example, based on pattern recognition and self-organization, by decomposing complex tasks in simpler cooperative agents. Reliable MAS-based data processing approaches can aid the material-integration of structural-monitoring applications, with agent processing platforms scaled to the microchip level. The agent behavior, based on a dynamic activity-transition graph (ATG) model, is implemented with program code storing the control and the data state of an agent, which is novel. The program code can be modified by the agent itself using code morphing techniques and is capable of migrating in the network between nodes. The program code is a self-contained unit (a container) and embeds the agent data, the initialization instructions and the ATG behavior implementation. The microchip agent processing platform used for the execution of the agent code is a standalone multi-core stack machine with a zero-operand instruction format, leading to a small-sized agent program code, low system complexity and high system performance. The agent processing is token-queue-based, similar to Petri-nets. The agent platform can be implemented in software, too, offering compatibility at the operational and code level, supporting agent processing in strong heterogeneous networks. In this work, the agent platform embedded in a large-scale distributed sensor network is simulated at the architectural level by using agent-based simulation techniques.

Measurement and control system for cryogenic helium gas bearing turbo-expander experimental platform based on Siemens PLC S7-300

NASA Astrophysics Data System (ADS)

Li, J.; Xiong, L. Y.; Peng, N.; Dong, B.; Wang, P.; Liu, L. Q.

2014-01-01

An experimental platform for cryogenic Helium gas bearing turbo-expanders is established at the Technical Institute of Physics and Chemistry, Chinese Academy of Sciences. This turbo-expander experimental platform is designed for performance testing and experimental research on Helium turbo-expanders with different sizes from the liquid hydrogen temperature to the room temperature region. A measurement and control system based on Siemens PLC S7-300 for this turbo-expander experimental platform is developed. Proper sensors are selected to measure such parameters as temperature, pressure, rotation speed and air flow rate. All the collected data to be processed are transformed and transmitted to S7-300 CPU. Siemens S7-300 series PLC CPU315-2PN/DP is as master station and two sets of ET200M DP remote expand I/O is as slave station. Profibus-DP field communication is established between master station and slave stations. The upper computer Human Machine Interface (HMI) is compiled using Siemens configuration software WinCC V6.2. The upper computer communicates with PLC by means of industrial Ethernet. Centralized monitoring and distributed control is achieved. Experimental results show that this measurement and control system has fulfilled the test requirement for the turbo-expander experimental platform.

Measurement and control system for cryogenic helium gas bearing turbo-expander experimental platform based on Siemens PLC S7-300

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

Li, J.; Xiong, L. Y.; Peng, N.

2014-01-29

An experimental platform for cryogenic Helium gas bearing turbo-expanders is established at the Technical Institute of Physics and Chemistry, Chinese Academy of Sciences. This turbo-expander experimental platform is designed for performance testing and experimental research on Helium turbo-expanders with different sizes from the liquid hydrogen temperature to the room temperature region. A measurement and control system based on Siemens PLC S7-300 for this turbo-expander experimental platform is developed. Proper sensors are selected to measure such parameters as temperature, pressure, rotation speed and air flow rate. All the collected data to be processed are transformed and transmitted to S7-300 CPU. Siemensmore » S7-300 series PLC CPU315-2PN/DP is as master station and two sets of ET200M DP remote expand I/O is as slave station. Profibus-DP field communication is established between master station and slave stations. The upper computer Human Machine Interface (HMI) is compiled using Siemens configuration software WinCC V6.2. The upper computer communicates with PLC by means of industrial Ethernet. Centralized monitoring and distributed control is achieved. Experimental results show that this measurement and control system has fulfilled the test requirement for the turbo-expander experimental platform.« less

Comparison of supervised machine learning algorithms for waterborne pathogen detection using mobile phone fluorescence microscopy

NASA Astrophysics Data System (ADS)

Ceylan Koydemir, Hatice; Feng, Steve; Liang, Kyle; Nadkarni, Rohan; Benien, Parul; Ozcan, Aydogan

2017-06-01

Giardia lamblia is a waterborne parasite that affects millions of people every year worldwide, causing a diarrheal illness known as giardiasis. Timely detection of the presence of the cysts of this parasite in drinking water is important to prevent the spread of the disease, especially in resource-limited settings. Here we provide extended experimental testing and evaluation of the performance and repeatability of a field-portable and cost-effective microscopy platform for automated detection and counting of Giardia cysts in water samples, including tap water, non-potable water, and pond water. This compact platform is based on our previous work, and is composed of a smartphone-based fluorescence microscope, a disposable sample processing cassette, and a custom-developed smartphone application. Our mobile phone microscope has a large field of view of 0.8 cm2 and weighs only 180 g, excluding the phone. A custom-developed smartphone application provides a user-friendly graphical interface, guiding the users to capture a fluorescence image of the sample filter membrane and analyze it automatically at our servers using an image processing algorithm and training data, consisting of >30,000 images of cysts and >100,000 images of other fluorescent particles that are captured, including, e.g. dust. The total time that it takes from sample preparation to automated cyst counting is less than an hour for each 10 ml of water sample that is tested. We compared the sensitivity and the specificity of our platform using multiple supervised classification models, including support vector machines and nearest neighbors, and demonstrated that a bootstrap aggregating (i.e. bagging) approach using raw image file format provides the best performance for automated detection of Giardia cysts. We evaluated the performance of this machine learning enabled pathogen detection device with water samples taken from different sources (e.g. tap water, non-potable water, pond water) and achieved a limit of detection of 12 cysts per 10 ml, an average cyst capture efficiency of 79%, and an accuracy of 95%. Providing rapid detection and quantification of waterborne pathogens without the need for a microbiology expert, this field-portable imaging and sensing platform running on a smartphone could be very useful for water quality monitoring in resource-limited settings.

Rapid imaging, detection and quantification of Giardia lamblia cysts using mobile-phone based fluorescent microscopy and machine learning.

PubMed

Koydemir, Hatice Ceylan; Gorocs, Zoltan; Tseng, Derek; Cortazar, Bingen; Feng, Steve; Chan, Raymond Yan Lok; Burbano, Jordi; McLeod, Euan; Ozcan, Aydogan

2015-03-07

Rapid and sensitive detection of waterborne pathogens in drinkable and recreational water sources is crucial for treating and preventing the spread of water related diseases, especially in resource-limited settings. Here we present a field-portable and cost-effective platform for detection and quantification of Giardia lamblia cysts, one of the most common waterborne parasites, which has a thick cell wall that makes it resistant to most water disinfection techniques including chlorination. The platform consists of a smartphone coupled with an opto-mechanical attachment weighing ~205 g, which utilizes a hand-held fluorescence microscope design aligned with the camera unit of the smartphone to image custom-designed disposable water sample cassettes. Each sample cassette is composed of absorbent pads and mechanical filter membranes; a membrane with 8 μm pore size is used as a porous spacing layer to prevent the backflow of particles to the upper membrane, while the top membrane with 5 μm pore size is used to capture the individual Giardia cysts that are fluorescently labeled. A fluorescence image of the filter surface (field-of-view: ~0.8 cm(2)) is captured and wirelessly transmitted via the mobile-phone to our servers for rapid processing using a machine learning algorithm that is trained on statistical features of Giardia cysts to automatically detect and count the cysts captured on the membrane. The results are then transmitted back to the mobile-phone in less than 2 minutes and are displayed through a smart application running on the phone. This mobile platform, along with our custom-developed sample preparation protocol, enables analysis of large volumes of water (e.g., 10-20 mL) for automated detection and enumeration of Giardia cysts in ~1 hour, including all the steps of sample preparation and analysis. We evaluated the performance of this approach using flow-cytometer-enumerated Giardia-contaminated water samples, demonstrating an average cyst capture efficiency of ~79% on our filter membrane along with a machine learning based cyst counting sensitivity of ~84%, yielding a limit-of-detection of ~12 cysts per 10 mL. Providing rapid detection and quantification of microorganisms, this field-portable imaging and sensing platform running on a mobile-phone could be useful for water quality monitoring in field and resource-limited settings.

IDEPI: rapid prediction of HIV-1 antibody epitopes and other phenotypic features from sequence data using a flexible machine learning platform.

PubMed

Hepler, N Lance; Scheffler, Konrad; Weaver, Steven; Murrell, Ben; Richman, Douglas D; Burton, Dennis R; Poignard, Pascal; Smith, Davey M; Kosakovsky Pond, Sergei L

2014-09-01

Since its identification in 1983, HIV-1 has been the focus of a research effort unprecedented in scope and difficulty, whose ultimate goals--a cure and a vaccine--remain elusive. One of the fundamental challenges in accomplishing these goals is the tremendous genetic variability of the virus, with some genes differing at as many as 40% of nucleotide positions among circulating strains. Because of this, the genetic bases of many viral phenotypes, most notably the susceptibility to neutralization by a particular antibody, are difficult to identify computationally. Drawing upon open-source general-purpose machine learning algorithms and libraries, we have developed a software package IDEPI (IDentify EPItopes) for learning genotype-to-phenotype predictive models from sequences with known phenotypes. IDEPI can apply learned models to classify sequences of unknown phenotypes, and also identify specific sequence features which contribute to a particular phenotype. We demonstrate that IDEPI achieves performance similar to or better than that of previously published approaches on four well-studied problems: finding the epitopes of broadly neutralizing antibodies (bNab), determining coreceptor tropism of the virus, identifying compartment-specific genetic signatures of the virus, and deducing drug-resistance associated mutations. The cross-platform Python source code (released under the GPL 3.0 license), documentation, issue tracking, and a pre-configured virtual machine for IDEPI can be found at https://github.com/veg/idepi.

Efficacy of Code Optimization on Cache-Based Processors

NASA Technical Reports Server (NTRS)

VanderWijngaart, Rob F.; Saphir, William C.; Chancellor, Marisa K. (Technical Monitor)

1997-01-01

In this paper a number of techniques for improving the cache performance of a representative piece of numerical software is presented. Target machines are popular processors from several vendors: MIPS R5000 (SGI Indy), MIPS R8000 (SGI PowerChallenge), MIPS R10000 (SGI Origin), DEC Alpha EV4 + EV5 (Cray T3D & T3E), IBM RS6000 (SP Wide-node), Intel PentiumPro (Ames' Whitney), Sun UltraSparc (NERSC's NOW). The optimizations all attempt to increase the locality of memory accesses. But they meet with rather varied and often counterintuitive success on the different computing platforms. We conclude that it may be genuinely impossible to obtain portable performance on the current generation of cache-based machines. At the least, it appears that the performance of modern commodity processors cannot be described with parameters defining the cache alone.

[A novel serial port auto trigger system for MOSFET dose acquisition].

PubMed

Luo, Guangwen; Qi, Zhenyu

2013-01-01

To synchronize the radiation of microSelectron-HDR (Nucletron afterloading machine) and measurement of MOSFET dose system, a trigger system based on interface circuit was designed and corresponding monitor and trigger program were developed on Qt platform. This interface and control system was tested and showed stable operate and reliable work. This adopted serial port detect technique may expand to trigger application of other medical devices.

Specifications and implementation of the RT MHD control system for the EC launcher of FTU

NASA Astrophysics Data System (ADS)

Galperti, C.; Alessi, E.; Boncagni, L.; Bruschi, A.; Granucci, G.; Grosso, A.; Iannone, F.; Marchetto, C.; Nowak, S.; Panella, M.; Sozzi, C.; Tilia, B.

2012-09-01

To perform real time plasma control experiments using EC heating waves by using the new fast launcher installed on FTU a dedicated data acquisition and elaboration system has been designed recently. A prototypical version of the acquisition/control system has been recently developed and will be tested on FTU machine in its next experimental campaign. The open-source framework MARTe (Multi-threaded Application Real-Time executor) on Linux/RTAI real-time operating system has been chosen as software platform to realize the control system. Standard open-architecture industrial PCs, based either on VME bus and CompactPCI bus equipped with standard input/output cards are the chosen hardware platform.

BrainLiner: A Neuroinformatics Platform for Sharing Time-Aligned Brain-Behavior Data

PubMed Central

Takemiya, Makoto; Majima, Kei; Tsukamoto, Mitsuaki; Kamitani, Yukiyasu

2016-01-01

Data-driven neuroscience aims to find statistical relationships between brain activity and task behavior from large-scale datasets. To facilitate high-throughput data processing and modeling, we created BrainLiner as a web platform for sharing time-aligned, brain-behavior data. Using an HDF5-based data format, BrainLiner treats brain activity and data related to behavior with the same salience, aligning both behavioral and brain activity data on a common time axis. This facilitates learning the relationship between behavior and brain activity. Using a common data file format also simplifies data processing and analyses. Properties describing data are unambiguously defined using a schema, allowing machine-readable definition of data. The BrainLiner platform allows users to upload and download data, as well as to explore and search for data from the web platform. A WebGL-based data explorer can visualize highly detailed neurophysiological data from within the web browser, and a data-driven search feature allows users to search for similar time windows of data. This increases transparency, and allows for visual inspection of neural coding. BrainLiner thus provides an essential set of tools for data sharing and data-driven modeling. PMID:26858636

Hybrid PolyLingual Object Model: An Efficient and Seamless Integration of Java and Native Components on the Dalvik Virtual Machine

PubMed Central

Huang, Yukun; Chen, Rong; Wei, Jingbo; Pei, Xilong; Cao, Jing; Prakash Jayaraman, Prem; Ranjan, Rajiv

2014-01-01

JNI in the Android platform is often observed with low efficiency and high coding complexity. Although many researchers have investigated the JNI mechanism, few of them solve the efficiency and the complexity problems of JNI in the Android platform simultaneously. In this paper, a hybrid polylingual object (HPO) model is proposed to allow a CAR object being accessed as a Java object and as vice in the Dalvik virtual machine. It is an acceptable substitute for JNI to reuse the CAR-compliant components in Android applications in a seamless and efficient way. The metadata injection mechanism is designed to support the automatic mapping and reflection between CAR objects and Java objects. A prototype virtual machine, called HPO-Dalvik, is implemented by extending the Dalvik virtual machine to support the HPO model. Lifespan management, garbage collection, and data type transformation of HPO objects are also handled in the HPO-Dalvik virtual machine automatically. The experimental result shows that the HPO model outweighs the standard JNI in lower overhead on native side, better executing performance with no JNI bridging code being demanded. PMID:25110745

Mathematical calibration procedure of a capacitive sensor-based indexed metrology platform

NASA Astrophysics Data System (ADS)

Brau-Avila, A.; Santolaria, J.; Acero, R.; Valenzuela-Galvan, M.; Herrera-Jimenez, V. M.; Aguilar, J. J.

2017-03-01

The demand for faster and more reliable measuring tasks for the control and quality assurance of modern production systems has created new challenges for the field of coordinate metrology. Thus, the search for new solutions in coordinate metrology systems and the need for the development of existing ones still persists. One example of such a system is the portable coordinate measuring machine (PCMM), the use of which in industry has considerably increased in recent years, mostly due to its flexibility for accomplishing in-line measuring tasks as well as its reduced cost and operational advantages compared to traditional coordinate measuring machines. Nevertheless, PCMMs have a significant drawback derived from the techniques applied in the verification and optimization procedures of their kinematic parameters. These techniques are based on the capture of data with the measuring instrument from a calibrated gauge object, fixed successively in various positions so that most of the instrument measuring volume is covered, which results in time-consuming, tedious and expensive verification and optimization procedures. In this work the mathematical calibration procedure of a capacitive sensor-based indexed metrology platform (IMP) is presented. This calibration procedure is based on the readings and geometric features of six capacitive sensors and their targets with nanometer resolution. The final goal of the IMP calibration procedure is to optimize the geometric features of the capacitive sensors and their targets in order to use the optimized data in the verification procedures of PCMMs.

[Machine Learning-based Prediction of Seizure-inducing Action as an Adverse Drug Effect].

PubMed

Gao, Mengxuan; Sato, Motoshige; Ikegaya, Yuji

2018-01-01

　During the preclinical research period of drug development, animal testing is widely used to help screen out a drug's dangerous side effects. However, it remains difficult to predict side effects within the central nervous system. Here, we introduce a machine learning-based in vitro system designed to detect seizure-inducing side effects before clinical trial. We recorded local field potentials from the CA1 alveus in acute mouse neocortico-hippocampal slices that were bath-perfused with each of 14 different drugs, and at 5 different concentrations of each drug. For each of these experimental conditions, we collected seizure-like neuronal activity and merged their waveforms as one graphic image, which was further converted into a feature vector using Caffe, an open framework for deep learning. In the space of the first two principal components, the support vector machine completely separated the vectors (i.e., doses of individual drugs) that induced seizure-like events, and identified diphenhydramine, enoxacin, strychnine and theophylline as "seizure-inducing" drugs, which have indeed been reported to induce seizures in clinical situations. Thus, this artificial intelligence-based classification may provide a new platform to pre-clinically detect seizure-inducing side effects of drugs.

Machine learning-based prediction of adverse drug effects: An example of seizure-inducing compounds.

PubMed

Gao, Mengxuan; Igata, Hideyoshi; Takeuchi, Aoi; Sato, Kaoru; Ikegaya, Yuji

2017-02-01

Various biological factors have been implicated in convulsive seizures, involving side effects of drugs. For the preclinical safety assessment of drug development, it is difficult to predict seizure-inducing side effects. Here, we introduced a machine learning-based in vitro system designed to detect seizure-inducing side effects. We recorded local field potentials from the CA1 alveus in acute mouse neocortico-hippocampal slices, while 14 drugs were bath-perfused at 5 different concentrations each. For each experimental condition, we collected seizure-like neuronal activity and merged their waveforms as one graphic image, which was further converted into a feature vector using Caffe, an open framework for deep learning. In the space of the first two principal components, the support vector machine completely separated the vectors (i.e., doses of individual drugs) that induced seizure-like events and identified diphenhydramine, enoxacin, strychnine and theophylline as "seizure-inducing" drugs, which indeed were reported to induce seizures in clinical situations. Thus, this artificial intelligence-based classification may provide a new platform to detect the seizure-inducing side effects of preclinical drugs. Copyright © 2017 The Authors. Production and hosting by Elsevier B.V. All rights reserved.

Three-Point Gear/Lead Screw Positioning

NASA Technical Reports Server (NTRS)

Calco, Frank S.

1993-01-01

Triple-ganged-lead-screw positioning mechanism drives movable plate toward or away from fixed plate and keeps plates parallel to each other. Designed for use in tuning microwave resonant cavity. Other potential applications include adjustable bed plates and cantilever tail stocks in machine tools, adjustable platforms for optical equipment, and lifting platforms.

High-speed DNA-based rolling motors powered by RNase H

PubMed Central

Yehl, Kevin; Mugler, Andrew; Vivek, Skanda; Liu, Yang; Zhang, Yun; Fan, Mengzhen; Weeks, Eric R.

2016-01-01

DNA-based machines that walk by converting chemical energy into controlled motion could be of use in applications such as next generation sensors, drug delivery platforms, and biological computing. Despite their exquisite programmability, DNA-based walkers are, however, challenging to work with due to their low fidelity and slow rates (~1 nm/min). Here, we report DNA-based machines that roll rather than walk, and consequently have a maximum speed and processivity that is three-orders of magnitude greater than conventional DNA motors. The motors are made from DNA-coated spherical particles that hybridise to a surface modified with complementary RNA; motion is achieved through the addition of RNase H, which selectively hydrolyses hybridised RNA. Spherical motors move in a self-avoiding manner, whereas anisotropic particles, such as dimerised particles or rod-shaped particles travel linearly without a track or external force. Finally, we demonstrate detection of single nucleotide polymorphism by measuring particle displacement using a smartphone camera. PMID:26619152

Engineering platform and experimental protocol for design and evaluation of a neurally-controlled powered transfemoral prosthesis.

PubMed

Zhang, Fan; Liu, Ming; Harper, Stephen; Lee, Michael; Huang, He

2014-07-22

To enable intuitive operation of powered artificial legs, an interface between user and prosthesis that can recognize the user's movement intent is desired. A novel neural-machine interface (NMI) based on neuromuscular-mechanical fusion developed in our previous study has demonstrated a great potential to accurately identify the intended movement of transfemoral amputees. However, this interface has not yet been integrated with a powered prosthetic leg for true neural control. This study aimed to report (1) a flexible platform to implement and optimize neural control of powered lower limb prosthesis and (2) an experimental setup and protocol to evaluate neural prosthesis control on patients with lower limb amputations. First a platform based on a PC and a visual programming environment were developed to implement the prosthesis control algorithms, including NMI training algorithm, NMI online testing algorithm, and intrinsic control algorithm. To demonstrate the function of this platform, in this study the NMI based on neuromuscular-mechanical fusion was hierarchically integrated with intrinsic control of a prototypical transfemoral prosthesis. One patient with a unilateral transfemoral amputation was recruited to evaluate our implemented neural controller when performing activities, such as standing, level-ground walking, ramp ascent, and ramp descent continuously in the laboratory. A novel experimental setup and protocol were developed in order to test the new prosthesis control safely and efficiently. The presented proof-of-concept platform and experimental setup and protocol could aid the future development and application of neurally-controlled powered artificial legs.

Toward a semi-mechanical harvesting platform system for harvesting blueberries with fresh-market quality

USDA-ARS?s Scientific Manuscript database

Major concerns related to harvesting blueberries for fresh market with over-the-row (OTR) harvesters are that the quality of the fruit harvested with OTR machines is generally low and ground loss is excessive. Machine-harvested blueberries have more internal bruise and usually soften rapidly in col...

Autonomous biomorphic robots as platforms for sensors

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

Tilden, M.; Hasslacher, B.; Mainieri, R.

1996-10-01

The idea of building autonomous robots that can carry out complex and nonrepetitive tasks is an old one, so far unrealized in any meaningful hardware. Tilden has shown recently that there are simple, processor-free solutions to building autonomous mobile machines that continuously adapt to unknown and hostile environments, are designed primarily to survive, and are extremely resistant to damage. These devices use smart mechanics and simple (low component count) electronic neuron control structures having the functionality of biological organisms from simple invertebrates to sophisticated members of the insect and crab family. These devices are paradigms for the development of autonomousmore » machines that can carry out directed goals. The machine then becomes a robust survivalist platform that can carry sensors or instruments. These autonomous roving machines, now in an early stage of development (several proof-of-concept prototype walkers have been built), can be developed so that they are inexpensive, robust, and versatile carriers for a variety of instrument packages. Applications are immediate and many, in areas as diverse as prosthetics, medicine, space, construction, nanoscience, defense, remote sensing, environmental cleanup, and biotechnology.« less

Formal Techniques for Synchronized Fault-Tolerant Systems

NASA Technical Reports Server (NTRS)

DiVito, Ben L.; Butler, Ricky W.

1992-01-01

We present the formal verification of synchronizing aspects of the Reliable Computing Platform (RCP), a fault-tolerant computing system for digital flight control applications. The RCP uses NMR-style redundancy to mask faults and internal majority voting to purge the effects of transient faults. The system design has been formally specified and verified using the EHDM verification system. Our formalization is based on an extended state machine model incorporating snapshots of local processors clocks.

Embedded System for Prosthetic Control Using Implanted Neuromuscular Interfaces Accessed Via an Osseointegrated Implant.

PubMed

Mastinu, Enzo; Doguet, Pascal; Botquin, Yohan; Hakansson, Bo; Ortiz-Catalan, Max

2017-08-01

Despite the technological progress in robotics achieved in the last decades, prosthetic limbs still lack functionality, reliability, and comfort. Recently, an implanted neuromusculoskeletal interface built upon osseointegration was developed and tested in humans, namely the Osseointegrated Human-Machine Gateway. Here, we present an embedded system to exploit the advantages of this technology. Our artificial limb controller allows for bioelectric signals acquisition, processing, decoding of motor intent, prosthetic control, and sensory feedback. It includes a neurostimulator to provide direct neural feedback based on sensory information. The system was validated using real-time tasks characterization, power consumption evaluation, and myoelectric pattern recognition performance. Functionality was proven in a first pilot patient from whom results of daily usage were obtained. The system was designed to be reliably used in activities of daily living, as well as a research platform to monitor prosthesis usage and training, machine-learning-based control algorithms, and neural stimulation paradigms.

Hierarchy of Gambling Choices: A Framework for Examining EGM Gambling Environment Preferences.

PubMed

Thorne, Hannah Briony; Rockloff, Matthew Justus; Langham, Erika; Li, En

2016-12-01

This paper presents the Hierarchy of Gambling Choices (HGC), which is a consumer-oriented framework for understanding the key environmental and contextual features that influence peoples' selections of online and venue-based electronic gaming machines (EGMs). The HGC framework proposes that EGM gamblers make choices in selection of EGM gambling experiences utilising Tversky's (Psychol Rev 79(4):281-299, 1972). Elimination-by-Aspects model, and organise their choice in a hierarchical manner by virtue of EGMs being an "experience good" (Nelson in J Polit Econ 78(2):311-329, 1970). EGM features are divided into three levels: the platform-including, online, mobile or land-based; the provider or specific venue in which the gambling occurs; and the game or machine characteristics, such as graphical themes and bonus features. This framework will contribute to the gambling field by providing a manner in which to systematically explore the environment surrounding EGM gambling and how it affects behaviour.

Hardware/software codesign for embedded RISC core

NASA Astrophysics Data System (ADS)

Liu, Peng

2001-12-01

This paper describes hardware/software codesign method of the extendible embedded RISC core VIRGO, which based on MIPS-I instruction set architecture. VIRGO is described by Verilog hardware description language that has five-stage pipeline with shared 32-bit cache/memory interface, and it is controlled by distributed control scheme. Every pipeline stage has one small controller, which controls the pipeline stage status and cooperation among the pipeline phase. Since description use high level language and structure is distributed, VIRGO core has highly extension that can meet the requirements of application. We take look at the high-definition television MPEG2 MPHL decoder chip, constructed the hardware/software codesign virtual prototyping machine that can research on VIRGO core instruction set architecture, and system on chip memory size requirements, and system on chip software, etc. We also can evaluate the system on chip design and RISC instruction set based on the virtual prototyping machine platform.

Signal Design for Improved Ranging Among Multiple Transceivers

NASA Technical Reports Server (NTRS)

Young, Lawrence; Tien, Jeffrey; Srinivasan, Jeffrey

2004-01-01

"Ultra-BOC" (where "BOC" signifies "binary offset carrier") is the name of an improved generic design of microwave signals to be used by a group of spacecraft flying in formation to measure ranges and bearings among themselves and to exchange telemetry needed for these measurements. Ultra-BOC could also be applied on Earth for diverse purposes -- for example, measuring relative positions of vehicles on highways for traffic-control purposes and determining the relative alignments of machines operating in mines and of construction machines and structures at construction sites. Ultra-BOC provides for rapid and robust acquisition of signals, even when signal-to-noise ratios are low. The design further provides that each spacecraft or other platform constantly strives to acquire and track the signals from the other platforms while simultaneously transmitting signals that provide full range, bearing, and telemetry service to the other platforms. In Ultra-BOC, unlike in other signal designs that have been considered for the same purposes, it is not necessary to maneuver the spacecraft or other platforms to obtain the data needed for resolving integer-carrier-cycle phase ambiguities.

Reconnaissance blind multi-chess: an experimentation platform for ISR sensor fusion and resource management

NASA Astrophysics Data System (ADS)

Newman, Andrew J.; Richardson, Casey L.; Kain, Sean M.; Stankiewicz, Paul G.; Guseman, Paul R.; Schreurs, Blake A.; Dunne, Jeffrey A.

2016-05-01

This paper introduces the game of reconnaissance blind multi-chess (RBMC) as a paradigm and test bed for understanding and experimenting with autonomous decision making under uncertainty and in particular managing a network of heterogeneous Intelligence, Surveillance and Reconnaissance (ISR) sensors to maintain situational awareness informing tactical and strategic decision making. The intent is for RBMC to serve as a common reference or challenge problem in fusion and resource management of heterogeneous sensor ensembles across diverse mission areas. We have defined a basic rule set and a framework for creating more complex versions, developed a web-based software realization to serve as an experimentation platform, and developed some initial machine intelligence approaches to playing it.

Build and Execute Environment

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

Guan, Qiang

At exascale, the challenge becomes to develop applications that run at scale and use exascale platforms reliably, efficiently, and flexibly. Workflows become much more complex because they must seamlessly integrate simulation and data analytics. They must include down-sampling, post-processing, feature extraction, and visualization. Power and data transfer limitations require these analysis tasks to be run in-situ or in-transit. We expect successful workflows will comprise multiple linked simulations along with tens of analysis routines. Users will have limited development time at scale and, therefore, must have rich tools to develop, debug, test, and deploy applications. At this scale, successful workflows willmore » compose linked computations from an assortment of reliable, well-defined computation elements, ones that can come and go as required, based on the needs of the workflow over time. We propose a novel framework that utilizes both virtual machines (VMs) and software containers to create a workflow system that establishes a uniform build and execution environment (BEE) beyond the capabilities of current systems. In this environment, applications will run reliably and repeatably across heterogeneous hardware and software. Containers, both commercial (Docker and Rocket) and open-source (LXC and LXD), define a runtime that isolates all software dependencies from the machine operating system. Workflows may contain multiple containers that run different operating systems, different software, and even different versions of the same software. We will run containers in open-source virtual machines (KVM) and emulators (QEMU) so that workflows run on any machine entirely in user-space. On this platform of containers and virtual machines, we will deliver workflow software that provides services, including repeatable execution, provenance, checkpointing, and future proofing. We will capture provenance about how containers were launched and how they interact to annotate workflows for repeatable and partial re-execution. We will coordinate the physical snapshots of virtual machines with parallel programming constructs, such as barriers, to automate checkpoint and restart. We will also integrate with HPC-specific container runtimes to gain access to accelerators and other specialized hardware to preserve native performance. Containers will link development to continuous integration. When application developers check code in, it will automatically be tested on a suite of different software and hardware architectures.« less

Advanced Monitoring to Improve Combustion Turbine/Combined Cycle Reliability, Availability & Maintainability

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

Leonard Angello

2005-09-30

Power generators are concerned with the maintenance costs associated with the advanced turbines that they are purchasing. Since these machines do not have fully established Operation and Maintenance (O&M) track records, power generators face financial risk due to uncertain future maintenance costs. This risk is of particular concern, as the electricity industry transitions to a competitive business environment in which unexpected O&M costs cannot be passed through to consumers. These concerns have accelerated the need for intelligent software-based diagnostic systems that can monitor the health of a combustion turbine in real time and provide valuable information on the machine's performancemore » to its owner/operators. EPRI, Impact Technologies, Boyce Engineering, and Progress Energy have teamed to develop a suite of intelligent software tools integrated with a diagnostic monitoring platform that, in real time, interpret data to assess the 'total health' of combustion turbines. The 'Combustion Turbine Health Management System' (CTHMS) will consist of a series of 'Dynamic Link Library' (DLL) programs residing on a diagnostic monitoring platform that accepts turbine health data from existing monitoring instrumentation. CTHMS interprets sensor and instrument outputs, correlates them to a machine's condition, provide interpretative analyses, project servicing intervals, and estimate remaining component life. In addition, the CTHMS enables real-time anomaly detection and diagnostics of performance and mechanical faults, enabling power producers to more accurately predict critical component remaining useful life and turbine degradation.« less

Mobile Landing Platform with Core Capability Set (MLP w/CCS): Combined Initial Operational Test and Evaluation and Live Fire Test and Evaluation Report

DTIC Science & Technology

2015-07-01

annex.   iii Self-defense testing was limited to structural test firing from each machine gun mount and an ammunition resupply drill. Robust self...provided in the classified annex. Self-   8 defense testing was limited to structural test firing from each machine gun mount and a single...Caliber Machine Gun Mount Structural Test Fire November 2014 San Diego, Offshore Ship Weapons Range Operating Independently       9 Section Three

Privacy preserving interactive record linkage (PPIRL).

PubMed

Kum, Hye-Chung; Krishnamurthy, Ashok; Machanavajjhala, Ashwin; Reiter, Michael K; Ahalt, Stanley

2014-01-01

Record linkage to integrate uncoordinated databases is critical in biomedical research using Big Data. Balancing privacy protection against the need for high quality record linkage requires a human-machine hybrid system to safely manage uncertainty in the ever changing streams of chaotic Big Data. In the computer science literature, private record linkage is the most published area. It investigates how to apply a known linkage function safely when linking two tables. However, in practice, the linkage function is rarely known. Thus, there are many data linkage centers whose main role is to be the trusted third party to determine the linkage function manually and link data for research via a master population list for a designated region. Recently, a more flexible computerized third-party linkage platform, Secure Decoupled Linkage (SDLink), has been proposed based on: (1) decoupling data via encryption, (2) obfuscation via chaffing (adding fake data) and universe manipulation; and (3) minimum information disclosure via recoding. We synthesize this literature to formalize a new framework for privacy preserving interactive record linkage (PPIRL) with tractable privacy and utility properties and then analyze the literature using this framework. Human-based third-party linkage centers for privacy preserving record linkage are the accepted norm internationally. We find that a computer-based third-party platform that can precisely control the information disclosed at the micro level and allow frequent human interaction during the linkage process, is an effective human-machine hybrid system that significantly improves on the linkage center model both in terms of privacy and utility.

An Intelligent and Interactive Simulation and Tutoring Environment for Exploring and Learning Simple Machines

NASA Astrophysics Data System (ADS)

Myneni, Lakshman Sundeep

Students in middle school science classes have difficulty mastering physics concepts such as energy and work, taught in the context of simple machines. Moreover, students' naive conceptions of physics often remain unchanged after completing a science class. To address this problem, I developed an intelligent tutoring system, called the Virtual Physics System (ViPS), which coaches students through problem solving with one class of simple machines, pulley systems. The tutor uses a unique cognitive based approach to teaching simple machines, and includes innovations in three areas. (1) It employs a teaching strategy that focuses on highlighting links among concepts of the domain that are essential for conceptual understanding yet are seldom learned by students. (2) Concepts are taught through a combination of effective human tutoring techniques (e.g., hinting) and simulations. (3) For each student, the system identifies which misconceptions he or she has, from a common set of student misconceptions gathered from domain experts, and tailors tutoring to match the correct line of scientific reasoning regarding the misconceptions. ViPS was implemented as a platform on which students can design and simulate pulley system experiments, integrated with a constraint-based tutor that intervenes when students make errors during problem solving to teach them and to help them. ViPS has a web-based client-server architecture, and has been implemented using Java technologies. ViPS is different from existing physics simulations and tutoring systems due to several original features. (1). It is the first system to integrate a simulation based virtual experimentation platform with an intelligent tutoring component. (2) It uses a novel approach, based on Bayesian networks, to help students construct correct pulley systems for experimental simulation. (3) It identifies student misconceptions based on a novel decision tree applied to student pretest scores, and tailors tutoring to individual students based on detected misconceptions. ViPS has been evaluated through usability and usefulness experiments with undergraduate engineering students taking their first college-level engineering physics course and undergraduate pre-service teachers taking their first college-level physics course. These experiments demonstrated that ViPS is highly usable and effective. Students using ViPS reduced their misconceptions, and students conducting virtual experiments in ViPS learned more than students who conducted experiments with physical pulley systems. Interestingly, it was also found that college students exhibited many of the same misconceptions that have been identified in middle school students.

Software platform for managing the classification of error- related potentials of observers

NASA Astrophysics Data System (ADS)

Asvestas, P.; Ventouras, E.-C.; Kostopoulos, S.; Sidiropoulos, K.; Korfiatis, V.; Korda, A.; Uzunolglu, A.; Karanasiou, I.; Kalatzis, I.; Matsopoulos, G.

2015-09-01

Human learning is partly based on observation. Electroencephalographic recordings of subjects who perform acts (actors) or observe actors (observers), contain a negative waveform in the Evoked Potentials (EPs) of the actors that commit errors and of observers who observe the error-committing actors. This waveform is called the Error-Related Negativity (ERN). Its detection has applications in the context of Brain-Computer Interfaces. The present work describes a software system developed for managing EPs of observers, with the aim of classifying them into observations of either correct or incorrect actions. It consists of an integrated platform for the storage, management, processing and classification of EPs recorded during error-observation experiments. The system was developed using C# and the following development tools and frameworks: MySQL, .NET Framework, Entity Framework and Emgu CV, for interfacing with the machine learning library of OpenCV. Up to six features can be computed per EP recording per electrode. The user can select among various feature selection algorithms and then proceed to train one of three types of classifiers: Artificial Neural Networks, Support Vector Machines, k-nearest neighbour. Next the classifier can be used for classifying any EP curve that has been inputted to the database.

FwWebViewPlus: integration of web technologies into WinCC OA based Human-Machine Interfaces at CERN

NASA Astrophysics Data System (ADS)

Golonka, Piotr; Fabian, Wojciech; Gonzalez-Berges, Manuel; Jasiun, Piotr; Varela-Rodriguez, Fernando

2014-06-01

The rapid growth in popularity of web applications gives rise to a plethora of reusable graphical components, such as Google Chart Tools and JQuery Sparklines, implemented in JavaScript and run inside a web browser. In the paper we describe the tool that allows for seamless integration of web-based widgets into WinCC Open Architecture, the SCADA system used commonly at CERN to build complex Human-Machine Interfaces. Reuse of widely available widget libraries and pushing the development efforts to a higher abstraction layer based on a scripting language allow for significant reduction in maintenance of the code in multi-platform environments compared to those currently used in C++ visualization plugins. Adequately designed interfaces allow for rapid integration of new web widgets into WinCC OA. At the same time, the mechanisms familiar to HMI developers are preserved, making the use of new widgets "native". Perspectives for further integration between the realms of WinCC OA and Web development are also discussed.

Multi-objective component sizing of a power-split plug-in hybrid electric vehicle powertrain using Pareto-based natural optimization machines

NASA Astrophysics Data System (ADS)

Mozaffari, Ahmad; Vajedi, Mahyar; Chehresaz, Maryyeh; Azad, Nasser L.

2016-03-01

The urgent need to meet increasingly tight environmental regulations and new fuel economy requirements has motivated system science researchers and automotive engineers to take advantage of emerging computational techniques to further advance hybrid electric vehicle and plug-in hybrid electric vehicle (PHEV) designs. In particular, research has focused on vehicle powertrain system design optimization, to reduce the fuel consumption and total energy cost while improving the vehicle's driving performance. In this work, two different natural optimization machines, namely the synchronous self-learning Pareto strategy and the elitism non-dominated sorting genetic algorithm, are implemented for component sizing of a specific power-split PHEV platform with a Toyota plug-in Prius as the baseline vehicle. To do this, a high-fidelity model of the Toyota plug-in Prius is employed for the numerical experiments using the Autonomie simulation software. Based on the simulation results, it is demonstrated that Pareto-based algorithms can successfully optimize the design parameters of the vehicle powertrain.

Automated Cough Assessment on a Mobile Platform

PubMed Central

2014-01-01

The development of an Automated System for Asthma Monitoring (ADAM) is described. This consists of a consumer electronics mobile platform running a custom application. The application acquires an audio signal from an external user-worn microphone connected to the device analog-to-digital converter (microphone input). This signal is processed to determine the presence or absence of cough sounds. Symptom tallies and raw audio waveforms are recorded and made easily accessible for later review by a healthcare provider. The symptom detection algorithm is based upon standard speech recognition and machine learning paradigms and consists of an audio feature extraction step followed by a Hidden Markov Model based Viterbi decoder that has been trained on a large database of audio examples from a variety of subjects. Multiple Hidden Markov Model topologies and orders are studied. Performance of the recognizer is presented in terms of the sensitivity and the rate of false alarm as determined in a cross-validation test. PMID:25506590

Wireless Sensor-Based Smart-Clothing Platform for ECG Monitoring

PubMed Central

Lin, Chung-Chih; Yu, Yan-Shuo

2015-01-01

The goal of this study is to use wireless sensor technologies to develop a smart clothes service platform for health monitoring. Our platform consists of smart clothes, a sensor node, a gateway server, and a health cloud. The smart clothes have fabric electrodes to detect electrocardiography (ECG) signals. The sensor node improves the accuracy of QRS complexes detection by morphology analysis and reduces power consumption by the power-saving transmission functionality. The gateway server provides a reconfigurable finite state machine (RFSM) software architecture for abnormal ECG detection to support online updating. Most normal ECG can be filtered out, and the abnormal ECG is further analyzed in the health cloud. Three experiments are conducted to evaluate the platform's performance. The results demonstrate that the signal-to-noise ratio (SNR) of the smart clothes exceeds 37 dB, which is within the “very good signal” interval. The average of the QRS sensitivity and positive prediction is above 99.5%. Power-saving transmission is reduced by nearly 1980 times the power consumption in the best-case analysis. PMID:26640512

Wireless Sensor-Based Smart-Clothing Platform for ECG Monitoring.

PubMed

Wang, Jie; Lin, Chung-Chih; Yu, Yan-Shuo; Yu, Tsang-Chu

2015-01-01

The goal of this study is to use wireless sensor technologies to develop a smart clothes service platform for health monitoring. Our platform consists of smart clothes, a sensor node, a gateway server, and a health cloud. The smart clothes have fabric electrodes to detect electrocardiography (ECG) signals. The sensor node improves the accuracy of QRS complexes detection by morphology analysis and reduces power consumption by the power-saving transmission functionality. The gateway server provides a reconfigurable finite state machine (RFSM) software architecture for abnormal ECG detection to support online updating. Most normal ECG can be filtered out, and the abnormal ECG is further analyzed in the health cloud. Three experiments are conducted to evaluate the platform's performance. The results demonstrate that the signal-to-noise ratio (SNR) of the smart clothes exceeds 37 dB, which is within the "very good signal" interval. The average of the QRS sensitivity and positive prediction is above 99.5%. Power-saving transmission is reduced by nearly 1980 times the power consumption in the best-case analysis.

Innovative lightweight substrate for stable optical benches and mirrors

NASA Astrophysics Data System (ADS)

Rugi Grond, E.; Herren, A.; Mérillat, S.; Fermé, J. J.

2017-11-01

High precision space optics, such as spectrometers, relay optics, and filters, require ultra stable, lightweight platforms. These equipped platforms have on one side to survive the launch loads, on the other side they have to maintain their stability also under the varying thermal loads occurring in space. Typically such platforms have their equipment (prisms, etalons, beam expanders, etc.) mounted by means of classical bonding, hydro-catalytic bonding or optical contacting. Therefore such an optical bench requires to provide an excellent flatness, minimal roughness and is usually made of the same material as the equipment it carries (glass, glass ceramics). For space systems, mass is a big penalty, therefore such optical platforms are in most cases light weighted by means of machining features (i.e. pockets). Besides of being not extremely mass efficient, such pockets reduce the load carrying capability of the base material significantly. The challenge for Oerlikon Space, in this context, was to develop, qualify and deliver such optical benches, providing a substantial mass reduction compared to actual light weighted systems, while maintaining most of the full load carrying capacity of the base material. Additionally such a substrate can find an attractive application for mirror substrates. The results of the first development and of the first test results will be presented.

Cache Sharing and Isolation Tradeoffs in Multicore Mixed-Criticality Systems

DTIC Science & Technology

2015-05-01

of lockdown registers, to provide way-based partitioning. These alternatives are illustrated in Fig. 1 with respect to a quad-core ARM Cortex A9...presented a cache-partitioning scheme that allows multiple tasks to share the same cache partition on a single processor (as we do for Level-A and...sets and determined the fraction that were schedulable on our target hardware platform, the quad-core ARM Cortex A9 machine mentioned earlier, the LLC

Software platform virtualization in chemistry research and university teaching

PubMed Central

2009-01-01

Background Modern chemistry laboratories operate with a wide range of software applications under different operating systems, such as Windows, LINUX or Mac OS X. Instead of installing software on different computers it is possible to install those applications on a single computer using Virtual Machine software. Software platform virtualization allows a single guest operating system to execute multiple other operating systems on the same computer. We apply and discuss the use of virtual machines in chemistry research and teaching laboratories. Results Virtual machines are commonly used for cheminformatics software development and testing. Benchmarking multiple chemistry software packages we have confirmed that the computational speed penalty for using virtual machines is low and around 5% to 10%. Software virtualization in a teaching environment allows faster deployment and easy use of commercial and open source software in hands-on computer teaching labs. Conclusion Software virtualization in chemistry, mass spectrometry and cheminformatics is needed for software testing and development of software for different operating systems. In order to obtain maximum performance the virtualization software should be multi-core enabled and allow the use of multiprocessor configurations in the virtual machine environment. Server consolidation, by running multiple tasks and operating systems on a single physical machine, can lead to lower maintenance and hardware costs especially in small research labs. The use of virtual machines can prevent software virus infections and security breaches when used as a sandbox system for internet access and software testing. Complex software setups can be created with virtual machines and are easily deployed later to multiple computers for hands-on teaching classes. We discuss the popularity of bioinformatics compared to cheminformatics as well as the missing cheminformatics education at universities worldwide. PMID:20150997

Software platform virtualization in chemistry research and university teaching.

PubMed

Kind, Tobias; Leamy, Tim; Leary, Julie A; Fiehn, Oliver

2009-11-16

Modern chemistry laboratories operate with a wide range of software applications under different operating systems, such as Windows, LINUX or Mac OS X. Instead of installing software on different computers it is possible to install those applications on a single computer using Virtual Machine software. Software platform virtualization allows a single guest operating system to execute multiple other operating systems on the same computer. We apply and discuss the use of virtual machines in chemistry research and teaching laboratories. Virtual machines are commonly used for cheminformatics software development and testing. Benchmarking multiple chemistry software packages we have confirmed that the computational speed penalty for using virtual machines is low and around 5% to 10%. Software virtualization in a teaching environment allows faster deployment and easy use of commercial and open source software in hands-on computer teaching labs. Software virtualization in chemistry, mass spectrometry and cheminformatics is needed for software testing and development of software for different operating systems. In order to obtain maximum performance the virtualization software should be multi-core enabled and allow the use of multiprocessor configurations in the virtual machine environment. Server consolidation, by running multiple tasks and operating systems on a single physical machine, can lead to lower maintenance and hardware costs especially in small research labs. The use of virtual machines can prevent software virus infections and security breaches when used as a sandbox system for internet access and software testing. Complex software setups can be created with virtual machines and are easily deployed later to multiple computers for hands-on teaching classes. We discuss the popularity of bioinformatics compared to cheminformatics as well as the missing cheminformatics education at universities worldwide.

Soft, Conformal Bioelectronics for a Wireless Human-Wheelchair Interface

PubMed Central

Mishra, Saswat; Norton, James J. S.; Lee, Yongkuk; Lee, Dong Sup; Agee, Nicolas; Chen, Yanfei; Chun, Youngjae; Yeo, Woon-Hong

2017-01-01

There are more than 3 million people in the world whose mobility relies on wheelchairs. Recent advancement on engineering technology enables more intuitive, easy-to-use rehabilitation systems. A human-machine interface that uses non-invasive, electrophysiological signals can allow a systematic interaction between human and devices; for example, eye movement-based wheelchair control. However, the existing machine-interface platforms are obtrusive, uncomfortable, and often cause skin irritations as they require a metal electrode affixed to the skin with a gel and acrylic pad. Here, we introduce a bioelectronic system that makes dry, conformal contact to the skin. The mechanically comfortable sensor records high-fidelity electrooculograms, comparable to the conventional gel electrode. Quantitative signal analysis and infrared thermographs show the advantages of the soft biosensor for an ergonomic human-machine interface. A classification algorithm with an optimized set of features shows the accuracy of 94% with five eye movements. A Bluetooth-enabled system incorporating the soft bioelectronics demonstrates a precise, hands-free control of a robotic wheelchair via electrooculograms. PMID:28152485

CloVR: a virtual machine for automated and portable sequence analysis from the desktop using cloud computing.

PubMed

Angiuoli, Samuel V; Matalka, Malcolm; Gussman, Aaron; Galens, Kevin; Vangala, Mahesh; Riley, David R; Arze, Cesar; White, James R; White, Owen; Fricke, W Florian

2011-08-30

Next-generation sequencing technologies have decentralized sequence acquisition, increasing the demand for new bioinformatics tools that are easy to use, portable across multiple platforms, and scalable for high-throughput applications. Cloud computing platforms provide on-demand access to computing infrastructure over the Internet and can be used in combination with custom built virtual machines to distribute pre-packaged with pre-configured software. We describe the Cloud Virtual Resource, CloVR, a new desktop application for push-button automated sequence analysis that can utilize cloud computing resources. CloVR is implemented as a single portable virtual machine (VM) that provides several automated analysis pipelines for microbial genomics, including 16S, whole genome and metagenome sequence analysis. The CloVR VM runs on a personal computer, utilizes local computer resources and requires minimal installation, addressing key challenges in deploying bioinformatics workflows. In addition CloVR supports use of remote cloud computing resources to improve performance for large-scale sequence processing. In a case study, we demonstrate the use of CloVR to automatically process next-generation sequencing data on multiple cloud computing platforms. The CloVR VM and associated architecture lowers the barrier of entry for utilizing complex analysis protocols on both local single- and multi-core computers and cloud systems for high throughput data processing.

Field precision machining technology of target chamber in ICF lasers

NASA Astrophysics Data System (ADS)

Xu, Yuanli; Wu, Wenkai; Shi, Sucun; Duan, Lin; Chen, Gang; Wang, Baoxu; Song, Yugang; Liu, Huilin; Zhu, Mingzhi

2016-10-01

In ICF lasers, many independent laser beams are required to be positioned on target with a very high degree of accuracy during a shot. The target chamber provides a precision platform and datum reference for final optics assembly and target collimation and location system. The target chamber consists of shell with welded flanges, reinforced concrete pedestal, and lateral support structure. The field precision machining technology of target chamber in ICF lasers have been developed based on ShenGuangIII (SGIII). The same center of the target chamber is adopted in the process of design, fabrication, and alignment. The technologies of beam collimation and datum reference transformation are developed for the fabrication, positioning and adjustment of target chamber. A supporting and rotating mechanism and a special drilling machine are developed to bore the holes of ports. An adjustment mechanism is designed to accurately position the target chamber. In order to ensure the collimation requirements of the beam leading and focusing and the target positioning, custom-machined spacers are used to accurately correct the alignment error of the ports. Finally, this paper describes the chamber center, orientation, and centering alignment error measurements of SGIII. The measurements show the field precision machining of SGIII target chamber meet its design requirement. These information can be used on similar systems.

New Design Concept for a Lifting Platform Made of Composite Material

NASA Astrophysics Data System (ADS)

Solazzi, L.; Scalmana, R.

2013-08-01

Elevating work platforms are hoists equipment that are increasingly used in many applications, like in the construction industry and in the maintenance field. The maintenance of the hub of the wind turbines, for example, can be done through the use of a working platform; these structures have to reach great heights and obviously they have to satisfy the constraints induced by the highway standards, like the maximum axle load and the maximum overall dimensions. To satisfy these requests the material of the structures changed from the classic structural steel (S235 JR, S275 JR or S355JR) to high strength steel (S700 to S1100 or more), characterized by a significantly higher specific resistance. The idea of this paper is to use a composite material for the construction of the arms of an elevating platform in order to reduce the global weight of the machine. The analyses on the new kind of platform show the technical possibility to change the material of the arms with composite materials and this produces a significant reduction of the weight of the machine components, about 50 %. Being a feasibility study, still remain open some problems such as the mechanical behavior of the used composite materials (fatigue, environment effects, etc.).

Developing a PLC-friendly state machine model: lessons learned

NASA Astrophysics Data System (ADS)

Pessemier, Wim; Deconinck, Geert; Raskin, Gert; Saey, Philippe; Van Winckel, Hans

2014-07-01

Modern Programmable Logic Controllers (PLCs) have become an attractive platform for controlling real-time aspects of astronomical telescopes and instruments due to their increased versatility, performance and standardization. Likewise, vendor-neutral middleware technologies such as OPC Unified Architecture (OPC UA) have recently demonstrated that they can greatly facilitate the integration of these industrial platforms into the overall control system. Many practical questions arise, however, when building multi-tiered control systems that consist of PLCs for low level control, and conventional software and platforms for higher level control. How should the PLC software be structured, so that it can rely on well-known programming paradigms on the one hand, and be mapped to a well-organized OPC UA interface on the other hand? Which programming languages of the IEC 61131-3 standard closely match the problem domains of the abstraction levels within this structure? How can the recent additions to the standard (such as the support for namespaces and object-oriented extensions) facilitate a model based development approach? To what degree can our applications already take advantage of the more advanced parts of the OPC UA standard, such as the high expressiveness of the semantic modeling language that it defines, or the support for events, aggregation of data, automatic discovery, ... ? What are the timing and concurrency problems to be expected for the higher level tiers of the control system due to the cyclic execution of control and communication tasks by the PLCs? We try to answer these questions by demonstrating a semantic state machine model that can readily be implemented using IEC 61131 and OPC UA. One that does not aim to capture all possible states of a system, but rather one that attempts to organize the course-grained structure and behaviour of a system. In this paper we focus on the intricacies of this seemingly simple task, and on the lessons that we've learned during the development process of such a "PLC-friendly" state machine model.

Combining a hybrid robotic system with a bain-machine interface for the rehabilitation of reaching movements: A case study with a stroke patient.

PubMed

Resquin, F; Ibañez, J; Gonzalez-Vargas, J; Brunetti, F; Dimbwadyo, I; Alves, S; Carrasco, L; Torres, L; Pons, Jose Luis

2016-08-01

Reaching and grasping are two of the most affected functions after stroke. Hybrid rehabilitation systems combining Functional Electrical Stimulation with Robotic devices have been proposed in the literature to improve rehabilitation outcomes. In this work, we present the combined use of a hybrid robotic system with an EEG-based Brain-Machine Interface to detect the user's movement intentions to trigger the assistance. The platform has been tested in a single session with a stroke patient. The results show how the patient could successfully interact with the BMI and command the assistance of the hybrid system with low latencies. Also, the Feedback Error Learning controller implemented in this system could adjust the required FES intensity to perform the task.

The vacuum platform

NASA Astrophysics Data System (ADS)

McNab, A.

2017-10-01

This paper describes GridPP’s Vacuum Platform for managing virtual machines (VMs), which has been used to run production workloads for WLCG and other HEP experiments. The platform provides a uniform interface between VMs and the sites they run at, whether the site is organised as an Infrastructure-as-a-Service cloud system such as OpenStack, or an Infrastructure-as-a-Client system such as Vac. The paper describes our experience in using this platform, in developing and operating VM lifecycle managers Vac and Vcycle, and in interacting with VMs provided by LHCb, ATLAS, ALICE, CMS, and the GridPP DIRAC service to run production workloads.

Precise positioning method for multi-process connecting based on binocular vision

NASA Astrophysics Data System (ADS)

Liu, Wei; Ding, Lichao; Zhao, Kai; Li, Xiao; Wang, Ling; Jia, Zhenyuan

2016-01-01

With the rapid development of aviation and aerospace, the demand for metal coating parts such as antenna reflector, eddy-current sensor and signal transmitter, etc. is more and more urgent. Such parts with varied feature dimensions, complex three-dimensional structures, and high geometric accuracy are generally fabricated by the combination of different manufacturing technology. However, it is difficult to ensure the machining precision because of the connection error between different processing methods. Therefore, a precise positioning method is proposed based on binocular micro stereo vision in this paper. Firstly, a novel and efficient camera calibration method for stereoscopic microscope is presented to solve the problems of narrow view field, small depth of focus and too many nonlinear distortions. Secondly, the extraction algorithms for law curve and free curve are given, and the spatial position relationship between the micro vision system and the machining system is determined accurately. Thirdly, a precise positioning system based on micro stereovision is set up and then embedded in a CNC machining experiment platform. Finally, the verification experiment of the positioning accuracy is conducted and the experimental results indicated that the average errors of the proposed method in the X and Y directions are 2.250 μm and 1.777 μm, respectively.

Integrating multisensor satellite data merging and image reconstruction in support of machine learning for better water quality management.

PubMed

Chang, Ni-Bin; Bai, Kaixu; Chen, Chi-Farn

2017-10-01

Monitoring water quality changes in lakes, reservoirs, estuaries, and coastal waters is critical in response to the needs for sustainable development. This study develops a remote sensing-based multiscale modeling system by integrating multi-sensor satellite data merging and image reconstruction algorithms in support of feature extraction with machine learning leading to automate continuous water quality monitoring in environmentally sensitive regions. This new Earth observation platform, termed "cross-mission data merging and image reconstruction with machine learning" (CDMIM), is capable of merging multiple satellite imageries to provide daily water quality monitoring through a series of image processing, enhancement, reconstruction, and data mining/machine learning techniques. Two existing key algorithms, including Spectral Information Adaptation and Synthesis Scheme (SIASS) and SMart Information Reconstruction (SMIR), are highlighted to support feature extraction and content-based mapping. Whereas SIASS can support various data merging efforts to merge images collected from cross-mission satellite sensors, SMIR can overcome data gaps by reconstructing the information of value-missing pixels due to impacts such as cloud obstruction. Practical implementation of CDMIM was assessed by predicting the water quality over seasons in terms of the concentrations of nutrients and chlorophyll-a, as well as water clarity in Lake Nicaragua, providing synergistic efforts to better monitor the aquatic environment and offer insightful lake watershed management strategies. Copyright © 2017 Elsevier Ltd. All rights reserved.

Cursor control by Kalman filter with a non-invasive body–machine interface

PubMed Central

Seáñez-González, Ismael; Mussa-Ivaldi, Ferdinando A

2015-01-01

Objective We describe a novel human–machine interface for the control of a two-dimensional (2D) computer cursor using four inertial measurement units (IMUs) placed on the user’s upper-body. Approach A calibration paradigm where human subjects follow a cursor with their body as if they were controlling it with their shoulders generates a map between shoulder motions and cursor kinematics. This map is used in a Kalman filter to estimate the desired cursor coordinates from upper-body motions. We compared cursor control performance in a centre-out reaching task performed by subjects using different amounts of information from the IMUs to control the 2D cursor. Main results Our results indicate that taking advantage of the redundancy of the signals from the IMUs improved overall performance. Our work also demonstrates the potential of non-invasive IMU-based body–machine interface systems as an alternative or complement to brain–machine interfaces for accomplishing cursor control in 2D space. Significance The present study may serve as a platform for people with high-tetraplegia to control assistive devices such as powered wheelchairs using a joystick. PMID:25242561

Semantic Agent-Based Service Middleware and Simulation for Smart Cities

PubMed Central

Liu, Ming; Xu, Yang; Hu, Haixiao; Mohammed, Abdul-Wahid

2016-01-01

With the development of Machine-to-Machine (M2M) technology, a variety of embedded and mobile devices is integrated to interact via the platform of the Internet of Things, especially in the domain of smart cities. One of the primary challenges is that selecting the appropriate services or service combination for upper layer applications is hard, which is due to the absence of a unified semantical service description pattern, as well as the service selection mechanism. In this paper, we define a semantic service representation model from four key properties: Capability (C), Deployment (D), Resource (R) and IOData (IO). Based on this model, an agent-based middleware is built to support semantic service enablement. In this middleware, we present an efficient semantic service discovery and matching approach for a service combination process, which calculates the semantic similarity between services, and a heuristic algorithm to search the service candidates for a specific service request. Based on this design, we propose a simulation of virtual urban fire fighting, and the experimental results manifest the feasibility and efficiency of our design. PMID:28009818

Semantic Agent-Based Service Middleware and Simulation for Smart Cities.

PubMed

Liu, Ming; Xu, Yang; Hu, Haixiao; Mohammed, Abdul-Wahid

2016-12-21

With the development of Machine-to-Machine (M2M) technology, a variety of embedded and mobile devices is integrated to interact via the platform of the Internet of Things, especially in the domain of smart cities. One of the primary challenges is that selecting the appropriate services or service combination for upper layer applications is hard, which is due to the absence of a unified semantical service description pattern, as well as the service selection mechanism. In this paper, we define a semantic service representation model from four key properties: Capability (C), Deployment (D), Resource (R) and IOData (IO). Based on this model, an agent-based middleware is built to support semantic service enablement. In this middleware, we present an efficient semantic service discovery and matching approach for a service combination process, which calculates the semantic similarity between services, and a heuristic algorithm to search the service candidates for a specific service request. Based on this design, we propose a simulation of virtual urban fire fighting, and the experimental results manifest the feasibility and efficiency of our design.

Design of penicillin fermentation process simulation system

NASA Astrophysics Data System (ADS)

Qi, Xiaoyu; Yuan, Zhonghu; Qi, Xiaoxuan; Zhang, Wenqi

2011-10-01

Real-time monitoring for batch process attracts increasing attention. It can ensure safety and provide products with consistent quality. The design of simulation system of batch process fault diagnosis is of great significance. In this paper, penicillin fermentation, a typical non-linear, dynamic, multi-stage batch production process, is taken as the research object. A visual human-machine interactive simulation software system based on Windows operation system is developed. The simulation system can provide an effective platform for the research of batch process fault diagnosis.

Optimization design about gimbal structure of high-precision autonomous celestial navigation tracking mirror system

NASA Astrophysics Data System (ADS)

Huang, Wei; Yang, Xiao-xu; Han, Jun-feng; Wei, Yu; Zhang, Jing; Xie, Mei-lin; Yue, Peng

2016-01-01

High precision tracking platform of celestial navigation with control mirror servo structure form, to solve the disadvantages of big volume and rotational inertia, slow response speed, and so on. It improved the stability and tracking accuracy of platform. Due to optical sensor and mirror are installed on the middle-gimbal, stiffness and resonant frequency requirement for high. Based on the application of finite element modality analysis theory, doing Research on dynamic characteristics of the middle-gimbal, and ANSYS was used for the finite element dynamic emulator analysis. According to the result of the computer to find out the weak links of the structure, and Put forward improvement suggestions and reanalysis. The lowest resonant frequency of optimization middle-gimbal avoid the bandwidth of the platform servo mechanism, and much higher than the disturbance frequency of carrier aircraft, and reduces mechanical resonance of the framework. Reaching provides a theoretical basis for the whole machine structure optimization design of high-precision of autonomous Celestial navigation tracking mirror system.

Collaborative mining and interpretation of large-scale data for biomedical research insights.

PubMed

Tsiliki, Georgia; Karacapilidis, Nikos; Christodoulou, Spyros; Tzagarakis, Manolis

2014-01-01

Biomedical research becomes increasingly interdisciplinary and collaborative in nature. Researchers need to efficiently and effectively collaborate and make decisions by meaningfully assembling, mining and analyzing available large-scale volumes of complex multi-faceted data residing in different sources. In line with related research directives revealing that, in spite of the recent advances in data mining and computational analysis, humans can easily detect patterns which computer algorithms may have difficulty in finding, this paper reports on the practical use of an innovative web-based collaboration support platform in a biomedical research context. Arguing that dealing with data-intensive and cognitively complex settings is not a technical problem alone, the proposed platform adopts a hybrid approach that builds on the synergy between machine and human intelligence to facilitate the underlying sense-making and decision making processes. User experience shows that the platform enables more informed and quicker decisions, by displaying the aggregated information according to their needs, while also exploiting the associated human intelligence.

Collaborative Mining and Interpretation of Large-Scale Data for Biomedical Research Insights

PubMed Central

Tsiliki, Georgia; Karacapilidis, Nikos; Christodoulou, Spyros; Tzagarakis, Manolis

2014-01-01

Biomedical research becomes increasingly interdisciplinary and collaborative in nature. Researchers need to efficiently and effectively collaborate and make decisions by meaningfully assembling, mining and analyzing available large-scale volumes of complex multi-faceted data residing in different sources. In line with related research directives revealing that, in spite of the recent advances in data mining and computational analysis, humans can easily detect patterns which computer algorithms may have difficulty in finding, this paper reports on the practical use of an innovative web-based collaboration support platform in a biomedical research context. Arguing that dealing with data-intensive and cognitively complex settings is not a technical problem alone, the proposed platform adopts a hybrid approach that builds on the synergy between machine and human intelligence to facilitate the underlying sense-making and decision making processes. User experience shows that the platform enables more informed and quicker decisions, by displaying the aggregated information according to their needs, while also exploiting the associated human intelligence. PMID:25268270

Big Data Analytics with Datalog Queries on Spark.

PubMed

Shkapsky, Alexander; Yang, Mohan; Interlandi, Matteo; Chiu, Hsuan; Condie, Tyson; Zaniolo, Carlo

2016-01-01

There is great interest in exploiting the opportunity provided by cloud computing platforms for large-scale analytics. Among these platforms, Apache Spark is growing in popularity for machine learning and graph analytics. Developing efficient complex analytics in Spark requires deep understanding of both the algorithm at hand and the Spark API or subsystem APIs (e.g., Spark SQL, GraphX). Our BigDatalog system addresses the problem by providing concise declarative specification of complex queries amenable to efficient evaluation. Towards this goal, we propose compilation and optimization techniques that tackle the important problem of efficiently supporting recursion in Spark. We perform an experimental comparison with other state-of-the-art large-scale Datalog systems and verify the efficacy of our techniques and effectiveness of Spark in supporting Datalog-based analytics.

Big Data Analytics with Datalog Queries on Spark

PubMed Central

Shkapsky, Alexander; Yang, Mohan; Interlandi, Matteo; Chiu, Hsuan; Condie, Tyson; Zaniolo, Carlo

2017-01-01

There is great interest in exploiting the opportunity provided by cloud computing platforms for large-scale analytics. Among these platforms, Apache Spark is growing in popularity for machine learning and graph analytics. Developing efficient complex analytics in Spark requires deep understanding of both the algorithm at hand and the Spark API or subsystem APIs (e.g., Spark SQL, GraphX). Our BigDatalog system addresses the problem by providing concise declarative specification of complex queries amenable to efficient evaluation. Towards this goal, we propose compilation and optimization techniques that tackle the important problem of efficiently supporting recursion in Spark. We perform an experimental comparison with other state-of-the-art large-scale Datalog systems and verify the efficacy of our techniques and effectiveness of Spark in supporting Datalog-based analytics. PMID:28626296

A Distributed Parallel Genetic Algorithm of Placement Strategy for Virtual Machines Deployment on Cloud Platform

PubMed Central

Dong, Yu-Shuang; Xu, Gao-Chao; Fu, Xiao-Dong

2014-01-01

The cloud platform provides various services to users. More and more cloud centers provide infrastructure as the main way of operating. To improve the utilization rate of the cloud center and to decrease the operating cost, the cloud center provides services according to requirements of users by sharding the resources with virtualization. Considering both QoS for users and cost saving for cloud computing providers, we try to maximize performance and minimize energy cost as well. In this paper, we propose a distributed parallel genetic algorithm (DPGA) of placement strategy for virtual machines deployment on cloud platform. It executes the genetic algorithm parallelly and distributedly on several selected physical hosts in the first stage. Then it continues to execute the genetic algorithm of the second stage with solutions obtained from the first stage as the initial population. The solution calculated by the genetic algorithm of the second stage is the optimal one of the proposed approach. The experimental results show that the proposed placement strategy of VM deployment can ensure QoS for users and it is more effective and more energy efficient than other placement strategies on the cloud platform. PMID:25097872

A distributed parallel genetic algorithm of placement strategy for virtual machines deployment on cloud platform.

PubMed

Dong, Yu-Shuang; Xu, Gao-Chao; Fu, Xiao-Dong

2014-01-01

The cloud platform provides various services to users. More and more cloud centers provide infrastructure as the main way of operating. To improve the utilization rate of the cloud center and to decrease the operating cost, the cloud center provides services according to requirements of users by sharding the resources with virtualization. Considering both QoS for users and cost saving for cloud computing providers, we try to maximize performance and minimize energy cost as well. In this paper, we propose a distributed parallel genetic algorithm (DPGA) of placement strategy for virtual machines deployment on cloud platform. It executes the genetic algorithm parallelly and distributedly on several selected physical hosts in the first stage. Then it continues to execute the genetic algorithm of the second stage with solutions obtained from the first stage as the initial population. The solution calculated by the genetic algorithm of the second stage is the optimal one of the proposed approach. The experimental results show that the proposed placement strategy of VM deployment can ensure QoS for users and it is more effective and more energy efficient than other placement strategies on the cloud platform.

Radial-piston pump for drive of test machines

NASA Astrophysics Data System (ADS)

Nizhegorodov, A. I.; Gavrilin, A. N.; Moyzes, B. B.; Cherkasov, A. I.; Zharkevich, O. M.; Zhetessova, G. S.; Savelyeva, N. A.

2018-01-01

The article reviews the development of radial-piston pump with phase control and alternating-flow mode for seismic-testing platforms and other test machines. The prospects for use of the developed device are proved. It is noted that the method of frequency modulation with the detection of the natural frequencies is easily realized by using the radial-piston pump. The prospects of further research are given proof.

Cloud-based opportunities in scientific computing: insights from processing Suomi National Polar-Orbiting Partnership (S-NPP) Direct Broadcast data

NASA Astrophysics Data System (ADS)

Evans, J. D.; Hao, W.; Chettri, S.

2013-12-01

The cloud is proving to be a uniquely promising platform for scientific computing. Our experience with processing satellite data using Amazon Web Services highlights several opportunities for enhanced performance, flexibility, and cost effectiveness in the cloud relative to traditional computing -- for example: - Direct readout from a polar-orbiting satellite such as the Suomi National Polar-Orbiting Partnership (S-NPP) requires bursts of processing a few times a day, separated by quiet periods when the satellite is out of receiving range. In the cloud, by starting and stopping virtual machines in minutes, we can marshal significant computing resources quickly when needed, but not pay for them when not needed. To take advantage of this capability, we are automating a data-driven approach to the management of cloud computing resources, in which new data availability triggers the creation of new virtual machines (of variable size and processing power) which last only until the processing workflow is complete. - 'Spot instances' are virtual machines that run as long as one's asking price is higher than the provider's variable spot price. Spot instances can greatly reduce the cost of computing -- for software systems that are engineered to withstand unpredictable interruptions in service (as occurs when a spot price exceeds the asking price). We are implementing an approach to workflow management that allows data processing workflows to resume with minimal delays after temporary spot price spikes. This will allow systems to take full advantage of variably-priced 'utility computing.' - Thanks to virtual machine images, we can easily launch multiple, identical machines differentiated only by 'user data' containing individualized instructions (e.g., to fetch particular datasets or to perform certain workflows or algorithms) This is particularly useful when (as is the case with S-NPP data) we need to launch many very similar machines to process an unpredictable number of data files concurrently. Our experience shows the viability and flexibility of this approach to workflow management for scientific data processing. - Finally, cloud computing is a promising platform for distributed volunteer ('interstitial') computing, via mechanisms such as the Berkeley Open Infrastructure for Network Computing (BOINC) popularized with the SETI@Home project and others such as ClimatePrediction.net and NASA's Climate@Home. Interstitial computing faces significant challenges as commodity computing shifts from (always on) desktop computers towards smartphones and tablets (untethered and running on scarce battery power); but cloud computing offers significant slack capacity. This capacity includes virtual machines with unused RAM or underused CPUs; virtual storage volumes allocated (& paid for) but not full; and virtual machines that are paid up for the current hour but whose work is complete. We are devising ways to facilitate the reuse of these resources (i.e., cloud-based interstitial computing) for satellite data processing and related analyses. We will present our findings and research directions on these and related topics.

Proba-V Mission Exploitation Platform

NASA Astrophysics Data System (ADS)

Goor, E.

2017-12-01

VITO and partners developed the Proba-V Mission Exploitation Platform (MEP) as an end-to-end solution to drastically improve the exploitation of the Proba-V (an EC Copernicus contributing mission) EO-data archive, the past mission SPOT-VEGETATION and derived vegetation parameters by researchers, service providers (e.g. the EC Copernicus Global Land Service) and end-users. The analysis of time series of data (PB range) is addressed, as well as the large scale on-demand processing of near real-time data on a powerful and scalable processing environment. New features are still developed, but the platform is yet fully operational since November 2016 and offers A time series viewer (browser web client and API), showing the evolution of Proba-V bands and derived vegetation parameters for any country, region, pixel or polygon defined by the user. Full-resolution viewing services for the complete data archive. On-demand processing chains on a powerfull Hadoop/Spark backend. Virtual Machines can be requested by users with access to the complete data archive mentioned above and pre-configured tools to work with this data, e.g. various toolboxes and support for R and Python. This allows users to immediately work with the data without having to install tools or download data, but as well to design, debug and test applications on the platform. Jupyter Notebooks is available with some examples python and R projects worked out to show the potential of the data. Today the platform is already used by several international third party projects to perform R&D activities on the data, and to develop/host data analysis toolboxes. From the Proba-V MEP, access to other data sources such as Sentinel-2 and landsat data is also addressed. Selected components of the MEP are also deployed on public cloud infrastructures in various R&D projects. Users can make use of powerful Web based tools and can self-manage virtual machines to perform their work on the infrastructure at VITO with access to the complete data archive. To realise this, private cloud technology (openStack) is used and a distributed processing environment is built based on Hadoop. The Hadoop ecosystem offers a lot of technologies (Spark, Yarn, Accumulo) which we integrate with several open-source components (e.g. Geotrellis).

Femtosecond laser fabrication of fiber based optofluidic platform for flow cytometry applications

NASA Astrophysics Data System (ADS)

Serhatlioglu, Murat; Elbuken, Caglar; Ortac, Bulend; Solmaz, Mehmet E.

2017-02-01

Miniaturized optofluidic platforms play an important role in bio-analysis, detection and diagnostic applications. The advantages of such miniaturized devices are extremely low sample requirement, low cost development and rapid analysis capabilities. Fused silica is advantageous for optofluidic systems due to properties such as being chemically inert, mechanically stable, and optically transparent to a wide spectrum of light. As a three dimensional manufacturing method, femtosecond laser scanning followed by chemical etching shows great potential to fabricate glass based optofluidic chips. In this study, we demonstrate fabrication of all-fiber based, optofluidic flow cytometer in fused silica glass by femtosecond laser machining. 3D particle focusing was achieved through a straightforward planar chip design with two separately fabricated fused silica glass slides thermally bonded together. Bioparticles in a fluid stream encounter with optical interrogation region specifically designed to allocate 405nm single mode fiber laser source and two multi-mode collection fibers for forward scattering (FSC) and side scattering (SSC) signals detection. Detected signal data collected with oscilloscope and post processed with MATLAB script file. We were able to count number of events over 4000events/sec, and achieve size distribution for 5.95μm monodisperse polystyrene beads using FSC and SSC signals. Our platform shows promise for optical and fluidic miniaturization of flow cytometry systems.

Cots Correlator Platform

NASA Astrophysics Data System (ADS)

Schaaf, Kjeld; Overeem, Ruud

2004-06-01

Moore’s law is best exploited by using consumer market hardware. In particular, the gaming industry pushes the limit of processor performance thus reducing the cost per raw flop even faster than Moore’s law predicts. Next to the cost benefits of Common-Of-The-Shelf (COTS) processing resources, there is a rapidly growing experience pool in cluster based processing. The typical Beowulf cluster of PC’s supercomputers are well known. Multiple examples exists of specialised cluster computers based on more advanced server nodes or even gaming stations. All these cluster machines build upon the same knowledge about cluster software management, scheduling, middleware libraries and mathematical libraries. In this study, we have integrated COTS processing resources and cluster nodes into a very high performance processing platform suitable for streaming data applications, in particular to implement a correlator. The required processing power for the correlator in modern radio telescopes is in the range of the larger supercomputers, which motivates the usage of supercomputer technology. Raw processing power is provided by graphical processors and is combined with an Infiniband host bus adapter with integrated data stream handling logic. With this processing platform a scalable correlator can be built with continuously growing processing power at consumer market prices.

Functional Interaction Network Construction and Analysis for Disease Discovery.

PubMed

Wu, Guanming; Haw, Robin

2017-01-01

Network-based approaches project seemingly unrelated genes or proteins onto a large-scale network context, therefore providing a holistic visualization and analysis platform for genomic data generated from high-throughput experiments, reducing the dimensionality of data via using network modules and increasing the statistic analysis power. Based on the Reactome database, the most popular and comprehensive open-source biological pathway knowledgebase, we have developed a highly reliable protein functional interaction network covering around 60 % of total human genes and an app called ReactomeFIViz for Cytoscape, the most popular biological network visualization and analysis platform. In this chapter, we describe the detailed procedures on how this functional interaction network is constructed by integrating multiple external data sources, extracting functional interactions from human curated pathway databases, building a machine learning classifier called a Naïve Bayesian Classifier, predicting interactions based on the trained Naïve Bayesian Classifier, and finally constructing the functional interaction database. We also provide an example on how to use ReactomeFIViz for performing network-based data analysis for a list of genes.

Privacy preserving interactive record linkage (PPIRL)

PubMed Central

Kum, Hye-Chung; Krishnamurthy, Ashok; Machanavajjhala, Ashwin; Reiter, Michael K; Ahalt, Stanley

2014-01-01

Objective Record linkage to integrate uncoordinated databases is critical in biomedical research using Big Data. Balancing privacy protection against the need for high quality record linkage requires a human–machine hybrid system to safely manage uncertainty in the ever changing streams of chaotic Big Data. Methods In the computer science literature, private record linkage is the most published area. It investigates how to apply a known linkage function safely when linking two tables. However, in practice, the linkage function is rarely known. Thus, there are many data linkage centers whose main role is to be the trusted third party to determine the linkage function manually and link data for research via a master population list for a designated region. Recently, a more flexible computerized third-party linkage platform, Secure Decoupled Linkage (SDLink), has been proposed based on: (1) decoupling data via encryption, (2) obfuscation via chaffing (adding fake data) and universe manipulation; and (3) minimum information disclosure via recoding. Results We synthesize this literature to formalize a new framework for privacy preserving interactive record linkage (PPIRL) with tractable privacy and utility properties and then analyze the literature using this framework. Conclusions Human-based third-party linkage centers for privacy preserving record linkage are the accepted norm internationally. We find that a computer-based third-party platform that can precisely control the information disclosed at the micro level and allow frequent human interaction during the linkage process, is an effective human–machine hybrid system that significantly improves on the linkage center model both in terms of privacy and utility. PMID:24201028

From the History of Conferences on the Machine and Mechanism Science

NASA Astrophysics Data System (ADS)

Wojnarowski, J.

2016-08-01

In the course of the past sixty years of the Polish Committee for the Theory of Machines and Mechanisms (PC TMM) 24 scientific and didactic conferences have been held. The subject matter of these conferences, generally organized every other year, comprised problems of the classification, analysis and synthesis of mechanisms, the dynamics of machine systems, investigations concerning self-excited vibrations, the stability of the systems, the control of machines and biomechanics. The numbers of submitted papers as well as the number of participants substantiate the need of organizing such conferences, their importance and the activity of the Polish Committee of TMM for the purpose of creating a platform for the presentation and discussion of new research methods in the domain of mechanisms, machines, biomechanics and mechatronics.

CloVR: A virtual machine for automated and portable sequence analysis from the desktop using cloud computing

PubMed Central

2011-01-01

Background Next-generation sequencing technologies have decentralized sequence acquisition, increasing the demand for new bioinformatics tools that are easy to use, portable across multiple platforms, and scalable for high-throughput applications. Cloud computing platforms provide on-demand access to computing infrastructure over the Internet and can be used in combination with custom built virtual machines to distribute pre-packaged with pre-configured software. Results We describe the Cloud Virtual Resource, CloVR, a new desktop application for push-button automated sequence analysis that can utilize cloud computing resources. CloVR is implemented as a single portable virtual machine (VM) that provides several automated analysis pipelines for microbial genomics, including 16S, whole genome and metagenome sequence analysis. The CloVR VM runs on a personal computer, utilizes local computer resources and requires minimal installation, addressing key challenges in deploying bioinformatics workflows. In addition CloVR supports use of remote cloud computing resources to improve performance for large-scale sequence processing. In a case study, we demonstrate the use of CloVR to automatically process next-generation sequencing data on multiple cloud computing platforms. Conclusion The CloVR VM and associated architecture lowers the barrier of entry for utilizing complex analysis protocols on both local single- and multi-core computers and cloud systems for high throughput data processing. PMID:21878105

Mission Exploitation Platform PROBA-V

NASA Astrophysics Data System (ADS)

Goor, Erwin

2016-04-01

VITO and partners developed an end-to-end solution to drastically improve the exploitation of the PROBA-V EO-data archive (http://proba-v.vgt.vito.be/), the past mission SPOT-VEGETATION and derived vegetation parameters by researchers, service providers and end-users. The analysis of time series of data (+1PB) is addressed, as well as the large scale on-demand processing of near real-time data. From November 2015 an operational Mission Exploitation Platform (MEP) PROBA-V, as an ESA pathfinder project, will be gradually deployed at the VITO data center with direct access to the complete data archive. Several applications will be released to the users, e.g. - A time series viewer, showing the evolution of PROBA-V bands and derived vegetation parameters for any area of interest. - Full-resolution viewing services for the complete data archive. - On-demand processing chains e.g. for the calculation of N-daily composites. - A Virtual Machine will be provided with access to the data archive and tools to work with this data, e.g. various toolboxes and support for R and Python. After an initial release in January 2016, a research platform will gradually be deployed allowing users to design, debug and test applications on the platform. From the MEP PROBA-V, access to Sentinel-2 and landsat data will be addressed as well, e.g. to support the Cal/Val activities of the users. Users can make use of powerful Web based tools and can self-manage virtual machines to perform their work on the infrastructure at VITO with access to the complete data archive. To realise this, private cloud technology (openStack) is used and a distributed processing environment is built based on Hadoop. The Hadoop ecosystem offers a lot of technologies (Spark, Yarn, Accumulo, etc.) which we integrate with several open-source components. The impact of this MEP on the user community will be high and will completely change the way of working with the data and hence open the large time series to a larger community of users. The presentation will address these benefits for the users and discuss on the technical challenges in implementing this MEP.

Research and implementation of SATA protocol link layer based on FPGA

NASA Astrophysics Data System (ADS)

Liu, Wen-long; Liu, Xue-bin; Qiang, Si-miao; Yan, Peng; Wen, Zhi-gang; Kong, Liang; Liu, Yong-zheng

2018-02-01

In order to solve the problem high-performance real-time, high-speed the image data storage generated by the detector. In this thesis, it choose an suitable portable image storage hard disk of SATA interface, it is relative to the existing storage media. It has a large capacity, high transfer rate, inexpensive, power-down data which is not lost, and many other advantages. This paper focuses on the link layer of the protocol, analysis the implementation process of SATA2.0 protocol, and build state machines. Then analyzes the characteristics resources of Kintex-7 FPGA family, builds state machines according to the agreement, write Verilog implement link layer modules, and run the simulation test. Finally, the test is on the Kintex-7 development board platform. It meets the requirements SATA2.0 protocol basically.

Space Spider - A concept for fabrication of large structures

NASA Technical Reports Server (NTRS)

Britton, W. R.; Johnston, J. D.

1978-01-01

The Space Spider concept for the automated fabrication of large space structures involves a specialized machine which roll-forms thin gauge material such as aluminum and develops continuous spiral structures with radial struts to sizes of 600-1,000 feet in diameter by 15 feet deep. This concept allows the machine and raw material to be integrated using the Orbiter capabilities, then boosting the rigid system to geosynchronous equatorial orbit (GEO) without high sensitivity to acceleration forces. As a teleoperator controlled device having repetitive operations, the fabrication process can be monitored and verified from a ground-based station without astronaut involvement in GEO. The resultant structure will be useful as an intermediate size platform or as a structural element to be used with other elements such as the space-fabricated beams or composite nested tubes.

Comparison of Ion Personal Genome Machine Platforms for the Detection of Variants in BRCA1 and BRCA2.

PubMed

Hwang, Sang Mee; Lee, Ki Chan; Lee, Min Seob; Park, Kyoung Un

2018-01-01

Transition to next generation sequencing (NGS) for BRCA1 / BRCA2 analysis in clinical laboratories is ongoing but different platforms and/or data analysis pipelines give different results resulting in difficulties in implementation. We have evaluated the Ion Personal Genome Machine (PGM) Platforms (Ion PGM, Ion PGM Dx, Thermo Fisher Scientific) for the analysis of BRCA1 /2. The results of Ion PGM with OTG-snpcaller, a pipeline based on Torrent mapping alignment program and Genome Analysis Toolkit, from 75 clinical samples and 14 reference DNA samples were compared with Sanger sequencing for BRCA1 / BRCA2 . Ten clinical samples and 14 reference DNA samples were additionally sequenced by Ion PGM Dx with Torrent Suite. Fifty types of variants including 18 pathogenic or variants of unknown significance were identified from 75 clinical samples and known variants of the reference samples were confirmed by Sanger sequencing and/or NGS. One false-negative results were present for Ion PGM/OTG-snpcaller for an indel variant misidentified as a single nucleotide variant. However, eight discordant results were present for Ion PGM Dx/Torrent Suite with both false-positive and -negative results. A 40-bp deletion, a 4-bp deletion and a 1-bp deletion variant was not called and a false-positive deletion was identified. Four other variants were misidentified as another variant. Ion PGM/OTG-snpcaller showed acceptable performance with good concordance with Sanger sequencing. However, Ion PGM Dx/Torrent Suite showed many discrepant results not suitable for use in a clinical laboratory, requiring further optimization of the data analysis for calling variants.

Craniux: A LabVIEW-Based Modular Software Framework for Brain-Machine Interface Research

PubMed Central

Degenhart, Alan D.; Kelly, John W.; Ashmore, Robin C.; Collinger, Jennifer L.; Tyler-Kabara, Elizabeth C.; Weber, Douglas J.; Wang, Wei

2011-01-01

This paper presents “Craniux,” an open-access, open-source software framework for brain-machine interface (BMI) research. Developed in LabVIEW, a high-level graphical programming environment, Craniux offers both out-of-the-box functionality and a modular BMI software framework that is easily extendable. Specifically, it allows researchers to take advantage of multiple features inherent to the LabVIEW environment for on-the-fly data visualization, parallel processing, multithreading, and data saving. This paper introduces the basic features and system architecture of Craniux and describes the validation of the system under real-time BMI operation using simulated and real electrocorticographic (ECoG) signals. Our results indicate that Craniux is able to operate consistently in real time, enabling a seamless work flow to achieve brain control of cursor movement. The Craniux software framework is made available to the scientific research community to provide a LabVIEW-based BMI software platform for future BMI research and development. PMID:21687575

Craniux: a LabVIEW-based modular software framework for brain-machine interface research.

PubMed

Degenhart, Alan D; Kelly, John W; Ashmore, Robin C; Collinger, Jennifer L; Tyler-Kabara, Elizabeth C; Weber, Douglas J; Wang, Wei

2011-01-01

This paper presents "Craniux," an open-access, open-source software framework for brain-machine interface (BMI) research. Developed in LabVIEW, a high-level graphical programming environment, Craniux offers both out-of-the-box functionality and a modular BMI software framework that is easily extendable. Specifically, it allows researchers to take advantage of multiple features inherent to the LabVIEW environment for on-the-fly data visualization, parallel processing, multithreading, and data saving. This paper introduces the basic features and system architecture of Craniux and describes the validation of the system under real-time BMI operation using simulated and real electrocorticographic (ECoG) signals. Our results indicate that Craniux is able to operate consistently in real time, enabling a seamless work flow to achieve brain control of cursor movement. The Craniux software framework is made available to the scientific research community to provide a LabVIEW-based BMI software platform for future BMI research and development.

GUIdock: Using Docker Containers with a Common Graphics User Interface to Address the Reproducibility of Research

PubMed Central

Yeung, Ka Yee

2016-01-01

Reproducibility is vital in science. For complex computational methods, it is often necessary, not just to recreate the code, but also the software and hardware environment to reproduce results. Virtual machines, and container software such as Docker, make it possible to reproduce the exact environment regardless of the underlying hardware and operating system. However, workflows that use Graphical User Interfaces (GUIs) remain difficult to replicate on different host systems as there is no high level graphical software layer common to all platforms. GUIdock allows for the facile distribution of a systems biology application along with its graphics environment. Complex graphics based workflows, ubiquitous in systems biology, can now be easily exported and reproduced on many different platforms. GUIdock uses Docker, an open source project that provides a container with only the absolutely necessary software dependencies and configures a common X Windows (X11) graphic interface on Linux, Macintosh and Windows platforms. As proof of concept, we present a Docker package that contains a Bioconductor application written in R and C++ called networkBMA for gene network inference. Our package also includes Cytoscape, a java-based platform with a graphical user interface for visualizing and analyzing gene networks, and the CyNetworkBMA app, a Cytoscape app that allows the use of networkBMA via the user-friendly Cytoscape interface. PMID:27045593

GUIdock: Using Docker Containers with a Common Graphics User Interface to Address the Reproducibility of Research.

PubMed

Hung, Ling-Hong; Kristiyanto, Daniel; Lee, Sung Bong; Yeung, Ka Yee

2016-01-01

Reproducibility is vital in science. For complex computational methods, it is often necessary, not just to recreate the code, but also the software and hardware environment to reproduce results. Virtual machines, and container software such as Docker, make it possible to reproduce the exact environment regardless of the underlying hardware and operating system. However, workflows that use Graphical User Interfaces (GUIs) remain difficult to replicate on different host systems as there is no high level graphical software layer common to all platforms. GUIdock allows for the facile distribution of a systems biology application along with its graphics environment. Complex graphics based workflows, ubiquitous in systems biology, can now be easily exported and reproduced on many different platforms. GUIdock uses Docker, an open source project that provides a container with only the absolutely necessary software dependencies and configures a common X Windows (X11) graphic interface on Linux, Macintosh and Windows platforms. As proof of concept, we present a Docker package that contains a Bioconductor application written in R and C++ called networkBMA for gene network inference. Our package also includes Cytoscape, a java-based platform with a graphical user interface for visualizing and analyzing gene networks, and the CyNetworkBMA app, a Cytoscape app that allows the use of networkBMA via the user-friendly Cytoscape interface.

Event-driven contrastive divergence for spiking neuromorphic systems.

PubMed

Neftci, Emre; Das, Srinjoy; Pedroni, Bruno; Kreutz-Delgado, Kenneth; Cauwenberghs, Gert

2013-01-01

Restricted Boltzmann Machines (RBMs) and Deep Belief Networks have been demonstrated to perform efficiently in a variety of applications, such as dimensionality reduction, feature learning, and classification. Their implementation on neuromorphic hardware platforms emulating large-scale networks of spiking neurons can have significant advantages from the perspectives of scalability, power dissipation and real-time interfacing with the environment. However, the traditional RBM architecture and the commonly used training algorithm known as Contrastive Divergence (CD) are based on discrete updates and exact arithmetics which do not directly map onto a dynamical neural substrate. Here, we present an event-driven variation of CD to train a RBM constructed with Integrate & Fire (I&F) neurons, that is constrained by the limitations of existing and near future neuromorphic hardware platforms. Our strategy is based on neural sampling, which allows us to synthesize a spiking neural network that samples from a target Boltzmann distribution. The recurrent activity of the network replaces the discrete steps of the CD algorithm, while Spike Time Dependent Plasticity (STDP) carries out the weight updates in an online, asynchronous fashion. We demonstrate our approach by training an RBM composed of leaky I&F neurons with STDP synapses to learn a generative model of the MNIST hand-written digit dataset, and by testing it in recognition, generation and cue integration tasks. Our results contribute to a machine learning-driven approach for synthesizing networks of spiking neurons capable of carrying out practical, high-level functionality.

Event-driven contrastive divergence for spiking neuromorphic systems

PubMed Central

Neftci, Emre; Das, Srinjoy; Pedroni, Bruno; Kreutz-Delgado, Kenneth; Cauwenberghs, Gert

2014-01-01

Restricted Boltzmann Machines (RBMs) and Deep Belief Networks have been demonstrated to perform efficiently in a variety of applications, such as dimensionality reduction, feature learning, and classification. Their implementation on neuromorphic hardware platforms emulating large-scale networks of spiking neurons can have significant advantages from the perspectives of scalability, power dissipation and real-time interfacing with the environment. However, the traditional RBM architecture and the commonly used training algorithm known as Contrastive Divergence (CD) are based on discrete updates and exact arithmetics which do not directly map onto a dynamical neural substrate. Here, we present an event-driven variation of CD to train a RBM constructed with Integrate & Fire (I&F) neurons, that is constrained by the limitations of existing and near future neuromorphic hardware platforms. Our strategy is based on neural sampling, which allows us to synthesize a spiking neural network that samples from a target Boltzmann distribution. The recurrent activity of the network replaces the discrete steps of the CD algorithm, while Spike Time Dependent Plasticity (STDP) carries out the weight updates in an online, asynchronous fashion. We demonstrate our approach by training an RBM composed of leaky I&F neurons with STDP synapses to learn a generative model of the MNIST hand-written digit dataset, and by testing it in recognition, generation and cue integration tasks. Our results contribute to a machine learning-driven approach for synthesizing networks of spiking neurons capable of carrying out practical, high-level functionality. PMID:24574952

Crop classification and mapping based on Sentinel missions data in cloud environment

NASA Astrophysics Data System (ADS)

Lavreniuk, M. S.; Kussul, N.; Shelestov, A.; Vasiliev, V.

2017-12-01

Availability of high resolution satellite imagery (Sentinel-1/2/3, Landsat) over large territories opens new opportunities in agricultural monitoring. In particular, it becomes feasible to solve crop classification and crop mapping task at country and regional scale using time series of heterogenous satellite imagery. But in this case, we face with the problem of Big Data. Dealing with time series of high resolution (10 m) multispectral imagery we need to download huge volumes of data and then process them. The solution is to move "processing chain" closer to data itself to drastically shorten time for data transfer. One more advantage of such approach is the possibility to parallelize data processing workflow and efficiently implement machine learning algorithms. This could be done with cloud platform where Sentinel imagery are stored. In this study, we investigate usability and efficiency of two different cloud platforms Amazon and Google for crop classification and crop mapping problems. Two pilot areas were investigated - Ukraine and England. Google provides user friendly environment Google Earth Engine for Earth observation applications with a lot of data processing and machine learning tools already deployed. At the same time with Amazon one gets much more flexibility in implementation of his own workflow. Detailed analysis of pros and cons will be done in the presentation.

Massively parallel digital high resolution melt for rapid and absolutely quantitative sequence profiling

NASA Astrophysics Data System (ADS)

Velez, Daniel Ortiz; Mack, Hannah; Jupe, Julietta; Hawker, Sinead; Kulkarni, Ninad; Hedayatnia, Behnam; Zhang, Yang; Lawrence, Shelley; Fraley, Stephanie I.

2017-02-01

In clinical diagnostics and pathogen detection, profiling of complex samples for low-level genotypes represents a significant challenge. Advances in speed, sensitivity, and extent of multiplexing of molecular pathogen detection assays are needed to improve patient care. We report the development of an integrated platform enabling the identification of bacterial pathogen DNA sequences in complex samples in less than four hours. The system incorporates a microfluidic chip and instrumentation to accomplish universal PCR amplification, High Resolution Melting (HRM), and machine learning within 20,000 picoliter scale reactions, simultaneously. Clinically relevant concentrations of bacterial DNA molecules are separated by digitization across 20,000 reactions and amplified with universal primers targeting the bacterial 16S gene. Amplification is followed by HRM sequence fingerprinting in all reactions, simultaneously. The resulting bacteria-specific melt curves are identified by Support Vector Machine learning, and individual pathogen loads are quantified. The platform reduces reaction volumes by 99.995% and achieves a greater than 200-fold increase in dynamic range of detection compared to traditional PCR HRM approaches. Type I and II error rates are reduced by 99% and 100% respectively, compared to intercalating dye-based digital PCR (dPCR) methods. This technology could impact a number of quantitative profiling applications, especially infectious disease diagnostics.

Rapid Prediction of Bacterial Heterotrophic Fluxomics Using Machine Learning and Constraint Programming.

PubMed

Wu, Stephen Gang; Wang, Yuxuan; Jiang, Wu; Oyetunde, Tolutola; Yao, Ruilian; Zhang, Xuehong; Shimizu, Kazuyuki; Tang, Yinjie J; Bao, Forrest Sheng

2016-04-01

13C metabolic flux analysis (13C-MFA) has been widely used to measure in vivo enzyme reaction rates (i.e., metabolic flux) in microorganisms. Mining the relationship between environmental and genetic factors and metabolic fluxes hidden in existing fluxomic data will lead to predictive models that can significantly accelerate flux quantification. In this paper, we present a web-based platform MFlux (http://mflux.org) that predicts the bacterial central metabolism via machine learning, leveraging data from approximately 100 13C-MFA papers on heterotrophic bacterial metabolisms. Three machine learning methods, namely Support Vector Machine (SVM), k-Nearest Neighbors (k-NN), and Decision Tree, were employed to study the sophisticated relationship between influential factors and metabolic fluxes. We performed a grid search of the best parameter set for each algorithm and verified their performance through 10-fold cross validations. SVM yields the highest accuracy among all three algorithms. Further, we employed quadratic programming to adjust flux profiles to satisfy stoichiometric constraints. Multiple case studies have shown that MFlux can reasonably predict fluxomes as a function of bacterial species, substrate types, growth rate, oxygen conditions, and cultivation methods. Due to the interest of studying model organism under particular carbon sources, bias of fluxome in the dataset may limit the applicability of machine learning models. This problem can be resolved after more papers on 13C-MFA are published for non-model species.

Rapid Prediction of Bacterial Heterotrophic Fluxomics Using Machine Learning and Constraint Programming

PubMed Central

Wu, Stephen Gang; Wang, Yuxuan; Jiang, Wu; Oyetunde, Tolutola; Yao, Ruilian; Zhang, Xuehong; Shimizu, Kazuyuki; Tang, Yinjie J.; Bao, Forrest Sheng

2016-01-01

13C metabolic flux analysis (13C-MFA) has been widely used to measure in vivo enzyme reaction rates (i.e., metabolic flux) in microorganisms. Mining the relationship between environmental and genetic factors and metabolic fluxes hidden in existing fluxomic data will lead to predictive models that can significantly accelerate flux quantification. In this paper, we present a web-based platform MFlux (http://mflux.org) that predicts the bacterial central metabolism via machine learning, leveraging data from approximately 100 13C-MFA papers on heterotrophic bacterial metabolisms. Three machine learning methods, namely Support Vector Machine (SVM), k-Nearest Neighbors (k-NN), and Decision Tree, were employed to study the sophisticated relationship between influential factors and metabolic fluxes. We performed a grid search of the best parameter set for each algorithm and verified their performance through 10-fold cross validations. SVM yields the highest accuracy among all three algorithms. Further, we employed quadratic programming to adjust flux profiles to satisfy stoichiometric constraints. Multiple case studies have shown that MFlux can reasonably predict fluxomes as a function of bacterial species, substrate types, growth rate, oxygen conditions, and cultivation methods. Due to the interest of studying model organism under particular carbon sources, bias of fluxome in the dataset may limit the applicability of machine learning models. This problem can be resolved after more papers on 13C-MFA are published for non-model species. PMID:27092947

A Self-Aware Machine Platform in Manufacturing Shop Floor Utilizing MTConnect Data

DTIC Science & Technology

2014-10-02

condition monitoring , and equipment time to failure prediction in manufacturing 1 ANNUAL CONFERENCE OF THE PROGNOSTICS AND HEALTH MANAGEMENT SOCIETY 2014 589...Component Level Health Monitoring and Prediction One of the characteristics of a self-aware machine is to be able to detect its components...the annual conference of the prognostics and health management society. Filzmoser, P., Garrett, R. G., & Reimann, C . (2005). Mul- tivariate outlier

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

Trujillo, Angelina Michelle

Strategy, Planning, Acquiring- very large scale computing platforms come and go and planning for immensely scalable machines often precedes actual procurement by 3 years. Procurement can be another year or more. Integration- After Acquisition, machines must be integrated into the computing environments at LANL. Connection to scalable storage via large scale storage networking, assuring correct and secure operations. Management and Utilization – Ongoing operations, maintenance, and trouble shooting of the hardware and systems software at massive scale is required.

Comparison of fMRI data analysis by SPM99 on different operating systems.

PubMed

Shinagawa, Hideo; Honda, Ei-ichi; Ono, Takashi; Kurabayashi, Tohru; Ohyama, Kimie

2004-09-01

The hardware chosen for fMRI data analysis may depend on the platform already present in the laboratory or the supporting software. In this study, we ran SPM99 software on multiple platforms to examine whether we could analyze fMRI data by SPM99, and to compare their differences and limitations in processing fMRI data, which can be attributed to hardware capabilities. Six normal right-handed volunteers participated in a study of hand-grasping to obtain fMRI data. Each subject performed a run that consisted of 98 images. The run was measured using a gradient echo-type echo planar imaging sequence on a 1.5T apparatus with a head coil. We used several personal computer (PC), Unix and Linux machines to analyze the fMRI data. There were no differences in the results obtained on several PC, Unix and Linux machines. The only limitations in processing large amounts of the fMRI data were found using PC machines. This suggests that the results obtained with different machines were not affected by differences in hardware components, such as the CPU, memory and hard drive. Rather, it is likely that the limitations in analyzing a huge amount of the fMRI data were due to differences in the operating system (OS).

Volumetric visualization algorithm development for an FPGA-based custom computing machine

NASA Astrophysics Data System (ADS)

Sallinen, Sami J.; Alakuijala, Jyrki; Helminen, Hannu; Laitinen, Joakim

1998-05-01

Rendering volumetric medical images is a burdensome computational task for contemporary computers due to the large size of the data sets. Custom designed reconfigurable hardware could considerably speed up volume visualization if an algorithm suitable for the platform is used. We present an algorithm and speedup techniques for visualizing volumetric medical CT and MR images with a custom-computing machine based on a Field Programmable Gate Array (FPGA). We also present simulated performance results of the proposed algorithm calculated with a software implementation running on a desktop PC. Our algorithm is capable of generating perspective projection renderings of single and multiple isosurfaces with transparency, simulated X-ray images, and Maximum Intensity Projections (MIP). Although more speedup techniques exist for parallel projection than for perspective projection, we have constrained ourselves to perspective viewing, because of its importance in the field of radiotherapy. The algorithm we have developed is based on ray casting, and the rendering is sped up by three different methods: shading speedup by gradient precalculation, a new generalized version of Ray-Acceleration by Distance Coding (RADC), and background ray elimination by speculative ray selection.

BLAISDELL SLOW SAND FILTER WASHING MACHINE. VIEW LOOKING SOUTH. THE ...

Library of Congress Historic Buildings Survey, Historic Engineering Record, Historic Landscapes Survey

BLAISDELL SLOW SAND FILTER WASHING MACHINE. VIEW LOOKING SOUTH. THE SUCTION (INTAKE) HOSE IS SEEN AT THE LEFT RESTING ON THE FILTER BED SURFACE; THE DISCHARGE HOSE IS AT THE RIGHT, RUNNING FROM THE BOTTOM OF THE CENTRAL VERTICAL AXLE TO THE CENTRIFUGAL PUMP. FROM THE PUMP WATER IS DISCHARGED THROUGH THE HORIZONTAL PIPE LOCATED UNDER THE EDGE OF PLATFORM DECK INTO THE WASTE-WATER TROUGH (NOT SEEN IN THIS VIEW). - Yuma Main Street Water Treatment Plant, Blaisdell Slow Sand Filter Washing Machine, Jones Street at foot of Main Street, Yuma, Yuma County, AZ

Analysis of parameters for technological equipment of parallel kinematics based on rods of variable length for processing accuracy assurance

NASA Astrophysics Data System (ADS)

Koltsov, A. G.; Shamutdinov, A. H.; Blokhin, D. A.; Krivonos, E. V.

2018-01-01

A new classification of parallel kinematics mechanisms on symmetry coefficient, being proportional to mechanism stiffness and accuracy of the processing product using the technological equipment under study, is proposed. A new version of the Stewart platform with a high symmetry coefficient is presented for analysis. The workspace of the mechanism under study is described, this space being a complex solid figure. The workspace end points are reached by the center of the mobile platform which moves in parallel related to the base plate. Parameters affecting the processing accuracy, namely the static and dynamic stiffness, natural vibration frequencies are determined. The capability assessment of the mechanism operation under various loads, taking into account resonance phenomena at different points of the workspace, was conducted. The study proved that stiffness and therefore, processing accuracy with the use of the above mentioned mechanisms are comparable with the stiffness and accuracy of medium-sized series-produced machines.

Proba-V Mission Exploitation Platform

NASA Astrophysics Data System (ADS)

Goor, Erwin; Dries, Jeroen

2017-04-01

VITO and partners developed the Proba-V Mission Exploitation Platform (MEP) as an end-to-end solution to drastically improve the exploitation of the Proba-V (a Copernicus contributing mission) EO-data archive (http://proba-v.vgt.vito.be/), the past mission SPOT-VEGETATION and derived vegetation parameters by researchers, service providers and end-users. The analysis of time series of data (+1PB) is addressed, as well as the large scale on-demand processing of near real-time data on a powerful and scalable processing environment. Furthermore data from the Copernicus Global Land Service is in scope of the platform. From November 2015 an operational Proba-V MEP environment, as an ESA operation service, is gradually deployed at the VITO data center with direct access to the complete data archive. Since autumn 2016 the platform is operational and yet several applications are released to the users, e.g. - A time series viewer, showing the evolution of Proba-V bands and derived vegetation parameters from the Copernicus Global Land Service for any area of interest. - Full-resolution viewing services for the complete data archive. - On-demand processing chains on a powerfull Hadoop/Spark backend e.g. for the calculation of N-daily composites. - Virtual Machines can be provided with access to the data archive and tools to work with this data, e.g. various toolboxes (GDAL, QGIS, GrassGIS, SNAP toolbox, …) and support for R and Python. This allows users to immediately work with the data without having to install tools or download data, but as well to design, debug and test applications on the platform. - A prototype of jupyter Notebooks is available with some examples worked out to show the potential of the data. Today the platform is used by several third party projects to perform R&D activities on the data, and to develop/host data analysis toolboxes. In parallel the platform is further improved and extended. From the MEP PROBA-V, access to Sentinel-2 and landsat data will be available as well soon. Users can make use of powerful Web based tools and can self-manage virtual machines to perform their work on the infrastructure at VITO with access to the complete data archive. To realise this, private cloud technology (openStack) is used and a distributed processing environment is built based on Hadoop. The Hadoop ecosystem offers a lot of technologies (Spark, Yarn, Accumulo, etc.) which we integrate with several open-source components (e.g. Geotrellis). The impact of this MEP on the user community will be high and will completely change the way of working with the data and hence open the large time series to a larger community of users. The presentation will address these benefits for the users and discuss on the technical challenges in implementing this MEP. Furthermore demonstrations will be done. Platform URL: https://proba-v-mep.esa.int/

The MiPACQ Clinical Question Answering System

PubMed Central

Cairns, Brian L.; Nielsen, Rodney D.; Masanz, James J.; Martin, James H.; Palmer, Martha S.; Ward, Wayne H.; Savova, Guergana K.

2011-01-01

The Multi-source Integrated Platform for Answering Clinical Questions (MiPACQ) is a QA pipeline that integrates a variety of information retrieval and natural language processing systems into an extensible question answering system. We present the system’s architecture and an evaluation of MiPACQ on a human-annotated evaluation dataset based on the Medpedia health and medical encyclopedia. Compared with our baseline information retrieval system, the MiPACQ rule-based system demonstrates 84% improvement in Precision at One and the MiPACQ machine-learning-based system demonstrates 134% improvement. Other performance metrics including mean reciprocal rank and area under the precision/recall curves also showed significant improvement, validating the effectiveness of the MiPACQ design and implementation. PMID:22195068

The MiPACQ clinical question answering system.

PubMed

Cairns, Brian L; Nielsen, Rodney D; Masanz, James J; Martin, James H; Palmer, Martha S; Ward, Wayne H; Savova, Guergana K

2011-01-01

The Multi-source Integrated Platform for Answering Clinical Questions (MiPACQ) is a QA pipeline that integrates a variety of information retrieval and natural language processing systems into an extensible question answering system. We present the system's architecture and an evaluation of MiPACQ on a human-annotated evaluation dataset based on the Medpedia health and medical encyclopedia. Compared with our baseline information retrieval system, the MiPACQ rule-based system demonstrates 84% improvement in Precision at One and the MiPACQ machine-learning-based system demonstrates 134% improvement. Other performance metrics including mean reciprocal rank and area under the precision/recall curves also showed significant improvement, validating the effectiveness of the MiPACQ design and implementation.

Research on target tracking in coal mine based on optical flow method

NASA Astrophysics Data System (ADS)

Xue, Hongye; Xiao, Qingwei

2015-03-01

To recognize, track and count the bolting machine in coal mine video images, a real-time target tracking method based on the Lucas-Kanade sparse optical flow is proposed in this paper. In the method, we judge whether the moving target deviate from its trajectory, predicate and correct the position of the moving target. The method solves the problem of failure to track the target or lose the target because of the weak light, uneven illumination and blocking. Using the VC++ platform and Opencv lib we complete the recognition and tracking. The validity of the method is verified by the result of the experiment.

A new class of high-G and long-duration shock testing machines

NASA Astrophysics Data System (ADS)

Rastegar, Jahangir

2018-03-01

Currently available methods and systems for testing components for survival and performance under shock loading suffer from several shortcomings for use to simulate high-G acceleration events with relatively long duration. Such events include most munitions firing and target impact, vehicular accidents, drops from relatively high heights, air drops, impact between machine components, and other similar events. In this paper, a new class of shock testing machines are presented that can be used to subject components to be tested to high-G acceleration pulses of prescribed amplitudes and relatively long durations. The machines provide for highly repeatable testing of components. The components are mounted on an open platform for ease of instrumentation and video recording of their dynamic behavior during shock loading tests.

Genomics Virtual Laboratory: A Practical Bioinformatics Workbench for the Cloud

PubMed Central

Afgan, Enis; Sloggett, Clare; Goonasekera, Nuwan; Makunin, Igor; Benson, Derek; Crowe, Mark; Gladman, Simon; Kowsar, Yousef; Pheasant, Michael; Horst, Ron; Lonie, Andrew

2015-01-01

Background Analyzing high throughput genomics data is a complex and compute intensive task, generally requiring numerous software tools and large reference data sets, tied together in successive stages of data transformation and visualisation. A computational platform enabling best practice genomics analysis ideally meets a number of requirements, including: a wide range of analysis and visualisation tools, closely linked to large user and reference data sets; workflow platform(s) enabling accessible, reproducible, portable analyses, through a flexible set of interfaces; highly available, scalable computational resources; and flexibility and versatility in the use of these resources to meet demands and expertise of a variety of users. Access to an appropriate computational platform can be a significant barrier to researchers, as establishing such a platform requires a large upfront investment in hardware, experience, and expertise. Results We designed and implemented the Genomics Virtual Laboratory (GVL) as a middleware layer of machine images, cloud management tools, and online services that enable researchers to build arbitrarily sized compute clusters on demand, pre-populated with fully configured bioinformatics tools, reference datasets and workflow and visualisation options. The platform is flexible in that users can conduct analyses through web-based (Galaxy, RStudio, IPython Notebook) or command-line interfaces, and add/remove compute nodes and data resources as required. Best-practice tutorials and protocols provide a path from introductory training to practice. The GVL is available on the OpenStack-based Australian Research Cloud (http://nectar.org.au) and the Amazon Web Services cloud. The principles, implementation and build process are designed to be cloud-agnostic. Conclusions This paper provides a blueprint for the design and implementation of a cloud-based Genomics Virtual Laboratory. We discuss scope, design considerations and technical and logistical constraints, and explore the value added to the research community through the suite of services and resources provided by our implementation. PMID:26501966

Genomics Virtual Laboratory: A Practical Bioinformatics Workbench for the Cloud.

PubMed

Afgan, Enis; Sloggett, Clare; Goonasekera, Nuwan; Makunin, Igor; Benson, Derek; Crowe, Mark; Gladman, Simon; Kowsar, Yousef; Pheasant, Michael; Horst, Ron; Lonie, Andrew

2015-01-01

Analyzing high throughput genomics data is a complex and compute intensive task, generally requiring numerous software tools and large reference data sets, tied together in successive stages of data transformation and visualisation. A computational platform enabling best practice genomics analysis ideally meets a number of requirements, including: a wide range of analysis and visualisation tools, closely linked to large user and reference data sets; workflow platform(s) enabling accessible, reproducible, portable analyses, through a flexible set of interfaces; highly available, scalable computational resources; and flexibility and versatility in the use of these resources to meet demands and expertise of a variety of users. Access to an appropriate computational platform can be a significant barrier to researchers, as establishing such a platform requires a large upfront investment in hardware, experience, and expertise. We designed and implemented the Genomics Virtual Laboratory (GVL) as a middleware layer of machine images, cloud management tools, and online services that enable researchers to build arbitrarily sized compute clusters on demand, pre-populated with fully configured bioinformatics tools, reference datasets and workflow and visualisation options. The platform is flexible in that users can conduct analyses through web-based (Galaxy, RStudio, IPython Notebook) or command-line interfaces, and add/remove compute nodes and data resources as required. Best-practice tutorials and protocols provide a path from introductory training to practice. The GVL is available on the OpenStack-based Australian Research Cloud (http://nectar.org.au) and the Amazon Web Services cloud. The principles, implementation and build process are designed to be cloud-agnostic. This paper provides a blueprint for the design and implementation of a cloud-based Genomics Virtual Laboratory. We discuss scope, design considerations and technical and logistical constraints, and explore the value added to the research community through the suite of services and resources provided by our implementation.

A Low-Cost Audio Prescription Labeling System Using RFID for Thai Visually-Impaired People.

PubMed

Lertwiriyaprapa, Titipong; Fakkheow, Pirapong

2015-01-01

This research aims to develop a low-cost audio prescription labeling (APL) system for visually-impaired people by using the RFID system. The developed APL system includes the APL machine and APL software. The APL machine is for visually-impaired people while APL software allows caregivers to record all important information into the APL machine. The main objective of the development of the APL machine is to reduce costs and size by designing all of the electronic devices to fit into one print circuit board. Also, it is designed so that it is easy to use and can become an electronic aid for daily living. The developed APL software is based on Java and MySQL, both of which can operate on various operating platforms and are easy to develop as commercial software. The developed APL system was first evaluated by 5 experts. The APL system was also evaluated by 50 actual visually-impaired people (30 elders and 20 blind individuals) and 20 caregivers, pharmacists and nurses. After using the APL system, evaluations were carried out, and it can be concluded from the evaluation results that this proposed APL system can be effectively used for helping visually-impaired people in terms of self-medication.

Finite element analysis of chip formation usingale method

NASA Astrophysics Data System (ADS)

Jayaprakash, V.

2017-05-01

In recent times, many studies made in FEM on plain isotropic metal plate formulation. The stress analysis plays the significant role in the stability of structural safety and system. The stress and distortion estimation is very helpful for designing and manufacturing product well. Usually the residual stress and plastic strain determine the fatigue life of structure, it also plays the significant role in designing and choosing material. When the load magnitude increases the crack starts to form, decreasing the work load and the residual stress reduces the damage of the metal. The manufacturing process is a key parameter in process and forming the part of any system. However, machining operation involves complex thing like hot development, material property and other estimates based on transition of the plastic strain and residual stress. The reduction of residual stress plays the complexity role in the finite element study. This paper deals with the manufacturing process with less residual stress and strain. The results shows that, by applying the ALE method in machining we can reduce the load on the work piece hence the life type of the work piece can be increased. We also investigate the cutting tool wear and there efficiency since it is a essential machine member in fabrication technology. ABAQUS platform used to solve the machining operation

Symmetrical compression distance for arrhythmia discrimination in cloud-based big-data services.

PubMed

Lillo-Castellano, J M; Mora-Jiménez, I; Santiago-Mozos, R; Chavarría-Asso, F; Cano-González, A; García-Alberola, A; Rojo-Álvarez, J L

2015-07-01

The current development of cloud computing is completely changing the paradigm of data knowledge extraction in huge databases. An example of this technology in the cardiac arrhythmia field is the SCOOP platform, a national-level scientific cloud-based big data service for implantable cardioverter defibrillators. In this scenario, we here propose a new methodology for automatic classification of intracardiac electrograms (EGMs) in a cloud computing system, designed for minimal signal preprocessing. A new compression-based similarity measure (CSM) is created for low computational burden, so-called weighted fast compression distance, which provides better performance when compared with other CSMs in the literature. Using simple machine learning techniques, a set of 6848 EGMs extracted from SCOOP platform were classified into seven cardiac arrhythmia classes and one noise class, reaching near to 90% accuracy when previous patient arrhythmia information was available and 63% otherwise, hence overcoming in all cases the classification provided by the majority class. Results show that this methodology can be used as a high-quality service of cloud computing, providing support to physicians for improving the knowledge on patient diagnosis.

Extended Kalman Doppler tracking and model determination for multi-sensor short-range radar

NASA Astrophysics Data System (ADS)

Mittermaier, Thomas J.; Siart, Uwe; Eibert, Thomas F.; Bonerz, Stefan

2016-09-01

A tracking solution for collision avoidance in industrial machine tools based on short-range millimeter-wave radar Doppler observations is presented. At the core of the tracking algorithm there is an Extended Kalman Filter (EKF) that provides dynamic estimation and localization in real-time. The underlying sensor platform consists of several homodyne continuous wave (CW) radar modules. Based on In-phase-Quadrature (IQ) processing and down-conversion, they provide only Doppler shift information about the observed target. Localization with Doppler shift estimates is a nonlinear problem that needs to be linearized before the linear KF can be applied. The accuracy of state estimation depends highly on the introduced linearization errors, the initialization and the models that represent the true physics as well as the stochastic properties. The important issue of filter consistency is addressed and an initialization procedure based on data fitting and maximum likelihood estimation is suggested. Models for both, measurement and process noise are developed. Tracking results from typical three-dimensional courses of movement at short distances in front of a multi-sensor radar platform are presented.

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

Scalable and reusable emulator for evaluating the performance of SS7 networks

NASA Astrophysics Data System (ADS)

Lazar, Aurel A.; Tseng, Kent H.; Lim, Koon Seng; Choe, Winston

1994-04-01

A scalable and reusable emulator was designed and implemented for studying the behavior of SS7 networks. The emulator design was largely based on public domain software. It was developed on top of an environment supported by PVM, the Parallel Virtual Machine, and managed by OSIMIS-the OSI Management Information Service platform. The emulator runs on top of a commercially available ATM LAN interconnecting engineering workstations. As a case study for evaluating the emulator, the behavior of the Singapore National SS7 Network under fault and unbalanced loading conditions was investigated.

Research on self-calibration biaxial autocollimator based on ZYNQ

NASA Astrophysics Data System (ADS)

Guo, Pan; Liu, Bingguo; Liu, Guodong; Zhong, Yao; Lu, Binghui

2018-01-01

Autocollimators are mainly based on computers or the electronic devices that can be connected to the internet, and its precision, measurement range and resolution are all defective, and external displays are needed to display images in real time. What's more, there is no real-time calibration for autocollimator in the market. In this paper, we propose a biaxial autocollimator based on the ZYNQ embedded platform to solve the above problems. Firstly, the traditional optical system is improved and a light path is added for real-time calibration. Then, in order to improve measurement speed, the embedded platform based on ZYNQ that combines Linux operating system with autocollimator is designed. In this part, image acquisition, image processing, image display and the man-machine interaction interface based on Qt are achieved. Finally, the system realizes two-dimensional small angle measurement. Experimental results showed that the proposed method can improve the angle measurement accuracy. The standard deviation of the close distance (1.5m) is 0.15" in horizontal direction of image and 0.24"in vertical direction, the repeatability of measurement of the long distance (10m) is improved by 0.12 in horizontal direction of image and 0.3 in vertical direction.

Peer-to-peer architectures for exascale computing : LDRD final report.

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

Vorobeychik, Yevgeniy; Mayo, Jackson R.; Minnich, Ronald G.

2010-09-01

The goal of this research was to investigate the potential for employing dynamic, decentralized software architectures to achieve reliability in future high-performance computing platforms. These architectures, inspired by peer-to-peer networks such as botnets that already scale to millions of unreliable nodes, hold promise for enabling scientific applications to run usefully on next-generation exascale platforms ({approx} 10{sup 18} operations per second). Traditional parallel programming techniques suffer rapid deterioration of performance scaling with growing platform size, as the work of coping with increasingly frequent failures dominates over useful computation. Our studies suggest that new architectures, in which failures are treated as ubiquitousmore » and their effects are considered as simply another controllable source of error in a scientific computation, can remove such obstacles to exascale computing for certain applications. We have developed a simulation framework, as well as a preliminary implementation in a large-scale emulation environment, for exploration of these 'fault-oblivious computing' approaches. High-performance computing (HPC) faces a fundamental problem of increasing total component failure rates due to increasing system sizes, which threaten to degrade system reliability to an unusable level by the time the exascale range is reached ({approx} 10{sup 18} operations per second, requiring of order millions of processors). As computer scientists seek a way to scale system software for next-generation exascale machines, it is worth considering peer-to-peer (P2P) architectures that are already capable of supporting 10{sup 6}-10{sup 7} unreliable nodes. Exascale platforms will require a different way of looking at systems and software because the machine will likely not be available in its entirety for a meaningful execution time. Realistic estimates of failure rates range from a few times per day to more than once per hour for these platforms. P2P architectures give us a starting point for crafting applications and system software for exascale. In the context of the Internet, P2P applications (e.g., file sharing, botnets) have already solved this problem for 10{sup 6}-10{sup 7} nodes. Usually based on a fractal distributed hash table structure, these systems have proven robust in practice to constant and unpredictable outages, failures, and even subversion. For example, a recent estimate of botnet turnover (i.e., the number of machines leaving and joining) is about 11% per week. Nonetheless, P2P networks remain effective despite these failures: The Conficker botnet has grown to {approx} 5 x 10{sup 6} peers. Unlike today's system software and applications, those for next-generation exascale machines cannot assume a static structure and, to be scalable over millions of nodes, must be decentralized. P2P architectures achieve both, and provide a promising model for 'fault-oblivious computing'. This project aimed to study the dynamics of P2P networks in the context of a design for exascale systems and applications. Having no single point of failure, the most successful P2P architectures are adaptive and self-organizing. While there has been some previous work applying P2P to message passing, little attention has been previously paid to the tightly coupled exascale domain. Typically, the per-node footprint of P2P systems is small, making them ideal for HPC use. The implementation on each peer node cooperates en masse to 'heal' disruptions rather than relying on a controlling 'master' node. Understanding this cooperative behavior from a complex systems viewpoint is essential to predicting useful environments for the inextricably unreliable exascale platforms of the future. We sought to obtain theoretical insight into the stability and large-scale behavior of candidate architectures, and to work toward leveraging Sandia's Emulytics platform to test promising candidates in a realistic (ultimately {ge} 10{sup 7} nodes) setting. Our primary example applications are drawn from linear algebra: a Jacobi relaxation solver for the heat equation, and the closely related technique of value iteration in optimization. We aimed to apply P2P concepts in designing implementations capable of surviving an unreliable machine of 10{sup 6} nodes.« less

Technology for robotic surface inspection in space

NASA Technical Reports Server (NTRS)

Volpe, Richard; Balaram, J.

1994-01-01

This paper presents on-going research in robotic inspection of space platforms. Three main areas of investigation are discussed: machine vision inspection techniques, an integrated sensor end-effector, and an orbital environment laboratory simulation. Machine vision inspection utilizes automatic comparison of new and reference images to detect on-orbit induced damage such as micrometeorite impacts. The cameras and lighting used for this inspection are housed in a multisensor end-effector, which also contains a suite of sensors for detection of temperature, gas leaks, proximity, and forces. To fully test all of these sensors, a realistic space platform mock-up has been created, complete with visual, temperature, and gas anomalies. Further, changing orbital lighting conditions are effectively mimicked by a robotic solar simulator. In the paper, each of these technology components will be discussed, and experimental results are provided.

Advances in industrial biopharmaceutical batch process monitoring: Machine-learning methods for small data problems.

PubMed

Tulsyan, Aditya; Garvin, Christopher; Ündey, Cenk

2018-04-06

Biopharmaceutical manufacturing comprises of multiple distinct processing steps that require effective and efficient monitoring of many variables simultaneously in real-time. The state-of-the-art real-time multivariate statistical batch process monitoring (BPM) platforms have been in use in recent years to ensure comprehensive monitoring is in place as a complementary tool for continued process verification to detect weak signals. This article addresses a longstanding, industry-wide problem in BPM, referred to as the "Low-N" problem, wherein a product has a limited production history. The current best industrial practice to address the Low-N problem is to switch from a multivariate to a univariate BPM, until sufficient product history is available to build and deploy a multivariate BPM platform. Every batch run without a robust multivariate BPM platform poses risk of not detecting potential weak signals developing in the process that might have an impact on process and product performance. In this article, we propose an approach to solve the Low-N problem by generating an arbitrarily large number of in silico batches through a combination of hardware exploitation and machine-learning methods. To the best of authors' knowledge, this is the first article to provide a solution to the Low-N problem in biopharmaceutical manufacturing using machine-learning methods. Several industrial case studies from bulk drug substance manufacturing are presented to demonstrate the efficacy of the proposed approach for BPM under various Low-N scenarios. © 2018 Wiley Periodicals, Inc.

An in-mold packaging process for plastic fluidic devices.

PubMed

Yoo, Y E; Lee, K H; Je, T J; Choi, D S; Kim, S K

2011-01-01

Micro or nanofluidic devices have many channel shapes to deliver chemical solutions, body fluids or any fluids. The channels in these devices should be covered to prevent the fluids from overflowing or leaking. A typical method to fabricate an enclosed channel is to bond or weld a cover plate to a channel plate. This solid-to-solid bonding process, however, takes a considerable amount of time for mass production. In this study, a new process for molding a cover layer that can enclose open micro or nanochannels without solid-to-solid bonding is proposed and its feasibility is estimated. First, based on the design of a model microchannel, a brass microchannel master core was machined and a plastic microchannel platform was injection-molded. Using this molded platform, a series of experiments was performed for four process or mold design parameters. Some feasible conditions were successfully found to enclosed channels without filling the microchannels for the injection molding of a cover layer over the plastic microchannel platform. In addition, the bond strength and seal performance were estimated in a comparison with those done by conventional bonding or welding processes.

Fast instantaneous center of rotation estimation algorithm for a skied-steered robot

NASA Astrophysics Data System (ADS)

Kniaz, V. V.

2015-05-01

Skid-steered robots are widely used as mobile platforms for machine vision systems. However it is hard to achieve a stable motion of such robots along desired trajectory due to an unpredictable wheel slip. It is possible to compensate the unpredictable wheel slip and stabilize the motion of the robot using visual odometry. This paper presents a fast optical flow based algorithm for estimation of instantaneous center of rotation, angular and longitudinal speed of the robot. The proposed algorithm is based on Horn-Schunck variational optical flow estimation method. The instantaneous center of rotation and motion of the robot is estimated by back projection of optical flow field to the ground surface. The developed algorithm was tested using skid-steered mobile robot. The robot is based on a mobile platform that includes two pairs of differential driven motors and a motor controller. Monocular visual odometry system consisting of a singleboard computer and a low cost webcam is mounted on the mobile platform. A state-space model of the robot was derived using standard black-box system identification. The input (commands) and the output (motion) were recorded using a dedicated external motion capture system. The obtained model was used to control the robot without visual odometry data. The paper is concluded with the algorithm quality estimation by comparison of the trajectories estimated by the algorithm with the data from motion capture system.

Observation of development of breast cancer cell lines in real time by fluorescence microscopy under simulated microgravity

NASA Astrophysics Data System (ADS)

Lavan, David; Valdivia-Silva, Julio E.; Sanabria, Gabriela; Orihuela, Diego; Suarez, Juan; Quispe, Marco; Chuchon, Mariano; Martin, David; Maroto, Marcos; Egea, Javier

2016-07-01

This project consist in the implementation of a fluorescence microscope for the in real time monitoring of biological labeled samples by several fluorophores in microgravity conditions keeping the temperature, humidity, and (CO)2 controlled by an electronic platform. The system (fluorescence microscope and incubator) is integrated to a microgravity simulator machine which was presented on the "30th Annual American Society for Gravitation and Space Research Meeting" October 2014 in Pasadena, CA, USA. Currently, we have the microgravity machine biologically validated by genetic expression studies in pupal stage of Drosophila melanogaster. The fluorescence microscope has a platform designed to hold a culture flask, and a fluorescence camera (Leica DFC3000 G) connected to an optical system (Fluorescence Light source Leica EL6000, optic fiber, fiber adapter, and fluorescence filter) in order to take images in real time. The mechanical system of the fluorescence microsc ope is designed to allow the displacement of the fluorescence camera through a parallel plane to the culture flask's plane and also the movement of the platform through a perpendicular axis to the culture flask in order to focus the samples to the optical system. The mechanical system is propelled by four DC moto-reductors with encoder (A-max 26 Maxon motor, GP 32S screw and MR encoder) that generate displacements in the order of micrometers. The angular position control of the DC motoreductor's shaft of all the DC moto-reductors is done by PWM signals based on the interpretation of the signals provided by the encoders during the movement. The system is remotely operated by a graphic interface installed on a personal computer or any mobile device (smartphone, laptop or tablet) by using the internet. Acknowledgments: Grant of INNOVATE PERU (Formerly FINCYT)

Online chemical modeling environment (OCHEM): web platform for data storage, model development and publishing of chemical information

NASA Astrophysics Data System (ADS)

Sushko, Iurii; Novotarskyi, Sergii; Körner, Robert; Pandey, Anil Kumar; Rupp, Matthias; Teetz, Wolfram; Brandmaier, Stefan; Abdelaziz, Ahmed; Prokopenko, Volodymyr V.; Tanchuk, Vsevolod Y.; Todeschini, Roberto; Varnek, Alexandre; Marcou, Gilles; Ertl, Peter; Potemkin, Vladimir; Grishina, Maria; Gasteiger, Johann; Schwab, Christof; Baskin, Igor I.; Palyulin, Vladimir A.; Radchenko, Eugene V.; Welsh, William J.; Kholodovych, Vladyslav; Chekmarev, Dmitriy; Cherkasov, Artem; Aires-de-Sousa, Joao; Zhang, Qing-You; Bender, Andreas; Nigsch, Florian; Patiny, Luc; Williams, Antony; Tkachenko, Valery; Tetko, Igor V.

2011-06-01

The Online Chemical Modeling Environment is a web-based platform that aims to automate and simplify the typical steps required for QSAR modeling. The platform consists of two major subsystems: the database of experimental measurements and the modeling framework. A user-contributed database contains a set of tools for easy input, search and modification of thousands of records. The OCHEM database is based on the wiki principle and focuses primarily on the quality and verifiability of the data. The database is tightly integrated with the modeling framework, which supports all the steps required to create a predictive model: data search, calculation and selection of a vast variety of molecular descriptors, application of machine learning methods, validation, analysis of the model and assessment of the applicability domain. As compared to other similar systems, OCHEM is not intended to re-implement the existing tools or models but rather to invite the original authors to contribute their results, make them publicly available, share them with other users and to become members of the growing research community. Our intention is to make OCHEM a widely used platform to perform the QSPR/QSAR studies online and share it with other users on the Web. The ultimate goal of OCHEM is collecting all possible chemoinformatics tools within one simple, reliable and user-friendly resource. The OCHEM is free for web users and it is available online at http://www.ochem.eu.

Time and Space Partition Platform for Safe and Secure Flight Software

NASA Astrophysics Data System (ADS)

Esquinas, Angel; Zamorano, Juan; de la Puente, Juan A.; Masmano, Miguel; Crespo, Alfons

2012-08-01

There are a number of research and development activities that are exploring Time and Space Partition (TSP) to implement safe and secure flight software. This approach allows to execute different real-time applications with different levels of criticality in the same computer board. In order to do that, flight applications must be isolated from each other in the temporal and spatial domains. This paper presents the first results of a partitioning platform based on the Open Ravenscar Kernel (ORK+) and the XtratuM hypervisor. ORK+ is a small, reliable realtime kernel supporting the Ada Ravenscar Computational model that is central to the ASSERT development process. XtratuM supports multiple virtual machines, i.e. partitions, on a single computer and is being used in the Integrated Modular Avionics for Space study. ORK+ executes in an XtratuM partition enabling Ada applications to share the computer board with other applications.

Automatic fall monitoring: a review.

PubMed

Pannurat, Natthapon; Thiemjarus, Surapa; Nantajeewarawat, Ekawit

2014-07-18

Falls and fall-related injuries are major incidents, especially for elderly people, which often mark the onset of major deterioration of health. More than one-third of home-dwelling people aged 65 or above and two-thirds of those in residential care fall once or more each year. Reliable fall detection, as well as prevention, is an important research topic for monitoring elderly living alone in residential or hospital units. The aim of this study is to review the existing fall detection systems and some of the key research challenges faced by the research community in this field. We categorize the existing platforms into two groups: wearable and ambient devices; the classification methods are divided into rule-based and machine learning techniques. The relative merit and potential drawbacks are discussed, and we also outline some of the outstanding research challenges that emerging new platforms need to address.

Automatic Fall Monitoring: A Review

PubMed Central

Pannurat, Natthapon; Thiemjarus, Surapa; Nantajeewarawat, Ekawit

2014-01-01

Falls and fall-related injuries are major incidents, especially for elderly people, which often mark the onset of major deterioration of health. More than one-third of home-dwelling people aged 65 or above and two-thirds of those in residential care fall once or more each year. Reliable fall detection, as well as prevention, is an important research topic for monitoring elderly living alone in residential or hospital units. The aim of this study is to review the existing fall detection systems and some of the key research challenges faced by the research community in this field. We categorize the existing platforms into two groups: wearable and ambient devices; the classification methods are divided into rule-based and machine learning techniques. The relative merit and potential drawbacks are discussed, and we also outline some of the outstanding research challenges that emerging new platforms need to address. PMID:25046016

The Machine / Job Features Mechanism

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

Alef, M.; Cass, T.; Keijser, J. J.

Within the HEPiX virtualization group and the Worldwide LHC Computing Grid’s Machine/Job Features Task Force, a mechanism has been developed which provides access to detailed information about the current host and the current job to the job itself. This allows user payloads to access meta information, independent of the current batch system or virtual machine model. The information can be accessed either locally via the filesystem on a worker node, or remotely via HTTP(S) from a webserver. This paper describes the final version of the specification from 2016 which was published as an HEP Software Foundation technical note, and themore » design of the implementations of this version for batch and virtual machine platforms. We discuss early experiences with these implementations and how they can be exploited by experiment frameworks.« less

The machine/job features mechanism

NASA Astrophysics Data System (ADS)

Alef, M.; Cass, T.; Keijser, J. J.; McNab, A.; Roiser, S.; Schwickerath, U.; Sfiligoi, I.

2017-10-01

Within the HEPiX virtualization group and the Worldwide LHC Computing Grid’s Machine/Job Features Task Force, a mechanism has been developed which provides access to detailed information about the current host and the current job to the job itself. This allows user payloads to access meta information, independent of the current batch system or virtual machine model. The information can be accessed either locally via the filesystem on a worker node, or remotely via HTTP(S) from a webserver. This paper describes the final version of the specification from 2016 which was published as an HEP Software Foundation technical note, and the design of the implementations of this version for batch and virtual machine platforms. We discuss early experiences with these implementations and how they can be exploited by experiment frameworks.

Computational Sensing of Staphylococcus aureus on Contact Lenses Using 3D Imaging of Curved Surfaces and Machine Learning.

PubMed

Veli, Muhammed; Ozcan, Aydogan

2018-03-27

We present a cost-effective and portable platform based on contact lenses for noninvasively detecting Staphylococcus aureus, which is part of the human ocular microbiome and resides on the cornea and conjunctiva. Using S. aureus-specific antibodies and a surface chemistry protocol that is compatible with human tears, contact lenses are designed to specifically capture S. aureus. After the bacteria capture on the lens and right before its imaging, the captured bacteria are tagged with surface-functionalized polystyrene microparticles. These microbeads provide sufficient signal-to-noise ratio for the quantification of the captured bacteria on the contact lens, without any fluorescent labels, by 3D imaging of the curved surface of each lens using only one hologram taken with a lens-free on-chip microscope. After the 3D surface of the contact lens is computationally reconstructed using rotational field transformations and holographic digital focusing, a machine learning algorithm is employed to automatically count the number of beads on the lens surface, revealing the count of the captured bacteria. To demonstrate its proof-of-concept, we created a field-portable and cost-effective holographic microscope, which weighs 77 g, controlled by a laptop. Using daily contact lenses that are spiked with bacteria, we demonstrated that this computational sensing platform provides a detection limit of ∼16 bacteria/μL. This contact-lens-based wearable sensor can be broadly applicable to detect various bacteria, viruses, and analytes in tears using a cost-effective and portable computational imager that might be used even at home by consumers.

Rapid insights from remote sensing in the geosciences

NASA Astrophysics Data System (ADS)

Plaza, Antonio

2015-03-01

The growing availability of capacity computing for atomistic materials modeling has encouraged the use of high-accuracy computationally intensive interatomic potentials, such as SNAP. These potentials also happen to scale well on petascale computing platforms. SNAP has a very general form and uses machine-learning techniques to reproduce the energies, forces, and stress tensors of a large set of small configurations of atoms, which are obtained using high-accuracy quantum electronic structure (QM) calculations. The local environment of each atom is characterized by a set of bispectrum components of the local neighbor density projected on to a basis of hyperspherical harmonics in four dimensions. The computational cost per atom is much greater than that of simpler potentials such as Lennard-Jones or EAM, while the communication cost remains modest. We discuss a variety of strategies for implementing SNAP in the LAMMPS molecular dynamics package. We present scaling results obtained running SNAP on three different classes of machine: a conventional Intel Xeon CPU cluster; the Titan GPU-based system; and the combined Sequoia and Vulcan BlueGene/Q. The growing availability of capacity computing for atomistic materials modeling has encouraged the use of high-accuracy computationally intensive interatomic potentials, such as SNAP. These potentials also happen to scale well on petascale computing platforms. SNAP has a very general form and uses machine-learning techniques to reproduce the energies, forces, and stress tensors of a large set of small configurations of atoms, which are obtained using high-accuracy quantum electronic structure (QM) calculations. The local environment of each atom is characterized by a set of bispectrum components of the local neighbor density projected on to a basis of hyperspherical harmonics in four dimensions. The computational cost per atom is much greater than that of simpler potentials such as Lennard-Jones or EAM, while the communication cost remains modest. We discuss a variety of strategies for implementing SNAP in the LAMMPS molecular dynamics package. We present scaling results obtained running SNAP on three different classes of machine: a conventional Intel Xeon CPU cluster; the Titan GPU-based system; and the combined Sequoia and Vulcan BlueGene/Q. Sandia National Laboratories is a multi-program laboratory managed and operated by Sandia Corporation, a wholly owned subsidiary of Lockheed Martin Corp., for the U.S. Dept. of Energy's National Nuclear Security Admin. under Contract DE-AC04-94AL85000.

Machine metaphors and ethics in synthetic biology.

PubMed

Boldt, Joachim

2018-06-04

The extent to which machine metaphors are used in synthetic biology is striking. These metaphors contain a specific perspective on organisms as well as on scientific and technological progress. Expressions such as "genetically engineered machine", "genetic circuit", and "platform organism", taken from the realms of electronic engineering, car manufacturing, and information technology, highlight specific aspects of the functioning of living beings while at the same time hiding others, such as evolutionary change and interdependencies in ecosystems. Since these latter aspects are relevant for, for example, risk evaluation of uncontained uses of synthetic organisms, it is ethically imperative to resist the thrust of machine metaphors in this respect. In addition, from the perspective of the machine metaphor viewing an entity as a moral agent or patient becomes dubious. If one were to regard living beings, including humans, as machines, it becomes difficult to justify ascriptions of moral status. Finally, the machine metaphor reinforces beliefs in the potential of synthetic biology to play a decisive role in solving societal problems, and downplays the role of alternative technological, and social and political measures.

Complete scanpaths analysis toolbox.

PubMed

Augustyniak, Piotr; Mikrut, Zbigniew

2006-01-01

This paper presents a complete open software environment for control, data processing and assessment of visual experiments. Visual experiments are widely used in research on human perception physiology and the results are applicable to various visual information-based man-machine interfacing, human-emulated automatic visual systems or scanpath-based learning of perceptual habits. The toolbox is designed for Matlab platform and supports infra-red reflection-based eyetracker in calibration and scanpath analysis modes. Toolbox procedures are organized in three layers: the lower one, communicating with the eyetracker output file, the middle detecting scanpath events on a physiological background and the one upper consisting of experiment schedule scripts, statistics and summaries. Several examples of visual experiments carried out with use of the presented toolbox complete the paper.

An Autonomous Gps-Denied Unmanned Vehicle Platform Based on Binocular Vision for Planetary Exploration

NASA Astrophysics Data System (ADS)

Qin, M.; Wan, X.; Shao, Y. Y.; Li, S. Y.

2018-04-01

Vision-based navigation has become an attractive solution for autonomous navigation for planetary exploration. This paper presents our work of designing and building an autonomous vision-based GPS-denied unmanned vehicle and developing an ARFM (Adaptive Robust Feature Matching) based VO (Visual Odometry) software for its autonomous navigation. The hardware system is mainly composed of binocular stereo camera, a pan-and tilt, a master machine, a tracked chassis. And the ARFM-based VO software system contains four modules: camera calibration, ARFM-based 3D reconstruction, position and attitude calculation, BA (Bundle Adjustment) modules. Two VO experiments were carried out using both outdoor images from open dataset and indoor images captured by our vehicle, the results demonstrate that our vision-based unmanned vehicle is able to achieve autonomous localization and has the potential for future planetary exploration.

Coupled Ocean/Atmosphere Mesoscale Prediction System (COAMPS), Version 5.0, Revision 2.0 (User’s Guide)

DTIC Science & Technology

2012-05-03

output (I/O) system. The framework provides tools for common modeling functions, as well as regridding, data decomposition, and communication on...Within this script, the user must specify both the site (DSRC or local) and the platform ( DAVINCI , EINSTEIN, or local machine) on which COAMPS is...being run. For example: site=navy_dsrc (for DSRC usage) site=nrlssc (for local NRL-SSC usage) platform= davinci or einstein (for DSRC usage

Coupled Ocean/Atmosphere Mesoscale Prediction System (COAMPS) Version 5.0, Rev. 2.0 (User’s Guide)

DTIC Science & Technology

2012-05-03

output (I/O) system. The framework provides tools for common modeling functions, as well as regridding, data decomposition, and communication on...Within this script, the user must specify both the site (DSRC or local) and the platform ( DAVINCI , EINSTEIN, or local machine) on which COAMPS is...being run. For example: site=navy_dsrc (for DSRC usage) site=nrlssc (for local NRL-SSC usage) platform= davinci or einstein (for DSRC usage

Empirical Evaluation of Conservative and Optimistic Discrete Event Execution on Cloud and VM Platforms

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

Yoginath, Srikanth B; Perumalla, Kalyan S

2013-01-01

Virtual machine (VM) technologies, especially those offered via Cloud platforms, present new dimensions with respect to performance and cost in executing parallel discrete event simulation (PDES) applications. Due to the introduction of overall cost as a metric, the choice of the highest-end computing configuration is no longer the most economical one. Moreover, runtime dynamics unique to VM platforms introduce new performance characteristics, and the variety of possible VM configurations give rise to a range of choices for hosting a PDES run. Here, an empirical study of these issues is undertaken to guide an understanding of the dynamics, trends and trade-offsmore » in executing PDES on VM/Cloud platforms. Performance results and cost measures are obtained from actual execution of a range of scenarios in two PDES benchmark applications on the Amazon Cloud offerings and on a high-end VM host machine. The data reveals interesting insights into the new VM-PDES dynamics that come into play and also leads to counter-intuitive guidelines with respect to choosing the best and second-best configurations when overall cost of execution is considered. In particular, it is found that choosing the highest-end VM configuration guarantees neither the best runtime nor the least cost. Interestingly, choosing a (suitably scaled) low-end VM configuration provides the least overall cost without adversely affecting the total runtime.« less

Implementation of a smartphone as a wireless gyroscope platform for quantifying reduced arm swing in hemiplegie gait with machine learning classification by multilayer perceptron neural network.

PubMed

LeMoyne, Robert; Mastroianni, Timothy

2016-08-01

Natural gait consists of synchronous and rhythmic patterns for both the lower and upper limb. People with hemiplegia can experience reduced arm swing, which can negatively impact the quality of gait. Wearable and wireless sensors, such as through a smartphone, have demonstrated the ability to quantify various features of gait. With a software application the smartphone (iPhone) can function as a wireless gyroscope platform capable of conveying a gyroscope signal recording as an email attachment by wireless connectivity to the Internet. The gyroscope signal recordings of the affected hemiplegic arm with reduced arm swing arm and the unaffected arm are post-processed into a feature set for machine learning. Using a multilayer perceptron neural network a considerable degree of classification accuracy is attained to distinguish between the affected hemiplegic arm with reduced arm swing arm and the unaffected arm.

Development of assembly and joint concepts for erectable space structures

NASA Technical Reports Server (NTRS)

Jacquemin, G. G.; Bluck, R. M.; Grotbeck, G. H.; Johnson, R. R.

1980-01-01

The technology associated with the on-orbit assembly of tetrahedral truss platforms erected of graphite epoxy tapered columns is examined. Associated with the assembly process is the design and fabrication of nine member node joints. Two such joints demonstrating somewhat different technology were designed and fabricated. Two methods of automatic assembly using the node designs were investigated, and the time of assembly of tetrahedral truss structures up to 1 square km in size was estimated. The effect of column and node joint packaging on the Space Shuttle cargo bay is examined. A brief discussion is included of operating cost considerations and the selection of energy sources. Consideration was given to the design assembly machines from 5 m to 20 m. The smaller machines, mounted on the Space Shuttle, are deployable and restowable. They provide a means of demonstrating the capabilities of the concept and of erecting small specialized platforms on relatively short notice.

Math Machines: Using Actuators in Physics Classes

NASA Astrophysics Data System (ADS)

Thomas, Frederick J.; Chaney, Robert A.; Gruesbeck, Marta

2018-01-01

Probeware (sensors combined with data-analysis software) is a well-established part of physics education. In engineering and technology, sensors are frequently paired with actuators—motors, heaters, buzzers, valves, color displays, medical dosing systems, and other devices that are activated by electrical signals to produce intentional physical change. This article describes how a 20-year project aimed at better integration of the STEM disciplines (science, technology, engineering and mathematics) uses brief actuator activities in physics instruction. Math Machines "actionware" includes software and hardware that convert virtually any free-form, time-dependent algebraic function into the dynamic actions of a stepper motor, servo motor, or RGB (red, green, blue) color mixer. With wheels and a platform, the stepper motor becomes LACI, a programmable vehicle. Adding a low-power laser module turns the servo motor into a programmable Pointer. Adding a gear and platform can transform the Pointer into an earthquake simulator.

A machine learning approach for automated wide-range frequency tagging analysis in embedded neuromonitoring systems.

PubMed

Montagna, Fabio; Buiatti, Marco; Benatti, Simone; Rossi, Davide; Farella, Elisabetta; Benini, Luca

2017-10-01

EEG is a standard non-invasive technique used in neural disease diagnostics and neurosciences. Frequency-tagging is an increasingly popular experimental paradigm that efficiently tests brain function by measuring EEG responses to periodic stimulation. Recently, frequency-tagging paradigms have proven successful with low stimulation frequencies (0.5-6Hz), but the EEG signal is intrinsically noisy in this frequency range, requiring heavy signal processing and significant human intervention for response estimation. This limits the possibility to process the EEG on resource-constrained systems and to design smart EEG based devices for automated diagnostic. We propose an algorithm for artifact removal and automated detection of frequency tagging responses in a wide range of stimulation frequencies, which we test on a visual stimulation protocol. The algorithm is rooted on machine learning based pattern recognition techniques and it is tailored for a new generation parallel ultra low power processing platform (PULP), reaching performance of more that 90% accuracy in the frequency detection even for very low stimulation frequencies (<1Hz) with a power budget of 56mW. Copyright © 2017 Elsevier Inc. All rights reserved.

Combined measurement system for double shield tunnel boring machine guidance based on optical and visual methods.

PubMed

Lin, Jiarui; Gao, Kai; Gao, Yang; Wang, Zheng

2017-10-01

In order to detect the position of the cutting shield at the head of a double shield tunnel boring machine (TBM) during the excavation, this paper develops a combined measurement system which is mainly composed of several optical feature points, a monocular vision sensor, a laser target sensor, and a total station. The different elements of the combined system are mounted on the TBM in suitable sequence, and the position of the cutting shield in the reference total station frame is determined by coordinate transformations. Subsequently, the structure of the feature points and matching technique for them are expounded, the position measurement method based on monocular vision is presented, and the calibration methods for the unknown relationships among different parts of the system are proposed. Finally, a set of experimental platforms to simulate the double shield TBM is established, and accuracy verification experiments are conducted. Experimental results show that the mean deviation of the system is 6.8 mm, which satisfies the requirements of double shield TBM guidance.

A wide-range programmable frequency synthesizer based on a finite state machine filter

NASA Astrophysics Data System (ADS)

Alser, Mohammed H.; Assaad, Maher M.; Hussin, Fawnizu A.

2013-11-01

In this article, an FPGA-based design and implementation of a fully digital wide-range programmable frequency synthesizer based on a finite state machine filter is presented. The advantages of the proposed architecture are that, it simultaneously generates a high frequency signal from a low frequency reference signal (i.e. synthesising), and synchronising the two signals (signals have the same phase, or a constant difference) without jitter accumulation issue. The architecture is portable and can be easily implemented for various platforms, such as FPGAs and integrated circuits. The frequency synthesizer circuit can be used as a part of SERDES devices in intra/inter chip communication in system-on-chip (SoC). The proposed circuit is designed using Verilog language and synthesized for the Altera DE2-70 development board, with the Cyclone II (EP2C35F672C6) device on board. Simulation and experimental results are included; they prove the synthesizing and tracking features of the proposed architecture. The generated clock signal frequency of a range from 19.8 MHz to 440 MHz is synchronized to the input reference clock with a frequency step of 0.12 MHz.

SVM Classifier - a comprehensive java interface for support vector machine classification of microarray data.

PubMed

Pirooznia, Mehdi; Deng, Youping

2006-12-12

Graphical user interface (GUI) software promotes novelty by allowing users to extend the functionality. SVM Classifier is a cross-platform graphical application that handles very large datasets well. The purpose of this study is to create a GUI application that allows SVM users to perform SVM training, classification and prediction. The GUI provides user-friendly access to state-of-the-art SVM methods embodied in the LIBSVM implementation of Support Vector Machine. We implemented the java interface using standard swing libraries. We used a sample data from a breast cancer study for testing classification accuracy. We achieved 100% accuracy in classification among the BRCA1-BRCA2 samples with RBF kernel of SVM. We have developed a java GUI application that allows SVM users to perform SVM training, classification and prediction. We have demonstrated that support vector machines can accurately classify genes into functional categories based upon expression data from DNA microarray hybridization experiments. Among the different kernel functions that we examined, the SVM that uses a radial basis kernel function provides the best performance. The SVM Classifier is available at http://mfgn.usm.edu/ebl/svm/.

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

PubMed

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

2015-05-01

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

expVIP: a Customizable RNA-seq Data Analysis and Visualization Platform1[OPEN

PubMed Central

2016-01-01

The majority of transcriptome sequencing (RNA-seq) expression studies in plants remain underutilized and inaccessible due to the use of disparate transcriptome references and the lack of skills and resources to analyze and visualize these data. We have developed expVIP, an expression visualization and integration platform, which allows easy analysis of RNA-seq data combined with an intuitive and interactive interface. Users can analyze public and user-specified data sets with minimal bioinformatics knowledge using the expVIP virtual machine. This generates a custom Web browser to visualize, sort, and filter the RNA-seq data and provides outputs for differential gene expression analysis. We demonstrate expVIP’s suitability for polyploid crops and evaluate its performance across a range of biologically relevant scenarios. To exemplify its use in crop research, we developed a flexible wheat (Triticum aestivum) expression browser (www.wheat-expression.com) that can be expanded with user-generated data in a local virtual machine environment. The open-access expVIP platform will facilitate the analysis of gene expression data from a wide variety of species by enabling the easy integration, visualization, and comparison of RNA-seq data across experiments. PMID:26869702

The Federal Geospatial Platform a shared infrastructure for publishing, discovering and exploiting public data and spatial applications.

NASA Astrophysics Data System (ADS)

Dabolt, T. O.

2016-12-01

The proliferation of open data and data services continues to thrive and is creating new challenges on how researchers, policy analysts and other decision makes can quickly discover and use relevant data. While traditional metadata catalog approaches used by applications such as data.gov prove to be useful starting points for data search they can quickly frustrate end users who are seeking ways to quickly find and then use data in machine to machine environs. The Geospatial Platform is overcoming these obstacles and providing end users and applications developers a richer more productive user experience. The Geospatial Platform leverages a collection of open source and commercial technology hosted on Amazon Web Services providing an ecosystem of services delivering trusted, consistent data in open formats to all users as well as a shared infrastructure for federal partners to serve their spatial data assets. It supports a diverse array of communities of practice ranging on topics from the 16 National Geospatial Data Assets Themes, to homeland security and climate adaptation. Come learn how you can contribute your data and leverage others or check it out on your own at https://www.geoplatform.gov/

Body-Machine Interfaces after Spinal Cord Injury: Rehabilitation and Brain Plasticity.

PubMed

Seáñez-González, Ismael; Pierella, Camilla; Farshchiansadegh, Ali; Thorp, Elias B; Wang, Xue; Parrish, Todd; Mussa-Ivaldi, Ferdinando A

2016-12-19

The purpose of this study was to identify rehabilitative effects and changes in white matter microstructure in people with high-level spinal cord injury following bilateral upper-extremity motor skill training. Five subjects with high-level (C5-C6) spinal cord injury (SCI) performed five visuo-spatial motor training tasks over 12 sessions (2-3 sessions per week). Subjects controlled a two-dimensional cursor with bilateral simultaneous movements of the shoulders using a non-invasive inertial measurement unit-based body-machine interface. Subjects' upper-body ability was evaluated before the start, in the middle and a day after the completion of training. MR imaging data were acquired before the start and within two days of the completion of training. Subjects learned to use upper-body movements that survived the injury to control the body-machine interface and improved their performance with practice. Motor training increased Manual Muscle Test scores and the isometric force of subjects' shoulders and upper arms. Moreover, motor training increased fractional anisotropy (FA) values in the cingulum of the left hemisphere by 6.02% on average, indicating localized white matter microstructure changes induced by activity-dependent modulation of axon diameter, myelin thickness or axon number. This body-machine interface may serve as a platform to develop a new generation of assistive-rehabilitative devices that promote the use of, and that re-strengthen, the motor and sensory functions that survived the injury.

CompactPCI/Linux Platform in FTU Slow Control System

NASA Astrophysics Data System (ADS)

Iannone, F.; Wang, L.; Centioli, C.; Panella, M.; Mazza, G.; Vitale, V.

2004-12-01

In large fusion experiments, such as tokamak devices, there is a common trend for slow control systems. Because of complexity of the plants, the so-called `Standard Model' (SM) in slow control has been adopted on several tokamak machines. This model is based on a three-level hierarchical control: 1) High-Level Control (HLC) with a supervisory function; 2) Medium-Level Control (MLC) to interface and concentrate I/O field equipments; 3) Low-Level Control (LLC) with hard real-time I/O function, often managed by PLCs. FTU control system designed with SM concepts has underwent several stages of developments in its fifteen years duration of runs. The latest evolution was inevitable, due to the obsolescence of the MLC CPUs, based on VME-MOTOROLA 68030 with OS9 operating system. A large amount of C code was developed for that platform to route the data flow from LLC, which is constituted by 24 Westinghouse Numalogic PC-700 PLCs with about 8000 field-points, to HLC, based on a commercial Object-Oriented Real-Time database on Alpha/CompaqTru64 platform. Therefore, we have to look for cost-effective solutions and finally a CompactPCI-Intel x86 platform with Linux operating system was chosen. A software porting has been done, taking into account the differences between OS9 and Linux operating system in terms of Inter/Network Processes Communications and I/O multi-ports serial driver. This paper describes the hardware/software architecture of the new MLC system, emphasizing the reliability and the low costs of the open source solutions. Moreover, a huge amount of software packages available in open source environment will assure a less painful maintenance, and will open the way to further improvements of the system itself.

High resolution image processing on low-cost microcomputers

NASA Technical Reports Server (NTRS)

Miller, R. L.

1993-01-01

Recent advances in microcomputer technology have resulted in systems that rival the speed, storage, and display capabilities of traditionally larger machines. Low-cost microcomputers can provide a powerful environment for image processing. A new software program which offers sophisticated image display and analysis on IBM-based systems is presented. Designed specifically for a microcomputer, this program provides a wide-range of functions normally found only on dedicated graphics systems, and therefore can provide most students, universities and research groups with an affordable computer platform for processing digital images. The processing of AVHRR images within this environment is presented as an example.

Challenges at Petascale for Pseudo-Spectral Methods on Spheres (A Last Hurrah?)

NASA Technical Reports Server (NTRS)

Clune, Thomas

2011-01-01

Conclusions: a) Proper software abstractions should enable rapid-exploration of platform-specific optimizations/ tradeoffs. b) Pseudo-spectra! methods are marginally viable for at least some classes of petascaie problems. i.e., GPU based machine with good bisection would be best. c) Scalability at exascale is possible, but the necessary resolution will make algorithm prohibitively expensive. Efficient implementations of realistic global transposes are mtricate and tedious in MPI. PS at petascaie requires exploration of a variety of strategies for spreading local and remote communic3tions. PGAS allows far simpler implementation and thus rapid exploration of variants.

CUDA-based real time surgery simulation.

PubMed

Liu, Youquan; De, Suvranu

2008-01-01

In this paper we present a general software platform that enables real time surgery simulation on the newly available compute unified device architecture (CUDA)from NVIDIA. CUDA-enabled GPUs harness the power of 128 processors which allow data parallel computations. Compared to the previous GPGPU, it is significantly more flexible with a C language interface. We report implementation of both collision detection and consequent deformation computation algorithms. Our test results indicate that the CUDA enables a twenty times speedup for collision detection and about fifteen times speedup for deformation computation on an Intel Core 2 Quad 2.66 GHz machine with GeForce 8800 GTX.

Analysis and optimization of gyrokinetic toroidal simulations on homogenous and heterogenous platforms

DOE PAGES

Ibrahim, Khaled Z.; Madduri, Kamesh; Williams, Samuel; ...

2013-07-18

The Gyrokinetic Toroidal Code (GTC) uses the particle-in-cell method to efficiently simulate plasma microturbulence. This paper presents novel analysis and optimization techniques to enhance the performance of GTC on large-scale machines. We introduce cell access analysis to better manage locality vs. synchronization tradeoffs on CPU and GPU-based architectures. Finally, our optimized hybrid parallel implementation of GTC uses MPI, OpenMP, and NVIDIA CUDA, achieves up to a 2× speedup over the reference Fortran version on multiple parallel systems, and scales efficiently to tens of thousands of cores.

Effect of Thermal and Chemical Treatment on the Microstructural, Mechanical and Machining Performance of W319 Al-Si-Cu Cast Alloy Engine Blocks and Directionally Solidified Machinability Test Blocks

NASA Astrophysics Data System (ADS)

Szablewski, Daniel

The research presented in this work is focused on making a link between casting microstructural, mechanical and machining properties for 319 Al-Si sand cast components. In order to achieve this, a unique Machinability Test Block (MTB) is designed to simulate the Nemak V6 Al-Si engine block solidification behavior. This MTB is then utilized to cast structures with in-situ nano-alumina particle master alloy additions that are Mg based, as well as independent in-situ Mg additions, and Sr additions to the MTB. The Universal Metallurgical Simulator and Analyzer (UMSA) Technology Platform is utilized for characterization of each cast structure at different Secondary Dendrite Arm Spacing (SDAS) levels. The rapid quench method and Jominy testing is used to assess the capability of the nano-alumina master alloy to modify the microstructure at different SDAS levels. Mechanical property assessment of the MTB is done at different SDAS levels on cast structures with master alloy additions described above. Weibull and Quality Index statistical analysis tools are then utilized to assess the mechanical properties. The MTB is also used to study single pass high speed face milling and bi-metallic cutting operations where the Al-Si hypoeutectic structure is combined with hypereutectoid Al-Si liners and cast iron cylinder liners. These studies are utilized to aid the implementation of Al-Si liners into the Nemak V6 engine block and bi-metallic cutting of the head decks. Machining behavior is also quantified for the investigated microstructures, and the Silicon Modification Level (SiML) is utilized for microstructural analysis as it relates to the machining behavior.

Assessment of loaded squat jump height with a free-weight barbell and Smith machine: comparison of the take-off velocity and flight time procedures.

PubMed

Pérez-Castilla, Alejandro; McMahon, John J; Comfort, Paul; García-Ramos, Amador

2017-07-31

The aims of this study were to compare the reliability and magnitude of jump height between the two standard procedures of analysing force platform data to estimate jump height (take-off velocity [TOV] and flight time [FT]) in the loaded squat jump (SJ) exercise performed with a free-weight barbell and in a Smith machine. Twenty-three collegiate men (age 23.1 ± 3.2 years, body mass 74.7 ± 7.3 kg, height 177.1 ± 7.0 cm) were tested twice for each SJ type (free-weight barbell and Smith machine) with 17, 30, 45, 60, and 75 kg loads. No substantial differences in reliability were observed between the TOV (Coefficient of variation [CV]: 9.88%; Intraclass correlation coefficient [ICC]: 0.82) and FT (CV: 8.68%; ICC: 0.88) procedures (CV ratio: 1.14), while the Smith SJ (CV: 7.74%; ICC: 0.87) revealed a higher reliability than the free-weight SJ (CV: 9.88%; ICC: 0.81) (CV ratio: 1.28). The TOV procedure provided higher magnitudes of jump height than the FT procedure for the loaded Smith machine SJ (systematic bias: 2.64 cm; P<0.05), while no significant differences between the TOV and FT procedures were observed in the free-weight SJ exercise (systematic bias: 0.26 cm; P>0.05). Heteroscedasticity of the errors was observed for the Smith machine SJ (r: 0.177) with increasing differences in favour of the TOV procedure for the trials with lower jump height (i.e. higher external loads). Based on these results the use of a Smith machine in conjunction with the FT more accurately determine jump height during the loaded SJ.

Task Assignment Heuristics for Distributed CFD Applications

NASA Technical Reports Server (NTRS)

Lopez-Benitez, N.; Djomehri, M. J.; Biswas, R.; Biegel, Bryan (Technical Monitor)

2001-01-01

CFD applications require high-performance computational platforms: 1. Complex physics and domain configuration demand strongly coupled solutions; 2. Applications are CPU and memory intensive; and 3. Huge resource requirements can only be satisfied by teraflop-scale machines or distributed computing.

Cyber-physical geographical information service-enabled control of diverse in-situ sensors.

PubMed

Chen, Nengcheng; Xiao, Changjiang; Pu, Fangling; Wang, Xiaolei; Wang, Chao; Wang, Zhili; Gong, Jianya

2015-01-23

Realization of open online control of diverse in-situ sensors is a challenge. This paper proposes a Cyber-Physical Geographical Information Service-enabled method for control of diverse in-situ sensors, based on location-based instant sensing of sensors, which provides closed-loop feedbacks. The method adopts the concepts and technologies of newly developed cyber-physical systems (CPSs) to combine control with sensing, communication, and computation, takes advantage of geographical information service such as services provided by the Tianditu which is a basic geographic information service platform in China and Sensor Web services to establish geo-sensor applications, and builds well-designed human-machine interfaces (HMIs) to support online and open interactions between human beings and physical sensors through cyberspace. The method was tested with experiments carried out in two geographically distributed scientific experimental fields, Baoxie Sensor Web Experimental Field in Wuhan city and Yemaomian Landslide Monitoring Station in Three Gorges, with three typical sensors chosen as representatives using the prototype system Geospatial Sensor Web Common Service Platform. The results show that the proposed method is an open, online, closed-loop means of control.

Link prediction in multiplex online social networks

NASA Astrophysics Data System (ADS)

Jalili, Mahdi; Orouskhani, Yasin; Asgari, Milad; Alipourfard, Nazanin; Perc, Matjaž

2017-02-01

Online social networks play a major role in modern societies, and they have shaped the way social relationships evolve. Link prediction in social networks has many potential applications such as recommending new items to users, friendship suggestion and discovering spurious connections. Many real social networks evolve the connections in multiple layers (e.g. multiple social networking platforms). In this article, we study the link prediction problem in multiplex networks. As an example, we consider a multiplex network of Twitter (as a microblogging service) and Foursquare (as a location-based social network). We consider social networks of the same users in these two platforms and develop a meta-path-based algorithm for predicting the links. The connectivity information of the two layers is used to predict the links in Foursquare network. Three classical classifiers (naive Bayes, support vector machines (SVM) and K-nearest neighbour) are used for the classification task. Although the networks are not highly correlated in the layers, our experiments show that including the cross-layer information significantly improves the prediction performance. The SVM classifier results in the best performance with an average accuracy of 89%.

Link prediction in multiplex online social networks.

PubMed

Jalili, Mahdi; Orouskhani, Yasin; Asgari, Milad; Alipourfard, Nazanin; Perc, Matjaž

2017-02-01

Online social networks play a major role in modern societies, and they have shaped the way social relationships evolve. Link prediction in social networks has many potential applications such as recommending new items to users, friendship suggestion and discovering spurious connections. Many real social networks evolve the connections in multiple layers (e.g. multiple social networking platforms). In this article, we study the link prediction problem in multiplex networks. As an example, we consider a multiplex network of Twitter (as a microblogging service) and Foursquare (as a location-based social network). We consider social networks of the same users in these two platforms and develop a meta-path-based algorithm for predicting the links. The connectivity information of the two layers is used to predict the links in Foursquare network. Three classical classifiers (naive Bayes, support vector machines (SVM) and K-nearest neighbour) are used for the classification task. Although the networks are not highly correlated in the layers, our experiments show that including the cross-layer information significantly improves the prediction performance. The SVM classifier results in the best performance with an average accuracy of 89%.

Cyber-Physical Geographical Information Service-Enabled Control of Diverse In-Situ Sensors

PubMed Central

Chen, Nengcheng; Xiao, Changjiang; Pu, Fangling; Wang, Xiaolei; Wang, Chao; Wang, Zhili; Gong, Jianya

2015-01-01

Realization of open online control of diverse in-situ sensors is a challenge. This paper proposes a Cyber-Physical Geographical Information Service-enabled method for control of diverse in-situ sensors, based on location-based instant sensing of sensors, which provides closed-loop feedbacks. The method adopts the concepts and technologies of newly developed cyber-physical systems (CPSs) to combine control with sensing, communication, and computation, takes advantage of geographical information service such as services provided by the Tianditu which is a basic geographic information service platform in China and Sensor Web services to establish geo-sensor applications, and builds well-designed human-machine interfaces (HMIs) to support online and open interactions between human beings and physical sensors through cyberspace. The method was tested with experiments carried out in two geographically distributed scientific experimental fields, Baoxie Sensor Web Experimental Field in Wuhan city and Yemaomian Landslide Monitoring Station in Three Gorges, with three typical sensors chosen as representatives using the prototype system Geospatial Sensor Web Common Service Platform. The results show that the proposed method is an open, online, closed-loop means of control. PMID:25625906

FLAME: A platform for high performance computing of complex systems, applied for three case studies

DOE PAGES

Kiran, Mariam; Bicak, Mesude; Maleki-Dizaji, Saeedeh; ...

2011-01-01

FLAME allows complex models to be automatically parallelised on High Performance Computing (HPC) grids enabling large number of agents to be simulated over short periods of time. Modellers are hindered by complexities of porting models on parallel platforms and time taken to run large simulations on a single machine, which FLAME overcomes. Three case studies from different disciplines were modelled using FLAME, and are presented along with their performance results on a grid.

1:50 Scale Testing of Three Floating Wind Turbines at MARIN and Numerical Model Validation Against Test Data

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

Dagher, Habib; Viselli, Anthony; Goupee, Andrew

The primary goal of the basin model test program discussed herein is to properly scale and accurately capture physical data of the rigid body motions, accelerations and loads for different floating wind turbine platform technologies. The intended use for this data is for performing comparisons with predictions from various aero-hydro-servo-elastic floating wind turbine simulators for calibration and validation. Of particular interest is validating the floating offshore wind turbine simulation capabilities of NREL’s FAST open-source simulation tool. Once the validation process is complete, coupled simulators such as FAST can be used with a much greater degree of confidence in design processesmore » for commercial development of floating offshore wind turbines. The test program subsequently described in this report was performed at MARIN (Maritime Research Institute Netherlands) in Wageningen, the Netherlands. The models considered consisted of the horizontal axis, NREL 5 MW Reference Wind Turbine (Jonkman et al., 2009) with a flexible tower affixed atop three distinct platforms: a tension leg platform (TLP), a spar-buoy modeled after the OC3 Hywind (Jonkman, 2010) and a semi-submersible. The three generic platform designs were intended to cover the spectrum of currently investigated concepts, each based on proven floating offshore structure technology. The models were tested under Froude scale wind and wave loads. The high-quality wind environments, unique to these tests, were realized in the offshore basin via a novel wind machine which exhibits negligible swirl and low turbulence intensity in the flow field. Recorded data from the floating wind turbine models included rotor torque and position, tower top and base forces and moments, mooring line tensions, six-axis platform motions and accelerations at key locations on the nacelle, tower, and platform. A large number of tests were performed ranging from simple free-decay tests to complex operating conditions with irregular sea states and dynamic winds.« less

40-Gbps optical backbone network deep packet inspection based on FPGA

NASA Astrophysics Data System (ADS)

Zuo, Yuan; Huang, Zhiping; Su, Shaojing

2014-11-01

In the era of information, the big data, which contains huge information, brings about some problems, such as high speed transmission, storage and real-time analysis and process. As the important media for data transmission, the Internet is the significant part for big data processing research. With the large-scale usage of the Internet, the data streaming of network is increasing rapidly. The speed level in the main fiber optic communication of the present has reached 40Gbps, even 100Gbps, therefore data on the optical backbone network shows some features of massive data. Generally, data services are provided via IP packets on the optical backbone network, which is constituted with SDH (Synchronous Digital Hierarchy). Hence this method that IP packets are directly mapped into SDH payload is named POS (Packet over SDH) technology. Aiming at the problems of real time process of high speed massive data, this paper designs a process system platform based on ATCA for 40Gbps POS signal data stream recognition and packet content capture, which employs the FPGA as the CPU. This platform offers pre-processing of clustering algorithms, service traffic identification and data mining for the following big data storage and analysis with high efficiency. Also, the operational procedure is proposed in this paper. Four channels of 10Gbps POS signal decomposed by the analysis module, which chooses FPGA as the kernel, are inputted to the flow classification module and the pattern matching component based on TCAM. Based on the properties of the length of payload and net flows, buffer management is added to the platform to keep the key flow information. According to data stream analysis, DPI (deep packet inspection) and flow balance distribute, the signal is transmitted to the backend machine through the giga Ethernet ports on back board. Practice shows that the proposed platform is superior to the traditional applications based on ASIC and NP.

Optimizing Irregular Applications for Energy and Performance on the Tilera Many-core Architecture

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

Chavarría-Miranda, Daniel; Panyala, Ajay R.; Halappanavar, Mahantesh

Optimizing applications simultaneously for energy and performance is a complex problem. High performance, parallel, irregular applications are notoriously hard to optimize due to their data-dependent memory accesses, lack of structured locality and complex data structures and code patterns. Irregular kernels are growing in importance in applications such as machine learning, graph analytics and combinatorial scientific computing. Performance- and energy-efficient implementation of these kernels on modern, energy efficient, multicore and many-core platforms is therefore an important and challenging problem. We present results from optimizing two irregular applications { the Louvain method for community detection (Grappolo), and high-performance conjugate gradient (HPCCG) {more » on the Tilera many-core system. We have significantly extended MIT's OpenTuner auto-tuning framework to conduct a detailed study of platform-independent and platform-specific optimizations to improve performance as well as reduce total energy consumption. We explore the optimization design space along three dimensions: memory layout schemes, compiler-based code transformations, and optimization of parallel loop schedules. Using auto-tuning, we demonstrate whole node energy savings of up to 41% relative to a baseline instantiation, and up to 31% relative to manually optimized variants.« less

Design and performance of the virtualization platform for offline computing on the ATLAS TDAQ Farm

NASA Astrophysics Data System (ADS)

Ballestrero, S.; Batraneanu, S. M.; Brasolin, F.; Contescu, C.; Di Girolamo, A.; Lee, C. J.; Pozo Astigarraga, M. E.; Scannicchio, D. A.; Twomey, M. S.; Zaytsev, A.

2014-06-01

With the LHC collider at CERN currently going through the period of Long Shutdown 1 there is an opportunity to use the computing resources of the experiments' large trigger farms for other data processing activities. In the case of the ATLAS experiment, the TDAQ farm, consisting of more than 1500 compute nodes, is suitable for running Monte Carlo (MC) production jobs that are mostly CPU and not I/O bound. This contribution gives a thorough review of the design and deployment of a virtualized platform running on this computing resource and of its use to run large groups of CernVM based virtual machines operating as a single CERN-P1 WLCG site. This platform has been designed to guarantee the security and the usability of the ATLAS private network, and to minimize interference with TDAQ's usage of the farm. Openstack has been chosen to provide a cloud management layer. The experience gained in the last 3.5 months shows that the use of the TDAQ farm for the MC simulation contributes to the ATLAS data processing at the level of a large Tier-1 WLCG site, despite the opportunistic nature of the underlying computing resources being used.

3D tissue engineered micro-tumors for optical-based therapeutic screening platform

NASA Astrophysics Data System (ADS)

Spano, Joseph L.; Schmitt, Trevor J.; Bailey, Ryan C.; Hannon, Timothy S.; Elmajdob, Mohamed; Mason, Eric M.; Ye, Guochang; Das, Soumen; Seal, Sudipta; Fenn, Michael B.

2016-03-01

Melanoma is an underserved area of cancer research, with little focus on studying the effects of tumor extracellular matrix (ECM) properties on melanoma tumor progression, metastasis, and treatment efficacy. We've developed a Raman spectral mapping-based in-vitro screening platform that allows for nondestructive in-situ, multi-time point assessment of a novel potential nanotherapeutic adjuvant, nanoceria (cerium oxide nanoparticles), for treating melanoma. We've focused primarily on understanding melanoma tumor ECM composition and how it influences cell morphology and ICC markers. Furthermore, we aim to correlate this with studies on nanotherapeutic efficacy to coincide with the goal of predicting and preventing metastasis based on ECM composition. We've compiled a Raman spectral database for substrates containing varying compositions of fibronectin, elastin, laminin, and collagens type I and IV. Furthermore, we've developed a machine learning-based semi-quantitative analysis platform utilizing dimensionality reduction with subsequent pixel classification and semi-quantitation of ECM composition using Direct Classical Least Squares for classification and estimation of the reorganization of these components by taking 2D maps using Raman spectroscopy. Gaining an understanding of how tissue properties influence ECM organization has laid the foundation for future work utilizing Raman spectroscopy to assess therapeutic efficacy and matrix reorganization imparted by nanoceria. Specifically, this will allow us to better understand the role of HIF1a in matrix reorganization of the tumor microenvironment. By studying the relationship between substrate modulus and nanoceria's ability to inhibit an ECM that is conducive to tumor formation, we endeavor to show that nanoceria may prevent or even revert tumor conducive microenvironments.

Cross-Platform Toxicogenomics for the Prediction of Non-Genotoxic Hepatocarcinogenesis in Rat

PubMed Central

Metzger, Ute; Templin, Markus F.; Plummer, Simon; Ellinger-Ziegelbauer, Heidrun; Zell, Andreas

2014-01-01

In the area of omics profiling in toxicology, i.e. toxicogenomics, characteristic molecular profiles have previously been incorporated into prediction models for early assessment of a carcinogenic potential and mechanism-based classification of compounds. Traditionally, the biomarker signatures used for model construction were derived from individual high-throughput techniques, such as microarrays designed for monitoring global mRNA expression. In this study, we built predictive models by integrating omics data across complementary microarray platforms and introduced new concepts for modeling of pathway alterations and molecular interactions between multiple biological layers. We trained and evaluated diverse machine learning-based models, differing in the incorporated features and learning algorithms on a cross-omics dataset encompassing mRNA, miRNA, and protein expression profiles obtained from rat liver samples treated with a heterogeneous set of substances. Most of these compounds could be unambiguously classified as genotoxic carcinogens, non-genotoxic carcinogens, or non-hepatocarcinogens based on evidence from published studies. Since mixed characteristics were reported for the compounds Cyproterone acetate, Thioacetamide, and Wy-14643, we reclassified these compounds as either genotoxic or non-genotoxic carcinogens based on their molecular profiles. Evaluating our toxicogenomics models in a repeated external cross-validation procedure, we demonstrated that the prediction accuracy of our models could be increased by joining the biomarker signatures across multiple biological layers and by adding complex features derived from cross-platform integration of the omics data. Furthermore, we found that adding these features resulted in a better separation of the compound classes and a more confident reclassification of the three undefined compounds as non-genotoxic carcinogens. PMID:24830643

Research on Visualization of Ground Laser Radar Data Based on Osg

NASA Astrophysics Data System (ADS)

Huang, H.; Hu, C.; Zhang, F.; Xue, H.

2018-04-01

Three-dimensional (3D) laser scanning is a new advanced technology integrating light, machine, electricity, and computer technologies. It can conduct 3D scanning to the whole shape and form of space objects with high precision. With this technology, you can directly collect the point cloud data of a ground object and create the structure of it for rendering. People use excellent 3D rendering engine to optimize and display the 3D model in order to meet the higher requirements of real time realism rendering and the complexity of the scene. OpenSceneGraph (OSG) is an open source 3D graphics engine. Compared with the current mainstream 3D rendering engine, OSG is practical, economical, and easy to expand. Therefore, OSG is widely used in the fields of virtual simulation, virtual reality, science and engineering visualization. In this paper, a dynamic and interactive ground LiDAR data visualization platform is constructed based on the OSG and the cross-platform C++ application development framework Qt. In view of the point cloud data of .txt format and the triangulation network data file of .obj format, the functions of 3D laser point cloud and triangulation network data display are realized. It is proved by experiments that the platform is of strong practical value as it is easy to operate and provides good interaction.

xdamp Version 6 : an IDL-based data and image manipulation program.

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

Ballard, William Parker

2012-04-01

The original DAMP (DAta Manipulation Program) was written by Mark Hedemann of Sandia National Laboratories and used the CA-DISSPLA{trademark} (available from Computer Associates International, Inc., Garden City, NY) graphics package as its engine. It was used to plot, modify, and otherwise manipulate the one-dimensional data waveforms (data vs. time) from a wide variety of accelerators. With the waning of CA-DISSPLA and the increasing popularity of Unix(reg sign)-based workstations, a replacement was needed. This package uses the IDL(reg sign) software, available from Research Systems Incorporated, a Xerox company, in Boulder, Colorado, as the engine, and creates a set of widgets tomore » manipulate the data in a manner similar to the original DAMP and earlier versions of xdamp. IDL is currently supported on a wide variety of Unix platforms such as IBM(reg sign) workstations, Hewlett Packard workstations, SUN(reg sign) workstations, Microsoft(reg sign) Windows{trademark} computers, Macintosh(reg sign) computers and Digital Equipment Corporation VMS(reg sign) and Alpha(reg sign) systems. Thus, xdamp is portable across many platforms. We have verified operation, albeit with some minor IDL bugs, on personal computers using Windows 7 and Windows Vista; Unix platforms; and Macintosh computers. Version 6 is an update that uses the IDL Virtual Machine to resolve the need for licensing IDL.« less

Adaptive MCS selection and resource planning for energy-efficient communication in LTE-M based IoT sensing platform.

PubMed

Dao, Nhu-Ngoc; Park, Minho; Kim, Joongheon; Cho, Sungrae

2017-01-01

As an important part of IoTization trends, wireless sensing technologies have been involved in many fields of human life. In cellular network evolution, the long term evolution advanced (LTE-A) networks including machine-type communication (MTC) features (named LTE-M) provide a promising infrastructure for a proliferation of Internet of things (IoT) sensing platform. However, LTE-M may not be optimally exploited for directly supporting such low-data-rate devices in terms of energy efficiency since it depends on core technologies of LTE that are originally designed for high-data-rate services. Focusing on this circumstance, we propose a novel adaptive modulation and coding selection (AMCS) algorithm to address the energy consumption problem in the LTE-M based IoT-sensing platform. The proposed algorithm determines the optimal pair of MCS and the number of primary resource blocks (#PRBs), at which the transport block size is sufficient to packetize the sensing data within the minimum transmit power. In addition, a quantity-oriented resource planning (QORP) technique that utilizes these optimal MCS levels as main criteria for spectrum allocation has been proposed for better adapting to the sensing node requirements. The simulation results reveal that the proposed approach significantly reduces the energy consumption of IoT sensing nodes and #PRBs up to 23.09% and 25.98%, respectively.

Adaptive MCS selection and resource planning for energy-efficient communication in LTE-M based IoT sensing platform

PubMed Central

Dao, Nhu-Ngoc; Park, Minho; Kim, Joongheon

2017-01-01

As an important part of IoTization trends, wireless sensing technologies have been involved in many fields of human life. In cellular network evolution, the long term evolution advanced (LTE-A) networks including machine-type communication (MTC) features (named LTE-M) provide a promising infrastructure for a proliferation of Internet of things (IoT) sensing platform. However, LTE-M may not be optimally exploited for directly supporting such low-data-rate devices in terms of energy efficiency since it depends on core technologies of LTE that are originally designed for high-data-rate services. Focusing on this circumstance, we propose a novel adaptive modulation and coding selection (AMCS) algorithm to address the energy consumption problem in the LTE-M based IoT-sensing platform. The proposed algorithm determines the optimal pair of MCS and the number of primary resource blocks (#PRBs), at which the transport block size is sufficient to packetize the sensing data within the minimum transmit power. In addition, a quantity-oriented resource planning (QORP) technique that utilizes these optimal MCS levels as main criteria for spectrum allocation has been proposed for better adapting to the sensing node requirements. The simulation results reveal that the proposed approach significantly reduces the energy consumption of IoT sensing nodes and #PRBs up to 23.09% and 25.98%, respectively. PMID:28796804

Powering a wireless sensor node with a vibration-driven piezoelectric energy harvester

NASA Astrophysics Data System (ADS)

Reilly, Elizabeth K.; Burghardt, Fred; Fain, Romy; Wright, Paul

2011-12-01

This paper discusses the direct application of scavenged energy to power a wireless sensor platform. A trapezoidal piezoelectric harvester was designed for a specific machine tool application and tested for robustness and longevity as well as performance. The design focused on resonant performance and distributed strain concentrations at a given resonant frequency and acceleration. Critical issues of power coupling and conditioning between harvester and wireless platform were addressed. The wireless platform consisted of a sensor, controller, power conditioning circuitry, and a custom low power radio. The system transmitted a sensor sample once every 10 s in a scavenging environment of 0.25 g and 100 Hz for a system duty cycle of approximately 0.2%.

Earth-Base: A Free And Open Source, RESTful Earth Sciences Platform

NASA Astrophysics Data System (ADS)

Kishor, P.; Heim, N. A.; Peters, S. E.; McClennen, M.

2012-12-01

This presentation describes the motivation, concept, and architecture behind Earth-Base, a web-based, RESTful data-management, analysis and visualization platform for earth sciences data. Traditionally web applications have been built directly accessing data from a database using a scripting language. While such applications are great at bring results to a wide audience, they are limited in scope to the imagination and capabilities of the application developer. Earth-Base decouples the data store from the web application by introducing an intermediate "data application" tier. The data application's job is to query the data store using self-documented, RESTful URIs, and send the results back formatted as JavaScript Object Notation (JSON). Decoupling the data store from the application allows virtually limitless flexibility in developing applications, both web-based for human consumption or programmatic for machine consumption. It also allows outside developers to use the data in their own applications, potentially creating applications that the original data creator and app developer may not have even thought of. Standardized specifications for URI-based querying and JSON-formatted results make querying and developing applications easy. URI-based querying also allows utilizing distributed datasets easily. Companion mechanisms for querying data snapshots aka time-travel, usage tracking and license management, and verification of semantic equivalence of data are also described. The latter promotes the "What You Expect Is What You Get" (WYEIWYG) principle that can aid in data citation and verification.

CNN universal machine as classificaton platform: an art-like clustering algorithm.

PubMed

Bálya, David

2003-12-01

Fast and robust classification of feature vectors is a crucial task in a number of real-time systems. A cellular neural/nonlinear network universal machine (CNN-UM) can be very efficient as a feature detector. The next step is to post-process the results for object recognition. This paper shows how a robust classification scheme based on adaptive resonance theory (ART) can be mapped to the CNN-UM. Moreover, this mapping is general enough to include different types of feed-forward neural networks. The designed analogic CNN algorithm is capable of classifying the extracted feature vectors keeping the advantages of the ART networks, such as robust, plastic and fault-tolerant behaviors. An analogic algorithm is presented for unsupervised classification with tunable sensitivity and automatic new class creation. The algorithm is extended for supervised classification. The presented binary feature vector classification is implemented on the existing standard CNN-UM chips for fast classification. The experimental evaluation shows promising performance after 100% accuracy on the training set.

Porous Foam Based Wick Structures for Loop Heat Pipes

NASA Technical Reports Server (NTRS)

Silk, Eric A.

2012-01-01

As part of an effort to identify cost efficient fabrication techniques for Loop Heat Pipe (LHP) construction, NASA Goddard Space Flight Center's Cryogenics and Fluids Branch collaborated with the U.S. Naval Academy s Aerospace Engineering Department in Spring 2012 to investigate the viability of carbon foam as a wick material within LHPs. The carbon foam was manufactured by ERG Aerospace and machined to geometric specifications at the U.S. Naval Academy s Materials, Mechanics and Structures Machine Shop. NASA GSFC s Fractal Loop Heat Pipe (developed under SBIR contract #NAS5-02112) was used as the validation LHP platform. In a horizontal orientation, the FLHP system demonstrated a heat flux of 75 Watts per square centimeter with deionized water as the working fluid. Also, no failed start-ups occurred during the 6 week performance testing period. The success of this study validated that foam can be used as a wick structure. Furthermore, given the COTS status of foam materials this study is one more step towards development of a low cost LHP.

A Loader for Executing Multi-Binary Applications on the Thinking Machines CM-5: It's Not Just for SPMD Anymore

NASA Technical Reports Server (NTRS)

Becker, Jeffrey C.

1995-01-01

The Thinking Machines CM-5 platform was designed to run single program, multiple data (SPMD) applications, i.e., to run a single binary across all nodes of a partition, with each node possibly operating on different data. Certain classes of applications, such as multi-disciplinary computational fluid dynamics codes, are facilitated by the ability to have subsets of the partition nodes running different binaries. In order to extend the CM-5 system software to permit such applications, a multi-program loader was developed. This system is based on the dld loader which was originally developed for workstations. This paper provides a high level description of dld, and describes how it was ported to the CM-5 to provide support for multi-binary applications. Finally, it elaborates how the loader has been used to implement the CM-5 version of MPIRUN, a portable facility for running multi-disciplinary/multi-zonal MPI (Message-Passing Interface Standard) codes.

Statistical analysis on the signals monitoring multiphase flow patterns in pipeline-riser system

NASA Astrophysics Data System (ADS)

Ye, Jing; Guo, Liejin

2013-07-01

The signals monitoring petroleum transmission pipeline in offshore oil industry usually contain abundant information about the multiphase flow on flow assurance which includes the avoidance of most undesirable flow pattern. Therefore, extracting reliable features form these signals to analyze is an alternative way to examine the potential risks to oil platform. This paper is focused on characterizing multiphase flow patterns in pipeline-riser system that is often appeared in offshore oil industry and finding an objective criterion to describe the transition of flow patterns. Statistical analysis on pressure signal at the riser top is proposed, instead of normal prediction method based on inlet and outlet flow conditions which could not be easily determined during most situations. Besides, machine learning method (least square supported vector machine) is also performed to classify automatically the different flow patterns. The experiment results from a small-scale loop show that the proposed method is effective for analyzing the multiphase flow pattern.

Progress Toward Fabrication of Machined Metal Shells for the First Double-Shell Implosions at the National Ignition Facility

DOE PAGES

Cardenas, Tana; Schmidt, Derek W.; Loomis, Eric N.; ...

2018-01-25

The double-shell platform fielded at the National Ignition Facility requires developments in new machining techniques and robotic assembly stations to meet the experimental specifications. Current double-shell target designs use a dense high-Z inner shell, a foam cushion, and a low-Z outer shell. The design requires that the inner shell be gas filled using a fill tube. This tube impacts the entire machining and assembly design. Other intermediate physics designs have to be fielded to answer physics questions and advance the technology to be able to fabricate the full point design in the near future. One of these intermediate designs ismore » a mid-Z imaging design. The methods of designing, fabricating, and characterizing each of the major components of an imaging double shell are discussed with an emphasis on the fabrication of the machined outer metal shell.« less

Using PVM to host CLIPS in distributed environments

NASA Technical Reports Server (NTRS)

Myers, Leonard; Pohl, Kym

1994-01-01

It is relatively easy to enhance CLIPS (C Language Integrated Production System) to support multiple expert systems running in a distributed environment with heterogeneous machines. The task is minimized by using the PVM (Parallel Virtual Machine) code from Oak Ridge Labs to provide the distributed utility. PVM is a library of C and FORTRAN subprograms that supports distributive computing on many different UNIX platforms. A PVM deamon is easily installed on each CPU that enters the virtual machine environment. Any user with rsh or rexec access to a machine can use the one PVM deamon to obtain a generous set of distributed facilities. The ready availability of both CLIPS and PVM makes the combination of software particularly attractive for budget conscious experimentation of heterogeneous distributive computing with multiple CLIPS executables. This paper presents a design that is sufficient to provide essential message passing functions in CLIPS and enable the full range of PVM facilities.

Progress Toward Fabrication of Machined Metal Shells for the First Double-Shell Implosions at the National Ignition Facility

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

Cardenas, Tana; Schmidt, Derek W.; Loomis, Eric N.

The double-shell platform fielded at the National Ignition Facility requires developments in new machining techniques and robotic assembly stations to meet the experimental specifications. Current double-shell target designs use a dense high-Z inner shell, a foam cushion, and a low-Z outer shell. The design requires that the inner shell be gas filled using a fill tube. This tube impacts the entire machining and assembly design. Other intermediate physics designs have to be fielded to answer physics questions and advance the technology to be able to fabricate the full point design in the near future. One of these intermediate designs ismore » a mid-Z imaging design. The methods of designing, fabricating, and characterizing each of the major components of an imaging double shell are discussed with an emphasis on the fabrication of the machined outer metal shell.« less

Machine learning of network metrics in ATLAS Distributed Data Management

NASA Astrophysics Data System (ADS)

Lassnig, Mario; Toler, Wesley; Vamosi, Ralf; Bogado, Joaquin; ATLAS Collaboration

2017-10-01

The increasing volume of physics data poses a critical challenge to the ATLAS experiment. In anticipation of high luminosity physics, automation of everyday data management tasks has become necessary. Previously many of these tasks required human decision-making and operation. Recent advances in hardware and software have made it possible to entrust more complicated duties to automated systems using models trained by machine learning algorithms. In this contribution we show results from one of our ongoing automation efforts that focuses on network metrics. First, we describe our machine learning framework built atop the ATLAS Analytics Platform. This framework can automatically extract and aggregate data, train models with various machine learning algorithms, and eventually score the resulting models and parameters. Second, we use these models to forecast metrics relevant for networkaware job scheduling and data brokering. We show the characteristics of the data and evaluate the forecasting accuracy of our models.

Enterprise Cloud Architecture for Chinese Ministry of Railway

NASA Astrophysics Data System (ADS)

Shan, Xumei; Liu, Hefeng

Enterprise like PRC Ministry of Railways (MOR), is facing various challenges ranging from highly distributed computing environment and low legacy system utilization, Cloud Computing is increasingly regarded as one workable solution to address this. This article describes full scale cloud solution with Intel Tashi as virtual machine infrastructure layer, Hadoop HDFS as computing platform, and self developed SaaS interface, gluing virtual machine and HDFS with Xen hypervisor. As a result, on demand computing task application and deployment have been tackled per MOR real working scenarios at the end of article.

The Portals 4.0 network programming interface.

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

Barrett, Brian W.; Brightwell, Ronald Brian; Pedretti, Kevin

2012-11-01

This report presents a specification for the Portals 4.0 network programming interface. Portals 4.0 is intended to allow scalable, high-performance network communication between nodes of a parallel computing system. Portals 4.0 is well suited to massively parallel processing and embedded systems. Portals 4.0 represents an adaption of the data movement layer developed for massively parallel processing platforms, such as the 4500-node Intel TeraFLOPS machine. Sandias Cplant cluster project motivated the development of Version 3.0, which was later extended to Version 3.3 as part of the Cray Red Storm machine and XT line. Version 4.0 is targeted to the next generationmore » of machines employing advanced network interface architectures that support enhanced offload capabilities.« less

A journey from nuclear criticality methods to high energy density radflow experiments

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

Urbatsch, Todd James

Los Alamos National Laboratory is a nuclear weapons laboratory supporting our nation's defense. In support of this mission is a high energy-density physics program in which we design and execute experiments to study radiationhydrodynamics phenomena and improve the predictive capability of our largescale multi-physics software codes on our big-iron computers. The Radflow project’s main experimental effort now is to understand why we haven't been able to predict opacities on Sandia National Laboratory's Z-machine. We are modeling an increasing fraction of the Z-machine's dynamic hohlraum to find multi-physics explanations for the experimental results. Further, we are building an entirely different opacitymore » platform on Lawrence Livermore National Laboratory's National Ignition Facility (NIF), which is set to get results early 2017. Will the results match our predictions, match the Z-machine, or give us something entirely different? The new platform brings new challenges such as designing hohlraums and spectrometers. The speaker will recount his history, starting with one-dimensional Monte Carlo nuclear criticality methods in graduate school, radiative transfer methods research and software development for his first 16 years at LANL, and, now, radflow technology and experiments. Who knew that the real world was more than just radiation transport? Experiments aren't easy, but they sure are fun.« less

Combination of mass spectrometry-based targeted lipidomics and supervised machine learning algorithms in detecting adulterated admixtures of white rice.

PubMed

Lim, Dong Kyu; Long, Nguyen Phuoc; Mo, Changyeun; Dong, Ziyuan; Cui, Lingmei; Kim, Giyoung; Kwon, Sung Won

2017-10-01

The mixing of extraneous ingredients with original products is a common adulteration practice in food and herbal medicines. In particular, authenticity of white rice and its corresponding blended products has become a key issue in food industry. Accordingly, our current study aimed to develop and evaluate a novel discrimination method by combining targeted lipidomics with powerful supervised learning methods, and eventually introduce a platform to verify the authenticity of white rice. A total of 30 cultivars were collected, and 330 representative samples of white rice from Korea and China as well as seven mixing ratios were examined. Random forests (RF), support vector machines (SVM) with a radial basis function kernel, C5.0, model averaged neural network, and k-nearest neighbor classifiers were used for the classification. We achieved desired results, and the classifiers effectively differentiated white rice from Korea to blended samples with high prediction accuracy for the contamination ratio as low as five percent. In addition, RF and SVM classifiers were generally superior to and more robust than the other techniques. Our approach demonstrated that the relative differences in lysoGPLs can be successfully utilized to detect the adulterated mixing of white rice originating from different countries. In conclusion, the present study introduces a novel and high-throughput platform that can be applied to authenticate adulterated admixtures from original white rice samples. Copyright © 2017 Elsevier Ltd. All rights reserved.

A method for using solid modeling CAD software to create an implant library for the fabrication of a custom abutment.

PubMed

Zhang, Jing; Zhang, Rimei; Ren, Guanghui; Zhang, Xiaojie

2017-02-01

This article describes a method that incorporates the solid modeling CAD software Solidworks with a dental milling machine to fabricate individual abutments in house. This process involves creating an implant library with 3-dimensional (3D) models and manufacturing a base, scan element, abutment, and crown anatomy. The 3D models can be imported into any dental computer-aided design and computer-aided (CAD-CAM) manufacturing system. This platform increases abutment design flexibility, as the base and scan elements can be designed to fit several shapes as needed to meet clinical requirements. Copyright © 2016 Editorial Council for the Journal of Prosthetic Dentistry. Published by Elsevier Inc. All rights reserved.

TAS::89 0927::TAS RECOVERY - The Lean Green Energy Controller Machine

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

Teeter, John; Wang, Gene; Moss, David

Achieving efficiency improvements and providing demand-response programs have been identified as key elements of our national energy initiative. The residential market is the largest, yet most difficult, segment to engage in efforts to meet these objectives. This project developed Energy Management System that engages the consumer and enables Smart Grid services, applications, and business processes to address this need. Our innovative solution provides smart controller providing dynamic optimization of energy consumption for the residential energy consumer. Our solution extends the technical platform to include a cloud based Internet of Things (IoT) aggregation of data sensors and actuators the go beyondmore » energy management and extend to life style services provided through compelling mobile and console based user experiences.« less

Rapid, portable and cost-effective yeast cell viability and concentration analysis using lensfree on-chip microscopy and machine learning.

PubMed

Feizi, Alborz; Zhang, Yibo; Greenbaum, Alon; Guziak, Alex; Luong, Michelle; Chan, Raymond Yan Lok; Berg, Brandon; Ozkan, Haydar; Luo, Wei; Wu, Michael; Wu, Yichen; Ozcan, Aydogan

2016-11-01

Monitoring yeast cell viability and concentration is important in brewing, baking and biofuel production. However, existing methods of measuring viability and concentration are relatively bulky, tedious and expensive. Here we demonstrate a compact and cost-effective automatic yeast analysis platform (AYAP), which can rapidly measure cell concentration and viability. AYAP is based on digital in-line holography and on-chip microscopy and rapidly images a large field-of-view of 22.5 mm 2 . This lens-free microscope weighs 70 g and utilizes a partially-coherent illumination source and an opto-electronic image sensor chip. A touch-screen user interface based on a tablet-PC is developed to reconstruct the holographic shadows captured by the image sensor chip and use a support vector machine (SVM) model to automatically classify live and dead cells in a yeast sample stained with methylene blue. In order to quantify its accuracy, we varied the viability and concentration of the cells and compared AYAP's performance with a fluorescence exclusion staining based gold-standard using regression analysis. The results agree very well with this gold-standard method and no significant difference was observed between the two methods within a concentration range of 1.4 × 10 5 to 1.4 × 10 6 cells per mL, providing a dynamic range suitable for various applications. This lensfree computational imaging technology that is coupled with machine learning algorithms would be useful for cost-effective and rapid quantification of cell viability and density even in field and resource-poor settings.

[A machine learning model using gut microbiome data for predicting changes of trimethylamine-N-oxide in healthy volunteers after choline consumption].

PubMed

Lu, Jun-Qi; Wang, Shan; Yin, Jia; Wu, Shan; He, Yan; Zheng, Hui-Min; Sheng, Hua-Fang; Zhou, Hong-Wei

2017-03-20

To establish a machine learning model based on gut microbiota for predicting the level of trimethylamine N-oxide (TMAO) metabolism in vivo after choline intake to provide guidance of individualized precision diet and evidence for screening population at high risks of cardiovascular disease. We quantified plasma levels of TMAO in 18 healthy volunteers before and 8 h after a choline challenge (ingestion of two boiled eggs). The volunteers were divided into two groups with increased or decreased TMAO level following choline challenge. Fresh fecal samples were collected before taking fasting blood samples for amplifying 16S rRNA V4 tags, and the PCR products were sequenced using the platform of Illumina HiSeq 2000. The differences in gut microbiata between subjects with increased and decreased plasma TMAO were analyzed using QIIME. Based on the gut microbiota data and TMAO levels in the two groups, the prediction model was established using the machine learning random forest algorithm, and the validity of the model was tested using a verified dataset. An obvious difference was found in beta diversity of the gut microbota between the subjects with increased and decreased plasma TMAO level following choline challenge. The area under the curve (AUC) of the model was 86.39% (95% CI: 72.7%-100%). Using the verified dataset, the model showed a much higher probability for correctly predicting TMAO variation following choline challenge. The model is feasible and reliable for predicting the level of TMAO metabolism in vivo based on gut microbiota.

What Do We Really Need? Visions of an Ideal Human-Machine Interface for NOTES Mechatronic Support Systems From the View of Surgeons, Gastroenterologists, and Medical Engineers.

PubMed

Kranzfelder, Michael; Schneider, Armin; Fiolka, Adam; Koller, Sebastian; Wilhelm, Dirk; Reiser, Silvano; Meining, Alexander; Feussner, Hubertus

2015-08-01

To investigate why natural orifice translumenal endoscopic surgery (NOTES) has not yet become widely accepted and to prove whether the main reason is still the lack of appropriate platforms due to the deficiency of applicable interfaces. To assess expectations of a suitable interface design, we performed a survey on human-machine interfaces for NOTES mechatronic support systems among surgeons, gastroenterologists, and medical engineers. Of 120 distributed questionnaires, each consisting of 14 distinct questions, 100 (83%) were eligible for analysis. A mechatronic platform for NOTES was considered "important" by 71% of surgeons, 83% of gastroenterologist,s and 56% of medical engineers. "Intuitivity" and "simple to use" were the most favored aspects (33% to 51%). Haptic feedback was considered "important" by 70% of participants. In all, 53% of surgeons, 50% of gastroenterologists, and 33% of medical engineers already had experience with NOTES platforms or other surgical robots; however, current interfaces only met expectations in just more than 50%. Whereas surgeons did not favor a certain working posture, gastroenterologists and medical engineers preferred a sitting position. Three-dimensional visualization was generally considered "nice to have" (67% to 72%); however, for 26% of surgeons, 17% of gastroenterologists, and 7% of medical engineers it did not matter (P = 0.018). Requests and expectations of human-machine interfaces for NOTES seem to be generally similar for surgeons, gastroenterologist, and medical engineers. Consensus exists on the importance of developing interfaces that should be both intuitive and simple to use, are similar to preexisting familiar instruments, and exceed current available systems. © The Author(s) 2014.

Exploring Google Earth Engine platform for big data processing: classification of multi-temporal satellite imagery for crop mapping

NASA Astrophysics Data System (ADS)

Shelestov, Andrii; Lavreniuk, Mykola; Kussul, Nataliia; Novikov, Alexei; Skakun, Sergii

2017-02-01

Many applied problems arising in agricultural monitoring and food security require reliable crop maps at national or global scale. Large scale crop mapping requires processing and management of large amount of heterogeneous satellite imagery acquired by various sensors that consequently leads to a “Big Data” problem. The main objective of this study is to explore efficiency of using the Google Earth Engine (GEE) platform when classifying multi-temporal satellite imagery with potential to apply the platform for a larger scale (e.g. country level) and multiple sensors (e.g. Landsat-8 and Sentinel-2). In particular, multiple state-of-the-art classifiers available in the GEE platform are compared to produce a high resolution (30 m) crop classification map for a large territory ( 28,100 km2 and 1.0 M ha of cropland). Though this study does not involve large volumes of data, it does address efficiency of the GEE platform to effectively execute complex workflows of satellite data processing required with large scale applications such as crop mapping. The study discusses strengths and weaknesses of classifiers, assesses accuracies that can be achieved with different classifiers for the Ukrainian landscape, and compares them to the benchmark classifier using a neural network approach that was developed in our previous studies. The study is carried out for the Joint Experiment of Crop Assessment and Monitoring (JECAM) test site in Ukraine covering the Kyiv region (North of Ukraine) in 2013. We found that Google Earth Engine (GEE) provides very good performance in terms of enabling access to the remote sensing products through the cloud platform and providing pre-processing; however, in terms of classification accuracy, the neural network based approach outperformed support vector machine (SVM), decision tree and random forest classifiers available in GEE.

3D hierarchical spatial representation and memory of multimodal sensory data

NASA Astrophysics Data System (ADS)

Khosla, Deepak; Dow, Paul A.; Huber, David J.

2009-04-01

This paper describes an efficient method and system for representing, processing and understanding multi-modal sensory data. More specifically, it describes a computational method and system for how to process and remember multiple locations in multimodal sensory space (e.g., visual, auditory, somatosensory, etc.). The multimodal representation and memory is based on a biologically-inspired hierarchy of spatial representations implemented with novel analogues of real representations used in the human brain. The novelty of the work is in the computationally efficient and robust spatial representation of 3D locations in multimodal sensory space as well as an associated working memory for storage and recall of these representations at the desired level for goal-oriented action. We describe (1) A simple and efficient method for human-like hierarchical spatial representations of sensory data and how to associate, integrate and convert between these representations (head-centered coordinate system, body-centered coordinate, etc.); (2) a robust method for training and learning a mapping of points in multimodal sensory space (e.g., camera-visible object positions, location of auditory sources, etc.) to the above hierarchical spatial representations; and (3) a specification and implementation of a hierarchical spatial working memory based on the above for storage and recall at the desired level for goal-oriented action(s). This work is most useful for any machine or human-machine application that requires processing of multimodal sensory inputs, making sense of it from a spatial perspective (e.g., where is the sensory information coming from with respect to the machine and its parts) and then taking some goal-oriented action based on this spatial understanding. A multi-level spatial representation hierarchy means that heterogeneous sensory inputs (e.g., visual, auditory, somatosensory, etc.) can map onto the hierarchy at different levels. When controlling various machine/robot degrees of freedom, the desired movements and action can be computed from these different levels in the hierarchy. The most basic embodiment of this machine could be a pan-tilt camera system, an array of microphones, a machine with arm/hand like structure or/and a robot with some or all of the above capabilities. We describe the approach, system and present preliminary results on a real-robotic platform.

Teaching Cybersecurity Using the Cloud

ERIC Educational Resources Information Center

Salah, Khaled; Hammoud, Mohammad; Zeadally, Sherali

2015-01-01

Cloud computing platforms can be highly attractive to conduct course assignments and empower students with valuable and indispensable hands-on experience. In particular, the cloud can offer teaching staff and students (whether local or remote) on-demand, elastic, dedicated, isolated, (virtually) unlimited, and easily configurable virtual machines.…

Remote Sensing as a Demonstration of Applied Physics.

ERIC Educational Resources Information Center

Colwell, Robert N.

1980-01-01

Provides information about the field of remote sensing, including discussions of geo-synchronous and sun-synchronous remote-sensing platforms, the actual physical processes and equipment involved in sensing, the analysis of images by humans and machines, and inexpensive, small scale methods, including aerial photography. (CS)

Performance of Machine Learning Algorithms for Qualitative and Quantitative Prediction Drug Blockade of hERG1 channel.

PubMed

Wacker, Soren; Noskov, Sergei Yu

2018-05-01

Drug-induced abnormal heart rhythm known as Torsades de Pointes (TdP) is a potential lethal ventricular tachycardia found in many patients. Even newly released anti-arrhythmic drugs, like ivabradine with HCN channel as a primary target, block the hERG potassium current in overlapping concentration interval. Promiscuous drug block to hERG channel may potentially lead to perturbation of the action potential duration (APD) and TdP, especially when with combined with polypharmacy and/or electrolyte disturbances. The example of novel anti-arrhythmic ivabradine illustrates clinically important and ongoing deficit in drug design and warrants for better screening methods. There is an urgent need to develop new approaches for rapid and accurate assessment of how drugs with complex interactions and multiple subcellular targets can predispose or protect from drug-induced TdP. One of the unexpected outcomes of compulsory hERG screening implemented in USA and European Union resulted in large datasets of IC 50 values for various molecules entering the market. The abundant data allows now to construct predictive machine-learning (ML) models. Novel ML algorithms and techniques promise better accuracy in determining IC 50 values of hERG blockade that is comparable or surpassing that of the earlier QSAR or molecular modeling technique. To test the performance of modern ML techniques, we have developed a computational platform integrating various workflows for quantitative structure activity relationship (QSAR) models using data from the ChEMBL database. To establish predictive powers of ML-based algorithms we computed IC 50 values for large dataset of molecules and compared it to automated patch clamp system for a large dataset of hERG blocking and non-blocking drugs, an industry gold standard in studies of cardiotoxicity. The optimal protocol with high sensitivity and predictive power is based on the novel eXtreme gradient boosting (XGBoost) algorithm. The ML-platform with XGBoost displays excellent performance with a coefficient of determination of up to R 2 ~0.8 for pIC 50 values in evaluation datasets, surpassing other metrics and approaches available in literature. Ultimately, the ML-based platform developed in our work is a scalable framework with automation potential to interact with other developing technologies in cardiotoxicity field, including high-throughput electrophysiology measurements delivering large datasets of profiled drugs, rapid synthesis and drug development via progress in synthetic biology.

A manned-machine space station construction concept

NASA Technical Reports Server (NTRS)

Mikulas, M. M., Jr.; Bush, H. G.; Wallsom, R. E.; Dorsey, J. T.; Rhodes, M. D.

1984-01-01

A design concept for the construction of a permanent manned space station is developed and discussed. The main considerations examined in developing the design concept are: (1) the support structure of the station be stiff enough to preclude the need for an elaborate on-orbit system to control structural response, (2) the station support structure and solar power system be compatible with existing technology, and (3) the station be capable of growing in a systematic modular fashion. The concept is developed around the assembly of truss platforms by pressure-suited astronauts operating in extravehicular activity (EVA), assisted by a machine (Assembly and Transport Vehicle, ATV) to position the astronauts at joint locations where they latch truss members in place. The ATV is a mobile platform that is attached to and moves on the station support structure using pegs attached to each truss joint. The operation of the ATV is described and a number of conceptual configurations for potential space stations are developed.

Experimental results in autonomous landing approaches by dynamic machine vision

NASA Astrophysics Data System (ADS)

Dickmanns, Ernst D.; Werner, Stefan; Kraus, S.; Schell, R.

1994-07-01

The 4-D approach to dynamic machine vision, exploiting full spatio-temporal models of the process to be controlled, has been applied to on board autonomous landing approaches of aircraft. Aside from image sequence processing, for which it was developed initially, it is also used for data fusion from a range of sensors. By prediction error feedback an internal representation of the aircraft state relative to the runway in 3-D space and time is servo- maintained in the interpretation process, from which the control applications required are being derived. The validity and efficiency of the approach have been proven both in hardware- in-the-loop simulations and in flight experiments with a twin turboprop aircraft Do128 under perturbations from cross winds and wind gusts. The software package has been ported to `C' and onto a new transputer image processing platform; the system has been expanded for bifocal vision with two cameras of different focal length mounted fixed relative to each other on a two-axes platform for viewing direction control.

New Web Server - the Java Version of Tempest - Produced

NASA Technical Reports Server (NTRS)

York, David W.; Ponyik, Joseph G.

2000-01-01

A new software design and development effort has produced a Java (Sun Microsystems, Inc.) version of the award-winning Tempest software (refs. 1 and 2). In 1999, the Embedded Web Technology (EWT) team received a prestigious R&D 100 Award for Tempest, Java Version. In this article, "Tempest" will refer to the Java version of Tempest, a World Wide Web server for desktop or embedded systems. Tempest was designed at the NASA Glenn Research Center at Lewis Field to run on any platform for which a Java Virtual Machine (JVM, Sun Microsystems, Inc.) exists. The JVM acts as a translator between the native code of the platform and the byte code of Tempest, which is compiled in Java. These byte code files are Java executables with a ".class" extension. Multiple byte code files can be zipped together as a "*.jar" file for more efficient transmission over the Internet. Today's popular browsers, such as Netscape (Netscape Communications Corporation) and Internet Explorer (Microsoft Corporation) have built-in Virtual Machines to display Java applets.

The Perseus computational platform for comprehensive analysis of (prote)omics data.

PubMed

Tyanova, Stefka; Temu, Tikira; Sinitcyn, Pavel; Carlson, Arthur; Hein, Marco Y; Geiger, Tamar; Mann, Matthias; Cox, Jürgen

2016-09-01

A main bottleneck in proteomics is the downstream biological analysis of highly multivariate quantitative protein abundance data generated using mass-spectrometry-based analysis. We developed the Perseus software platform (http://www.perseus-framework.org) to support biological and biomedical researchers in interpreting protein quantification, interaction and post-translational modification data. Perseus contains a comprehensive portfolio of statistical tools for high-dimensional omics data analysis covering normalization, pattern recognition, time-series analysis, cross-omics comparisons and multiple-hypothesis testing. A machine learning module supports the classification and validation of patient groups for diagnosis and prognosis, and it also detects predictive protein signatures. Central to Perseus is a user-friendly, interactive workflow environment that provides complete documentation of computational methods used in a publication. All activities in Perseus are realized as plugins, and users can extend the software by programming their own, which can be shared through a plugin store. We anticipate that Perseus's arsenal of algorithms and its intuitive usability will empower interdisciplinary analysis of complex large data sets.

Workflow4Metabolomics: a collaborative research infrastructure for computational metabolomics

PubMed Central

Giacomoni, Franck; Le Corguillé, Gildas; Monsoor, Misharl; Landi, Marion; Pericard, Pierre; Pétéra, Mélanie; Duperier, Christophe; Tremblay-Franco, Marie; Martin, Jean-François; Jacob, Daniel; Goulitquer, Sophie; Thévenot, Etienne A.; Caron, Christophe

2015-01-01

Summary: The complex, rapidly evolving field of computational metabolomics calls for collaborative infrastructures where the large volume of new algorithms for data pre-processing, statistical analysis and annotation can be readily integrated whatever the language, evaluated on reference datasets and chained to build ad hoc workflows for users. We have developed Workflow4Metabolomics (W4M), the first fully open-source and collaborative online platform for computational metabolomics. W4M is a virtual research environment built upon the Galaxy web-based platform technology. It enables ergonomic integration, exchange and running of individual modules and workflows. Alternatively, the whole W4M framework and computational tools can be downloaded as a virtual machine for local installation. Availability and implementation: http://workflow4metabolomics.org homepage enables users to open a private account and access the infrastructure. W4M is developed and maintained by the French Bioinformatics Institute (IFB) and the French Metabolomics and Fluxomics Infrastructure (MetaboHUB). Contact: contact@workflow4metabolomics.org PMID:25527831

Workflow4Metabolomics: a collaborative research infrastructure for computational metabolomics.

PubMed

Giacomoni, Franck; Le Corguillé, Gildas; Monsoor, Misharl; Landi, Marion; Pericard, Pierre; Pétéra, Mélanie; Duperier, Christophe; Tremblay-Franco, Marie; Martin, Jean-François; Jacob, Daniel; Goulitquer, Sophie; Thévenot, Etienne A; Caron, Christophe

2015-05-01

The complex, rapidly evolving field of computational metabolomics calls for collaborative infrastructures where the large volume of new algorithms for data pre-processing, statistical analysis and annotation can be readily integrated whatever the language, evaluated on reference datasets and chained to build ad hoc workflows for users. We have developed Workflow4Metabolomics (W4M), the first fully open-source and collaborative online platform for computational metabolomics. W4M is a virtual research environment built upon the Galaxy web-based platform technology. It enables ergonomic integration, exchange and running of individual modules and workflows. Alternatively, the whole W4M framework and computational tools can be downloaded as a virtual machine for local installation. http://workflow4metabolomics.org homepage enables users to open a private account and access the infrastructure. W4M is developed and maintained by the French Bioinformatics Institute (IFB) and the French Metabolomics and Fluxomics Infrastructure (MetaboHUB). contact@workflow4metabolomics.org. © The Author 2014. Published by Oxford University Press.

NGS tools for traceability in candies as high processed food products: Ion Torrent PGM versus conventional PCR-cloning.

PubMed

Muñoz-Colmenero, Marta; Martínez, Jose Luis; Roca, Agustín; Garcia-Vazquez, Eva

2017-01-01

The Next Generation Sequencing methodologies are considered the next step within DNA-based methods and their applicability in different fields is being evaluated. Here, we tested the usefulness of the Ion Torrent Personal Genome Machine (PGM) in food traceability analyzing candies as a model of high processed foods, and compared the results with those obtained by PCR-cloning-sequencing (PCR-CS). The majority of samples exhibited consistency between methodologies, yielding more information and species per product from the PGM platform than PCR-CS. Significantly higher AT-content in sequences of the same species was also obtained from PGM. This together with some taxonomical discrepancies between methodologies suggest that the PGM platform is still pre-mature for its use in food traceability of complex highly processed products. It could be a good option for analysis of less complex food, saving time and cost per sample. Copyright © 2016 Elsevier Ltd. All rights reserved.

Computer Vision Malaria Diagnostic Systems-Progress and Prospects.

PubMed

Pollak, Joseph Joel; Houri-Yafin, Arnon; Salpeter, Seth J

2017-01-01

Accurate malaria diagnosis is critical to prevent malaria fatalities, curb overuse of antimalarial drugs, and promote appropriate management of other causes of fever. While several diagnostic tests exist, the need for a rapid and highly accurate malaria assay remains. Microscopy and rapid diagnostic tests are the main diagnostic modalities available, yet they can demonstrate poor performance and accuracy. Automated microscopy platforms have the potential to significantly improve and standardize malaria diagnosis. Based on image recognition and machine learning algorithms, these systems maintain the benefits of light microscopy and provide improvements such as quicker scanning time, greater scanning area, and increased consistency brought by automation. While these applications have been in development for over a decade, recently several commercial platforms have emerged. In this review, we discuss the most advanced computer vision malaria diagnostic technologies and investigate several of their features which are central to field use. Additionally, we discuss the technological and policy barriers to implementing these technologies in low-resource settings world-wide.

Organomatics and organometrics: Novel platforms for long-term whole-organ culture

PubMed Central

Bruinsma, Bote G.; Yarmush, Martin L.; Uygun, Korkut

2014-01-01

Organ culture systems are instrumental as experimental whole-organ models of physiology and disease, as well as preservation modalities facilitating organ replacement therapies such as transplantation. Nevertheless, a coordinated system of machine perfusion components and integrated regulatory control has yet to be fully developed to achieve long-term maintenance of organ function ex vivo. Here we outline current strategies for organ culture, or organomatics, and how these systems can be regulated by means of computational algorithms, or organometrics, to achieve the organ culture platforms anticipated in modern-day biomedicine. PMID:25035864

Cooling system for a gas turbine using a cylindrical insert having V-shaped notch weirs

DOEpatents

Grondahl, Clayton M.; Germain, Malcolm R.

1981-01-01

An improved cooling system for a gas turbine is disclosed. A plurality of V-shaped notch weirs are utilized to meter a coolant liquid from a pool of coolant into a plurality of platform and airfoil coolant channels formed in the buckets of the turbine. The V-shaped notch weirs are formed in a separately machined cylindrical insert and serve to desensitize the flow of coolant into the individual platform and airfoil coolant channels to design tolerances and non-uniform flow distribution.

Argumentation Based Joint Learning: A Novel Ensemble Learning Approach

PubMed Central

Xu, Junyi; Yao, Li; Li, Le

2015-01-01

Recently, ensemble learning methods have been widely used to improve classification performance in machine learning. In this paper, we present a novel ensemble learning method: argumentation based multi-agent joint learning (AMAJL), which integrates ideas from multi-agent argumentation, ensemble learning, and association rule mining. In AMAJL, argumentation technology is introduced as an ensemble strategy to integrate multiple base classifiers and generate a high performance ensemble classifier. We design an argumentation framework named Arena as a communication platform for knowledge integration. Through argumentation based joint learning, high quality individual knowledge can be extracted, and thus a refined global knowledge base can be generated and used independently for classification. We perform numerous experiments on multiple public datasets using AMAJL and other benchmark methods. The results demonstrate that our method can effectively extract high quality knowledge for ensemble classifier and improve the performance of classification. PMID:25966359

Testing single point incremental forming molds for thermoforming operations

NASA Astrophysics Data System (ADS)

Afonso, Daniel; de Sousa, Ricardo Alves; Torcato, Ricardo

2016-10-01

Low pressure polymer processing processes as thermoforming or rotational molding use much simpler molds then high pressure processes like injection. However, despite the low forces involved with the process, molds manufacturing for this operations is still a very material, energy and time consuming operation. The goal of the research is to develop and validate a method for manufacturing plastically formed sheets metal molds by single point incremental forming (SPIF) operation for thermoforming operation. Stewart platform based SPIF machines allow the forming of thick metal sheets, granting the required structural stiffness for the mold surface, and keeping the short lead time manufacture and low thermal inertia.

Uncertainties in cylindrical anode current inferences on pulsed power drivers

NASA Astrophysics Data System (ADS)

Porwitzky, Andrew; Brown, Justin

2018-06-01

For over a decade, velocimetry based techniques have been used to infer the electrical current delivered to dynamic materials properties experiments on pulsed power drivers such as the Z Machine. Though originally developed for planar load geometries, in recent years, inferring the current delivered to cylindrical coaxial loads has become a valuable diagnostic tool for numerous platforms. Presented is a summary of uncertainties that can propagate through the current inference technique when applied to expanding cylindrical anodes. An equation representing quantitative uncertainty is developed which shows the unfold method to be accurate to a few percent above 10 MA of load current.

Program Helps Decompose Complex Design Systems

NASA Technical Reports Server (NTRS)

Rogers, James L., Jr.; Hall, Laura E.

1995-01-01

DeMAID (Design Manager's Aid for Intelligent Decomposition) computer program is knowledge-based software system for ordering sequence of modules and identifying possible multilevel structure for design problems such as large platforms in outer space. Groups modular subsystems on basis of interactions among them. Saves considerable amount of money and time in total design process, particularly in new design problem in which order of modules has not been defined. Originally written for design problems, also applicable to problems containing modules (processes) that take inputs and generate outputs. Available in three machine versions: Macintosh written in Symantec's Think C 3.01, Sun, and SGI IRIS in C language.

Reconfigurable Mobile System - Ground, sea and air applications

NASA Astrophysics Data System (ADS)

Lamonica, Gary L.; Sturges, James W.

1990-11-01

The Reconfigurable Mobile System (RMS) is a highly mobile data-processing unit for military users requiring real-time access to data gathered by airborne (and other) reconnaissance data. RMS combines high-performance computation and image processing workstations with resources for command/control/communications in a single, lightweight shelter. RMS is composed of off-the-shelf components, and is easily reconfigurable to land-vehicle or shipboard versions. Mission planning, which involves an airborne sensor platform's sensor coverage, considered aircraft/sensor capabilities in conjunction with weather, terrain, and threat scenarios. RMS's man-machine interface concept facilitates user familiarization and features iron-based function selection and windowing.

Evaluation of different time domain peak models using extreme learning machine-based peak detection for EEG signal.

PubMed

Adam, Asrul; Ibrahim, Zuwairie; Mokhtar, Norrima; Shapiai, Mohd Ibrahim; Cumming, Paul; Mubin, Marizan

2016-01-01

Various peak models have been introduced to detect and analyze peaks in the time domain analysis of electroencephalogram (EEG) signals. In general, peak model in the time domain analysis consists of a set of signal parameters, such as amplitude, width, and slope. Models including those proposed by Dumpala, Acir, Liu, and Dingle are routinely used to detect peaks in EEG signals acquired in clinical studies of epilepsy or eye blink. The optimal peak model is the most reliable peak detection performance in a particular application. A fair measure of performance of different models requires a common and unbiased platform. In this study, we evaluate the performance of the four different peak models using the extreme learning machine (ELM)-based peak detection algorithm. We found that the Dingle model gave the best performance, with 72 % accuracy in the analysis of real EEG data. Statistical analysis conferred that the Dingle model afforded significantly better mean testing accuracy than did the Acir and Liu models, which were in the range 37-52 %. Meanwhile, the Dingle model has no significant difference compared to Dumpala model.

The study on the nanomachining property and cutting model of single-crystal sapphire by atomic force microscopy.

PubMed

Huang, Jen-Ching; Weng, Yung-Jin

2014-01-01

This study focused on the nanomachining property and cutting model of single-crystal sapphire during nanomachining. The coated diamond probe is used to as a tool, and the atomic force microscopy (AFM) is as an experimental platform for nanomachining. To understand the effect of normal force on single-crystal sapphire machining, this study tested nano-line machining and nano-rectangular pattern machining at different normal force. In nano-line machining test, the experimental results showed that the normal force increased, the groove depth from nano-line machining also increased. And the trend is logarithmic type. In nano-rectangular pattern machining test, it is found when the normal force increases, the groove depth also increased, but rather the accumulation of small chips. This paper combined the blew by air blower, the cleaning by ultrasonic cleaning machine and using contact mode probe to scan the surface topology after nanomaching, and proposed the "criterion of nanomachining cutting model," in order to determine the cutting model of single-crystal sapphire in the nanomachining is ductile regime cutting model or brittle regime cutting model. After analysis, the single-crystal sapphire substrate is processed in small normal force during nano-linear machining; its cutting modes are ductile regime cutting model. In the nano-rectangular pattern machining, due to the impact of machined zones overlap, the cutting mode is converted into a brittle regime cutting model. © 2014 Wiley Periodicals, Inc.

The IHMC CmapTools software in research and education: a multi-level use case in Space Meteorology

NASA Astrophysics Data System (ADS)

Messerotti, Mauro

2010-05-01

The IHMC (Institute for Human and Machine Cognition, Florida University System, USA) CmapTools software is a powerful multi-platform tool for knowledge modelling in graphical form based on concept maps. In this work we present its application for the high-level development of a set of multi-level concept maps in the framework of Space Meteorology to act as the kernel of a space meteorology domain ontology. This is an example of a research use case, as a domain ontology coded in machine-readable form via e.g. OWL (Web Ontology Language) is suitable to be an active layer of any knowledge management system embedded in a Virtual Observatory (VO). Apart from being manageable at machine level, concept maps developed via CmapTools are intrinsically human-readable and can embed hyperlinks and objects of many kinds. Therefore they are suitable to be published on the web: the coded knowledge can be exploited for educational purposes by the students and the public, as the level of information can be naturally organized among linked concept maps in progressively increasing complexity levels. Hence CmapTools and its advanced version COE (Concept-map Ontology Editor) represent effective and user-friendly software tools for high-level knowledge represention in research and education.

MetaJC++: A flexible and automatic program transformation technique using meta framework

NASA Astrophysics Data System (ADS)

Beevi, Nadera S.; Reghu, M.; Chitraprasad, D.; Vinodchandra, S. S.

2014-09-01

Compiler is a tool to translate abstract code containing natural language terms to machine code. Meta compilers are available to compile more than one languages. We have developed a meta framework intends to combine two dissimilar programming languages, namely C++ and Java to provide a flexible object oriented programming platform for the user. Suitable constructs from both the languages have been combined, thereby forming a new and stronger Meta-Language. The framework is developed using the compiler writing tools, Flex and Yacc to design the front end of the compiler. The lexer and parser have been developed to accommodate the complete keyword set and syntax set of both the languages. Two intermediate representations have been used in between the translation of the source program to machine code. Abstract Syntax Tree has been used as a high level intermediate representation that preserves the hierarchical properties of the source program. A new machine-independent stack-based byte-code has also been devised to act as a low level intermediate representation. The byte-code is essentially organised into an output class file that can be used to produce an interpreted output. The results especially in the spheres of providing C++ concepts in Java have given an insight regarding the potential strong features of the resultant meta-language.

Overlay improvements using a real time machine learning algorithm

NASA Astrophysics Data System (ADS)

Schmitt-Weaver, Emil; Kubis, Michael; Henke, Wolfgang; Slotboom, Daan; Hoogenboom, Tom; Mulkens, Jan; Coogans, Martyn; ten Berge, Peter; Verkleij, Dick; van de Mast, Frank

2014-04-01

While semiconductor manufacturing is moving towards the 14nm node using immersion lithography, the overlay requirements are tightened to below 5nm. Next to improvements in the immersion scanner platform, enhancements in the overlay optimization and process control are needed to enable these low overlay numbers. Whereas conventional overlay control methods address wafer and lot variation autonomously with wafer pre exposure alignment metrology and post exposure overlay metrology, we see a need to reduce these variations by correlating more of the TWINSCAN system's sensor data directly to the post exposure YieldStar metrology in time. In this paper we will present the results of a study on applying a real time control algorithm based on machine learning technology. Machine learning methods use context and TWINSCAN system sensor data paired with post exposure YieldStar metrology to recognize generic behavior and train the control system to anticipate on this generic behavior. Specific for this study, the data concerns immersion scanner context, sensor data and on-wafer measured overlay data. By making the link between the scanner data and the wafer data we are able to establish a real time relationship. The result is an inline controller that accounts for small changes in scanner hardware performance in time while picking up subtle lot to lot and wafer to wafer deviations introduced by wafer processing.

A Data Parallel Multizone Navier-Stokes Code

NASA Technical Reports Server (NTRS)

Jespersen, Dennis C.; Levit, Creon; Kwak, Dochan (Technical Monitor)

1995-01-01

We have developed a data parallel multizone compressible Navier-Stokes code on the Connection Machine CM-5. The code is set up for implicit time-stepping on single or multiple structured grids. For multiple grids and geometrically complex problems, we follow the "chimera" approach, where flow data on one zone is interpolated onto another in the region of overlap. We will describe our design philosophy and give some timing results for the current code. The design choices can be summarized as: 1. finite differences on structured grids; 2. implicit time-stepping with either distributed solves or data motion and local solves; 3. sequential stepping through multiple zones with interzone data transfer via a distributed data structure. We have implemented these ideas on the CM-5 using CMF (Connection Machine Fortran), a data parallel language which combines elements of Fortran 90 and certain extensions, and which bears a strong similarity to High Performance Fortran (HPF). One interesting feature is the issue of turbulence modeling, where the architecture of a parallel machine makes the use of an algebraic turbulence model awkward, whereas models based on transport equations are more natural. We will present some performance figures for the code on the CM-5, and consider the issues involved in transitioning the code to HPF for portability to other parallel platforms.

Biotea: RDFizing PubMed Central in support for the paper as an interface to the Web of Data

PubMed Central

2013-01-01

Background The World Wide Web has become a dissemination platform for scientific and non-scientific publications. However, most of the information remains locked up in discrete documents that are not always interconnected or machine-readable. The connectivity tissue provided by RDF technology has not yet been widely used to support the generation of self-describing, machine-readable documents. Results In this paper, we present our approach to the generation of self-describing machine-readable scholarly documents. We understand the scientific document as an entry point and interface to the Web of Data. We have semantically processed the full-text, open-access subset of PubMed Central. Our RDF model and resulting dataset make extensive use of existing ontologies and semantic enrichment services. We expose our model, services, prototype, and datasets at http://biotea.idiginfo.org/ Conclusions The semantic processing of biomedical literature presented in this paper embeds documents within the Web of Data and facilitates the execution of concept-based queries against the entire digital library. Our approach delivers a flexible and adaptable set of tools for metadata enrichment and semantic processing of biomedical documents. Our model delivers a semantically rich and highly interconnected dataset with self-describing content so that software can make effective use of it. PMID:23734622

Visualization of Vgi Data Through the New NASA Web World Wind Virtual Globe

NASA Astrophysics Data System (ADS)

Brovelli, M. A.; Kilsedar, C. E.; Zamboni, G.

2016-06-01

GeoWeb 2.0, laying the foundations of Volunteered Geographic Information (VGI) systems, has led to platforms where users can contribute to the geographic knowledge that is open to access. Moreover, as a result of the advancements in 3D visualization, virtual globes able to visualize geographic data even on browsers emerged. However the integration of VGI systems and virtual globes has not been fully realized. The study presented aims to visualize volunteered data in 3D, considering also the ease of use aspects for general public, using Free and Open Source Software (FOSS). The new Application Programming Interface (API) of NASA, Web World Wind, written in JavaScript and based on Web Graphics Library (WebGL) is cross-platform and cross-browser, so that the virtual globe created using this API can be accessible through any WebGL supported browser on different operating systems and devices, as a result not requiring any installation or configuration on the client-side, making the collected data more usable to users, which is not the case with the World Wind for Java as installation and configuration of the Java Virtual Machine (JVM) is required. Furthermore, the data collected through various VGI platforms might be in different formats, stored in a traditional relational database or in a NoSQL database. The project developed aims to visualize and query data collected through Open Data Kit (ODK) platform and a cross-platform application, where data is stored in a relational PostgreSQL and NoSQL CouchDB databases respectively.

Cloud BioLinux: pre-configured and on-demand bioinformatics computing for the genomics community.

PubMed

Krampis, Konstantinos; Booth, Tim; Chapman, Brad; Tiwari, Bela; Bicak, Mesude; Field, Dawn; Nelson, Karen E

2012-03-19

A steep drop in the cost of next-generation sequencing during recent years has made the technology affordable to the majority of researchers, but downstream bioinformatic analysis still poses a resource bottleneck for smaller laboratories and institutes that do not have access to substantial computational resources. Sequencing instruments are typically bundled with only the minimal processing and storage capacity required for data capture during sequencing runs. Given the scale of sequence datasets, scientific value cannot be obtained from acquiring a sequencer unless it is accompanied by an equal investment in informatics infrastructure. Cloud BioLinux is a publicly accessible Virtual Machine (VM) that enables scientists to quickly provision on-demand infrastructures for high-performance bioinformatics computing using cloud platforms. Users have instant access to a range of pre-configured command line and graphical software applications, including a full-featured desktop interface, documentation and over 135 bioinformatics packages for applications including sequence alignment, clustering, assembly, display, editing, and phylogeny. Each tool's functionality is fully described in the documentation directly accessible from the graphical interface of the VM. Besides the Amazon EC2 cloud, we have started instances of Cloud BioLinux on a private Eucalyptus cloud installed at the J. Craig Venter Institute, and demonstrated access to the bioinformatic tools interface through a remote connection to EC2 instances from a local desktop computer. Documentation for using Cloud BioLinux on EC2 is available from our project website, while a Eucalyptus cloud image and VirtualBox Appliance is also publicly available for download and use by researchers with access to private clouds. Cloud BioLinux provides a platform for developing bioinformatics infrastructures on the cloud. An automated and configurable process builds Virtual Machines, allowing the development of highly customized versions from a shared code base. This shared community toolkit enables application specific analysis platforms on the cloud by minimizing the effort required to prepare and maintain them.

Cloud BioLinux: pre-configured and on-demand bioinformatics computing for the genomics community

PubMed Central

2012-01-01

Background A steep drop in the cost of next-generation sequencing during recent years has made the technology affordable to the majority of researchers, but downstream bioinformatic analysis still poses a resource bottleneck for smaller laboratories and institutes that do not have access to substantial computational resources. Sequencing instruments are typically bundled with only the minimal processing and storage capacity required for data capture during sequencing runs. Given the scale of sequence datasets, scientific value cannot be obtained from acquiring a sequencer unless it is accompanied by an equal investment in informatics infrastructure. Results Cloud BioLinux is a publicly accessible Virtual Machine (VM) that enables scientists to quickly provision on-demand infrastructures for high-performance bioinformatics computing using cloud platforms. Users have instant access to a range of pre-configured command line and graphical software applications, including a full-featured desktop interface, documentation and over 135 bioinformatics packages for applications including sequence alignment, clustering, assembly, display, editing, and phylogeny. Each tool's functionality is fully described in the documentation directly accessible from the graphical interface of the VM. Besides the Amazon EC2 cloud, we have started instances of Cloud BioLinux on a private Eucalyptus cloud installed at the J. Craig Venter Institute, and demonstrated access to the bioinformatic tools interface through a remote connection to EC2 instances from a local desktop computer. Documentation for using Cloud BioLinux on EC2 is available from our project website, while a Eucalyptus cloud image and VirtualBox Appliance is also publicly available for download and use by researchers with access to private clouds. Conclusions Cloud BioLinux provides a platform for developing bioinformatics infrastructures on the cloud. An automated and configurable process builds Virtual Machines, allowing the development of highly customized versions from a shared code base. This shared community toolkit enables application specific analysis platforms on the cloud by minimizing the effort required to prepare and maintain them. PMID:22429538

Optimization of classification and regression analysis of four monoclonal antibodies from Raman spectra using collaborative machine learning approach.

PubMed

Le, Laetitia Minh Maï; Kégl, Balázs; Gramfort, Alexandre; Marini, Camille; Nguyen, David; Cherti, Mehdi; Tfaili, Sana; Tfayli, Ali; Baillet-Guffroy, Arlette; Prognon, Patrice; Chaminade, Pierre; Caudron, Eric

2018-07-01

The use of monoclonal antibodies (mAbs) constitutes one of the most important strategies to treat patients suffering from cancers such as hematological malignancies and solid tumors. These antibodies are prescribed by the physician and prepared by hospital pharmacists. An analytical control enables the quality of the preparations to be ensured. The aim of this study was to explore the development of a rapid analytical method for quality control. The method used four mAbs (Infliximab, Bevacizumab, Rituximab and Ramucirumab) at various concentrations and was based on recording Raman data and coupling them to a traditional chemometric and machine learning approach for data analysis. Compared to conventional linear approach, prediction errors are reduced with a data-driven approach using statistical machine learning methods. In the latter, preprocessing and predictive models are jointly optimized. An additional original aspect of the work involved on submitting the problem to a collaborative data challenge platform called Rapid Analytics and Model Prototyping (RAMP). This allowed using solutions from about 300 data scientists in collaborative work. Using machine learning, the prediction of the four mAbs samples was considerably improved. The best predictive model showed a combined error of 2.4% versus 14.6% using linear approach. The concentration and classification errors were 5.8% and 0.7%, only three spectra were misclassified over the 429 spectra of the test set. This large improvement obtained with machine learning techniques was uniform for all molecules but maximal for Bevacizumab with an 88.3% reduction on combined errors (2.1% versus 17.9%). Copyright © 2018 Elsevier B.V. All rights reserved.

Smartphone-based quantitative measurements on holographic sensors.

PubMed

Khalili Moghaddam, Gita; Lowe, Christopher Robin

2017-01-01

The research reported herein integrates a generic holographic sensor platform and a smartphone-based colour quantification algorithm in order to standardise and improve the determination of the concentration of analytes of interest. The utility of this approach has been exemplified by analysing the replay colour of the captured image of a holographic pH sensor in near real-time. Personalised image encryption followed by a wavelet-based image compression method were applied to secure the image transfer across a bandwidth-limited network to the cloud. The decrypted and decompressed image was processed through four principal steps: Recognition of the hologram in the image with a complex background using a template-based approach, conversion of device-dependent RGB values to device-independent CIEXYZ values using a polynomial model of the camera and computation of the CIEL*a*b* values, use of the colour coordinates of the captured image to segment the image, select the appropriate colour descriptors and, ultimately, locate the region of interest (ROI), i.e. the hologram in this case, and finally, application of a machine learning-based algorithm to correlate the colour coordinates of the ROI to the analyte concentration. Integrating holographic sensors and the colour image processing algorithm potentially offers a cost-effective platform for the remote monitoring of analytes in real time in readily accessible body fluids by minimally trained individuals.

Smartphone-based quantitative measurements on holographic sensors

PubMed Central

Khalili Moghaddam, Gita

2017-01-01

The research reported herein integrates a generic holographic sensor platform and a smartphone-based colour quantification algorithm in order to standardise and improve the determination of the concentration of analytes of interest. The utility of this approach has been exemplified by analysing the replay colour of the captured image of a holographic pH sensor in near real-time. Personalised image encryption followed by a wavelet-based image compression method were applied to secure the image transfer across a bandwidth-limited network to the cloud. The decrypted and decompressed image was processed through four principal steps: Recognition of the hologram in the image with a complex background using a template-based approach, conversion of device-dependent RGB values to device-independent CIEXYZ values using a polynomial model of the camera and computation of the CIEL*a*b* values, use of the colour coordinates of the captured image to segment the image, select the appropriate colour descriptors and, ultimately, locate the region of interest (ROI), i.e. the hologram in this case, and finally, application of a machine learning-based algorithm to correlate the colour coordinates of the ROI to the analyte concentration. Integrating holographic sensors and the colour image processing algorithm potentially offers a cost-effective platform for the remote monitoring of analytes in real time in readily accessible body fluids by minimally trained individuals. PMID:29141008

A New Parallel Approach for Accelerating the GPU-Based Execution of Edge Detection Algorithms

PubMed Central

Emrani, Zahra; Bateni, Soroosh; Rabbani, Hossein

2017-01-01

Real-time image processing is used in a wide variety of applications like those in medical care and industrial processes. This technique in medical care has the ability to display important patient information graphi graphically, which can supplement and help the treatment process. Medical decisions made based on real-time images are more accurate and reliable. According to the recent researches, graphic processing unit (GPU) programming is a useful method for improving the speed and quality of medical image processing and is one of the ways of real-time image processing. Edge detection is an early stage in most of the image processing methods for the extraction of features and object segments from a raw image. The Canny method, Sobel and Prewitt filters, and the Roberts’ Cross technique are some examples of edge detection algorithms that are widely used in image processing and machine vision. In this work, these algorithms are implemented using the Compute Unified Device Architecture (CUDA), Open Source Computer Vision (OpenCV), and Matrix Laboratory (MATLAB) platforms. An existing parallel method for Canny approach has been modified further to run in a fully parallel manner. This has been achieved by replacing the breadth- first search procedure with a parallel method. These algorithms have been compared by testing them on a database of optical coherence tomography images. The comparison of results shows that the proposed implementation of the Canny method on GPU using the CUDA platform improves the speed of execution by 2–100× compared to the central processing unit-based implementation using the OpenCV and MATLAB platforms. PMID:28487831

A New Parallel Approach for Accelerating the GPU-Based Execution of Edge Detection Algorithms.

PubMed

Emrani, Zahra; Bateni, Soroosh; Rabbani, Hossein

2017-01-01

Real-time image processing is used in a wide variety of applications like those in medical care and industrial processes. This technique in medical care has the ability to display important patient information graphi graphically, which can supplement and help the treatment process. Medical decisions made based on real-time images are more accurate and reliable. According to the recent researches, graphic processing unit (GPU) programming is a useful method for improving the speed and quality of medical image processing and is one of the ways of real-time image processing. Edge detection is an early stage in most of the image processing methods for the extraction of features and object segments from a raw image. The Canny method, Sobel and Prewitt filters, and the Roberts' Cross technique are some examples of edge detection algorithms that are widely used in image processing and machine vision. In this work, these algorithms are implemented using the Compute Unified Device Architecture (CUDA), Open Source Computer Vision (OpenCV), and Matrix Laboratory (MATLAB) platforms. An existing parallel method for Canny approach has been modified further to run in a fully parallel manner. This has been achieved by replacing the breadth- first search procedure with a parallel method. These algorithms have been compared by testing them on a database of optical coherence tomography images. The comparison of results shows that the proposed implementation of the Canny method on GPU using the CUDA platform improves the speed of execution by 2-100× compared to the central processing unit-based implementation using the OpenCV and MATLAB platforms.

Using data from the Microsoft Kinect 2 to determine postural stability in healthy subjects: A feasibility trial

PubMed Central

Smeragliuolo, Anna H.; Long, John Davis; Bumanlag, Silverio Joseph; He, Victor; Lampe, Anna

2017-01-01

The objective of this study was to determine whether kinematic data collected by the Microsoft Kinect 2 (MK2) could be used to quantify postural stability in healthy subjects. Twelve subjects were recruited for the project, and were instructed to perform a sequence of simple postural stability tasks. The movement sequence was performed as subjects were seated on top of a force platform, and the MK2 was positioned in front of them. This sequence of tasks was performed by each subject under three different postural conditions: “both feet on the ground” (1), “One foot off the ground” (2), and “both feet off the ground” (3). We compared force platform and MK2 data to quantify the degree to which the MK2 was returning reliable data across subjects. We then applied a novel machine-learning paradigm to the MK2 data in order to determine the extent to which data from the MK2 could be used to reliably classify different postural conditions. Our initial comparison of force plate and MK2 data showed a strong agreement between the two devices, with strong Pearson correlations between the trunk centroids “Spine_Mid” (0.85 ± 0.06), “Neck” (0.86 ± 0.07) and “Head” (0.87 ± 0.07), and the center of pressure centroid inferred by the force platform. Mean accuracy for the machine learning classifier from MK2 was 97.0%, with a specific classification accuracy breakdown of 90.9%, 100%, and 100% for conditions 1 through 3, respectively. Mean accuracy for the machine learning classifier derived from the force platform data was lower at 84.4%. We conclude that data from the MK2 has sufficient information content to allow us to classify sequences of tasks being performed under different levels of postural stability. Future studies will focus on validating this protocol on large populations of individuals with actual balance impairments in order to create a toolkit that is clinically validated and available to the medical community. PMID:28196139

Using data from the Microsoft Kinect 2 to determine postural stability in healthy subjects: A feasibility trial.

PubMed

Dehbandi, Behdad; Barachant, Alexandre; Smeragliuolo, Anna H; Long, John Davis; Bumanlag, Silverio Joseph; He, Victor; Lampe, Anna; Putrino, David

2017-01-01

The objective of this study was to determine whether kinematic data collected by the Microsoft Kinect 2 (MK2) could be used to quantify postural stability in healthy subjects. Twelve subjects were recruited for the project, and were instructed to perform a sequence of simple postural stability tasks. The movement sequence was performed as subjects were seated on top of a force platform, and the MK2 was positioned in front of them. This sequence of tasks was performed by each subject under three different postural conditions: "both feet on the ground" (1), "One foot off the ground" (2), and "both feet off the ground" (3). We compared force platform and MK2 data to quantify the degree to which the MK2 was returning reliable data across subjects. We then applied a novel machine-learning paradigm to the MK2 data in order to determine the extent to which data from the MK2 could be used to reliably classify different postural conditions. Our initial comparison of force plate and MK2 data showed a strong agreement between the two devices, with strong Pearson correlations between the trunk centroids "Spine_Mid" (0.85 ± 0.06), "Neck" (0.86 ± 0.07) and "Head" (0.87 ± 0.07), and the center of pressure centroid inferred by the force platform. Mean accuracy for the machine learning classifier from MK2 was 97.0%, with a specific classification accuracy breakdown of 90.9%, 100%, and 100% for conditions 1 through 3, respectively. Mean accuracy for the machine learning classifier derived from the force platform data was lower at 84.4%. We conclude that data from the MK2 has sufficient information content to allow us to classify sequences of tasks being performed under different levels of postural stability. Future studies will focus on validating this protocol on large populations of individuals with actual balance impairments in order to create a toolkit that is clinically validated and available to the medical community.

The portals 4.0.1 network programming interface.

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

Barrett, Brian W.; Brightwell, Ronald Brian; Pedretti, Kevin

2013-04-01

This report presents a specification for the Portals 4.0 network programming interface. Portals 4.0 is intended to allow scalable, high-performance network communication between nodes of a parallel computing system. Portals 4.0 is well suited to massively parallel processing and embedded systems. Portals 4.0 represents an adaption of the data movement layer developed for massively parallel processing platforms, such as the 4500-node Intel TeraFLOPS machine. Sandias Cplant cluster project motivated the development of Version 3.0, which was later extended to Version 3.3 as part of the Cray Red Storm machine and XT line. Version 4.0 is targeted to the next generationmore » of machines employing advanced network interface architectures that support enhanced offload capabilities. 3« less

Study on magnetic force of electromagnetic levitation circular knitting machine

NASA Astrophysics Data System (ADS)

Wu, X. G.; Zhang, C.; Xu, X. S.; Zhang, J. G.; Yan, N.; Zhang, G. Z.

2018-06-01

The structure of the driving coil and the electromagnetic force of the test prototype of electromagnetic-levitation (EL) circular knitting machine are studied. In this paper, the driving coil’s structure and working principle of the EL circular knitting machine are firstly introduced, then the mathematical modelling analysis of the driving electromagnetic force is carried out, and through the Ansoft Maxwell finite element simulation software the coil’s magnetic induction intensity and the needle’s electromagnetic force is simulated, finally an experimental platform is built to measure the coil’s magnetic induction intensity and the needle’s electromagnetic force. The results show that the theoretical analysis, the simulation analysis and the results of the test are very close, which proves the correctness of the proposed model.

Implementation of a Web-Based Collaborative Process Planning System

NASA Astrophysics Data System (ADS)

Wang, Huifen; Liu, Tingting; Qiao, Li; Huang, Shuangxi

Under the networked manufacturing environment, all phases of product manufacturing involving design, process planning, machining and assembling may be accomplished collaboratively by different enterprises, even different manufacturing stages of the same part may be finished collaboratively by different enterprises. Based on the self-developed networked manufacturing platform eCWS(e-Cooperative Work System), a multi-agent-based system framework for collaborative process planning is proposed. In accordance with requirements of collaborative process planning, share resources provided by cooperative enterprises in the course of collaboration are classified into seven classes. Then a reconfigurable and extendable resource object model is built. Decision-making strategy is also studied in this paper. Finally a collaborative process planning system e-CAPP is developed and applied. It provides strong support for distributed designers to collaboratively plan and optimize product process though network.

Distributed Processing of Sentinel-2 Products using the BIGEARTH Platform

NASA Astrophysics Data System (ADS)

Bacu, Victor; Stefanut, Teodor; Nandra, Constantin; Mihon, Danut; Gorgan, Dorian

2017-04-01

The constellation of observational satellites orbiting around Earth is constantly increasing, providing more data that need to be processed in order to extract meaningful information and knowledge from it. Sentinel-2 satellites, part of the Copernicus Earth Observation program, aim to be used in agriculture, forestry and many other land management applications. ESA's SNAP toolbox can be used to process data gathered by Sentinel-2 satellites but is limited to the resources provided by a stand-alone computer. In this paper we present a cloud based software platform that makes use of this toolbox together with other remote sensing software applications to process Sentinel-2 products. The BIGEARTH software platform [1] offers an integrated solution for processing Earth Observation data coming from different sources (such as satellites or on-site sensors). The flow of processing is defined as a chain of tasks based on the WorDeL description language [2]. Each task could rely on a different software technology (such as Grass GIS and ESA's SNAP) in order to process the input data. One important feature of the BIGEARTH platform comes from this possibility of interconnection and integration, throughout the same flow of processing, of the various well known software technologies. All this integration is transparent from the user perspective. The proposed platform extends the SNAP capabilities by enabling specialists to easily scale the processing over distributed architectures, according to their specific needs and resources. The software platform [3] can be used in multiple configurations. In the basic one the software platform runs as a standalone application inside a virtual machine. Obviously in this case the computational resources are limited but it will give an overview of the functionalities of the software platform, and also the possibility to define the flow of processing and later on to execute it on a more complex infrastructure. The most complex and robust configuration is based on cloud computing and allows the installation on a private or public cloud infrastructure. In this configuration, the processing resources can be dynamically allocated and the execution time can be considerably improved by the available virtual resources and the number of parallelizable sequences in the processing flow. The presentation highlights the benefits and issues of the proposed solution by analyzing some significant experimental use cases. Main references for further information: [1] BigEarth project, http://cgis.utcluj.ro/projects/bigearth [2] Constantin Nandra, Dorian Gorgan: "Defining Earth data batch processing tasks by means of a flexible workflow description language", ISPRS Ann. Photogramm. Remote Sens. Spatial Inf. Sci., III-4, 59-66, (2016). [3] Victor Bacu, Teodor Stefanut, Dorian Gorgan, "Adaptive Processing of Earth Observation Data on Cloud Infrastructures Based on Workflow Description", Proceedings of the Intelligent Computer Communication and Processing (ICCP), IEEE-Press, pp.444-454, (2015).

GISpark: A Geospatial Distributed Computing Platform for Spatiotemporal Big Data

NASA Astrophysics Data System (ADS)

Wang, S.; Zhong, E.; Wang, E.; Zhong, Y.; Cai, W.; Li, S.; Gao, S.

2016-12-01

Geospatial data are growing exponentially because of the proliferation of cost effective and ubiquitous positioning technologies such as global remote-sensing satellites and location-based devices. Analyzing large amounts of geospatial data can provide great value for both industrial and scientific applications. Data- and compute- intensive characteristics inherent in geospatial big data increasingly pose great challenges to technologies of data storing, computing and analyzing. Such challenges require a scalable and efficient architecture that can store, query, analyze, and visualize large-scale spatiotemporal data. Therefore, we developed GISpark - a geospatial distributed computing platform for processing large-scale vector, raster and stream data. GISpark is constructed based on the latest virtualized computing infrastructures and distributed computing architecture. OpenStack and Docker are used to build multi-user hosting cloud computing infrastructure for GISpark. The virtual storage systems such as HDFS, Ceph, MongoDB are combined and adopted for spatiotemporal data storage management. Spark-based algorithm framework is developed for efficient parallel computing. Within this framework, SuperMap GIScript and various open-source GIS libraries can be integrated into GISpark. GISpark can also integrated with scientific computing environment (e.g., Anaconda), interactive computing web applications (e.g., Jupyter notebook), and machine learning tools (e.g., TensorFlow/Orange). The associated geospatial facilities of GISpark in conjunction with the scientific computing environment, exploratory spatial data analysis tools, temporal data management and analysis systems make up a powerful geospatial computing tool. GISpark not only provides spatiotemporal big data processing capacity in the geospatial field, but also provides spatiotemporal computational model and advanced geospatial visualization tools that deals with other domains related with spatial property. We tested the performance of the platform based on taxi trajectory analysis. Results suggested that GISpark achieves excellent run time performance in spatiotemporal big data applications.

Classification of single-trial auditory events using dry-wireless EEG during real and motion simulated flight.

PubMed

Callan, Daniel E; Durantin, Gautier; Terzibas, Cengiz

2015-01-01

Application of neuro-augmentation technology based on dry-wireless EEG may be considerably beneficial for aviation and space operations because of the inherent dangers involved. In this study we evaluate classification performance of perceptual events using a dry-wireless EEG system during motion platform based flight simulation and actual flight in an open cockpit biplane to determine if the system can be used in the presence of considerable environmental and physiological artifacts. A passive task involving 200 random auditory presentations of a chirp sound was used for evaluation. The advantage of this auditory task is that it does not interfere with the perceptual motor processes involved with piloting the plane. Classification was based on identifying the presentation of a chirp sound vs. silent periods. Evaluation of Independent component analysis (ICA) and Kalman filtering to enhance classification performance by extracting brain activity related to the auditory event from other non-task related brain activity and artifacts was assessed. The results of permutation testing revealed that single trial classification of presence or absence of an auditory event was significantly above chance for all conditions on a novel test set. The best performance could be achieved with both ICA and Kalman filtering relative to no processing: Platform Off (83.4% vs. 78.3%), Platform On (73.1% vs. 71.6%), Biplane Engine Off (81.1% vs. 77.4%), and Biplane Engine On (79.2% vs. 66.1%). This experiment demonstrates that dry-wireless EEG can be used in environments with considerable vibration, wind, acoustic noise, and physiological artifacts and achieve good single trial classification performance that is necessary for future successful application of neuro-augmentation technology based on brain-machine interfaces.

A native IP satellite communications system

NASA Astrophysics Data System (ADS)

Koudelka, O.; Schmidt, M.; Ebert, J.; Schlemmer, H.; Kastner-Puschl, S.; Riedler, W.

2004-08-01

≪ In the framework of ESA's ARTES-5 program the Institute of Applied Systems Technology (Joanneum Research) in cooperation with the Department of Communications and Wave Propagation has developed a novel meshed satellite communications system which is optimised for Internet traffic and applications (L*IP—Local Network Interconnection via Satellite Systems Using the IP Protocol Suite). Both symmetrical and asymmetrical connections are supported. Bandwidth on demand and guaranteed quality of service are key features of the system. A novel multi-frequency TDMA access scheme utilises efficient methods of IP encapsulation. In contrast to other solutions it avoids legacy transport network techniques. While the DVB-RCS standard is based on ATM or MPEG transport cells, the solution of the L*IP system uses variable-length cells which reduces the overhead significantly. A flexible and programmable platform based on Linux machines was chosen to allow the easy implementation and adaptation to different standards. This offers the possibility to apply the system not only to satellite communications, but provides seamless integration with terrestrial fixed broadcast wireless access systems. The platform is also an ideal test-bed for a variety of interactive broadband communications systems. The paper describes the system architecture and the key features of the system.

Integrated Semantics Service Platform for the Internet of Things: A Case Study of a Smart Office

PubMed Central

Ryu, Minwoo; Kim, Jaeho; Yun, Jaeseok

2015-01-01

The Internet of Things (IoT) allows machines and devices in the world to connect with each other and generate a huge amount of data, which has a great potential to provide useful knowledge across service domains. Combining the context of IoT with semantic technologies, we can build integrated semantic systems to support semantic interoperability. In this paper, we propose an integrated semantic service platform (ISSP) to support ontological models in various IoT-based service domains of a smart city. In particular, we address three main problems for providing integrated semantic services together with IoT systems: semantic discovery, dynamic semantic representation, and semantic data repository for IoT resources. To show the feasibility of the ISSP, we develop a prototype service for a smart office using the ISSP, which can provide a preset, personalized office environment by interpreting user text input via a smartphone. We also discuss a scenario to show how the ISSP-based method would help build a smart city, where services in each service domain can discover and exploit IoT resources that are wanted across domains. We expect that our method could eventually contribute to providing people in a smart city with more integrated, comprehensive services based on semantic interoperability. PMID:25608216

Integrated semantics service platform for the Internet of Things: a case study of a smart office.

PubMed

Ryu, Minwoo; Kim, Jaeho; Yun, Jaeseok

2015-01-19

The Internet of Things (IoT) allows machines and devices in the world to connect with each other and generate a huge amount of data, which has a great potential to provide useful knowledge across service domains. Combining the context of IoT with semantic technologies, we can build integrated semantic systems to support semantic interoperability. In this paper, we propose an integrated semantic service platform (ISSP) to support ontological models in various IoT-based service domains of a smart city. In particular, we address three main problems for providing integrated semantic services together with IoT systems: semantic discovery, dynamic semantic representation, and semantic data repository for IoT resources. To show the feasibility of the ISSP, we develop a prototype service for a smart office using the ISSP, which can provide a preset, personalized office environment by interpreting user text input via a smartphone. We also discuss a scenario to show how the ISSP-based method would help build a smart city, where services in each service domain can discover and exploit IoT resources that are wanted across domains. We expect that our method could eventually contribute to providing people in a smart city with more integrated, comprehensive services based on semantic interoperability.

An adaptive process-based cloud infrastructure for space situational awareness applications

NASA Astrophysics Data System (ADS)

Liu, Bingwei; Chen, Yu; Shen, Dan; Chen, Genshe; Pham, Khanh; Blasch, Erik; Rubin, Bruce

2014-06-01

Space situational awareness (SSA) and defense space control capabilities are top priorities for groups that own or operate man-made spacecraft. Also, with the growing amount of space debris, there is an increase in demand for contextual understanding that necessitates the capability of collecting and processing a vast amount sensor data. Cloud computing, which features scalable and flexible storage and computing services, has been recognized as an ideal candidate that can meet the large data contextual challenges as needed by SSA. Cloud computing consists of physical service providers and middleware virtual machines together with infrastructure, platform, and software as service (IaaS, PaaS, SaaS) models. However, the typical Virtual Machine (VM) abstraction is on a per operating systems basis, which is at too low-level and limits the flexibility of a mission application architecture. In responding to this technical challenge, a novel adaptive process based cloud infrastructure for SSA applications is proposed in this paper. In addition, the details for the design rationale and a prototype is further examined. The SSA Cloud (SSAC) conceptual capability will potentially support space situation monitoring and tracking, object identification, and threat assessment. Lastly, the benefits of a more granular and flexible cloud computing resources allocation are illustrated for data processing and implementation considerations within a representative SSA system environment. We show that the container-based virtualization performs better than hypervisor-based virtualization technology in an SSA scenario.

Air-condition Control System of Weaving Workshop Based on LabVIEW

NASA Astrophysics Data System (ADS)

Song, Jian

The project of air-condition measurement and control system based on LabVIEW is put forward for the sake of controlling effectively the environmental targets in the weaving workshop. In this project, which is based on the virtual instrument technology and in which LabVIEW development platform by NI is adopted, the system is constructed on the basis of the virtual instrument technology. It is composed of the upper PC, central control nodes based on CC2530, sensor nodes, sensor modules and executive device. Fuzzy control algorithm is employed to achieve the accuracy control of the temperature and humidity. A user-friendly man-machine interaction interface is designed with virtual instrument technology at the core of the software. It is shown by experiments that the measurement and control system can run stably and reliably and meet the functional requirements for controlling the weaving workshop.

Design and deployment of an elastic network test-bed in IHEP data center based on SDN

NASA Astrophysics Data System (ADS)

Zeng, Shan; Qi, Fazhi; Chen, Gang

2017-10-01

High energy physics experiments produce huge amounts of raw data, while because of the sharing characteristics of the network resources, there is no guarantee of the available bandwidth for each experiment which may cause link congestion problems. On the other side, with the development of cloud computing technologies, IHEP have established a cloud platform based on OpenStack which can ensure the flexibility of the computing and storage resources, and more and more computing applications have been deployed on virtual machines established by OpenStack. However, under the traditional network architecture, network capability can’t be required elastically, which becomes the bottleneck of restricting the flexible application of cloud computing. In order to solve the above problems, we propose an elastic cloud data center network architecture based on SDN, and we also design a high performance controller cluster based on OpenDaylight. In the end, we present our current test results.

Microfluidic microscopy-assisted label-free approach for cancer screening: automated microfluidic cytology for cancer screening.

PubMed

Jagannadh, Veerendra Kalyan; Gopakumar, G; Subrahmanyam, Gorthi R K Sai; Gorthi, Sai Siva

2017-05-01

Each year, about 7-8 million deaths occur due to cancer around the world. More than half of the cancer-related deaths occur in the less-developed parts of the world. Cancer mortality rate can be reduced with early detection and subsequent treatment of the disease. In this paper, we introduce a microfluidic microscopy-based cost-effective and label-free approach for identification of cancerous cells. We outline a diagnostic framework for the same and detail an instrumentation layout. We have employed classical computer vision techniques such as 2D principal component analysis-based cell type representation followed by support vector machine-based classification. Analogous to criminal face recognition systems implemented with help of surveillance cameras, a signature-based approach for cancerous cell identification using microfluidic microscopy surveillance is demonstrated. Such a platform would facilitate affordable mass screening camps in the developing countries and therefore help decrease cancer mortality rate.

Method and system for enabling real-time speckle processing using hardware platforms

NASA Technical Reports Server (NTRS)

Ortiz, Fernando E. (Inventor); Kelmelis, Eric (Inventor); Durbano, James P. (Inventor); Curt, Peterson F. (Inventor)

2012-01-01

An accelerator for the speckle atmospheric compensation algorithm may enable real-time speckle processing of video feeds that may enable the speckle algorithm to be applied in numerous real-time applications. The accelerator may be implemented in various forms, including hardware, software, and/or machine-readable media.

Design and implementation of laser target simulator in hardware-in-the-loop simulation system based on LabWindows/CVI and RTX

NASA Astrophysics Data System (ADS)

Tong, Qiujie; Wang, Qianqian; Li, Xiaoyang; Shan, Bin; Cui, Xuntai; Li, Chenyu; Peng, Zhong

2016-11-01

In order to satisfy the requirements of the real-time and generality, a laser target simulator in semi-physical simulation system based on RTX+LabWindows/CVI platform is proposed in this paper. Compared with the upper-lower computers simulation platform architecture used in the most of the real-time system now, this system has better maintainability and portability. This system runs on the Windows platform, using Windows RTX real-time extension subsystem to ensure the real-time performance of the system combining with the reflective memory network to complete some real-time tasks such as calculating the simulation model, transmitting the simulation data, and keeping real-time communication. The real-time tasks of simulation system run under the RTSS process. At the same time, we use the LabWindows/CVI to compile a graphical interface, and complete some non-real-time tasks in the process of simulation such as man-machine interaction, display and storage of the simulation data, which run under the Win32 process. Through the design of RTX shared memory and task scheduling algorithm, the data interaction between the real-time tasks process of RTSS and non-real-time tasks process of Win32 is completed. The experimental results show that this system has the strongly real-time performance, highly stability, and highly simulation accuracy. At the same time, it also has the good performance of human-computer interaction.

Improving the interactivity and functionality of Web-based radiology teaching files with the Java programming language.

PubMed

Eng, J

1997-01-01

Java is a programming language that runs on a "virtual machine" built into World Wide Web (WWW)-browsing programs on multiple hardware platforms. Web pages were developed with Java to enable Web-browsing programs to overlay transparent graphics and text on displayed images so that the user could control the display of labels and annotations on the images, a key feature not available with standard Web pages. This feature was extended to include the presentation of normal radiologic anatomy. Java programming was also used to make Web browsers compatible with the Digital Imaging and Communications in Medicine (DICOM) file format. By enhancing the functionality of Web pages, Java technology should provide greater incentive for using a Web-based approach in the development of radiology teaching material.

A journey from nuclear criticality methods to high energy density radflow experiments

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

Urbatsch, Todd James

Los Alamos National Laboratory is a nuclear weapons laboratory supporting our nation's defense. In support of this mission is a high energy-density physics program in which we design and execute experiments to study radiationhydrodynamics phenomena and improve the predictive capability of our largescale multi-physics software codes on our big-iron computers. The Radflow project’s main experimental effort now is to understand why we haven't been able to predict opacities on Sandia National Laboratory's Z-machine. We are modeling an increasing fraction of the Z-machine's dynamic hohlraum to find multi-physics explanations for the experimental results. Further, we are building an entirely different opacitymore » platform on Lawrence Livermore National Laboratory's National Ignition Facility (NIF), which is set to get results early 2017. Will the results match our predictions, match the Z-machine, or give us something entirely different? The new platform brings new challenges such as designing hohlraums and spectrometers. The speaker will recount his history, starting with one-dimensional Monte Carlo nuclear criticality methods in graduate school, radiative transfer methods research and software development for his first 16 years at LANL, and, now, radflow technology and experiments. Who knew that the real world was more than just radiation transport? Experiments aren't easy and they are as saturated with politics as a presidential election, but they sure are fun.« less

Downscaling of Aircraft-, Landsat-, and MODIS-based Land Surface Temperature Images with Support Vector Machines

NASA Astrophysics Data System (ADS)

Ha, W.; Gowda, P. H.; Oommen, T.; Howell, T. A.; Hernandez, J. E.

2010-12-01

High spatial resolution Land Surface Temperature (LST) images are required to estimate evapotranspiration (ET) at a field scale for irrigation scheduling purposes. Satellite sensors such as Landsat 5 Thematic Mapper (TM) and Moderate Resolution Imaging Spectroradiometer (MODIS) can offer images at several spectral bandwidths including visible, near-infrared (NIR), shortwave-infrared, and thermal-infrared (TIR). The TIR images usually have coarser spatial resolutions than those from non-thermal infrared bands. Due to this technical constraint of the satellite sensors on these platforms, image downscaling has been proposed in the field of ET remote sensing. This paper explores the potential of the Support Vector Machines (SVM) to perform downscaling of LST images derived from aircraft (4 m spatial resolution), TM (120 m), and MODIS (1000 m) using normalized difference vegetation index images derived from simultaneously acquired high resolution visible and NIR data (1 m for aircraft, 30 m for TM, and 250 m for MODIS). The SVM is a new generation machine learning algorithm that has found a wide application in the field of pattern recognition and time series analysis. The SVM would be ideally suited for downscaling problems due to its generalization ability in capturing non-linear regression relationship between the predictand and the multiple predictors. Remote sensing data acquired over the Texas High Plains during the 2008 summer growing season will be used in this study. Accuracy assessment of the downscaled 1, 30, and 250 m LST images will be made by comparing them with LST data measured with infrared thermometers at a small spatial scale, upscaled 30 m aircraft-based LST images, and upscaled 250 m TM-based LST images, respectively.

Product quality management based on CNC machine fault prognostics and diagnosis

NASA Astrophysics Data System (ADS)

Kozlov, A. M.; Al-jonid, Kh M.; Kozlov, A. A.; Antar, Sh D.

2018-03-01

This paper presents a new fault classification model and an integrated approach to fault diagnosis which involves the combination of ideas of Neuro-fuzzy Networks (NF), Dynamic Bayesian Networks (DBN) and Particle Filtering (PF) algorithm on a single platform. In the new model, faults are categorized in two aspects, namely first and second degree faults. First degree faults are instantaneous in nature, and second degree faults are evolutional and appear as a developing phenomenon which starts from the initial stage, goes through the development stage and finally ends at the mature stage. These categories of faults have a lifetime which is inversely proportional to a machine tool's life according to the modified version of Taylor’s equation. For fault diagnosis, this framework consists of two phases: the first one is focusing on fault prognosis, which is done online, and the second one is concerned with fault diagnosis which depends on both off-line and on-line modules. In the first phase, a neuro-fuzzy predictor is used to take a decision on whether to embark Conditional Based Maintenance (CBM) or fault diagnosis based on the severity of a fault. The second phase only comes into action when an evolving fault goes beyond a critical threshold limit called a CBM limit for a command to be issued for fault diagnosis. During this phase, DBN and PF techniques are used as an intelligent fault diagnosis system to determine the severity, time and location of the fault. The feasibility of this approach was tested in a simulation environment using the CNC machine as a case study and the results were studied and analyzed.

Wireless brain-machine interface using EEG and EOG: brain wave classification and robot control

NASA Astrophysics Data System (ADS)

Oh, Sechang; Kumar, Prashanth S.; Kwon, Hyeokjun; Varadan, Vijay K.

2012-04-01

A brain-machine interface (BMI) links a user's brain activity directly to an external device. It enables a person to control devices using only thought. Hence, it has gained significant interest in the design of assistive devices and systems for people with disabilities. In addition, BMI has also been proposed to replace humans with robots in the performance of dangerous tasks like explosives handling/diffusing, hazardous materials handling, fire fighting etc. There are mainly two types of BMI based on the measurement method of brain activity; invasive and non-invasive. Invasive BMI can provide pristine signals but it is expensive and surgery may lead to undesirable side effects. Recent advances in non-invasive BMI have opened the possibility of generating robust control signals from noisy brain activity signals like EEG and EOG. A practical implementation of a non-invasive BMI such as robot control requires: acquisition of brain signals with a robust wearable unit, noise filtering and signal processing, identification and extraction of relevant brain wave features and finally, an algorithm to determine control signals based on the wave features. In this work, we developed a wireless brain-machine interface with a small platform and established a BMI that can be used to control the movement of a robot by using the extracted features of the EEG and EOG signals. The system records and classifies EEG as alpha, beta, delta, and theta waves. The classified brain waves are then used to define the level of attention. The acceleration and deceleration or stopping of the robot is controlled based on the attention level of the wearer. In addition, the left and right movements of eye ball control the direction of the robot.

Using virtual machine monitors to overcome the challenges of monitoring and managing virtualized cloud infrastructures

NASA Astrophysics Data System (ADS)

Bamiah, Mervat Adib; Brohi, Sarfraz Nawaz; Chuprat, Suriayati

2012-01-01

Virtualization is one of the hottest research topics nowadays. Several academic researchers and developers from IT industry are designing approaches for solving security and manageability issues of Virtual Machines (VMs) residing on virtualized cloud infrastructures. Moving the application from a physical to a virtual platform increases the efficiency, flexibility and reduces management cost as well as effort. Cloud computing is adopting the paradigm of virtualization, using this technique, memory, CPU and computational power is provided to clients' VMs by utilizing the underlying physical hardware. Beside these advantages there are few challenges faced by adopting virtualization such as management of VMs and network traffic, unexpected additional cost and resource allocation. Virtual Machine Monitor (VMM) or hypervisor is the tool used by cloud providers to manage the VMs on cloud. There are several heterogeneous hypervisors provided by various vendors that include VMware, Hyper-V, Xen and Kernel Virtual Machine (KVM). Considering the challenge of VM management, this paper describes several techniques to monitor and manage virtualized cloud infrastructures.

Alchemy: A Web 2.0 Real-time Quality Assurance Platform for Human Immunodeficiency Virus, Hepatitis C Virus, and BK Virus Quantitation Assays.

PubMed

Agosto-Arroyo, Emmanuel; Coshatt, Gina M; Winokur, Thomas S; Harada, Shuko; Park, Seung L

2017-01-01

The molecular diagnostics laboratory faces the challenge of improving test turnaround time (TAT). Low and consistent TATs are of great clinical and regulatory importance, especially for molecular virology tests. Laboratory information systems (LISs) contain all the data elements necessary to do accurate quality assurance (QA) reporting of TAT and other measures, but these reports are in most cases still performed manually: a time-consuming and error-prone task. The aim of this study was to develop a web-based real-time QA platform that would automate QA reporting in the molecular diagnostics laboratory at our institution, and minimize the time expended in preparing these reports. Using a standard Linux, Nginx, MariaDB, PHP stack virtual machine running atop a Dell Precision 5810, we designed and built a web-based QA platform, code-named Alchemy. Data files pulled periodically from the LIS in comma-separated value format were used to autogenerate QA reports for the human immunodeficiency virus (HIV) quantitation, hepatitis C virus (HCV) quantitation, and BK virus (BKV) quantitation. Alchemy allowed the user to select a specific timeframe to be analyzed and calculated key QA statistics in real-time, including the average TAT in days, tests falling outside the expected TAT ranges, and test result ranges. Before implementing Alchemy, reporting QA for the HIV, HCV, and BKV quantitation assays took 45-60 min of personnel time per test every month. With Alchemy, that time has decreased to 15 min total per month. Alchemy allowed the user to select specific periods of time and analyzed the TAT data in-depth without the need of extensive manual calculations. Alchemy has significantly decreased the time and the human error associated with QA report generation in our molecular diagnostics laboratory. Other tests will be added to this web-based platform in future updates. This effort shows the utility of informatician-supervised resident/fellow programming projects as learning opportunities and workflow improvements in the molecular laboratory.

Human-machine analytics for closed-loop sense-making in time-dominant cyber defense problems

NASA Astrophysics Data System (ADS)

Henry, Matthew H.

2017-05-01

Many defense problems are time-dominant: attacks progress at speeds that outpace human-centric systems designed for monitoring and response. Despite this shortcoming, these well-honed and ostensibly reliable systems pervade most domains, including cyberspace. The argument that often prevails when considering the automation of defense is that while technological systems are suitable for simple, well-defined tasks, only humans possess sufficiently nuanced understanding of problems to act appropriately under complicated circumstances. While this perspective is founded in verifiable truths, it does not account for a middle ground in which human-managed technological capabilities extend well into the territory of complex reasoning, thereby automating more nuanced sense-making and dramatically increasing the speed at which it can be applied. Snort1 and platforms like it enable humans to build, refine, and deploy sense-making tools for network defense. Shortcomings of these platforms include a reliance on rule-based logic, which confounds analyst knowledge of how bad actors behave with the means by which bad behaviors can be detected, and a lack of feedback-informed automation of sensor deployment. We propose an approach in which human-specified computational models hypothesize bad behaviors independent of indicators and then allocate sensors to estimate and forecast the state of an intrusion. State estimates and forecasts inform the proactive deployment of additional sensors and detection logic, thereby closing the sense-making loop. All the while, humans are on the loop, rather than in it, permitting nuanced management of fast-acting automated measurement, detection, and inference engines. This paper motivates and conceptualizes analytics to facilitate this human-machine partnership.

Introducing meta-services for biomedical information extraction

PubMed Central

Leitner, Florian; Krallinger, Martin; Rodriguez-Penagos, Carlos; Hakenberg, Jörg; Plake, Conrad; Kuo, Cheng-Ju; Hsu, Chun-Nan; Tsai, Richard Tzong-Han; Hung, Hsi-Chuan; Lau, William W; Johnson, Calvin A; Sætre, Rune; Yoshida, Kazuhiro; Chen, Yan Hua; Kim, Sun; Shin, Soo-Yong; Zhang, Byoung-Tak; Baumgartner, William A; Hunter, Lawrence; Haddow, Barry; Matthews, Michael; Wang, Xinglong; Ruch, Patrick; Ehrler, Frédéric; Özgür, Arzucan; Erkan, Güneş; Radev, Dragomir R; Krauthammer, Michael; Luong, ThaiBinh; Hoffmann, Robert; Sander, Chris; Valencia, Alfonso

2008-01-01

We introduce the first meta-service for information extraction in molecular biology, the BioCreative MetaServer (BCMS; ). This prototype platform is a joint effort of 13 research groups and provides automatically generated annotations for PubMed/Medline abstracts. Annotation types cover gene names, gene IDs, species, and protein-protein interactions. The annotations are distributed by the meta-server in both human and machine readable formats (HTML/XML). This service is intended to be used by biomedical researchers and database annotators, and in biomedical language processing. The platform allows direct comparison, unified access, and result aggregation of the annotations. PMID:18834497

A Machine Learned Classifier That Uses Gene Expression Data to Accurately Predict Estrogen Receptor Status

PubMed Central

Bastani, Meysam; Vos, Larissa; Asgarian, Nasimeh; Deschenes, Jean; Graham, Kathryn; Mackey, John; Greiner, Russell

2013-01-01

Background Selecting the appropriate treatment for breast cancer requires accurately determining the estrogen receptor (ER) status of the tumor. However, the standard for determining this status, immunohistochemical analysis of formalin-fixed paraffin embedded samples, suffers from numerous technical and reproducibility issues. Assessment of ER-status based on RNA expression can provide more objective, quantitative and reproducible test results. Methods To learn a parsimonious RNA-based classifier of hormone receptor status, we applied a machine learning tool to a training dataset of gene expression microarray data obtained from 176 frozen breast tumors, whose ER-status was determined by applying ASCO-CAP guidelines to standardized immunohistochemical testing of formalin fixed tumor. Results This produced a three-gene classifier that can predict the ER-status of a novel tumor, with a cross-validation accuracy of 93.17±2.44%. When applied to an independent validation set and to four other public databases, some on different platforms, this classifier obtained over 90% accuracy in each. In addition, we found that this prediction rule separated the patients' recurrence-free survival curves with a hazard ratio lower than the one based on the IHC analysis of ER-status. Conclusions Our efficient and parsimonious classifier lends itself to high throughput, highly accurate and low-cost RNA-based assessments of ER-status, suitable for routine high-throughput clinical use. This analytic method provides a proof-of-principle that may be applicable to developing effective RNA-based tests for other biomarkers and conditions. PMID:24312637

Hadoop-Based Distributed System for Online Prediction of Air Pollution Based on Support Vector Machine

NASA Astrophysics Data System (ADS)

Ghaemi, Z.; Farnaghi, M.; Alimohammadi, A.

2015-12-01

The critical impact of air pollution on human health and environment in one hand and the complexity of pollutant concentration behavior in the other hand lead the scientists to look for advance techniques for monitoring and predicting the urban air quality. Additionally, recent developments in data measurement techniques have led to collection of various types of data about air quality. Such data is extremely voluminous and to be useful it must be processed at high velocity. Due to the complexity of big data analysis especially for dynamic applications, online forecasting of pollutant concentration trends within a reasonable processing time is still an open problem. The purpose of this paper is to present an online forecasting approach based on Support Vector Machine (SVM) to predict the air quality one day in advance. In order to overcome the computational requirements for large-scale data analysis, distributed computing based on the Hadoop platform has been employed to leverage the processing power of multiple processing units. The MapReduce programming model is adopted for massive parallel processing in this study. Based on the online algorithm and Hadoop framework, an online forecasting system is designed to predict the air pollution of Tehran for the next 24 hours. The results have been assessed on the basis of Processing Time and Efficiency. Quite accurate predictions of air pollutant indicator levels within an acceptable processing time prove that the presented approach is very suitable to tackle large scale air pollution prediction problems.

"First generation" automated DNA sequencing technology.

PubMed

Slatko, Barton E; Kieleczawa, Jan; Ju, Jingyue; Gardner, Andrew F; Hendrickson, Cynthia L; Ausubel, Frederick M

2011-10-01

Beginning in the 1980s, automation of DNA sequencing has greatly increased throughput, reduced costs, and enabled large projects to be completed more easily. The development of automation technology paralleled the development of other aspects of DNA sequencing: better enzymes and chemistry, separation and imaging technology, sequencing protocols, robotics, and computational advancements (including base-calling algorithms with quality scores, database developments, and sequence analysis programs). Despite the emergence of high-throughput sequencing platforms, automated Sanger sequencing technology remains useful for many applications. This unit provides background and a description of the "First-Generation" automated DNA sequencing technology. It also includes protocols for using the current Applied Biosystems (ABI) automated DNA sequencing machines. © 2011 by John Wiley & Sons, Inc.

[A computer-aided image diagnosis and study system].

PubMed

Li, Zhangyong; Xie, Zhengxiang

2004-08-01

The revolution in information processing, particularly the digitizing of medicine, has changed the medical study, work and management. This paper reports a method to design a system for computer-aided image diagnosis and study. Combined with some good idea of graph-text system and picture archives communicate system (PACS), the system was realized and used for "prescription through computer", "managing images" and "reading images under computer and helping the diagnosis". Also typical examples were constructed in a database and used to teach the beginners. The system was developed by the visual developing tools based on object oriented programming (OOP) and was carried into operation on the Windows 9X platform. The system possesses friendly man-machine interface.

The use of open and machine vision technologies for development of gesture recognition intelligent systems

NASA Astrophysics Data System (ADS)

Cherkasov, Kirill V.; Gavrilova, Irina V.; Chernova, Elena V.; Dokolin, Andrey S.

2018-05-01

The article is devoted to reflection of separate aspects of intellectual system gesture recognition development. The peculiarity of the system is its intellectual block which completely based on open technologies: OpenCV library and Microsoft Cognitive Toolkit (CNTK) platform. The article presents the rationale for the choice of such set of tools, as well as the functional scheme of the system and the hierarchy of its modules. Experiments have shown that the system correctly recognizes about 85% of images received from sensors. The authors assume that the improvement of the algorithmic block of the system will increase the accuracy of gesture recognition up to 95%.

BIRD: Bio-Image Referral Database. Design and implementation of a new web based and patient multimedia data focused system for effective medical diagnosis and therapy.

PubMed

Pinciroli, Francesco; Masseroli, Marco; Acerbo, Livio A; Bonacina, Stefano; Ferrari, Roberto; Marchente, Mario

2004-01-01

This paper presents a low cost software platform prototype supporting health care personnel in retrieving patient referral multimedia data. These information are centralized in a server machine and structured by using a flexible eXtensible Markup Language (XML) Bio-Image Referral Database (BIRD). Data are distributed on demand to requesting client in an Intranet network and transformed via eXtensible Stylesheet Language (XSL) to be visualized in an uniform way on market browsers. The core server operation software has been developed in PHP Hypertext Preprocessor scripting language, which is very versatile and useful for crafting a dynamic Web environment.

Fluid Lensing based Machine Learning for Augmenting Earth Science Coral Datasets

NASA Astrophysics Data System (ADS)

Li, A.; Instrella, R.; Chirayath, V.

2016-12-01

Recently, there has been increased interest in monitoring the effects of climate change upon the world's marine ecosystems, particularly coral reefs. These delicate ecosystems are especially threatened due to their sensitivity to ocean warming and acidification, leading to unprecedented levels of coral bleaching and die-off in recent years. However, current global aquatic remote sensing datasets are unable to quantify changes in marine ecosystems at spatial and temporal scales relevant to their growth. In this project, we employ various supervised and unsupervised machine learning algorithms to augment existing datasets from NASA's Earth Observing System (EOS), using high resolution airborne imagery. This method utilizes NASA's ongoing airborne campaigns as well as its spaceborne assets to collect remote sensing data over these afflicted regions, and employs Fluid Lensing algorithms to resolve optical distortions caused by the fluid surface, producing cm-scale resolution imagery of these diverse ecosystems from airborne platforms. Support Vector Machines (SVMs) and K-mean clustering methods were applied to satellite imagery at 0.5m resolution, producing segmented maps classifying coral based on percent cover and morphology. Compared to a previous study using multidimensional maximum a posteriori (MAP) estimation to separate these features in high resolution airborne datasets, SVMs are able to achieve above 75% accuracy when augmented with existing MAP estimates, while unsupervised methods such as K-means achieve roughly 68% accuracy, verified by manually segmented reference data provided by a marine biologist. This effort thus has broad applications for coastal remote sensing, by helping marine biologists quantify behavioral trends spanning large areas and over longer timescales, and to assess the health of coral reefs worldwide.

Integrative Data Analysis of Multi-Platform Cancer Data with a Multimodal Deep Learning Approach.

PubMed

Liang, Muxuan; Li, Zhizhong; Chen, Ting; Zeng, Jianyang

2015-01-01

Identification of cancer subtypes plays an important role in revealing useful insights into disease pathogenesis and advancing personalized therapy. The recent development of high-throughput sequencing technologies has enabled the rapid collection of multi-platform genomic data (e.g., gene expression, miRNA expression, and DNA methylation) for the same set of tumor samples. Although numerous integrative clustering approaches have been developed to analyze cancer data, few of them are particularly designed to exploit both deep intrinsic statistical properties of each input modality and complex cross-modality correlations among multi-platform input data. In this paper, we propose a new machine learning model, called multimodal deep belief network (DBN), to cluster cancer patients from multi-platform observation data. In our integrative clustering framework, relationships among inherent features of each single modality are first encoded into multiple layers of hidden variables, and then a joint latent model is employed to fuse common features derived from multiple input modalities. A practical learning algorithm, called contrastive divergence (CD), is applied to infer the parameters of our multimodal DBN model in an unsupervised manner. Tests on two available cancer datasets show that our integrative data analysis approach can effectively extract a unified representation of latent features to capture both intra- and cross-modality correlations, and identify meaningful disease subtypes from multi-platform cancer data. In addition, our approach can identify key genes and miRNAs that may play distinct roles in the pathogenesis of different cancer subtypes. Among those key miRNAs, we found that the expression level of miR-29a is highly correlated with survival time in ovarian cancer patients. These results indicate that our multimodal DBN based data analysis approach may have practical applications in cancer pathogenesis studies and provide useful guidelines for personalized cancer therapy.

Machine Learning Technologies Translates Vigilant Surveillance Satellite Big Data into Predictive Alerts for Environmental Stressors

NASA Astrophysics Data System (ADS)

Johnson, S. P.; Rohrer, M. E.

2017-12-01

The application of scientific research pertaining to satellite imaging and data processing has facilitated the development of dynamic methodologies and tools that utilize nanosatellites and analytical platforms to address the increasing scope, scale, and intensity of emerging environmental threats to national security. While the use of remotely sensed data to monitor the environment at local and global scales is not a novel proposition, the application of advances in nanosatellites and analytical platforms are capable of overcoming the data availability and accessibility barriers that have historically impeded the timely detection, identification, and monitoring of these stressors. Commercial and university-based applications of these technologies were used to identify and evaluate their capacity as security-motivated environmental monitoring tools. Presently, nanosatellites can provide consumers with 1-meter resolution imaging, frequent revisits, and customizable tasking, allowing users to define an appropriate temporal scale for high resolution data collection that meets their operational needs. Analytical platforms are capable of ingesting increasingly large and diverse volumes of data, delivering complex analyses in the form of interpretation-ready data products and solutions. The synchronous advancement of these technologies creates the capability of analytical platforms to deliver interpretable products from persistently collected high-resolution data that meet varying temporal and geographic scale requirements. In terms of emerging environmental threats, these advances translate into customizable and flexible tools that can respond to and accommodate the evolving nature of environmental stressors. This presentation will demonstrate the capability of nanosatellites and analytical platforms to provide timely, relevant, and actionable information that enables environmental analysts and stakeholders to make informed decisions regarding the prevention, intervention, and prediction of emerging environmental threats.

Integrated computer-aided design using minicomputers

NASA Technical Reports Server (NTRS)

Storaasli, O. O.

1980-01-01

Computer-Aided Design/Computer-Aided Manufacturing (CAD/CAM), a highly interactive software, has been implemented on minicomputers at the NASA Langley Research Center. CAD/CAM software integrates many formerly fragmented programs and procedures into one cohesive system; it also includes finite element modeling and analysis, and has been interfaced via a computer network to a relational data base management system and offline plotting devices on mainframe computers. The CAD/CAM software system requires interactive graphics terminals operating at a minimum of 4800 bits/sec transfer rate to a computer. The system is portable and introduces 'interactive graphics', which permits the creation and modification of models interactively. The CAD/CAM system has already produced designs for a large area space platform, a national transonic facility fan blade, and a laminar flow control wind tunnel model. Besides the design/drafting element analysis capability, CAD/CAM provides options to produce an automatic program tooling code to drive a numerically controlled (N/C) machine. Reductions in time for design, engineering, drawing, finite element modeling, and N/C machining will benefit productivity through reduced costs, fewer errors, and a wider range of configuration.

Modeling human-machine interactions for operations room layouts

NASA Astrophysics Data System (ADS)

Hendy, Keith C.; Edwards, Jack L.; Beevis, David

2000-11-01

The LOCATE layout analysis tool was used to analyze three preliminary configurations for the Integrated Command Environment (ICE) of a future USN platform. LOCATE develops a cost function reflecting the quality of all human-human and human-machine communications within a workspace. This proof- of-concept study showed little difference between the efficacy of the preliminary designs selected for comparison. This was thought to be due to the limitations of the study, which included the assumption of similar size for each layout and a lack of accurate measurement data for various objects in the designs, due largely to their notional nature. Based on these results, the USN offered an opportunity to conduct a LOCATE analysis using more appropriate assumptions. A standard crew was assumed, and subject matter experts agreed on the communications patterns for the analysis. Eight layouts were evaluated with the concepts of coordination and command factored into the analysis. Clear differences between the layouts emerged. The most promising design was refined further by the USN, and a working mock-up built for human-in-the-loop evaluation. LOCATE was applied to this configuration for comparison with the earlier analyses.

Axis: Generating Explanations at Scale with Learnersourcing and Machine Learning

ERIC Educational Resources Information Center

Williams, Joseph Jay; Kim, Juho; Rafferty, Anna; Heffernan, Neil; Maldonado, Samuel; Gajos, Krzysztof Z.; Lasecki, Walter S.; Heffernan, Neil

2016-01-01

While explanations may help people learn by providing information about why an answer is correct, many problems on online platforms lack high-quality explanations. This paper presents AXIS (Adaptive eXplanation Improvement System), a system for obtaining explanations. AXIS asks learners to generate, revise, and evaluate explanations as they solve…

Biomedical Big Data Training Collaborative (BBDTC): An effort to bridge the talent gap in biomedical science and research.

PubMed

Purawat, Shweta; Cowart, Charles; Amaro, Rommie E; Altintas, Ilkay

2016-06-01

The BBDTC (https://biobigdata.ucsd.edu) is a community-oriented platform to encourage high-quality knowledge dissemination with the aim of growing a well-informed biomedical big data community through collaborative efforts on training and education. The BBDTC collaborative is an e-learning platform that supports the biomedical community to access, develop and deploy open training materials. The BBDTC supports Big Data skill training for biomedical scientists at all levels, and from varied backgrounds. The natural hierarchy of courses allows them to be broken into and handled as modules . Modules can be reused in the context of multiple courses and reshuffled, producing a new and different, dynamic course called a playlist . Users may create playlists to suit their learning requirements and share it with individual users or the wider public. BBDTC leverages the maturity and design of the HUBzero content-management platform for delivering educational content. To facilitate the migration of existing content, the BBDTC supports importing and exporting course material from the edX platform. Migration tools will be extended in the future to support other platforms. Hands-on training software packages, i.e., toolboxes , are supported through Amazon EC2 and Virtualbox virtualization technologies, and they are available as: ( i ) downloadable lightweight Virtualbox Images providing a standardized software tool environment with software packages and test data on their personal machines, and ( ii ) remotely accessible Amazon EC2 Virtual Machines for accessing biomedical big data tools and scalable big data experiments. At the moment, the BBDTC site contains three open Biomedical big data training courses with lecture contents, videos and hands-on training utilizing VM toolboxes, covering diverse topics. The courses have enhanced the hands-on learning environment by providing structured content that users can use at their own pace. A four course biomedical big data series is planned for development in 2016.

Machine learning plus optical flow: a simple and sensitive method to detect cardioactive drugs

NASA Astrophysics Data System (ADS)

Lee, Eugene K.; Kurokawa, Yosuke K.; Tu, Robin; George, Steven C.; Khine, Michelle

2015-07-01

Current preclinical screening methods do not adequately detect cardiotoxicity. Using human induced pluripotent stem cell-derived cardiomyocytes (iPS-CMs), more physiologically relevant preclinical or patient-specific screening to detect potential cardiotoxic effects of drug candidates may be possible. However, one of the persistent challenges for developing a high-throughput drug screening platform using iPS-CMs is the need to develop a simple and reliable method to measure key electrophysiological and contractile parameters. To address this need, we have developed a platform that combines machine learning paired with brightfield optical flow as a simple and robust tool that can automate the detection of cardiomyocyte drug effects. Using three cardioactive drugs of different mechanisms, including those with primarily electrophysiological effects, we demonstrate the general applicability of this screening method to detect subtle changes in cardiomyocyte contraction. Requiring only brightfield images of cardiomyocyte contractions, we detect changes in cardiomyocyte contraction comparable to - and even superior to - fluorescence readouts. This automated method serves as a widely applicable screening tool to characterize the effects of drugs on cardiomyocyte function.

Machine Learning of Human Pluripotent Stem Cell-Derived Engineered Cardiac Tissue Contractility for Automated Drug Classification.

PubMed

Lee, Eugene K; Tran, David D; Keung, Wendy; Chan, Patrick; Wong, Gabriel; Chan, Camie W; Costa, Kevin D; Li, Ronald A; Khine, Michelle

2017-11-14

Accurately predicting cardioactive effects of new molecular entities for therapeutics remains a daunting challenge. Immense research effort has been focused toward creating new screening platforms that utilize human pluripotent stem cell (hPSC)-derived cardiomyocytes and three-dimensional engineered cardiac tissue constructs to better recapitulate human heart function and drug responses. As these new platforms become increasingly sophisticated and high throughput, the drug screens result in larger multidimensional datasets. Improved automated analysis methods must therefore be developed in parallel to fully comprehend the cellular response across a multidimensional parameter space. Here, we describe the use of machine learning to comprehensively analyze 17 functional parameters derived from force readouts of hPSC-derived ventricular cardiac tissue strips (hvCTS) electrically paced at a range of frequencies and exposed to a library of compounds. A generated metric is effective for then determining the cardioactivity of a given drug. Furthermore, we demonstrate a classification model that can automatically predict the mechanistic action of an unknown cardioactive drug. Copyright © 2017 The Authors. Published by Elsevier Inc. All rights reserved.

FDR Soil Moisture Sensor for Environmental Testing and Evaluation

NASA Astrophysics Data System (ADS)

Linmao, Ye; longqin, Xue; guangzhou, Zhang; haibo, Chen; likuai, Shi; zhigang, Wu; gouhe, Yu; yanbin, Wang; sujun, Niu; Jin, Ye; Qi, Jin

To test the affect of environmental stresses on a adaptability of soil moisture capacitance sensor(FDR) a number of stresses were induced including vibrational shock as well as temperature and humidity through the use of a CH-I constant humidity chamber with variable temperature. A Vibrational platform was used to exam the resistance and structural integrity of the sensor after vibrations simulating the process of using, transporting and handling the sensor. A Impactive trial platform was used to test the resistance and structural integrity of the sensor after enduring repeated mechanical shocks. An CH-I constant humidity chamber with high-low temperature was used to test the adaptability of sensor in different environments with high temperature, low temperature and constant humidity. Otherwise, scope of magnetic force line of sensor was also tested in this paper. Test show:the capacitance type soil moisture sensor spread a feeling machine to bear heat, high wet and low temperature, at bear impact and vibration experiment in pass an examination, is a kind of environment to adapt to ability very strong instrument;Spread a feeling machine moreover electric field strength function radius scope 7 cms.

Using the Sandia Z Machine to Probe Water at Planetary Conditions: Redefining the Properties of Water in the Ice Giants

NASA Astrophysics Data System (ADS)

Knudson, M. D.; Desjarlais, M.; Lemke, R.; Mattsson, T.; French, M.; Nettelmann, N.; Redmer, R.

2012-12-01

Recently, there has been a tremendous increase in the number of identified extrasolar planetary systems. Our understanding of their formation is tied to exoplanet internal structure models, which rely upon equation of state (EOS) models of light elements and compounds such as water at multi-Mbar pressure conditions. For the past decade, a large, interdisciplinary team at Sandia National Laboratories has been refining the Z Machine (20+ MA and 10+ MGauss) into a mature, robust, and precise platform for material dynamics experiments in the multi-Mbar pressure regime. In particular, significant effort has gone into effectively coupling condensed matter theory, magneto-hydrodynamic simulation, and electromagnetic modeling to produce a fully self-consistent simulation capability able to very accurately predict the performance of the Z machine and various experimental load configurations. This capability has been instrumental in the ability to develop experimental platforms to routinely perform magnetic ramp compression experiments to over 4 Mbar, and magnetically accelerate flyer plates to over 40 km/s, creating over 20 Mbar impact pressures. Furthermore, a strong tie has been developed between the condensed matter theory and the experimental program. This coupling has been proven time and again to be extremely fruitful, with the capability of both theory and experiment being challenged and advanced through this close interrelationship. This presentation will provide a short overview of the material dynamics platform and discuss in more detail the use of Z to perform extreme material dynamics studies with unprecedented accuracy on water in support of basic science, planetary astrophysics, and the emerging field of high energy density laboratory physics. It was found that widely used EOSs for water are much too compressible (up to 30 percent) at pressures and temperatures relevant to planetary interiors. Furthermore, it is shown that the behavior of water at these conditions, including its reflectivity and isentropic response, is well-described by an EOS for water based on recent first-principles calculations. These findings advocate that this water model be used as the standard for modeling Neptune, Uranus, and "hot Neptune" exoplanets, and should improve our understanding of these types of planetary systems. Sandia National Laboratories is a multiprogram laboratory managed and operated by Sandia Corporation, a wholly owned subsidiary of Lockheed Martin Corporation, for the U.S. Department of Energy's National Nuclear Security Administration under Contract No. DE-AC04-94AL85000.

Comparing the performance-enhancing effects of squats on a vibration platform with conventional squats in recreationally resistance-trained men.

PubMed

Rønnestad, Bent R

2004-11-01

The purpose of this investigation was to compare the performance-enhancing effects of squats on a vibration platform with conventional squats in recreationally resistance-trained men. The subjects were 14 recreationally resistance-trained men (age, 21-40 years) and the intervention period consisted of 5 weeks. After the initial testing, subjects were randomly assigned to either the "squat whole body vibration" (SWBV) group (n = 7), which performed squats on a vibration platform on a Smith Machine, or the "squat"(S) group (n = 7), which performed conventional squats with no vibrations on a Smith Machine. Testing was performed at the beginning and the end of the study and consisted of 1 repetition maximum (1RM) in squat and maximum jump height in countermovement jump (CMJ). A modified daily undulating periodization program was used during the intervention period in both groups. Both groups trained at the same percentage of 1RM in squats (6-10RM). After the intervention, CMJ performance increased significantly only in the SWBV (p < 0.01), but there was no significant difference between groups in relative jump height increase (p = 0.088). Both groups showed significant increases in 1RM performance in squats (p < 0.01). Although there was a trend toward a greater relative strength increase in the SWBV group, it did not reach a significant level. In conclusion, the preliminary results of this study point toward a tendency of superiority of squats performed on a vibration platform compared with squats without vibrations regarding maximal strength and explosive power as long as the external load is similar in recreationally resistance-trained men.

Yeast viability and concentration analysis using lens-free computational microscopy and machine learning

NASA Astrophysics Data System (ADS)

Feizi, Alborz; Zhang, Yibo; Greenbaum, Alon; Guziak, Alex; Luong, Michelle; Chan, Raymond Yan Lok; Berg, Brandon; Ozkan, Haydar; Luo, Wei; Wu, Michael; Wu, Yichen; Ozcan, Aydogan

2017-03-01

Research laboratories and the industry rely on yeast viability and concentration measurements to adjust fermentation parameters such as pH, temperature, and pressure. Beer-brewing processes as well as biofuel production can especially utilize a cost-effective and portable way of obtaining data on cell viability and concentration. However, current methods of analysis are relatively costly and tedious. Here, we demonstrate a rapid, portable, and cost-effective platform for imaging and measuring viability and concentration of yeast cells. Our platform features a lens-free microscope that weighs 70 g and has dimensions of 12 × 4 × 4 cm. A partially-coherent illumination source (a light-emitting-diode), a band-pass optical filter, and a multimode optical fiber are used to illuminate the sample. The yeast sample is directly placed on a complementary metal-oxide semiconductor (CMOS) image sensor chip, which captures an in-line hologram of the sample over a large field-of-view of >20 mm2. The hologram is transferred to a touch-screen interface, where a trained Support Vector Machine model classifies yeast cells stained with methylene blue as live or dead and measures cell viability as well as concentration. We tested the accuracy of our platform against manual counting of live and dead cells using fluorescent exclusion staining and a bench-top fluorescence microscope. Our regression analysis showed no significant difference between the two methods within a concentration range of 1.4 × 105 to 1.4 × 106 cells/mL. This compact and cost-effective yeast analysis platform will enable automatic quantification of yeast viability and concentration in field settings and resource-limited environments.

AstroCloud, a Cyber-Infrastructure for Astronomy Research: Data Access and Interoperability

NASA Astrophysics Data System (ADS)

Fan, D.; He, B.; Xiao, J.; Li, S.; Li, C.; Cui, C.; Yu, C.; Hong, Z.; Yin, S.; Wang, C.; Cao, Z.; Fan, Y.; Mi, L.; Wan, W.; Wang, J.

2015-09-01

Data access and interoperability module connects the observation proposals, data, virtual machines and software. According to the unique identifier of PI (principal investigator), an email address or an internal ID, data can be collected by PI's proposals, or by the search interfaces, e.g. conesearch. Files associated with the searched results could be easily transported to cloud storages, including the storage with virtual machines, or several commercial platforms like Dropbox. Benefitted from the standards of IVOA (International Observatories Alliance), VOTable formatted searching result could be sent to kinds of VO software. Latter endeavor will try to integrate more data and connect archives and some other astronomical resources.

Ethoscopes: An open platform for high-throughput ethomics.

PubMed

Geissmann, Quentin; Garcia Rodriguez, Luis; Beckwith, Esteban J; French, Alice S; Jamasb, Arian R; Gilestro, Giorgio F

2017-10-01

Here, we present the use of ethoscopes, which are machines for high-throughput analysis of behavior in Drosophila and other animals. Ethoscopes provide a software and hardware solution that is reproducible and easily scalable. They perform, in real-time, tracking and profiling of behavior by using a supervised machine learning algorithm, are able to deliver behaviorally triggered stimuli to flies in a feedback-loop mode, and are highly customizable and open source. Ethoscopes can be built easily by using 3D printing technology and rely on Raspberry Pi microcomputers and Arduino boards to provide affordable and flexible hardware. All software and construction specifications are available at http://lab.gilest.ro/ethoscope.

Compliance analysis of a 3-DOF spindle head by considering gravitational effects

NASA Astrophysics Data System (ADS)

Li, Qi; Wang, Manxin; Huang, Tian; Chetwynd, Derek G.

2015-01-01

The compliance modeling is one of the most significant issues in the stage of preliminary design for parallel kinematic machine(PKM). The gravity ignored in traditional compliance analysis has a significant effect on pose accuracy of tool center point(TCP) when a PKM is horizontally placed. By taking gravity into account, this paper presents a semi-analytical approach for compliance analysis of a 3-DOF spindle head named the A3 head. The architecture behind the A3 head is a 3-R PS parallel mechanism having one translational and two rotational movement capabilities, which can be employed to form the main body of a 5-DOF hybrid kinematic machine especially designed for high-speed machining of large aircraft components. The force analysis is carried out by considering both the externally applied wrench imposed upon the platform as well as gravity of all moving components. Then, the deflection analysis is investigated to establish the relationship between the deflection twist and compliances of all joints and links using semi-analytical method. The merits of this approach lie in that platform deflection twist throughout the entire task workspace can be evaluated in a very efficient manner. The effectiveness of the proposed approach is verified by the FEA and experiment at different configurations and the results show that the discrepancy of the compliances is less than 0.04 μm/N-1 and that of the deformations is less than 10μm. The computational and experimental results show that the deflection twist induced by gravity forces of the moving components has significant bearings on pose accuracy of the platform, providing an informative guidance for the improvement of the current design. The proposed approach can be easily applied to the compliance analysis of PKM by considering gravitational effects and to evaluate the deformation caused by gravity throughout the entire workspace.

Human-Assisted Machine Information Exploitation: a crowdsourced investigation of information-based problem solving

NASA Astrophysics Data System (ADS)

Kase, Sue E.; Vanni, Michelle; Caylor, Justine; Hoye, Jeff

2017-05-01

The Human-Assisted Machine Information Exploitation (HAMIE) investigation utilizes large-scale online data collection for developing models of information-based problem solving (IBPS) behavior in a simulated time-critical operational environment. These types of environments are characteristic of intelligence workflow processes conducted during human-geo-political unrest situations when the ability to make the best decision at the right time ensures strategic overmatch. The project takes a systems approach to Human Information Interaction (HII) by harnessing the expertise of crowds to model the interaction of the information consumer and the information required to solve a problem at different levels of system restrictiveness and decisional guidance. The design variables derived from Decision Support Systems (DSS) research represent the experimental conditions in this online single-player against-the-clock game where the player, acting in the role of an intelligence analyst, is tasked with a Commander's Critical Information Requirement (CCIR) in an information overload scenario. The player performs a sequence of three information processing tasks (annotation, relation identification, and link diagram formation) with the assistance of `HAMIE the robot' who offers varying levels of information understanding dependent on question complexity. We provide preliminary results from a pilot study conducted with Amazon Mechanical Turk (AMT) participants on the Volunteer Science scientific research platform.

An open-source solution for advanced imaging flow cytometry data analysis using machine learning.

PubMed

Hennig, Holger; Rees, Paul; Blasi, Thomas; Kamentsky, Lee; Hung, Jane; Dao, David; Carpenter, Anne E; Filby, Andrew

2017-01-01

Imaging flow cytometry (IFC) enables the high throughput collection of morphological and spatial information from hundreds of thousands of single cells. This high content, information rich image data can in theory resolve important biological differences among complex, often heterogeneous biological samples. However, data analysis is often performed in a highly manual and subjective manner using very limited image analysis techniques in combination with conventional flow cytometry gating strategies. This approach is not scalable to the hundreds of available image-based features per cell and thus makes use of only a fraction of the spatial and morphometric information. As a result, the quality, reproducibility and rigour of results are limited by the skill, experience and ingenuity of the data analyst. Here, we describe a pipeline using open-source software that leverages the rich information in digital imagery using machine learning algorithms. Compensated and corrected raw image files (.rif) data files from an imaging flow cytometer (the proprietary .cif file format) are imported into the open-source software CellProfiler, where an image processing pipeline identifies cells and subcellular compartments allowing hundreds of morphological features to be measured. This high-dimensional data can then be analysed using cutting-edge machine learning and clustering approaches using "user-friendly" platforms such as CellProfiler Analyst. Researchers can train an automated cell classifier to recognize different cell types, cell cycle phases, drug treatment/control conditions, etc., using supervised machine learning. This workflow should enable the scientific community to leverage the full analytical power of IFC-derived data sets. It will help to reveal otherwise unappreciated populations of cells based on features that may be hidden to the human eye that include subtle measured differences in label free detection channels such as bright-field and dark-field imagery. Copyright © 2016 The Authors. Published by Elsevier Inc. All rights reserved.

Towards a Web-Enabled Geovisualization and Analytics Platform for the Energy and Water Nexus

NASA Astrophysics Data System (ADS)

Sanyal, J.; Chandola, V.; Sorokine, A.; Allen, M.; Berres, A.; Pang, H.; Karthik, R.; Nugent, P.; McManamay, R.; Stewart, R.; Bhaduri, B. L.

2017-12-01

Interactive data analytics are playing an increasingly vital role in the generation of new, critical insights regarding the complex dynamics of the energy/water nexus (EWN) and its interactions with climate variability and change. Integration of impacts, adaptation, and vulnerability (IAV) science with emerging, and increasingly critical, data science capabilities offers a promising potential to meet the needs of the EWN community. To enable the exploration of pertinent research questions, a web-based geospatial visualization platform is being built that integrates a data analysis toolbox with advanced data fusion and data visualization capabilities to create a knowledge discovery framework for the EWN. The system, when fully built out, will offer several geospatial visualization capabilities including statistical visual analytics, clustering, principal-component analysis, dynamic time warping, support uncertainty visualization and the exploration of data provenance, as well as support machine learning discoveries to render diverse types of geospatial data and facilitate interactive analysis. Key components in the system architecture includes NASA's WebWorldWind, the Globus toolkit, postgresql, as well as other custom built software modules.

Update on PISCES

NASA Technical Reports Server (NTRS)

Pearson, Don; Hamm, Dustin; Kubena, Brian; Weaver, Jonathan K.

2010-01-01

An updated version of the Platform Independent Software Components for the Exploration of Space (PISCES) software library is available. A previous version was reported in Library for Developing Spacecraft-Mission-Planning Software (MSC-22983), NASA Tech Briefs, Vol. 25, No. 7 (July 2001), page 52. To recapitulate: This software provides for Web-based, collaborative development of computer programs for planning trajectories and trajectory- related aspects of spacecraft-mission design. The library was built using state-of-the-art object-oriented concepts and software-development methodologies. The components of PISCES include Java-language application programs arranged in a hierarchy of classes that facilitates the reuse of the components. As its full name suggests, the PISCES library affords platform-independence: The Java language makes it possible to use the classes and application programs with a Java virtual machine, which is available in most Web-browser programs. Another advantage is expandability: Object orientation facilitates expansion of the library through creation of a new class. Improvements in the library since the previous version include development of orbital-maneuver- planning and rendezvous-launch-window application programs, enhancement of capabilities for propagation of orbits, and development of a desktop user interface.

Three legged walking mobile platform: Kinematic and dynamic analysis and simulation

NASA Technical Reports Server (NTRS)

Mcmurray, Gary V.; Maclaren, Brice K.

1988-01-01

The three legged walker is proposed as a mobile work platform for numerous tasks associated with lunar base site preparation and construction. It is seen as one of several forms of surface transportation, each of which will be best suited for its respective tasks. Utilizing the principle of dynamic stability and taking advantage of the Moon's gravity, it appears to be capable of walking in any radial direction and rotating about a point. Typical curved path walking could involve some combination of the radial and rotational movements. Comprised mainly of a body, six actuators, and six moving parts, it is mechanically quite simple. Each leg connects to the body at a hip joint and has a femur, a knee joint, and a tibia that terminates at a foot. Also capable of enabling or enhancing the dexterity of a series of implements, the walker concept provides a mechanically simple and weight efficient means of drilling, digging, mining, and transporting cargo, and performing other like tasks. A proof of principle machine demonstrated the feasibility of the walking concept.

Identification of COPD patients' health status using an intelligent system in the CHRONIOUS wearable platform.

PubMed

Bellos, Christos C; Papadopoulos, Athanasios; Rosso, Roberto; Fotiadis, Dimitrios I

2014-05-01

The CHRONIOUS system offers an integrated platform aiming at the effective management and real-time assessment of the health status of the patient suffering from chronic obstructive pulmonary disease (COPD). An intelligent system is developed for the analysis and the real-time evaluation of patient's condition. A hybrid classifier has been implemented on a personal digital assistant, combining a support vector machine, a random forest, and a rule-based system to provide a more advanced categorization scheme for the early and in real-time characterization of a COPD episode. This is followed by a severity estimation algorithm which classifies the identified pathological situation in different levels and triggers an alerting mechanism to provide an informative and instructive message/advice to the patient and the clinical supervisor. The system has been validated using data collected from 30 patients that have been annotated by experts indicating 1) the severity level of the current patient's health status, and 2) the COPD disease level of the recruited patients according to the GOLD guidelines. The achieved characterization accuracy has been found 94%.

System Architecture Development for Energy and Water Infrastructure Data Management and Geovisual Analytics

NASA Astrophysics Data System (ADS)

Berres, A.; Karthik, R.; Nugent, P.; Sorokine, A.; Myers, A.; Pang, H.

2017-12-01

Building an integrated data infrastructure that can meet the needs of a sustainable energy-water resource management requires a robust data management and geovisual analytics platform, capable of cross-domain scientific discovery and knowledge generation. Such a platform can facilitate the investigation of diverse complex research and policy questions for emerging priorities in Energy-Water Nexus (EWN) science areas. Using advanced data analytics, machine learning techniques, multi-dimensional statistical tools, and interactive geovisualization components, such a multi-layered federated platform is being developed, the Energy-Water Nexus Knowledge Discovery Framework (EWN-KDF). This platform utilizes several enterprise-grade software design concepts and standards such as extensible service-oriented architecture, open standard protocols, event-driven programming model, enterprise service bus, and adaptive user interfaces to provide a strategic value to the integrative computational and data infrastructure. EWN-KDF is built on the Compute and Data Environment for Science (CADES) environment in Oak Ridge National Laboratory (ORNL).

Micro-algae come of age as a platform for recombinant protein production

PubMed Central

Specht, Elizabeth; Miyake-Stoner, Shigeki

2010-01-01

A complete set of genetic tools is still being developed for the micro-alga Chlamydomonas reinhardtii. Yet even with this incomplete set, this photosynthetic single-celled plant has demonstrated significant promise as a platform for recombinant protein expression. In recent years, techniques have been developed that allow for robust expression of genes from both the nuclear and plastid genome. With these advances, many research groups have examined the pliability of this and other micro-algae as biological machines capable of producing recombinant peptides and proteins. This review describes recent successes in recombinant protein production in Chlamydomonas, including production of complex mammalian therapeutic proteins and monoclonal antibodies at levels sufficient for production at economic parity with existing production platforms. These advances have also shed light on the details of algal protein production at the molecular level, and provide insight into the next steps for optimizing micro-algae as a useful platform for the production of therapeutic and industrially relevant recombinant proteins. PMID:20556634

OCILOW-Wheeled Platform Controls Executable Set

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

Jansen, John F.

2005-11-30

The OCILOW Controls Executable Set is the complete set of machine executable instructions to control the motion of wheeled platforms that incorporate Off-Centered In-Line Omni-directional Wheels (OCILOW). The controls utilize command signals for the desired motion of the platform (X, Y and Theta) and calculate and control the steering and rolling motion required of each OCILOW wheels to achieve the desired translational and rotational platform motion. The controls utilize signals from the wheel steering and rolling resolvers, and from three load cells located at each wheels, to coordinate the motion of all wheels, while respecting their non-holonomic constraints (i.e., keepingmore » internal stresses and slippage due to possible errors, uneven floors, bumps, misalignment, etc. bounded). The OCILOW Controls Executable Set, which is copyrighted here, is an embodiment of the generic OCILOW algorithms (patented separately) developed specifically for controls of the Proof-of-Principle-Transporter (POP-T) system that has been developed to demonstrate the overall OCILOW controls feasibility and capabilities.« less

Mining Twitter Data to Improve Detection of Schizophrenia

PubMed Central

McManus, Kimberly; Mallory, Emily K.; Goldfeder, Rachel L.; Haynes, Winston A.; Tatum, Jonathan D.

2015-01-01

Individuals who suffer from schizophrenia comprise I percent of the United States population and are four times more likely to die of suicide than the general US population. Identification of at-risk individuals with schizophrenia is challenging when they do not seek treatment. Microblogging platforms allow users to share their thoughts and emotions with the world in short snippets of text. In this work, we leveraged the large corpus of Twitter posts and machine-learning methodologies to detect individuals with schizophrenia. Using features from tweets such as emoticon use, posting time of day, and dictionary terms, we trained, built, and validated several machine learning models. Our support vector machine model achieved the best performance with 92% precision and 71% recall on the held-out test set. Additionally, we built a web application that dynamically displays summary statistics between cohorts. This enables outreach to undiagnosed individuals, improved physician diagnoses, and destigmatization of schizophrenia. PMID:26306253

Bionimbus: a cloud for managing, analyzing and sharing large genomics datasets

PubMed Central

Heath, Allison P; Greenway, Matthew; Powell, Raymond; Spring, Jonathan; Suarez, Rafael; Hanley, David; Bandlamudi, Chai; McNerney, Megan E; White, Kevin P; Grossman, Robert L

2014-01-01

Background As large genomics and phenotypic datasets are becoming more common, it is increasingly difficult for most researchers to access, manage, and analyze them. One possible approach is to provide the research community with several petabyte-scale cloud-based computing platforms containing these data, along with tools and resources to analyze it. Methods Bionimbus is an open source cloud-computing platform that is based primarily upon OpenStack, which manages on-demand virtual machines that provide the required computational resources, and GlusterFS, which is a high-performance clustered file system. Bionimbus also includes Tukey, which is a portal, and associated middleware that provides a single entry point and a single sign on for the various Bionimbus resources; and Yates, which automates the installation, configuration, and maintenance of the software infrastructure required. Results Bionimbus is used by a variety of projects to process genomics and phenotypic data. For example, it is used by an acute myeloid leukemia resequencing project at the University of Chicago. The project requires several computational pipelines, including pipelines for quality control, alignment, variant calling, and annotation. For each sample, the alignment step requires eight CPUs for about 12 h. BAM file sizes ranged from 5 GB to 10 GB for each sample. Conclusions Most members of the research community have difficulty downloading large genomics datasets and obtaining sufficient storage and computer resources to manage and analyze the data. Cloud computing platforms, such as Bionimbus, with data commons that contain large genomics datasets, are one choice for broadening access to research data in genomics. PMID:24464852

LHCb Dockerized Build Environment

NASA Astrophysics Data System (ADS)

Clemencic, M.; Belin, M.; Closier, J.; Couturier, B.

2017-10-01

Used as lightweight virtual machines or as enhanced chroot environments, Linux containers, and in particular the Docker abstraction over them, are more and more popular in the virtualization communities. The LHCb Core Software team decided to investigate how to use Docker containers to provide stable and reliable build environments for the different supported platforms, including the obsolete ones which cannot be installed on modern hardware, to be used in integration builds, releases and by any developer. We present here the techniques and procedures set up to define and maintain the Docker images and how these images can be used to develop on modern Linux distributions for platforms otherwise not accessible.

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

Messer, Bronson; Harris, James A; Parete-Koon, Suzanne T

We describe recent development work on the core-collapse supernova code CHIMERA. CHIMERA has consumed more than 100 million cpu-hours on Oak Ridge Leadership Computing Facility (OLCF) platforms in the past 3 years, ranking it among the most important applications at the OLCF. Most of the work described has been focused on exploiting the multicore nature of the current platform (Jaguar) via, e.g., multithreading using OpenMP. In addition, we have begun a major effort to marshal the computational power of GPUs with CHIMERA. The impending upgrade of Jaguar to Titan a 20+ PF machine with an NVIDIA GPU on many nodesmore » makes this work essential.« less

A blood-based predictor for neocortical Aβ burden in Alzheimer's disease: results from the AIBL study.

PubMed

Burnham, S C; Faux, N G; Wilson, W; Laws, S M; Ames, D; Bedo, J; Bush, A I; Doecke, J D; Ellis, K A; Head, R; Jones, G; Kiiveri, H; Martins, R N; Rembach, A; Rowe, C C; Salvado, O; Macaulay, S L; Masters, C L; Villemagne, V L

2014-04-01

Dementia is a global epidemic with Alzheimer's disease (AD) being the leading cause. Early identification of patients at risk of developing AD is now becoming an international priority. Neocortical Aβ (extracellular β-amyloid) burden (NAB), as assessed by positron emission tomography (PET), represents one such marker for early identification. These scans are expensive and are not widely available, thus, there is a need for cheaper and more widely accessible alternatives. Addressing this need, a blood biomarker-based signature having efficacy for the prediction of NAB and which can be easily adapted for population screening is described. Blood data (176 analytes measured in plasma) and Pittsburgh Compound B (PiB)-PET measurements from 273 participants from the Australian Imaging, Biomarkers and Lifestyle (AIBL) study were utilised. Univariate analysis was conducted to assess the difference of plasma measures between high and low NAB groups, and cross-validated machine-learning models were generated for predicting NAB. These models were applied to 817 non-imaged AIBL subjects and 82 subjects from the Alzheimer's Disease Neuroimaging Initiative (ADNI) for validation. Five analytes showed significant difference between subjects with high compared to low NAB. A machine-learning model (based on nine markers) achieved sensitivity and specificity of 80 and 82%, respectively, for predicting NAB. Validation using the ADNI cohort yielded similar results (sensitivity 79% and specificity 76%). These results show that a panel of blood-based biomarkers is able to accurately predict NAB, supporting the hypothesis for a relationship between a blood-based signature and Aβ accumulation, therefore, providing a platform for developing a population-based screen.

Efficient full-chip SRAF placement using machine learning for best accuracy and improved consistency

NASA Astrophysics Data System (ADS)

Wang, Shibing; Baron, Stanislas; Kachwala, Nishrin; Kallingal, Chidam; Sun, Dezheng; Shu, Vincent; Fong, Weichun; Li, Zero; Elsaid, Ahmad; Gao, Jin-Wei; Su, Jing; Ser, Jung-Hoon; Zhang, Quan; Chen, Been-Der; Howell, Rafael; Hsu, Stephen; Luo, Larry; Zou, Yi; Zhang, Gary; Lu, Yen-Wen; Cao, Yu

2018-03-01

Various computational approaches from rule-based to model-based methods exist to place Sub-Resolution Assist Features (SRAF) in order to increase process window for lithography. Each method has its advantages and drawbacks, and typically requires the user to make a trade-off between time of development, accuracy, consistency and cycle time. Rule-based methods, used since the 90 nm node, require long development time and struggle to achieve good process window performance for complex patterns. Heuristically driven, their development is often iterative and involves significant engineering time from multiple disciplines (Litho, OPC and DTCO). Model-based approaches have been widely adopted since the 20 nm node. While the development of model-driven placement methods is relatively straightforward, they often become computationally expensive when high accuracy is required. Furthermore these methods tend to yield less consistent SRAFs due to the nature of the approach: they rely on a model which is sensitive to the pattern placement on the native simulation grid, and can be impacted by such related grid dependency effects. Those undesirable effects tend to become stronger when more iterations or complexity are needed in the algorithm to achieve required accuracy. ASML Brion has developed a new SRAF placement technique on the Tachyon platform that is assisted by machine learning and significantly improves the accuracy of full chip SRAF placement while keeping consistency and runtime under control. A Deep Convolutional Neural Network (DCNN) is trained using the target wafer layout and corresponding Continuous Transmission Mask (CTM) images. These CTM images have been fully optimized using the Tachyon inverse mask optimization engine. The neural network generated SRAF guidance map is then used to place SRAF on full-chip. This is different from our existing full-chip MB-SRAF approach which utilizes a SRAF guidance map (SGM) of mask sensitivity to improve the contrast of optical image at the target pattern edges. In this paper, we demonstrate that machine learning assisted SRAF placement can achieve a superior process window compared to the SGM model-based SRAF method, while keeping the full-chip runtime affordable, and maintain consistency of SRAF placement . We describe the current status of this machine learning assisted SRAF technique and demonstrate its application to full chip mask synthesis and discuss how it can extend the computational lithography roadmap.

FROG - Fingerprinting Genomic Variation Ontology

PubMed Central

Bhardwaj, Anshu

2015-01-01

Genetic variations play a crucial role in differential phenotypic outcomes. Given the complexity in establishing this correlation and the enormous data available today, it is imperative to design machine-readable, efficient methods to store, label, search and analyze this data. A semantic approach, FROG: “FingeRprinting Ontology of Genomic variations” is implemented to label variation data, based on its location, function and interactions. FROG has six levels to describe the variation annotation, namely, chromosome, DNA, RNA, protein, variations and interactions. Each level is a conceptual aggregation of logically connected attributes each of which comprises of various properties for the variant. For example, in chromosome level, one of the attributes is location of variation and which has two properties, allosomes or autosomes. Another attribute is variation kind which has four properties, namely, indel, deletion, insertion, substitution. Likewise, there are 48 attributes and 278 properties to capture the variation annotation across six levels. Each property is then assigned a bit score which in turn leads to generation of a binary fingerprint based on the combination of these properties (mostly taken from existing variation ontologies). FROG is a novel and unique method designed for the purpose of labeling the entire variation data generated till date for efficient storage, search and analysis. A web-based platform is designed as a test case for users to navigate sample datasets and generate fingerprints. The platform is available at http://ab-openlab.csir.res.in/frog. PMID:26244889

HIVprotI: an integrated web based platform for prediction and design of HIV proteins inhibitors.

PubMed

Qureshi, Abid; Rajput, Akanksha; Kaur, Gazaldeep; Kumar, Manoj

2018-03-09

A number of anti-retroviral drugs are being used for treating Human Immunodeficiency Virus (HIV) infection. Due to emergence of drug resistant strains, there is a constant quest to discover more effective anti-HIV compounds. In this endeavor, computational tools have proven useful in accelerating drug discovery. Although methods were published to design a class of compounds against a specific HIV protein, but an integrated web server for the same is lacking. Therefore, we have developed support vector machine based regression models using experimentally validated data from ChEMBL repository. Quantitative structure activity relationship based features were selected for predicting inhibition activity of a compound against HIV proteins namely protease (PR), reverse transcriptase (RT) and integrase (IN). The models presented a maximum Pearson correlation coefficient of 0.78, 0.76, 0.74 and 0.76, 0.68, 0.72 during tenfold cross-validation on IC 50 and percent inhibition datasets of PR, RT, IN respectively. These models performed equally well on the independent datasets. Chemical space mapping, applicability domain analyses and other statistical tests further support robustness of the predictive models. Currently, we have identified a number of chemical descriptors that are imperative in predicting the compound inhibition potential. HIVprotI platform ( http://bioinfo.imtech.res.in/manojk/hivproti ) would be useful in virtual screening of inhibitors as well as designing of new molecules against the important HIV proteins for therapeutics development.

Robustness of spiking Deep Belief Networks to noise and reduced bit precision of neuro-inspired hardware platforms.

PubMed

Stromatias, Evangelos; Neil, Daniel; Pfeiffer, Michael; Galluppi, Francesco; Furber, Steve B; Liu, Shih-Chii

2015-01-01

Increasingly large deep learning architectures, such as Deep Belief Networks (DBNs) are the focus of current machine learning research and achieve state-of-the-art results in different domains. However, both training and execution of large-scale Deep Networks require vast computing resources, leading to high power requirements and communication overheads. The on-going work on design and construction of spike-based hardware platforms offers an alternative for running deep neural networks with significantly lower power consumption, but has to overcome hardware limitations in terms of noise and limited weight precision, as well as noise inherent in the sensor signal. This article investigates how such hardware constraints impact the performance of spiking neural network implementations of DBNs. In particular, the influence of limited bit precision during execution and training, and the impact of silicon mismatch in the synaptic weight parameters of custom hybrid VLSI implementations is studied. Furthermore, the network performance of spiking DBNs is characterized with regard to noise in the spiking input signal. Our results demonstrate that spiking DBNs can tolerate very low levels of hardware bit precision down to almost two bits, and show that their performance can be improved by at least 30% through an adapted training mechanism that takes the bit precision of the target platform into account. Spiking DBNs thus present an important use-case for large-scale hybrid analog-digital or digital neuromorphic platforms such as SpiNNaker, which can execute large but precision-constrained deep networks in real time.

Robustness of spiking Deep Belief Networks to noise and reduced bit precision of neuro-inspired hardware platforms

PubMed Central

Stromatias, Evangelos; Neil, Daniel; Pfeiffer, Michael; Galluppi, Francesco; Furber, Steve B.; Liu, Shih-Chii

2015-01-01

Increasingly large deep learning architectures, such as Deep Belief Networks (DBNs) are the focus of current machine learning research and achieve state-of-the-art results in different domains. However, both training and execution of large-scale Deep Networks require vast computing resources, leading to high power requirements and communication overheads. The on-going work on design and construction of spike-based hardware platforms offers an alternative for running deep neural networks with significantly lower power consumption, but has to overcome hardware limitations in terms of noise and limited weight precision, as well as noise inherent in the sensor signal. This article investigates how such hardware constraints impact the performance of spiking neural network implementations of DBNs. In particular, the influence of limited bit precision during execution and training, and the impact of silicon mismatch in the synaptic weight parameters of custom hybrid VLSI implementations is studied. Furthermore, the network performance of spiking DBNs is characterized with regard to noise in the spiking input signal. Our results demonstrate that spiking DBNs can tolerate very low levels of hardware bit precision down to almost two bits, and show that their performance can be improved by at least 30% through an adapted training mechanism that takes the bit precision of the target platform into account. Spiking DBNs thus present an important use-case for large-scale hybrid analog-digital or digital neuromorphic platforms such as SpiNNaker, which can execute large but precision-constrained deep networks in real time. PMID:26217169

A droplet-based microfluidic chip as a platform for leukemia cell lysate identification using surface-enhanced Raman scattering.

PubMed

Hassoun, Mohamed; Rüger, Jan; Kirchberger-Tolstik, Tatiana; Schie, Iwan W; Henkel, Thomas; Weber, Karina; Cialla-May, Dana; Krafft, Christoph; Popp, Jürgen

2018-01-01

A new approach is presented for cell lysate identification which uses SERS-active silver nanoparticles and a droplet-based microfluidic chip. Eighty-nanoliter droplets are generated by injecting silver nanoparticles, KCl as aggregation agent, and cell lysate containing cell constituents, such as nucleic acids, carbohydrates, metabolites, and proteins into a continuous flow of mineral oil. This platform enables accurate mixing of small volumes inside the meandering channels of the quartz chip and allows acquisition of thousands of SERS spectra with 785 nm excitation at an integration time of 1 s. Preparation of three batches of three leukemia cell lines demonstrated the experimental reproducibility. The main advantage of a high number of reproducible spectra is to apply statistics for large sample populations with robust classification results. A support vector machine with leave-one-batch-out cross-validation classified SERS spectra with sensitivities, specificities, and accuracies better than 99% to differentiate Jurkat, THP-1, and MONO-MAC-6 leukemia cell lysates. This approach is compared with previous published reports about Raman spectroscopy for leukemia detection, and an outlook is given for transfer to single cells. A quartz chip was designed for SERS at 785 nm excitation. Principal component analysis of SERS spectra clearly separates cell lysates using variations in band intensity ratios.

Human Detection from a Mobile Robot Using Fusion of Laser and Vision Information

PubMed Central

Fotiadis, Efstathios P.; Garzón, Mario; Barrientos, Antonio

2013-01-01

This paper presents a human detection system that can be employed on board a mobile platform for use in autonomous surveillance of large outdoor infrastructures. The prediction is based on the fusion of two detection modules, one for the laser and another for the vision data. In the laser module, a novel feature set that better encapsulates variations due to noise, distance and human pose is proposed. This enhances the generalization of the system, while at the same time, increasing the outdoor performance in comparison with current methods. The vision module uses the combination of the histogram of oriented gradients descriptor and the linear support vector machine classifier. Current approaches use a fixed-size projection to define regions of interest on the image data using the range information from the laser range finder. When applied to small size unmanned ground vehicles, these techniques suffer from misalignment, due to platform vibrations and terrain irregularities. This is effectively addressed in this work by using a novel adaptive projection technique, which is based on a probabilistic formulation of the classifier performance. Finally, a probability calibration step is introduced in order to optimally fuse the information from both modules. Experiments in real world environments demonstrate the robustness of the proposed method. PMID:24008280

Human detection from a mobile robot using fusion of laser and vision information.

PubMed

Fotiadis, Efstathios P; Garzón, Mario; Barrientos, Antonio

2013-09-04

This paper presents a human detection system that can be employed on board a mobile platform for use in autonomous surveillance of large outdoor infrastructures. The prediction is based on the fusion of two detection modules, one for the laser and another for the vision data. In the laser module, a novel feature set that better encapsulates variations due to noise, distance and human pose is proposed. This enhances the generalization of the system, while at the same time, increasing the outdoor performance in comparison with current methods. The vision module uses the combination of the histogram of oriented gradients descriptor and the linear support vector machine classifier. Current approaches use a fixed-size projection to define regions of interest on the image data using the range information from the laser range finder. When applied to small size unmanned ground vehicles, these techniques suffer from misalignment, due to platform vibrations and terrain irregularities. This is effectively addressed in this work by using a novel adaptive projection technique, which is based on a probabilistic formulation of the classifier performance. Finally, a probability calibration step is introduced in order to optimally fuse the information from both modules. Experiments in real world environments demonstrate the robustness of the proposed method.

ODISEES: A New Paradigm in Data Access

NASA Astrophysics Data System (ADS)

Huffer, E.; Little, M. M.; Kusterer, J.

2013-12-01

As part of its ongoing efforts to improve access to data, the Atmospheric Science Data Center has developed a high-precision Earth Science domain ontology (the 'ES Ontology') implemented in a graph database ('the Semantic Metadata Repository') that is used to store detailed, semantically-enhanced, parameter-level metadata for ASDC data products. The ES Ontology provides the semantic infrastructure needed to drive the ASDC's Ontology-Driven Interactive Search Environment for Earth Science ('ODISEES'), a data discovery and access tool, and will support additional data services such as analytics and visualization. The ES ontology is designed on the premise that naming conventions alone are not adequate to provide the information needed by prospective data consumers to assess the suitability of a given dataset for their research requirements; nor are current metadata conventions adequate to support seamless machine-to-machine interactions between file servers and end-user applications. Data consumers need information not only about what two data elements have in common, but also about how they are different. End-user applications need consistent, detailed metadata to support real-time data interoperability. The ES ontology is a highly precise, bottom-up, queriable model of the Earth Science domain that focuses on critical details about the measurable phenomena, instrument techniques, data processing methods, and data file structures. Earth Science parameters are described in detail in the ES Ontology and mapped to the corresponding variables that occur in ASDC datasets. Variables are in turn mapped to well-annotated representations of the datasets that they occur in, the instrument(s) used to create them, the instrument platforms, the processing methods, etc., creating a linked-data structure that allows both human and machine users to access a wealth of information critical to understanding and manipulating the data. The mappings are recorded in the Semantic Metadata Repository as RDF-triples. An off-the-shelf Ontology Development Environment and a custom Metadata Conversion Tool comprise a human-machine/machine-machine hybrid tool that partially automates the creation of metadata as RDF-triples by interfacing with existing metadata repositories and providing a user interface that solicits input from a human user, when needed. RDF-triples are pushed to the Ontology Development Environment, where a reasoning engine executes a series of inference rules whose antecedent conditions can be satisfied by the initial set of RDF-triples, thereby generating the additional detailed metadata that is missing in existing repositories. A SPARQL Endpoint, a web-based query service and a Graphical User Interface allow prospective data consumers - even those with no familiarity with NASA data products - to search the metadata repository to find and order data products that meet their exact specifications. A web-based API will provide an interface for machine-to-machine transactions.

Mobile Robotics Activities in DOE Laboratories

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

Ron Lujan; Jerry Harbour; John T. Feddema

This paper will briefly outline major activities in Department of Energy (DOE) Laboratories focused on mobile platforms, both Unmanned Ground Vehicles (UGV’s) as well as Unmanned Air Vehicles (UAV’s). The activities will be discussed in the context of the science and technology construct used by the DOE Technology Roadmap for Robotics and Intelligent Machines (RIM)1 published in 1998; namely, Perception, Reasoning, Action, and Integration. The activities to be discussed span from research and development to deployment in field operations. The activities support customers in other agencies. The discussion of "perception" will include hyperspectral sensors, complex patterns discrimination, multisensor fusion andmore » advances in LADAR technologies, including real-world perception. "Reasoning" activities to be covered include cooperative controls, distributed systems, ad-hoc networks, platform-centric intelligence, and adaptable communications. The paper will discuss "action" activities such as advanced mobility and various air and ground platforms. In the RIM construct, "integration" includes the Human-Machine Integration. Accordingly the paper will discuss adjustable autonomy and the collaboration of operator(s) with distributed UGV’s and UAV’s. Integration also refers to the applications of these technologies into systems to perform operations such as perimeter surveillance, large-area monitoring and reconnaissance. Unique facilities and test beds for advanced mobile systems will be described. Given that this paper is an overview, rather than delve into specific detail in these activities, other more exhaustive references and sources will be cited extensively.« less

PCM-SABRE: a platform for benchmarking and comparing outcome prediction methods in precision cancer medicine.

PubMed

Eyal-Altman, Noah; Last, Mark; Rubin, Eitan

2017-01-17

Numerous publications attempt to predict cancer survival outcome from gene expression data using machine-learning methods. A direct comparison of these works is challenging for the following reasons: (1) inconsistent measures used to evaluate the performance of different models, and (2) incomplete specification of critical stages in the process of knowledge discovery. There is a need for a platform that would allow researchers to replicate previous works and to test the impact of changes in the knowledge discovery process on the accuracy of the induced models. We developed the PCM-SABRE platform, which supports the entire knowledge discovery process for cancer outcome analysis. PCM-SABRE was developed using KNIME. By using PCM-SABRE to reproduce the results of previously published works on breast cancer survival, we define a baseline for evaluating future attempts to predict cancer outcome with machine learning. We used PCM-SABRE to replicate previous work that describe predictive models of breast cancer recurrence, and tested the performance of all possible combinations of feature selection methods and data mining algorithms that was used in either of the works. We reconstructed the work of Chou et al. observing similar trends - superior performance of Probabilistic Neural Network (PNN) and logistic regression (LR) algorithms and inconclusive impact of feature pre-selection with the decision tree algorithm on subsequent analysis. PCM-SABRE is a software tool that provides an intuitive environment for rapid development of predictive models in cancer precision medicine.

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

Kowalkowski, Jim; Lyon, Adam; Paterno, Marc

Over the past few years, container technology has become increasingly promising as a means to seamlessly make our software available across a wider range of platforms. In December 2015, we decided to put together a set of docker images that serve as a demonstration of this container technology for managing a run-time environment for art-related software projects, and also serve as a set of test cases for evaluation of performance. Docker[1] containers provide a way to “wrap up a piece of software in a complete filesystem that contains everything it needs to run”. In combination with Shifter[2], such containers providemore » a way to run software developed and deployed on “typical” HEP platforms (such as SLF 6, in common use at Fermilab and on OSG platforms) on HPC facilities at NERSC. Docker containers provide a means of delivering software that can be run on a variety of hosts without needing to be compiled specially for each OS to be supported. This could substantially reduce the effort required to create and validate a new release, since one build could be suitable for use on both grid machines (both FermiGrid and OSG) as well as any machine capable of running the Docker container. In addition, docker containers may provide for a quick and easy way for users to install and use a software release in a standardized environment. This report contains the results and status of this demonstration and evaluation.« less

CMS Connect

NASA Astrophysics Data System (ADS)

Balcas, J.; Bockelman, B.; Gardner, R., Jr.; Hurtado Anampa, K.; Jayatilaka, B.; Aftab Khan, F.; Lannon, K.; Larson, K.; Letts, J.; Marra Da Silva, J.; Mascheroni, M.; Mason, D.; Perez-Calero Yzquierdo, A.; Tiradani, A.

2017-10-01

The CMS experiment collects and analyzes large amounts of data coming from high energy particle collisions produced by the Large Hadron Collider (LHC) at CERN. This involves a huge amount of real and simulated data processing that needs to be handled in batch-oriented platforms. The CMS Global Pool of computing resources provide +100K dedicated CPU cores and another 50K to 100K CPU cores from opportunistic resources for these kind of tasks and even though production and event processing analysis workflows are already managed by existing tools, there is still a lack of support to submit final stage condor-like analysis jobs familiar to Tier-3 or local Computing Facilities users into these distributed resources in an integrated (with other CMS services) and friendly way. CMS Connect is a set of computing tools and services designed to augment existing services in the CMS Physics community focusing on these kind of condor analysis jobs. It is based on the CI-Connect platform developed by the Open Science Grid and uses the CMS GlideInWMS infrastructure to transparently plug CMS global grid resources into a virtual pool accessed via a single submission machine. This paper describes the specific developments and deployment of CMS Connect beyond the CI-Connect platform in order to integrate the service with CMS specific needs, including specific Site submission, accounting of jobs and automated reporting to standard CMS monitoring resources in an effortless way to their users.

The implementation of depth measurement and related algorithms based on binocular vision in embedded AM5728

NASA Astrophysics Data System (ADS)

Deng, Zhiwei; Li, Xicai; Shi, Junsheng; Huang, Xiaoqiao; Li, Feiyan

2018-01-01

Depth measurement is the most basic measurement in various machine vision, such as automatic driving, unmanned aerial vehicle (UAV), robot and so on. And it has a wide range of use. With the development of image processing technology and the improvement of hardware miniaturization and processing speed, real-time depth measurement using dual cameras has become a reality. In this paper, an embedded AM5728 and the ordinary low-cost dual camera is used as the hardware platform. The related algorithms of dual camera calibration, image matching and depth calculation have been studied and implemented on the hardware platform, and hardware design and the rationality of the related algorithms of the system are tested. The experimental results show that the system can realize simultaneous acquisition of binocular images, switching of left and right video sources, display of depth image and depth range. For images with a resolution of 640 × 480, the processing speed of the system can be up to 25 fps. The experimental results show that the optimal measurement range of the system is from 0.5 to 1.5 meter, and the relative error of the distance measurement is less than 5%. Compared with the PC, ARM11 and DMCU hardware platforms, the embedded AM5728 hardware is good at meeting real-time depth measurement requirements in ensuring the image resolution.

Quantum Machine Learning

NASA Technical Reports Server (NTRS)

Biswas, Rupak

2018-01-01

Quantum computing promises an unprecedented ability to solve intractable problems by harnessing quantum mechanical effects such as tunneling, superposition, and entanglement. The Quantum Artificial Intelligence Laboratory (QuAIL) at NASA Ames Research Center is the space agency's primary facility for conducting research and development in quantum information sciences. QuAIL conducts fundamental research in quantum physics but also explores how best to exploit and apply this disruptive technology to enable NASA missions in aeronautics, Earth and space sciences, and space exploration. At the same time, machine learning has become a major focus in computer science and captured the imagination of the public as a panacea to myriad big data problems. In this talk, we will discuss how classical machine learning can take advantage of quantum computing to significantly improve its effectiveness. Although we illustrate this concept on a quantum annealer, other quantum platforms could be used as well. If explored fully and implemented efficiently, quantum machine learning could greatly accelerate a wide range of tasks leading to new technologies and discoveries that will significantly change the way we solve real-world problems.

Quantum-assisted Helmholtz machines: A quantum–classical deep learning framework for industrial datasets in near-term devices

NASA Astrophysics Data System (ADS)

Benedetti, Marcello; Realpe-Gómez, John; Perdomo-Ortiz, Alejandro

2018-07-01

Machine learning has been presented as one of the key applications for near-term quantum technologies, given its high commercial value and wide range of applicability. In this work, we introduce the quantum-assisted Helmholtz machine:a hybrid quantum–classical framework with the potential of tackling high-dimensional real-world machine learning datasets on continuous variables. Instead of using quantum computers only to assist deep learning, as previous approaches have suggested, we use deep learning to extract a low-dimensional binary representation of data, suitable for processing on relatively small quantum computers. Then, the quantum hardware and deep learning architecture work together to train an unsupervised generative model. We demonstrate this concept using 1644 quantum bits of a D-Wave 2000Q quantum device to model a sub-sampled version of the MNIST handwritten digit dataset with 16 × 16 continuous valued pixels. Although we illustrate this concept on a quantum annealer, adaptations to other quantum platforms, such as ion-trap technologies or superconducting gate-model architectures, could be explored within this flexible framework.

gProcess and ESIP Platforms for Satellite Imagery Processing over the Grid

NASA Astrophysics Data System (ADS)

Bacu, Victor; Gorgan, Dorian; Rodila, Denisa; Pop, Florin; Neagu, Gabriel; Petcu, Dana

2010-05-01

The Environment oriented Satellite Data Processing Platform (ESIP) is developed through the SEE-GRID-SCI (SEE-GRID eInfrastructure for regional eScience) co-funded by the European Commission through FP7 [1]. The gProcess Platform [2] is a set of tools and services supporting the development and the execution over the Grid of the workflow based processing, and particularly the satelite imagery processing. The ESIP [3], [4] is build on top of the gProcess platform by adding a set of satellite image processing software modules and meteorological algorithms. The satellite images can reveal and supply important information on earth surface parameters, climate data, pollution level, weather conditions that can be used in different research areas. Generally, the processing algorithms of the satellite images can be decomposed in a set of modules that forms a graph representation of the processing workflow. Two types of workflows can be defined in the gProcess platform: abstract workflow (PDG - Process Description Graph), in which the user defines conceptually the algorithm, and instantiated workflow (iPDG - instantiated PDG), which is the mapping of the PDG pattern on particular satellite image and meteorological data [5]. The gProcess platform allows the definition of complex workflows by combining data resources, operators, services and sub-graphs. The gProcess platform is developed for the gLite middleware that is available in EGEE and SEE-GRID infrastructures [6]. gProcess exposes the specific functionality through web services [7]. The Editor Web Service retrieves information on available resources that are used to develop complex workflows (available operators, sub-graphs, services, supported resources, etc.). The Manager Web Service deals with resources management (uploading new resources such as workflows, operators, services, data, etc.) and in addition retrieves information on workflows. The Executor Web Service manages the execution of the instantiated workflows on the Grid infrastructure. In addition, this web service monitors the execution and generates statistical data that are important to evaluate performances and to optimize execution. The Viewer Web Service allows access to input and output data. To prove and to validate the utility of the gProcess and ESIP platforms there were developed the GreenView and GreenLand applications. The GreenView related functionality includes the refinement of some meteorological data such as temperature, and the calibration of the satellite images based on field measurements. The GreenLand application performs the classification of the satellite images by using a set of vegetation indices. The gProcess and ESIP platforms are used as well in GiSHEO project [8] to support the processing of Earth Observation data over the Grid in eGLE (GiSHEO eLearning Environment). Experiments of performance assessment were conducted and they have revealed that the workflow-based execution could improve the execution time of a satellite image processing algorithm [9]. It is not a reliable solution to execute all the workflow nodes on different machines. The execution of some nodes can be more time consuming and they will be performed in a longer time than other nodes. The total execution time will be affected because some nodes will slow down the execution. It is important to correctly balance the workflow nodes. Based on some optimization strategy the workflow nodes can be grouped horizontally, vertically or in a hybrid approach. In this way, those operators will be executed on one machine and also the data transfer between workflow nodes will be lower. The dynamic nature of the Grid infrastructure makes it more exposed to the occurrence of failures. These failures can occur at worker node, services availability, storage element, etc. Currently gProcess has support for some basic error prevention and error management solutions. In future, some more advanced error prevention and management solutions will be integrated in the gProcess platform. References [1] SEE-GRID-SCI Project, http://www.see-grid-sci.eu/ [2] Bacu V., Stefanut T., Rodila D., Gorgan D., Process Description Graph Composition by gProcess Platform. HiPerGRID - 3rd International Workshop on High Performance Grid Middleware, 28 May, Bucharest. Proceedings of CSCS-17 Conference, Vol.2., ISSN 2066-4451, pp. 423-430, (2009). [3] ESIP Platform, http://wiki.egee-see.org/index.php/JRA1_Commonalities [4] Gorgan D., Bacu V., Rodila D., Pop Fl., Petcu D., Experiments on ESIP - Environment oriented Satellite Data Processing Platform. SEE-GRID-SCI User Forum, 9-10 Dec 2009, Bogazici University, Istanbul, Turkey, ISBN: 978-975-403-510-0, pp. 157-166 (2009). [5] Radu, A., Bacu, V., Gorgan, D., Diagrammatic Description of Satellite Image Processing Workflow. Workshop on Grid Computing Applications Development (GridCAD) at the SYNASC Symposium, 28 September 2007, Timisoara, IEEE Computer Press, ISBN 0-7695-3078-8, 2007, pp. 341-348 (2007). [6] Gorgan D., Bacu V., Stefanut T., Rodila D., Mihon D., Grid based Satellite Image Processing Platform for Earth Observation Applications Development. IDAACS'2009 - IEEE Fifth International Workshop on "Intelligent Data Acquisition and Advanced Computing Systems: Technology and Applications", 21-23 September, Cosenza, Italy, IEEE Published in Computer Press, 247-252 (2009). [7] Rodila D., Bacu V., Gorgan D., Integration of Satellite Image Operators as Workflows in the gProcess Application. Proceedings of ICCP2009 - IEEE 5th International Conference on Intelligent Computer Communication and Processing, 27-29 Aug, 2009 Cluj-Napoca. ISBN: 978-1-4244-5007-7, pp. 355-358 (2009). [8] GiSHEO consortium, Project site, http://gisheo.info.uvt.ro [9] Bacu V., Gorgan D., Graph Based Evaluation of Satellite Imagery Processing over Grid. ISPDC 2008 - 7th International Symposium on Parallel and Distributed Computing, July 1-5, 2008, Krakow, Poland. IEEE Computer Society 2008, ISBN: 978-0-7695-3472-5, pp. 147-154.

An electromagnetic signals monitoring and analysis wireless platform employing personal digital assistants and pattern analysis techniques

NASA Astrophysics Data System (ADS)

Ninos, K.; Georgiadis, P.; Cavouras, D.; Nomicos, C.

2010-05-01

This study presents the design and development of a mobile wireless platform to be used for monitoring and analysis of seismic events and related electromagnetic (EM) signals, employing Personal Digital Assistants (PDAs). A prototype custom-developed application was deployed on a 3G enabled PDA that could connect to the FTP server of the Institute of Geodynamics of the National Observatory of Athens and receive and display EM signals at 4 receiver frequencies (3 KHz (E-W, N-S), 10 KHz (E-W, N-S), 41 MHz and 46 MHz). Signals may originate from any one of the 16 field-stations located around the Greek territory. Employing continuous recordings of EM signals gathered from January 2003 till December 2007, a Support Vector Machines (SVM)-based classification system was designed to distinguish EM precursor signals within noisy background. EM-signals corresponding to recordings preceding major seismic events (Ms≥5R) were segmented, by an experienced scientist, and five features (mean, variance, skewness, kurtosis, and a wavelet based feature), derived from the EM-signals were calculated. These features were used to train the SVM-based classification scheme. The performance of the system was evaluated by the exhaustive search and leave-one-out methods giving 87.2% overall classification accuracy, in correctly identifying EM precursor signals within noisy background employing all calculated features. Due to the insufficient processing power of the PDAs, this task was performed on a typical desktop computer. This optimal trained context of the SVM classifier was then integrated in the PDA based application rendering the platform capable to discriminate between EM precursor signals and noise. System's efficiency was evaluated by an expert who reviewed 1/ multiple EM-signals, up to 18 days prior to corresponding past seismic events, and 2/ the possible EM-activity of a specific region employing the trained SVM classifier. Additionally, the proposed architecture can form a base platform for a future integrated system that will incorporate services such as notifications for field station power failures, disruption of data flow, occurring SEs, and even other types of measurement and analysis processes such as the integration of a special analysis algorithm based on the ratio of short term to long term signal average.

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

Sadayappan, Ponnuswamy

Exascale computing systems will provide a thousand-fold increase in parallelism and a proportional increase in failure rate relative to today's machines. Systems software for exascale machines must provide the infrastructure to support existing applications while simultaneously enabling efficient execution of new programming models that naturally express dynamic, adaptive, irregular computation; coupled simulations; and massive data analysis in a highly unreliable hardware environment with billions of threads of execution. We propose a new approach to the data and work distribution model provided by system software based on the unifying formalism of an abstract file system. The proposed hierarchical data model providesmore » simple, familiar visibility and access to data structures through the file system hierarchy, while providing fault tolerance through selective redundancy. The hierarchical task model features work queues whose form and organization are represented as file system objects. Data and work are both first class entities. By exposing the relationships between data and work to the runtime system, information is available to optimize execution time and provide fault tolerance. The data distribution scheme provides replication (where desirable and possible) for fault tolerance and efficiency, and it is hierarchical to make it possible to take advantage of locality. The user, tools, and applications, including legacy applications, can interface with the data, work queues, and one another through the abstract file model. This runtime environment will provide multiple interfaces to support traditional Message Passing Interface applications, languages developed under DARPA's High Productivity Computing Systems program, as well as other, experimental programming models. We will validate our runtime system with pilot codes on existing platforms and will use simulation to validate for exascale-class platforms. In this final report, we summarize research results from the work done at the Ohio State University towards the larger goals of the project listed above.« less

Dispel4py: An Open-Source Python library for Data-Intensive Seismology

NASA Astrophysics Data System (ADS)

Filgueira, Rosa; Krause, Amrey; Spinuso, Alessandro; Klampanos, Iraklis; Danecek, Peter; Atkinson, Malcolm

2015-04-01

Scientific workflows are a necessary tool for many scientific communities as they enable easy composition and execution of applications on computing resources while scientists can focus on their research without being distracted by the computation management. Nowadays, scientific communities (e.g. Seismology) have access to a large variety of computing resources and their computational problems are best addressed using parallel computing technology. However, successful use of these technologies requires a lot of additional machinery whose use is not straightforward for non-experts: different parallel frameworks (MPI, Storm, multiprocessing, etc.) must be used depending on the computing resources (local machines, grids, clouds, clusters) where applications are run. This implies that for achieving the best applications' performance, users usually have to change their codes depending on the features of the platform selected for running them. This work presents dispel4py, a new open-source Python library for describing abstract stream-based workflows for distributed data-intensive applications. Special care has been taken to provide dispel4py with the ability to map abstract workflows to different platforms dynamically at run-time. Currently dispel4py has four mappings: Apache Storm, MPI, multi-threading and sequential. The main goal of dispel4py is to provide an easy-to-use tool to develop and test workflows in local resources by using the sequential mode with a small dataset. Later, once a workflow is ready for long runs, it can be automatically executed on different parallel resources. dispel4py takes care of the underlying mappings by performing an efficient parallelisation. Processing Elements (PE) represent the basic computational activities of any dispel4Py workflow, which can be a seismologic algorithm, or a data transformation process. For creating a dispel4py workflow, users only have to write very few lines of code to describe their PEs and how they are connected by using Python, which is widely supported on many platforms and is popular in many scientific domains, such as in geosciences. Once, a dispel4py workflow is written, a user only has to select which mapping they would like to use, and everything else (parallelisation, distribution of data) is carried on by dispel4py without any cost to the user. Among all dispel4py features we would like to highlight the following: * The PEs are connected by streams and not by writing to and reading from intermediate files, avoiding many IO operations. * The PEs can be stored into a registry. Therefore, different users can recombine PEs in many different workflows. * dispel4py has been enriched with a provenance mechanism to support runtime provenance analysis. We have adopted the W3C-PROV data model, which is accessible via a prototypal browser-based user interface and a web API. It supports the users with the visualisation of graphical products and offers combined operations to access and download the data, which may be selectively stored at runtime, into dedicated data archives. dispel4py has been already used by seismologists in the VERCE project to develop different seismic workflows. One of them is the Seismic Ambient Noise Cross-Correlation workflow, which preprocesses and cross-correlates traces from several stations. First, this workflow was tested on a local machine by using a small number of stations as input data. Later, it was executed on different parallel platforms (SuperMUC cluster, and Terracorrelator machine), automatically scaling up by using MPI and multiprocessing mappings and up to 1000 stations as input data. The results show that the dispel4py achieves scalable performance in both mappings tested on different parallel platforms.

Design and implementation of a UNIX based distributed computing system

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

Love, J.S.; Michael, M.W.

1994-12-31

We have designed, implemented, and are running a corporate-wide distributed processing batch queue on a large number of networked workstations using the UNIX{reg_sign} operating system. Atlas Wireline researchers and scientists have used the system for over a year. The large increase in available computer power has greatly reduced the time required for nuclear and electromagnetic tool modeling. Use of remote distributed computing has simultaneously reduced computation costs and increased usable computer time. The system integrates equipment from different manufacturers, using various CPU architectures, distinct operating system revisions, and even multiple processors per machine. Various differences between the machines have tomore » be accounted for in the master scheduler. These differences include shells, command sets, swap spaces, memory sizes, CPU sizes, and OS revision levels. Remote processing across a network must be performed in a manner that is seamless from the users` perspective. The system currently uses IBM RISC System/6000{reg_sign}, SPARCstation{sup TM}, HP9000s700, HP9000s800, and DEC Alpha AXP{sup TM} machines. Each CPU in the network has its own speed rating, allowed working hours, and workload parameters. The system if designed so that all of the computers in the network can be optimally scheduled without adversely impacting the primary users of the machines. The increase in the total usable computational capacity by means of distributed batch computing can change corporate computing strategy. The integration of disparate computer platforms eliminates the need to buy one type of computer for computations, another for graphics, and yet another for day-to-day operations. It might be possible, for example, to meet all research and engineering computing needs with existing networked computers.« less

3D Printing of Biosamples: A Concise Review

NASA Astrophysics Data System (ADS)

Zhao, Victoria Xin Ting; Wong, Ten It; Zhou, Xiaodong

This paper reviews the recent development of 3D printing of biosamples, in terms of the 3D structure design, suitable printing technology, and available materials. Successfully printed 3D biosamples should possess the properties of high cell viability, vascularization and good biocompatibility. These goals are attained by printing the materials of hydrogels, polymers and cells, with a carefully selected 3D printer from the categories of inkjet printing, extrusion printing and laser printing, based on the uniqueness, advantages and disadvantages of these technologies. For recent developments, we introduce the 3D applications of creating scaffolds, printing cells for self-assembly and testing platforms. We foresee more bio-applications of 3D printing will be developed, with the advancements on materials and 3D printing machines.

Research and realization of key technology in HILS interactive system

NASA Astrophysics Data System (ADS)

Liu, Che; Lu, Huiming; Wang, Fankai

2018-03-01

This paper designed HILS (Hardware In the Loop Simulation) interactive system based on xPC platform . Through the interface between C++ and MATLAB engine, establish the seamless data connection between Simulink and interactive system, complete data interaction between system and Simulink, realize the function development of model configuration, parameter modification and off line simulation. We establish the data communication between host and target machine through TCP/IP protocol to realize the model download and real-time simulation. Use database to store simulation data, implement real-time simulation monitoring and simulation data management. Realize system function integration by Qt graphic interface library and dynamic link library. At last, take the typical control system as an example to verify the feasibility of HILS interactive system.

Stereodivergent synthesis with a programmable molecular machine

NASA Astrophysics Data System (ADS)

Kassem, Salma; Lee, Alan T. L.; Leigh, David A.; Marcos, Vanesa; Palmer, Leoni I.; Pisano, Simone

2017-09-01

It has been convincingly argued that molecular machines that manipulate individual atoms, or highly reactive clusters of atoms, with Ångström precision are unlikely to be realized. However, biological molecular machines routinely position rather less reactive substrates in order to direct chemical reaction sequences, from sequence-specific synthesis by the ribosome to polyketide synthases, where tethered molecules are passed from active site to active site in multi-enzyme complexes. Artificial molecular machines have been developed for tasks that include sequence-specific oligomer synthesis and the switching of product chirality, a photo-responsive host molecule has been described that is able to mechanically twist a bound molecular guest, and molecular fragments have been selectively transported in either direction between sites on a molecular platform through a ratchet mechanism. Here we detail an artificial molecular machine that moves a substrate between different activating sites to achieve different product outcomes from chemical synthesis. This molecular robot can be programmed to stereoselectively produce, in a sequential one-pot operation, an excess of any one of four possible diastereoisomers from the addition of a thiol and an alkene to an α,β-unsaturated aldehyde in a tandem reaction process. The stereodivergent synthesis includes diastereoisomers that cannot be selectively synthesized through conventional iminium-enamine organocatalysis. We anticipate that future generations of programmable molecular machines may have significant roles in chemical synthesis and molecular manufacturing.

Array-based disease diagnostics using lipid/polydiacetylene vesicles encapsulated in a sol-gel matrix.

PubMed

Kolusheva, S; Yossef, R; Kugel, A; Katz, M; Volinsky, R; Welt, M; Hadad, U; Drory, V; Kliger, M; Rubin, E; Porgador, A; Jelinek, R

2012-07-17

We demonstrate a novel array-based diagnostic platform comprising lipid/polydiacetylene (PDA) vesicles embedded within a transparent silica-gel matrix. The diagnostic scheme is based upon the unique chromatic properties of PDA, which undergoes blue-red transformations induced by interactions with amphiphilic or membrane-active analytes. We show that constructing a gel matrix array hosting PDA vesicles with different lipid compositions and applying to blood plasma obtained from healthy individuals and from patients suffering from disease, respectively, allow distinguishing among the disease conditions through application of a simple machine-learning algorithm, using the colorimetric response of the lipid/PDA/gel matrix as the input. Importantly, the new colorimetric diagnostic approach does not require a priori knowledge on the exact metabolite compositions of the blood plasma, since the concept relies only on identifying statistically significant changes in overall disease-induced chromatic response. The chromatic lipid/PDA/gel array-based "fingerprinting" concept is generic, easy to apply, and could be implemented for varied diagnostic and screening applications.

SIP: A Web-Based Astronomical Image Processing Program

NASA Astrophysics Data System (ADS)

Simonetti, J. H.

1999-12-01

I have written an astronomical image processing and analysis program designed to run over the internet in a Java-compatible web browser. The program, Sky Image Processor (SIP), is accessible at the SIP webpage (http://www.phys.vt.edu/SIP). Since nothing is installed on the user's machine, there is no need to download upgrades; the latest version of the program is always instantly available. Furthermore, the Java programming language is designed to work on any computer platform (any machine and operating system). The program could be used with students in web-based instruction or in a computer laboratory setting; it may also be of use in some research or outreach applications. While SIP is similar to other image processing programs, it is unique in some important respects. For example, SIP can load images from the user's machine or from the Web. An instructor can put images on a web server for students to load and analyze on their own personal computer. Or, the instructor can inform the students of images to load from any other web server. Furthermore, since SIP was written with students in mind, the philosophy is to present the user with the most basic tools necessary to process and analyze astronomical images. Images can be combined (by addition, subtraction, multiplication, or division), multiplied by a constant, smoothed, cropped, flipped, rotated, and so on. Statistics can be gathered for pixels within a box drawn by the user. Basic tools are available for gathering data from an image which can be used for performing simple differential photometry, or astrometry. Therefore, students can learn how astronomical image processing works. Since SIP is not part of a commercial CCD camera package, the program is written to handle the most common denominator image file, the FITS format.

myChEMBL: a virtual machine implementation of open data and cheminformatics tools.

PubMed

Ochoa, Rodrigo; Davies, Mark; Papadatos, George; Atkinson, Francis; Overington, John P

2014-01-15

myChEMBL is a completely open platform, which combines public domain bioactivity data with open source database and cheminformatics technologies. myChEMBL consists of a Linux (Ubuntu) Virtual Machine featuring a PostgreSQL schema with the latest version of the ChEMBL database, as well as the latest RDKit cheminformatics libraries. In addition, a self-contained web interface is available, which can be modified and improved according to user specifications. The VM is available at: ftp://ftp.ebi.ac.uk/pub/databases/chembl/VM/myChEMBL/current. The web interface and web services code is available at: https://github.com/rochoa85/myChEMBL.

Ethoscopes: An open platform for high-throughput ethomics

PubMed Central

Geissmann, Quentin; Garcia Rodriguez, Luis; Beckwith, Esteban J.; French, Alice S.; Jamasb, Arian R.

2017-01-01

Here, we present the use of ethoscopes, which are machines for high-throughput analysis of behavior in Drosophila and other animals. Ethoscopes provide a software and hardware solution that is reproducible and easily scalable. They perform, in real-time, tracking and profiling of behavior by using a supervised machine learning algorithm, are able to deliver behaviorally triggered stimuli to flies in a feedback-loop mode, and are highly customizable and open source. Ethoscopes can be built easily by using 3D printing technology and rely on Raspberry Pi microcomputers and Arduino boards to provide affordable and flexible hardware. All software and construction specifications are available at http://lab.gilest.ro/ethoscope. PMID:29049280

A testpart for interdisciplinary analyses in micro production engineering

DOE PAGES

Möhring, H. -C.; Kersting, P.; Carmignato, S.; ...

2015-04-26

In 2011, a round robin test was initiated within the group of CIRP Research Affiliates. The aim was to establish a platform for linking interdisciplinary research in order to share the expertise and experiences of participants all over the world. This paper introduces a testpart which has been designed to allow an analysis of different manufacturing technologies, simulation methods, machinery and metrology as well as process and production planning aspects. Current investigations are presented focusing on the machining and additive processes to produce the geometry, simulation approaches, machine analysis, and a comparison of measuring technologies. Challenges and limitations regarding themore » manufacturing and evaluation of the testpart features by the applied methods are discussed.« less

Are Mash-Ups the Future for Online Learning Platforms? Psychology A-Level Students' Judgements about VLE and MUPPLE Interfaces

ERIC Educational Resources Information Center

Jarvis, Matt; Gauntlett, Lizzie; Collins, Hayley

2011-01-01

Virtual Learning Environments (VLEs) have become ubiquitous in colleges and universities but have failed to consistently improve learning (Machin, 2007). An alternative interface can be provided in the form of a mashed-up personal learning environment (MUPPLE). The aim of this study was to investigate student perceptions of its desirability and…

Simultaneous Planning and Control for Autonomous Ground Vehicles

DTIC Science & Technology

2009-02-01

these applications is called A * ( A -star), and it was originally developed by Hart, Nilsson, and Raphael [HAR68]. Their research presented the formal...sequence, rather than a dynamic programming approach. A * search is a technique originally developed for Artificial Intelligence 43 applications ... developed at the Center for Intelligent Machines and Robotics, serves as a platform for the implementation and testing discussed. autonomous

Tactical Aviation Mission System Simulation Situational Awareness Project

DTIC Science & Technology

2004-04-01

prototyping and exercising human-machine systems and for measuring the impact of new technologies in a dynamic simulation environment. Theoretical...31 2.4.1 The Impact of an ERSTA-Like System on the CH-146 Mission Commander...was proven to be an effective platform for prototyping and exercising systems and for measuring the impact of new technologies in a dynamic simulation

Virtual network computing: cross-platform remote display and collaboration software.

PubMed

Konerding, D E

1999-04-01

VNC (Virtual Network Computing) is a computer program written to address the problem of cross-platform remote desktop/application display. VNC uses a client/server model in which an image of the desktop of the server is transmitted to the client and displayed. The client collects mouse and keyboard input from the user and transmits them back to the server. The VNC client and server can run on Windows 95/98/NT, MacOS, and Unix (including Linux) operating systems. VNC is multi-user on Unix machines (any number of servers can be run are unrelated to the primary display of the computer), while it is effectively single-user on Macintosh and Windows machines (only one server can be run, displaying the contents of the primary display of the server). The VNC servers can be configured to allow more than one client to connect at one time, effectively allowing collaboration through the shared desktop. I describe the function of VNC, provide details of installation, describe how it achieves its goal, and evaluate the use of VNC for molecular modelling. VNC is an extremely useful tool for collaboration, instruction, software development, and debugging of graphical programs with remote users.

Applications of Deep Learning in Biomedicine.

PubMed

Mamoshina, Polina; Vieira, Armando; Putin, Evgeny; Zhavoronkov, Alex

2016-05-02

Increases in throughput and installed base of biomedical research equipment led to a massive accumulation of -omics data known to be highly variable, high-dimensional, and sourced from multiple often incompatible data platforms. While this data may be useful for biomarker identification and drug discovery, the bulk of it remains underutilized. Deep neural networks (DNNs) are efficient algorithms based on the use of compositional layers of neurons, with advantages well matched to the challenges -omics data presents. While achieving state-of-the-art results and even surpassing human accuracy in many challenging tasks, the adoption of deep learning in biomedicine has been comparatively slow. Here, we discuss key features of deep learning that may give this approach an edge over other machine learning methods. We then consider limitations and review a number of applications of deep learning in biomedical studies demonstrating proof of concept and practical utility.

3D printing of robotic soft actuators with programmable bioinspired architectures.

PubMed

Schaffner, Manuel; Faber, Jakob A; Pianegonda, Lucas; Rühs, Patrick A; Coulter, Fergal; Studart, André R

2018-02-28

Soft actuation allows robots to interact safely with humans, other machines, and their surroundings. Full exploitation of the potential of soft actuators has, however, been hindered by the lack of simple manufacturing routes to generate multimaterial parts with intricate shapes and architectures. Here, we report a 3D printing platform for the seamless digital fabrication of pneumatic silicone actuators exhibiting programmable bioinspired architectures and motions. The actuators comprise an elastomeric body whose surface is decorated with reinforcing stripes at a well-defined lead angle. Similar to the fibrous architectures found in muscular hydrostats, the lead angle can be altered to achieve elongation, contraction, or twisting motions. Using a quantitative model based on lamination theory, we establish design principles for the digital fabrication of silicone-based soft actuators whose functional response is programmed within the material's properties and architecture. Exploring such programmability enables 3D printing of a broad range of soft morphing structures.

Social Sentiment Sensor in Twitter for Predicting Cyber-Attacks Using ℓ1 Regularization

PubMed Central

Sanchez-Perez, Gabriel; Toscano-Medina, Karina; Martinez-Hernandez, Victor; Olivares-Mercado, Jesus; Sanchez, Victor

2018-01-01

In recent years, online social media information has been the subject of study in several data science fields due to its impact on users as a communication and expression channel. Data gathered from online platforms such as Twitter has the potential to facilitate research over social phenomena based on sentiment analysis, which usually employs Natural Language Processing and Machine Learning techniques to interpret sentimental tendencies related to users’ opinions and make predictions about real events. Cyber-attacks are not isolated from opinion subjectivity on online social networks. Various security attacks are performed by hacker activists motivated by reactions from polemic social events. In this paper, a methodology for tracking social data that can trigger cyber-attacks is developed. Our main contribution lies in the monthly prediction of tweets with content related to security attacks and the incidents detected based on ℓ1 regularization. PMID:29710833

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

NASA Astrophysics Data System (ADS)

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

2018-05-01

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

Project summaries

NASA Technical Reports Server (NTRS)

1990-01-01

Lunar base projects, including a reconfigurable lunar cargo launcher, a thermal and micrometeorite protection system, a versatile lifting machine with robotic capabilities, a cargo transport system, the design of a road construction system for a lunar base, and the design of a device for removing lunar dust from material surfaces, are discussed. The emphasis on the Gulf of Mexico project was on the development of a computer simulation model for predicting vessel station keeping requirements. An existing code, used in predicting station keeping requirements for oil drilling platforms operating in North Shore (Alaska) waters was used as a basis for the computer simulation. Modifications were made to the existing code. The input into the model consists of satellite altimeter readings and water velocity readings from buoys stationed in the Gulf of Mexico. The satellite data consists of altimeter readings (wave height) taken during the spring of 1989. The simulation model predicts water velocity and direction, and wind velocity.

Social Sentiment Sensor in Twitter for Predicting Cyber-Attacks Using ℓ₁ Regularization.

PubMed

Hernandez-Suarez, Aldo; Sanchez-Perez, Gabriel; Toscano-Medina, Karina; Martinez-Hernandez, Victor; Perez-Meana, Hector; Olivares-Mercado, Jesus; Sanchez, Victor

2018-04-29

In recent years, online social media information has been the subject of study in several data science fields due to its impact on users as a communication and expression channel. Data gathered from online platforms such as Twitter has the potential to facilitate research over social phenomena based on sentiment analysis, which usually employs Natural Language Processing and Machine Learning techniques to interpret sentimental tendencies related to users’ opinions and make predictions about real events. Cyber-attacks are not isolated from opinion subjectivity on online social networks. Various security attacks are performed by hacker activists motivated by reactions from polemic social events. In this paper, a methodology for tracking social data that can trigger cyber-attacks is developed. Our main contribution lies in the monthly prediction of tweets with content related to security attacks and the incidents detected based on ℓ 1 regularization.

MetaDP: a comprehensive web server for disease prediction of 16S rRNA metagenomic datasets.

PubMed

Xu, Xilin; Wu, Aiping; Zhang, Xinlei; Su, Mingming; Jiang, Taijiao; Yuan, Zhe-Ming

2016-01-01

High-throughput sequencing-based metagenomics has garnered considerable interest in recent years. Numerous methods and tools have been developed for the analysis of metagenomic data. However, it is still a daunting task to install a large number of tools and complete a complicated analysis, especially for researchers with minimal bioinformatics backgrounds. To address this problem, we constructed an automated software named MetaDP for 16S rRNA sequencing data analysis, including data quality control, operational taxonomic unit clustering, diversity analysis, and disease risk prediction modeling. Furthermore, a support vector machine-based prediction model for intestinal bowel syndrome (IBS) was built by applying MetaDP to microbial 16S sequencing data from 108 children. The success of the IBS prediction model suggests that the platform may also be applied to other diseases related to gut microbes, such as obesity, metabolic syndrome, or intestinal cancer, among others (http://metadp.cn:7001/).

Analysis of multigrid methods on massively parallel computers: Architectural implications

NASA Technical Reports Server (NTRS)

Matheson, Lesley R.; Tarjan, Robert E.

1993-01-01

We study the potential performance of multigrid algorithms running on massively parallel computers with the intent of discovering whether presently envisioned machines will provide an efficient platform for such algorithms. We consider the domain parallel version of the standard V cycle algorithm on model problems, discretized using finite difference techniques in two and three dimensions on block structured grids of size 10(exp 6) and 10(exp 9), respectively. Our models of parallel computation were developed to reflect the computing characteristics of the current generation of massively parallel multicomputers. These models are based on an interconnection network of 256 to 16,384 message passing, 'workstation size' processors executing in an SPMD mode. The first model accomplishes interprocessor communications through a multistage permutation network. The communication cost is a logarithmic function which is similar to the costs in a variety of different topologies. The second model allows single stage communication costs only. Both models were designed with information provided by machine developers and utilize implementation derived parameters. With the medium grain parallelism of the current generation and the high fixed cost of an interprocessor communication, our analysis suggests an efficient implementation requires the machine to support the efficient transmission of long messages, (up to 1000 words) or the high initiation cost of a communication must be significantly reduced through an alternative optimization technique. Furthermore, with variable length message capability, our analysis suggests the low diameter multistage networks provide little or no advantage over a simple single stage communications network.

Base-CP proteasome can serve as a platform for stepwise lid formation

PubMed Central

Yu, Zanlin; Livnat-Levanon, Nurit; Kleifeld, Oded; Mansour, Wissam; Nakasone, Mark A.; Castaneda, Carlos A.; Dixon, Emma K.; Fushman, David; Reis, Noa; Pick, Elah; Glickman, Michael H.

2015-01-01

26S proteasome, a major regulatory protease in eukaryotes, consists of a 20S proteolytic core particle (CP) capped by a 19S regulatory particle (RP). The 19S RP is divisible into base and lid sub-complexes. Even within the lid, subunits have been demarcated into two modules: module 1 (Rpn5, Rpn6, Rpn8, Rpn9 and Rpn11), which interacts with both CP and base sub-complexes and module 2 (Rpn3, Rpn7, Rpn12 and Rpn15) that is attached mainly to module 1. We now show that suppression of RPN11 expression halted lid assembly yet enabled the base and 20S CP to pre-assemble and form a base-CP. A key role for Regulatory particle non-ATPase 11 (Rpn11) in bridging lid module 1 and module 2 subunits together is inferred from observing defective proteasomes in rpn11–m1, a mutant expressing a truncated form of Rpn11 and displaying mitochondrial phenotypes. An incomplete lid made up of five module 1 subunits attached to base-CP was identified in proteasomes isolated from this mutant. Re-introducing the C-terminal portion of Rpn11 enabled recruitment of missing module 2 subunits. In vitro, module 1 was reconstituted stepwise, initiated by Rpn11–Rpn8 heterodimerization. Upon recruitment of Rpn6, the module 1 intermediate was competent to lock into base-CP and reconstitute an incomplete 26S proteasome. Thus, base-CP can serve as a platform for gradual incorporation of lid, along a proteasome assembly pathway. Identification of proteasome intermediates and reconstitution of minimal functional units should clarify aspects of the inner workings of this machine and how multiple catalytic processes are synchronized within the 26S proteasome holoenzymes. PMID:26182356

Data Randomization and Cluster-Based Partitioning for Botnet Intrusion Detection.

PubMed

Al-Jarrah, Omar Y; Alhussein, Omar; Yoo, Paul D; Muhaidat, Sami; Taha, Kamal; Kim, Kwangjo

2016-08-01

Botnets, which consist of remotely controlled compromised machines called bots, provide a distributed platform for several threats against cyber world entities and enterprises. Intrusion detection system (IDS) provides an efficient countermeasure against botnets. It continually monitors and analyzes network traffic for potential vulnerabilities and possible existence of active attacks. A payload-inspection-based IDS (PI-IDS) identifies active intrusion attempts by inspecting transmission control protocol and user datagram protocol packet's payload and comparing it with previously seen attacks signatures. However, the PI-IDS abilities to detect intrusions might be incapacitated by packet encryption. Traffic-based IDS (T-IDS) alleviates the shortcomings of PI-IDS, as it does not inspect packet payload; however, it analyzes packet header to identify intrusions. As the network's traffic grows rapidly, not only the detection-rate is critical, but also the efficiency and the scalability of IDS become more significant. In this paper, we propose a state-of-the-art T-IDS built on a novel randomized data partitioned learning model (RDPLM), relying on a compact network feature set and feature selection techniques, simplified subspacing and a multiple randomized meta-learning technique. The proposed model has achieved 99.984% accuracy and 21.38 s training time on a well-known benchmark botnet dataset. Experiment results demonstrate that the proposed methodology outperforms other well-known machine-learning models used in the same detection task, namely, sequential minimal optimization, deep neural network, C4.5, reduced error pruning tree, and randomTree.

Alchemy: A Web 2.0 Real-time Quality Assurance Platform for Human Immunodeficiency Virus, Hepatitis C Virus, and BK Virus Quantitation Assays

PubMed Central

Agosto-Arroyo, Emmanuel; Coshatt, Gina M.; Winokur, Thomas S.; Harada, Shuko; Park, Seung L.

2017-01-01

Background: The molecular diagnostics laboratory faces the challenge of improving test turnaround time (TAT). Low and consistent TATs are of great clinical and regulatory importance, especially for molecular virology tests. Laboratory information systems (LISs) contain all the data elements necessary to do accurate quality assurance (QA) reporting of TAT and other measures, but these reports are in most cases still performed manually: a time-consuming and error-prone task. The aim of this study was to develop a web-based real-time QA platform that would automate QA reporting in the molecular diagnostics laboratory at our institution, and minimize the time expended in preparing these reports. Methods: Using a standard Linux, Nginx, MariaDB, PHP stack virtual machine running atop a Dell Precision 5810, we designed and built a web-based QA platform, code-named Alchemy. Data files pulled periodically from the LIS in comma-separated value format were used to autogenerate QA reports for the human immunodeficiency virus (HIV) quantitation, hepatitis C virus (HCV) quantitation, and BK virus (BKV) quantitation. Alchemy allowed the user to select a specific timeframe to be analyzed and calculated key QA statistics in real-time, including the average TAT in days, tests falling outside the expected TAT ranges, and test result ranges. Results: Before implementing Alchemy, reporting QA for the HIV, HCV, and BKV quantitation assays took 45–60 min of personnel time per test every month. With Alchemy, that time has decreased to 15 min total per month. Alchemy allowed the user to select specific periods of time and analyzed the TAT data in-depth without the need of extensive manual calculations. Conclusions: Alchemy has significantly decreased the time and the human error associated with QA report generation in our molecular diagnostics laboratory. Other tests will be added to this web-based platform in future updates. This effort shows the utility of informatician-supervised resident/fellow programming projects as learning opportunities and workflow improvements in the molecular laboratory. PMID:28480121

Cloud computing geospatial application for water resources based on free and open source software and open standards - a prototype

NASA Astrophysics Data System (ADS)

Delipetrev, Blagoj

2016-04-01

Presently, most of the existing software is desktop-based, designed to work on a single computer, which represents a major limitation in many ways, starting from limited computer processing, storage power, accessibility, availability, etc. The only feasible solution lies in the web and cloud. This abstract presents research and development of a cloud computing geospatial application for water resources based on free and open source software and open standards using hybrid deployment model of public - private cloud, running on two separate virtual machines (VMs). The first one (VM1) is running on Amazon web services (AWS) and the second one (VM2) is running on a Xen cloud platform. The presented cloud application is developed using free and open source software, open standards and prototype code. The cloud application presents a framework how to develop specialized cloud geospatial application that needs only a web browser to be used. This cloud application is the ultimate collaboration geospatial platform because multiple users across the globe with internet connection and browser can jointly model geospatial objects, enter attribute data and information, execute algorithms, and visualize results. The presented cloud application is: available all the time, accessible from everywhere, it is scalable, works in a distributed computer environment, it creates a real-time multiuser collaboration platform, the programing languages code and components are interoperable, and it is flexible in including additional components. The cloud geospatial application is implemented as a specialized water resources application with three web services for 1) data infrastructure (DI), 2) support for water resources modelling (WRM), 3) user management. The web services are running on two VMs that are communicating over the internet providing services to users. The application was tested on the Zletovica river basin case study with concurrent multiple users. The application is a state-of-the-art cloud geospatial collaboration platform. The presented solution is a prototype and can be used as a foundation for developing of any specialized cloud geospatial applications. Further research will be focused on distributing the cloud application on additional VMs, testing the scalability and availability of services.

An Efficient Statistical Computation Technique for Health Care Big Data using R

NASA Astrophysics Data System (ADS)

Sushma Rani, N.; Srinivasa Rao, P., Dr; Parimala, P.

2017-08-01

Due to the changes in living conditions and other factors many critical health related problems are arising. The diagnosis of the problem at earlier stages will increase the chances of survival and fast recovery. This reduces the time of recovery and the cost associated for the treatment. One such medical related issue is cancer and breast cancer has been identified as the second leading cause of cancer death. If detected in the early stage it can be cured. Once a patient is detected with breast cancer tumor, it should be classified whether it is cancerous or non-cancerous. So the paper uses k-nearest neighbors(KNN) algorithm which is one of the simplest machine learning algorithms and is an instance-based learning algorithm to classify the data. Day-to -day new records are added which leds to increase in the data to be classified and this tends to be big data problem. The algorithm is implemented in R whichis the most popular platform applied to machine learning algorithms for statistical computing. Experimentation is conducted by using various classification evaluation metric onvarious values of k. The results show that the KNN algorithm out performes better than existing models.

Large-scale machine learning and evaluation platform for real-time traffic surveillance

NASA Astrophysics Data System (ADS)

Eichel, Justin A.; Mishra, Akshaya; Miller, Nicholas; Jankovic, Nicholas; Thomas, Mohan A.; Abbott, Tyler; Swanson, Douglas; Keller, Joel

2016-09-01

In traffic engineering, vehicle detectors are trained on limited datasets, resulting in poor accuracy when deployed in real-world surveillance applications. Annotating large-scale high-quality datasets is challenging. Typically, these datasets have limited diversity; they do not reflect the real-world operating environment. There is a need for a large-scale, cloud-based positive and negative mining process and a large-scale learning and evaluation system for the application of automatic traffic measurements and classification. The proposed positive and negative mining process addresses the quality of crowd sourced ground truth data through machine learning review and human feedback mechanisms. The proposed learning and evaluation system uses a distributed cloud computing framework to handle data-scaling issues associated with large numbers of samples and a high-dimensional feature space. The system is trained using AdaBoost on 1,000,000 Haar-like features extracted from 70,000 annotated video frames. The trained real-time vehicle detector achieves an accuracy of at least 95% for 1/2 and about 78% for 19/20 of the time when tested on ˜7,500,000 video frames. At the end of 2016, the dataset is expected to have over 1 billion annotated video frames.

OpenNEX, a private-public partnership in support of the national climate assessment

NASA Astrophysics Data System (ADS)

Nemani, R. R.; Wang, W.; Michaelis, A.; Votava, P.; Ganguly, S.

2016-12-01

The NASA Earth Exchange (NEX) is a collaborative computing platform that has been developed with the objective of bringing scientists together with the software tools, massive global datasets, and supercomputing resources necessary to accelerate research in Earth systems science and global change. NEX is funded as an enabling tool for sustaining the national climate assessment. Over the past five years, researchers have used the NEX platform and produced a number of data sets highly relevant to the National Climate Assessment. These include high-resolution climate projections using different downscaling techniques and trends in historical climate from satellite data. To enable a broader community in exploiting the above datasets, the NEX team partnered with public cloud providers to create the OpenNEX platform. OpenNEX provides ready access to NEX data holdings on a number of public cloud platforms along with pertinent analysis tools and workflows in the form of Machine Images and Docker Containers, lectures and tutorials by experts. We will showcase some of the applications of OpenNEX data and tools by the community on Amazon Web Services, Google Cloud and the NEX Sandbox.

Machine learning-based patient specific prompt-gamma dose monitoring in proton therapy

NASA Astrophysics Data System (ADS)

Gueth, P.; Dauvergne, D.; Freud, N.; Létang, J. M.; Ray, C.; Testa, E.; Sarrut, D.

2013-07-01

Online dose monitoring in proton therapy is currently being investigated with prompt-gamma (PG) devices. PG emission was shown to be correlated with dose deposition. This relationship is mostly unknown under real conditions. We propose a machine learning approach based on simulations to create optimized treatment-specific classifiers that detect discrepancies between planned and delivered dose. Simulations were performed with the Monte-Carlo platform Gate/Geant4 for a spot-scanning proton therapy treatment and a PG camera prototype currently under investigation. The method first builds a learning set of perturbed situations corresponding to a range of patient translation. This set is then used to train a combined classifier using distal falloff and registered correlation measures. Classifier performances were evaluated using receiver operating characteristic curves and maximum associated specificity and sensitivity. A leave-one-out study showed that it is possible to detect discrepancies of 5 mm with specificity and sensitivity of 85% whereas using only distal falloff decreases the sensitivity down to 77% on the same data set. The proposed method could help to evaluate performance and to optimize the design of PG monitoring devices. It is generic: other learning sets of deviations, other measures and other types of classifiers could be studied to potentially reach better performance. At the moment, the main limitation lies in the computation time needed to perform the simulations.

Neurofeedback-based functional near-infrared spectroscopy upregulates motor cortex activity in imagined motor tasks

PubMed Central

Lapborisuth, Pawan; Zhang, Xian; Noah, Adam; Hirsch, Joy

2017-01-01

Abstract. Neurofeedback is a method for using neural activity displayed on a computer to regulate one’s own brain function and has been shown to be a promising technique for training individuals to interact with brain–machine interface applications such as neuroprosthetic limbs. The goal of this study was to develop a user-friendly functional near-infrared spectroscopy (fNIRS)-based neurofeedback system to upregulate neural activity associated with motor imagery, which is frequently used in neuroprosthetic applications. We hypothesized that fNIRS neurofeedback would enhance activity in motor cortex during a motor imagery task. Twenty-two participants performed active and imaginary right-handed squeezing movements using an elastic ball while wearing a 98-channel fNIRS device. Neurofeedback traces representing localized cortical hemodynamic responses were graphically presented to participants in real time. Participants were instructed to observe this graphical representation and use the information to increase signal amplitude. Neural activity was compared during active and imaginary squeezing with and without neurofeedback. Active squeezing resulted in activity localized to the left premotor and supplementary motor cortex, and activity in the motor cortex was found to be modulated by neurofeedback. Activity in the motor cortex was also shown in the imaginary squeezing condition only in the presence of neurofeedback. These findings demonstrate that real-time fNIRS neurofeedback is a viable platform for brain–machine interface applications. PMID:28680906

Neurofeedback-based functional near-infrared spectroscopy upregulates motor cortex activity in imagined motor tasks.

PubMed

Lapborisuth, Pawan; Zhang, Xian; Noah, Adam; Hirsch, Joy

2017-04-01

Neurofeedback is a method for using neural activity displayed on a computer to regulate one's own brain function and has been shown to be a promising technique for training individuals to interact with brain-machine interface applications such as neuroprosthetic limbs. The goal of this study was to develop a user-friendly functional near-infrared spectroscopy (fNIRS)-based neurofeedback system to upregulate neural activity associated with motor imagery, which is frequently used in neuroprosthetic applications. We hypothesized that fNIRS neurofeedback would enhance activity in motor cortex during a motor imagery task. Twenty-two participants performed active and imaginary right-handed squeezing movements using an elastic ball while wearing a 98-channel fNIRS device. Neurofeedback traces representing localized cortical hemodynamic responses were graphically presented to participants in real time. Participants were instructed to observe this graphical representation and use the information to increase signal amplitude. Neural activity was compared during active and imaginary squeezing with and without neurofeedback. Active squeezing resulted in activity localized to the left premotor and supplementary motor cortex, and activity in the motor cortex was found to be modulated by neurofeedback. Activity in the motor cortex was also shown in the imaginary squeezing condition only in the presence of neurofeedback. These findings demonstrate that real-time fNIRS neurofeedback is a viable platform for brain-machine interface applications.

Monte Carlo verification of radiotherapy treatments with CloudMC.

PubMed

Miras, Hector; Jiménez, Rubén; Perales, Álvaro; Terrón, José Antonio; Bertolet, Alejandro; Ortiz, Antonio; Macías, José

2018-06-27

A new implementation has been made on CloudMC, a cloud-based platform presented in a previous work, in order to provide services for radiotherapy treatment verification by means of Monte Carlo in a fast, easy and economical way. A description of the architecture of the application and the new developments implemented is presented together with the results of the tests carried out to validate its performance. CloudMC has been developed over Microsoft Azure cloud. It is based on a map/reduce implementation for Monte Carlo calculations distribution over a dynamic cluster of virtual machines in order to reduce calculation time. CloudMC has been updated with new methods to read and process the information related to radiotherapy treatment verification: CT image set, treatment plan, structures and dose distribution files in DICOM format. Some tests have been designed in order to determine, for the different tasks, the most suitable type of virtual machines from those available in Azure. Finally, the performance of Monte Carlo verification in CloudMC is studied through three real cases that involve different treatment techniques, linac models and Monte Carlo codes. Considering computational and economic factors, D1_v2 and G1 virtual machines were selected as the default type for the Worker Roles and the Reducer Role respectively. Calculation times up to 33 min and costs of 16 € were achieved for the verification cases presented when a statistical uncertainty below 2% (2σ) was required. The costs were reduced to 3-6 € when uncertainty requirements are relaxed to 4%. Advantages like high computational power, scalability, easy access and pay-per-usage model, make Monte Carlo cloud-based solutions, like the one presented in this work, an important step forward to solve the long-lived problem of truly introducing the Monte Carlo algorithms in the daily routine of the radiotherapy planning process.

3D Printed Bionic Nanodevices.

PubMed

Kong, Yong Lin; Gupta, Maneesh K; Johnson, Blake N; McAlpine, Michael C

2016-06-01

The ability to three-dimensionally interweave biological and functional materials could enable the creation of bionic devices possessing unique and compelling geometries, properties, and functionalities. Indeed, interfacing high performance active devices with biology could impact a variety of fields, including regenerative bioelectronic medicines, smart prosthetics, medical robotics, and human-machine interfaces. Biology, from the molecular scale of DNA and proteins, to the macroscopic scale of tissues and organs, is three-dimensional, often soft and stretchable, and temperature sensitive. This renders most biological platforms incompatible with the fabrication and materials processing methods that have been developed and optimized for functional electronics, which are typically planar, rigid and brittle. A number of strategies have been developed to overcome these dichotomies. One particularly novel approach is the use of extrusion-based multi-material 3D printing, which is an additive manufacturing technology that offers a freeform fabrication strategy. This approach addresses the dichotomies presented above by (1) using 3D printing and imaging for customized, hierarchical, and interwoven device architectures; (2) employing nanotechnology as an enabling route for introducing high performance materials, with the potential for exhibiting properties not found in the bulk; and (3) 3D printing a range of soft and nanoscale materials to enable the integration of a diverse palette of high quality functional nanomaterials with biology. Further, 3D printing is a multi-scale platform, allowing for the incorporation of functional nanoscale inks, the printing of microscale features, and ultimately the creation of macroscale devices. This blending of 3D printing, novel nanomaterial properties, and 'living' platforms may enable next-generation bionic systems. In this review, we highlight this synergistic integration of the unique properties of nanomaterials with the versatility of extrusion-based 3D printing technologies to interweave nanomaterials and fabricate novel bionic devices.

3D Printed Bionic Nanodevices

PubMed Central

Kong, Yong Lin; Gupta, Maneesh K.; Johnson, Blake N.; McAlpine, Michael C.

2016-01-01

Summary The ability to three-dimensionally interweave biological and functional materials could enable the creation of bionic devices possessing unique and compelling geometries, properties, and functionalities. Indeed, interfacing high performance active devices with biology could impact a variety of fields, including regenerative bioelectronic medicines, smart prosthetics, medical robotics, and human-machine interfaces. Biology, from the molecular scale of DNA and proteins, to the macroscopic scale of tissues and organs, is three-dimensional, often soft and stretchable, and temperature sensitive. This renders most biological platforms incompatible with the fabrication and materials processing methods that have been developed and optimized for functional electronics, which are typically planar, rigid and brittle. A number of strategies have been developed to overcome these dichotomies. One particularly novel approach is the use of extrusion-based multi-material 3D printing, which is an additive manufacturing technology that offers a freeform fabrication strategy. This approach addresses the dichotomies presented above by (1) using 3D printing and imaging for customized, hierarchical, and interwoven device architectures; (2) employing nanotechnology as an enabling route for introducing high performance materials, with the potential for exhibiting properties not found in the bulk; and (3) 3D printing a range of soft and nanoscale materials to enable the integration of a diverse palette of high quality functional nanomaterials with biology. Further, 3D printing is a multi-scale platform, allowing for the incorporation of functional nanoscale inks, the printing of microscale features, and ultimately the creation of macroscale devices. This blending of 3D printing, novel nanomaterial properties, and ‘living’ platforms may enable next-generation bionic systems. In this review, we highlight this synergistic integration of the unique properties of nanomaterials with the versatility of extrusion-based 3D printing technologies to interweave nanomaterials and fabricate novel bionic devices. PMID:27617026

Bionimbus: a cloud for managing, analyzing and sharing large genomics datasets.

PubMed

Heath, Allison P; Greenway, Matthew; Powell, Raymond; Spring, Jonathan; Suarez, Rafael; Hanley, David; Bandlamudi, Chai; McNerney, Megan E; White, Kevin P; Grossman, Robert L

2014-01-01

As large genomics and phenotypic datasets are becoming more common, it is increasingly difficult for most researchers to access, manage, and analyze them. One possible approach is to provide the research community with several petabyte-scale cloud-based computing platforms containing these data, along with tools and resources to analyze it. Bionimbus is an open source cloud-computing platform that is based primarily upon OpenStack, which manages on-demand virtual machines that provide the required computational resources, and GlusterFS, which is a high-performance clustered file system. Bionimbus also includes Tukey, which is a portal, and associated middleware that provides a single entry point and a single sign on for the various Bionimbus resources; and Yates, which automates the installation, configuration, and maintenance of the software infrastructure required. Bionimbus is used by a variety of projects to process genomics and phenotypic data. For example, it is used by an acute myeloid leukemia resequencing project at the University of Chicago. The project requires several computational pipelines, including pipelines for quality control, alignment, variant calling, and annotation. For each sample, the alignment step requires eight CPUs for about 12 h. BAM file sizes ranged from 5 GB to 10 GB for each sample. Most members of the research community have difficulty downloading large genomics datasets and obtaining sufficient storage and computer resources to manage and analyze the data. Cloud computing platforms, such as Bionimbus, with data commons that contain large genomics datasets, are one choice for broadening access to research data in genomics. Published by the BMJ Publishing Group Limited. For permission to use (where not already granted under a licence) please go to http://group.bmj.com/group/rights-licensing/permissions.

A mobile platform for automated screening of asthma and chronic obstructive pulmonary disease.

PubMed

Chamberlain, Daniel B; Kodgule, Rahul; Fletcher, Richard Ribon

2016-08-01

Chronic Obstructive Pulmonary Disease (COPD) and asthma each represent a large proportion of the global disease burden; COPD is the third leading cause of death worldwide and asthma is one of the most prevalent chronic diseases, afflicting over 300 million people. Much of this burden is concentrated in the developing world, where patients lack access to physicians trained in the diagnosis of pulmonary disease. As a result, these patients experience high rates of underdiagnosis and misdiagnosis. To address this need, we present a mobile platform capable of screening for Asthma and COPD. Our solution is based on a mobile smart phone and consists of an electronic stethoscope, a peak flow meter application, and a patient questionnaire. This data is combined with a machine learning algorithm to identify patients with asthma and COPD. To test and validate the design, we collected data from 119 healthy and sick participants using our custom mobile application and ran the analysis on a PC computer. For comparison, all subjects were examined by an experienced pulmonologist using a full pulmonary testing laboratory. Employing a two-stage logistic regression model, our algorithms were first able to identify patients with either asthma or COPD from the general population, yielding an ROC curve with an AUC of 0.95. Then, after identifying these patients, our algorithm was able to distinguish between patients with asthma and patients with COPD, yielding an ROC curve with AUC of 0.97. This work represents an important milestone towards creating a self-contained mobile phone-based platform that can be used for screening and diagnosis of pulmonary disease in many parts of the world.

New controller for high voltage converter modulator at spallation neutron source

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

Wezensky, Mark W; Brown, David L; Lee, Sung-Woo

2017-01-01

The Spallation Neutron Source (SNS) has developed a new control system for the High Voltage Convertor Modulator (HVCM) at the SNS to replace the original control system which is approaching obsolescence. The original system was based on controllers for similar high voltage systems that were already in use [1]. The new controller, based on National Instruments PXI/FlexRIO Field Programmable Gate Array (FPGA) platform, offers enhancements such as modular construction, flexibility and non-proprietary software. The new controller also provides new capabilities like various methods for modulator pulse flattening, waveform capture, and first fault detection. This paper will discuss the design ofmore » the system, including the human machine interface, based on lessons learned at the SNS and other projects. It will also discuss performance and other issues related to its operation in an accelerator facility which requires high availability. To date, 73% of the operational HVCMs have been upgraded to with the new controller, and the remainder are scheduled for completion by mid-2017.« less

Web-based newborn screening system for metabolic diseases: machine learning versus clinicians.

PubMed

Chen, Wei-Hsin; Hsieh, Sheau-Ling; Hsu, Kai-Ping; Chen, Han-Ping; Su, Xing-Yu; Tseng, Yi-Ju; Chien, Yin-Hsiu; Hwu, Wuh-Liang; Lai, Feipei

2013-05-23

A hospital information system (HIS) that integrates screening data and interpretation of the data is routinely requested by hospitals and parents. However, the accuracy of disease classification may be low because of the disease characteristics and the analytes used for classification. The objective of this study is to describe a system that enhanced the neonatal screening system of the Newborn Screening Center at the National Taiwan University Hospital. The system was designed and deployed according to a service-oriented architecture (SOA) framework under the Web services .NET environment. The system consists of sample collection, testing, diagnosis, evaluation, treatment, and follow-up services among collaborating hospitals. To improve the accuracy of newborn screening, machine learning and optimal feature selection mechanisms were investigated for screening newborns for inborn errors of metabolism. The framework of the Newborn Screening Hospital Information System (NSHIS) used the embedded Health Level Seven (HL7) standards for data exchanges among heterogeneous platforms integrated by Web services in the C# language. In this study, machine learning classification was used to predict phenylketonuria (PKU), hypermethioninemia, and 3-methylcrotonyl-CoA-carboxylase (3-MCC) deficiency. The classification methods used 347,312 newborn dried blood samples collected at the Center between 2006 and 2011. Of these, 220 newborns had values over the diagnostic cutoffs (positive cases) and 1557 had values that were over the screening cutoffs but did not meet the diagnostic cutoffs (suspected cases). The original 35 analytes and the manifested features were ranked based on F score, then combinations of the top 20 ranked features were selected as input features to support vector machine (SVM) classifiers to obtain optimal feature sets. These feature sets were tested using 5-fold cross-validation and optimal models were generated. The datasets collected in year 2011 were used as predicting cases. The feature selection strategies were implemented and the optimal markers for PKU, hypermethioninemia, and 3-MCC deficiency were obtained. The results of the machine learning approach were compared with the cutoff scheme. The number of the false positive cases were reduced from 21 to 2 for PKU, from 30 to 10 for hypermethioninemia, and 209 to 46 for 3-MCC deficiency. This SOA Web service-based newborn screening system can accelerate screening procedures effectively and efficiently. An SVM learning methodology for PKU, hypermethioninemia, and 3-MCC deficiency metabolic diseases classification, including optimal feature selection strategies, is presented. By adopting the results of this study, the number of suspected cases could be reduced dramatically.

Web-Based Newborn Screening System for Metabolic Diseases: Machine Learning Versus Clinicians

PubMed Central

Chen, Wei-Hsin; Hsu, Kai-Ping; Chen, Han-Ping; Su, Xing-Yu; Tseng, Yi-Ju; Chien, Yin-Hsiu; Hwu, Wuh-Liang; Lai, Feipei

2013-01-01

Background A hospital information system (HIS) that integrates screening data and interpretation of the data is routinely requested by hospitals and parents. However, the accuracy of disease classification may be low because of the disease characteristics and the analytes used for classification. Objective The objective of this study is to describe a system that enhanced the neonatal screening system of the Newborn Screening Center at the National Taiwan University Hospital. The system was designed and deployed according to a service-oriented architecture (SOA) framework under the Web services .NET environment. The system consists of sample collection, testing, diagnosis, evaluation, treatment, and follow-up services among collaborating hospitals. To improve the accuracy of newborn screening, machine learning and optimal feature selection mechanisms were investigated for screening newborns for inborn errors of metabolism. Methods The framework of the Newborn Screening Hospital Information System (NSHIS) used the embedded Health Level Seven (HL7) standards for data exchanges among heterogeneous platforms integrated by Web services in the C# language. In this study, machine learning classification was used to predict phenylketonuria (PKU), hypermethioninemia, and 3-methylcrotonyl-CoA-carboxylase (3-MCC) deficiency. The classification methods used 347,312 newborn dried blood samples collected at the Center between 2006 and 2011. Of these, 220 newborns had values over the diagnostic cutoffs (positive cases) and 1557 had values that were over the screening cutoffs but did not meet the diagnostic cutoffs (suspected cases). The original 35 analytes and the manifested features were ranked based on F score, then combinations of the top 20 ranked features were selected as input features to support vector machine (SVM) classifiers to obtain optimal feature sets. These feature sets were tested using 5-fold cross-validation and optimal models were generated. The datasets collected in year 2011 were used as predicting cases. Results The feature selection strategies were implemented and the optimal markers for PKU, hypermethioninemia, and 3-MCC deficiency were obtained. The results of the machine learning approach were compared with the cutoff scheme. The number of the false positive cases were reduced from 21 to 2 for PKU, from 30 to 10 for hypermethioninemia, and 209 to 46 for 3-MCC deficiency. Conclusions This SOA Web service–based newborn screening system can accelerate screening procedures effectively and efficiently. An SVM learning methodology for PKU, hypermethioninemia, and 3-MCC deficiency metabolic diseases classification, including optimal feature selection strategies, is presented. By adopting the results of this study, the number of suspected cases could be reduced dramatically. PMID:23702487

A lightweight messaging-based distributed processing and workflow execution framework for real-time and big data analysis

NASA Astrophysics Data System (ADS)

Laban, Shaban; El-Desouky, Aly

2014-05-01

To achieve a rapid, simple and reliable parallel processing of different types of tasks and big data processing on any compute cluster, a lightweight messaging-based distributed applications processing and workflow execution framework model is proposed. The framework is based on Apache ActiveMQ and Simple (or Streaming) Text Oriented Message Protocol (STOMP). ActiveMQ , a popular and powerful open source persistence messaging and integration patterns server with scheduler capabilities, acts as a message broker in the framework. STOMP provides an interoperable wire format that allows framework programs to talk and interact between each other and ActiveMQ easily. In order to efficiently use the message broker a unified message and topic naming pattern is utilized to achieve the required operation. Only three Python programs and simple library, used to unify and simplify the implementation of activeMQ and STOMP protocol, are needed to use the framework. A watchdog program is used to monitor, remove, add, start and stop any machine and/or its different tasks when necessary. For every machine a dedicated one and only one zoo keeper program is used to start different functions or tasks, stompShell program, needed for executing the user required workflow. The stompShell instances are used to execute any workflow jobs based on received message. A well-defined, simple and flexible message structure, based on JavaScript Object Notation (JSON), is used to build any complex workflow systems. Also, JSON format is used in configuration, communication between machines and programs. The framework is platform independent. Although, the framework is built using Python the actual workflow programs or jobs can be implemented by any programming language. The generic framework can be used in small national data centres for processing seismological and radionuclide data received from the International Data Centre (IDC) of the Preparatory Commission for the Comprehensive Nuclear-Test-Ban Treaty Organization (CTBTO). Also, it is possible to extend the use of the framework in monitoring the IDC pipeline. The detailed design, implementation,conclusion and future work of the proposed framework will be presented.

Scalable Rapidly Deployable Convex Optimization for Data Analytics

DTIC Science & Technology

SOCPs , SDPs, exponential cone programs, and power cone programs. CVXPY supports basic methods for distributed optimization, on...multiple heterogenous platforms. We have also done basic research in various application areas , using CVXPY , to demonstrate its usefulness. See attached report for publication information....Over the period of the contract we have developed the full stack for wide use of convex optimization, in machine learning and many other areas .

Crux: Rapid Open Source Protein Tandem Mass Spectrometry Analysis

PubMed Central

2015-01-01

Efficiently and accurately analyzing big protein tandem mass spectrometry data sets requires robust software that incorporates state-of-the-art computational, machine learning, and statistical methods. The Crux mass spectrometry analysis software toolkit (http://cruxtoolkit.sourceforge.net) is an open source project that aims to provide users with a cross-platform suite of analysis tools for interpreting protein mass spectrometry data. PMID:25182276

Concurrent Collections (CnC): A new approach to parallel programming

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

Knobe, Kathleen

2010-05-07

A common approach in designing parallel languages is to provide some high level handles to manipulate the use of the parallel platform. This exposes some aspects of the target platform, for example, shared vs. distributed memory. It may expose some but not all types of parallelism, for example, data parallelism but not task parallelism. This approach must find a balance between the desire to provide a simple view for the domain expert and provide sufficient power for tuning. This is hard for any given architecture and harder if the language is to apply to a range of architectures. Either simplicitymore » or power is lost. Instead of viewing the language design problem as one of providing the programmer with high level handles, we view the problem as one of designing an interface. On one side of this interface is the programmer (domain expert) who knows the application but needs no knowledge of any aspects of the platform. On the other side of the interface is the performance expert (programmer or program) who demands maximal flexibility for optimizing the mapping to a wide range of target platforms (parallel / serial, shared / distributed, homogeneous / heterogeneous, etc.) but needs no knowledge of the domain. Concurrent Collections (CnC) is based on this separation of concerns. The talk will present CnC and its benefits. About the speaker. Kathleen Knobe has focused throughout her career on parallelism especially compiler technology, runtime system design and language design. She worked at Compass (aka Massachusetts Computer Associates) from 1980 to 1991 designing compilers for a wide range of parallel platforms for Thinking Machines, MasPar, Alliant, Numerix, and several government projects. In 1991 she decided to finish her education. After graduating from MIT in 1997, she joined Digital Equipment’s Cambridge Research Lab (CRL). She stayed through the DEC/Compaq/HP mergers and when CRL was acquired and absorbed by Intel. She currently works in the Software and Services Group / Technology Pathfinding and Innovation.« less

Concurrent Collections (CnC): A new approach to parallel programming

ScienceCinema

Knobe, Kathleen

2018-04-16

A common approach in designing parallel languages is to provide some high level handles to manipulate the use of the parallel platform. This exposes some aspects of the target platform, for example, shared vs. distributed memory. It may expose some but not all types of parallelism, for example, data parallelism but not task parallelism. This approach must find a balance between the desire to provide a simple view for the domain expert and provide sufficient power for tuning. This is hard for any given architecture and harder if the language is to apply to a range of architectures. Either simplicity or power is lost. Instead of viewing the language design problem as one of providing the programmer with high level handles, we view the problem as one of designing an interface. On one side of this interface is the programmer (domain expert) who knows the application but needs no knowledge of any aspects of the platform. On the other side of the interface is the performance expert (programmer or program) who demands maximal flexibility for optimizing the mapping to a wide range of target platforms (parallel / serial, shared / distributed, homogeneous / heterogeneous, etc.) but needs no knowledge of the domain. Concurrent Collections (CnC) is based on this separation of concerns. The talk will present CnC and its benefits. About the speaker. Kathleen Knobe has focused throughout her career on parallelism especially compiler technology, runtime system design and language design. She worked at Compass (aka Massachusetts Computer Associates) from 1980 to 1991 designing compilers for a wide range of parallel platforms for Thinking Machines, MasPar, Alliant, Numerix, and several government projects. In 1991 she decided to finish her education. After graduating from MIT in 1997, she joined Digital Equipment’s Cambridge Research Lab (CRL). She stayed through the DEC/Compaq/HP mergers and when CRL was acquired and absorbed by Intel. She currently works in the Software and Services Group / Technology Pathfinding and Innovation.

Concept and realization of unmanned aerial system with different modes of operation

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

Czyba, Roman; Szafrański, Grzegorz; Janusz, Wojciech

2014-12-10

In this paper we describe the development process of unmanned aerial system, its mechanical components, electronics and software solutions. During the stage of design, we have formulated some necessary requirements for the multirotor vehicle and ground control station in order to build an optimal system which can be used for the reconnaissance missions. Platform is controlled by use of the ground control station (GCS) and has possibility of accomplishing video based observation tasks. In order to fulfill this requirement the on-board payload consists of mechanically stabilized camera augmented with machine vision algorithms to enable object tracking tasks. Novelty of themore » system are four modes of flight, which give full functionality of the developed UAV system. Designed ground control station is consisted not only of the application itself, but also a built-in dedicated components located inside the chassis, which together creates an advanced UAV system supporting the control and management of the flight. Mechanical part of quadrotor is designed to ensure its robustness while meeting objectives of minimizing weight of the platform. Finally the designed electronics allows for implementation of control and estimation algorithms without the needs for their excessive computational optimization.« less

High performance 3D adaptive filtering for DSP based portable medical imaging systems

NASA Astrophysics Data System (ADS)

Bockenbach, Olivier; Ali, Murtaza; Wainwright, Ian; Nadeski, Mark

2015-03-01

Portable medical imaging devices have proven valuable for emergency medical services both in the field and hospital environments and are becoming more prevalent in clinical settings where the use of larger imaging machines is impractical. Despite their constraints on power, size and cost, portable imaging devices must still deliver high quality images. 3D adaptive filtering is one of the most advanced techniques aimed at noise reduction and feature enhancement, but is computationally very demanding and hence often cannot be run with sufficient performance on a portable platform. In recent years, advanced multicore digital signal processors (DSP) have been developed that attain high processing performance while maintaining low levels of power dissipation. These processors enable the implementation of complex algorithms on a portable platform. In this study, the performance of a 3D adaptive filtering algorithm on a DSP is investigated. The performance is assessed by filtering a volume of size 512x256x128 voxels sampled at a pace of 10 MVoxels/sec with an Ultrasound 3D probe. Relative performance and power is addressed between a reference PC (Quad Core CPU) and a TMS320C6678 DSP from Texas Instruments.

Decision tree and ensemble learning algorithms with their applications in bioinformatics.

PubMed

Che, Dongsheng; Liu, Qi; Rasheed, Khaled; Tao, Xiuping

2011-01-01

Machine learning approaches have wide applications in bioinformatics, and decision tree is one of the successful approaches applied in this field. In this chapter, we briefly review decision tree and related ensemble algorithms and show the successful applications of such approaches on solving biological problems. We hope that by learning the algorithms of decision trees and ensemble classifiers, biologists can get the basic ideas of how machine learning algorithms work. On the other hand, by being exposed to the applications of decision trees and ensemble algorithms in bioinformatics, computer scientists can get better ideas of which bioinformatics topics they may work on in their future research directions. We aim to provide a platform to bridge the gap between biologists and computer scientists.

Harnessing Disordered-Ensemble Quantum Dynamics for Machine Learning

NASA Astrophysics Data System (ADS)

Fujii, Keisuke; Nakajima, Kohei

2017-08-01

The quantum computer has an amazing potential of fast information processing. However, the realization of a digital quantum computer is still a challenging problem requiring highly accurate controls and key application strategies. Here we propose a platform, quantum reservoir computing, to solve these issues successfully by exploiting the natural quantum dynamics of ensemble systems, which are ubiquitous in laboratories nowadays, for machine learning. This framework enables ensemble quantum systems to universally emulate nonlinear dynamical systems including classical chaos. A number of numerical experiments show that quantum systems consisting of 5-7 qubits possess computational capabilities comparable to conventional recurrent neural networks of 100-500 nodes. This discovery opens up a paradigm for information processing with artificial intelligence powered by quantum physics.

Performance Comparison of a Set of Periodic and Non-Periodic Tridiagonal Solvers on SP2 and Paragon Parallel Computers

NASA Technical Reports Server (NTRS)

Sun, Xian-He; Moitra, Stuti

1996-01-01

Various tridiagonal solvers have been proposed in recent years for different parallel platforms. In this paper, the performance of three tridiagonal solvers, namely, the parallel partition LU algorithm, the parallel diagonal dominant algorithm, and the reduced diagonal dominant algorithm, is studied. These algorithms are designed for distributed-memory machines and are tested on an Intel Paragon and an IBM SP2 machines. Measured results are reported in terms of execution time and speedup. Analytical study are conducted for different communication topologies and for different tridiagonal systems. The measured results match the analytical results closely. In addition to address implementation issues, performance considerations such as problem sizes and models of speedup are also discussed.

Automated Data Assimilation and Flight Planning for Multi-Platform Observation Missions

NASA Technical Reports Server (NTRS)

Oza, Nikunj; Morris, Robert A.; Strawa, Anthony; Kurklu, Elif; Keely, Leslie

2008-01-01

This is a progress report on an effort in which our goal is to demonstrate the effectiveness of automated data mining and planning for the daily management of Earth Science missions. Currently, data mining and machine learning technologies are being used by scientists at research labs for validating Earth science models. However, few if any of these advanced techniques are currently being integrated into daily mission operations. Consequently, there are significant gaps in the knowledge that can be derived from the models and data that are used each day for guiding mission activities. The result can be sub-optimal observation plans, lack of useful data, and wasteful use of resources. Recent advances in data mining, machine learning, and planning make it feasible to migrate these technologies into the daily mission planning cycle. We describe the design of a closed loop system for data acquisition, processing, and flight planning that integrates the results of machine learning into the flight planning process.

Designing a holistic end-to-end intelligent network analysis and security platform

NASA Astrophysics Data System (ADS)

Alzahrani, M.

2018-03-01

Firewall protects a network from outside attacks, however, once an attack entering a network, it is difficult to detect. Recent significance accidents happened. i.e.: millions of Yahoo email account were stolen and crucial data from institutions are held for ransom. Within two year Yahoo’s system administrators were not aware that there are intruder inside the network. This happened due to the lack of intelligent tools to monitor user behaviour in internal network. This paper discusses a design of an intelligent anomaly/malware detection system with proper proactive actions. The aim is to equip the system administrator with a proper tool to battle the insider attackers. The proposed system adopts machine learning to analyse user’s behaviour through the runtime behaviour of each node in the network. The machine learning techniques include: deep learning, evolving machine learning perceptron, hybrid of Neural Network and Fuzzy, as well as predictive memory techniques. The proposed system is expanded to deal with larger network using agent techniques.

Morphological change in machines accelerates the evolution of robust behavior

PubMed Central

Bongard, Josh

2011-01-01

Most animals exhibit significant neurological and morphological change throughout their lifetime. No robots to date, however, grow new morphological structure while behaving. This is due to technological limitations but also because it is unclear that morphological change provides a benefit to the acquisition of robust behavior in machines. Here I show that in evolving populations of simulated robots, if robots grow from anguilliform into legged robots during their lifetime in the early stages of evolution, and the anguilliform body plan is gradually lost during later stages of evolution, gaits are evolved for the final, legged form of the robot more rapidly—and the evolved gaits are more robust—compared to evolving populations of legged robots that do not transition through the anguilliform body plan. This suggests that morphological change, as well as the evolution of development, are two important processes that improve the automatic generation of robust behaviors for machines. It also provides an experimental platform for investigating the relationship between the evolution of development and robust behavior in biological organisms. PMID:21220304

Cost-effective lightweight mirrors for aerospace and defense

NASA Astrophysics Data System (ADS)

Woodard, Kenneth S.; Comstock, Lovell E.; Wamboldt, Leonard; Roy, Brian P.

2015-05-01

The demand for high performance, lightweight mirrors was historically driven by aerospace and defense (A&D) but now we are also seeing similar requirements for commercial applications. These applications range from aerospace-like platforms such as small unmanned aircraft for agricultural, mineral and pollutant aerial mapping to an eye tracking gimbaled mirror for optometry offices. While aerospace and defense businesses can often justify the high cost of exotic, low density materials, commercial products rarely can. Also, to obtain high performance with low overall optical system weight, aspheric surfaces are often prescribed. This may drive the manufacturing process to diamond machining thus requiring the reflective side of the mirror to be a diamond machinable material. This paper summarizes the diamond machined finishing and coating of some high performance, lightweight designs using non-exotic substrates to achieve cost effective mirrors. The results indicate that these processes can meet typical aerospace and defense requirements but may also be competitive in some commercial applications.

Machine Vision Applied to Navigation of Confined Spaces

NASA Technical Reports Server (NTRS)

Briscoe, Jeri M.; Broderick, David J.; Howard, Ricky; Corder, Eric L.

2004-01-01

The reliability of space related assets has been emphasized after the second loss of a Space Shuttle. The intricate nature of the hardware being inspected often requires a complete disassembly to perform a thorough inspection which can be difficult as well as costly. Furthermore, it is imperative that the hardware under inspection not be altered in any other manner than that which is intended. In these cases the use of machine vision can allow for inspection with greater frequency using less intrusive methods. Such systems can provide feedback to guide, not only manually controlled instrumentation, but autonomous robotic platforms as well. This paper serves to detail a method using machine vision to provide such sensing capabilities in a compact package. A single camera is used in conjunction with a projected reference grid to ascertain precise distance measurements. The design of the sensor focuses on the use of conventional components in an unconventional manner with the goal of providing a solution for systems that do not require or cannot accommodate more complex vision systems.

Unconstrained and contactless hand geometry biometrics.

PubMed

de-Santos-Sierra, Alberto; Sánchez-Ávila, Carmen; Del Pozo, Gonzalo Bailador; Guerra-Casanova, Javier

2011-01-01

This paper presents a hand biometric system for contact-less, platform-free scenarios, proposing innovative methods in feature extraction, template creation and template matching. The evaluation of the proposed method considers both the use of three contact-less publicly available hand databases, and the comparison of the performance to two competitive pattern recognition techniques existing in literature: namely support vector machines (SVM) and k-nearest neighbour (k-NN). Results highlight the fact that the proposed method outcomes existing approaches in literature in terms of computational cost, accuracy in human identification, number of extracted features and number of samples for template creation. The proposed method is a suitable solution for human identification in contact-less scenarios based on hand biometrics, providing a feasible solution to devices with limited hardware requirements like mobile devices.

Efficient and self-adaptive in-situ learning in multilayer memristor neural networks.

PubMed

Li, Can; Belkin, Daniel; Li, Yunning; Yan, Peng; Hu, Miao; Ge, Ning; Jiang, Hao; Montgomery, Eric; Lin, Peng; Wang, Zhongrui; Song, Wenhao; Strachan, John Paul; Barnell, Mark; Wu, Qing; Williams, R Stanley; Yang, J Joshua; Xia, Qiangfei

2018-06-19

Memristors with tunable resistance states are emerging building blocks of artificial neural networks. However, in situ learning on a large-scale multiple-layer memristor network has yet to be demonstrated because of challenges in device property engineering and circuit integration. Here we monolithically integrate hafnium oxide-based memristors with a foundry-made transistor array into a multiple-layer neural network. We experimentally demonstrate in situ learning capability and achieve competitive classification accuracy on a standard machine learning dataset, which further confirms that the training algorithm allows the network to adapt to hardware imperfections. Our simulation using the experimental parameters suggests that a larger network would further increase the classification accuracy. The memristor neural network is a promising hardware platform for artificial intelligence with high speed-energy efficiency.

New technique for simulation of microgravity and variable gravity conditions

NASA Astrophysics Data System (ADS)

de la Rosa, R.; Alonso, A.; Abasolo, D. E.; Hornero, R.; Abasolo, D. E.

2005-08-01

This paper suggests a microgravity or variable gravity conditions simulator based on a Neuromuscular Control System (NCS), working as a man-machine interface. The subject under training lies on an active platform that counteracts his weight. And a Virtual Reality (VR) system displays a simulated environment, where the subject can interact a number of settings: extravehicular activity (EVA), walking on the Moon or training the limb response faced with variable acceleration scenes. Results related to real-time voluntary control have been achieved with neuromuscular interfaces at the Bioengineering Group in the University of Valladolid. It has been employed a custom real-time system to train arm movements. This paper outlines a more complex design that can complement other training facilities, like the buoyancy pool, in the task of microgravity simulation.

Unconstrained and Contactless Hand Geometry Biometrics

PubMed Central

de-Santos-Sierra, Alberto; Sánchez-Ávila, Carmen; del Pozo, Gonzalo Bailador; Guerra-Casanova, Javier

2011-01-01

This paper presents a hand biometric system for contact-less, platform-free scenarios, proposing innovative methods in feature extraction, template creation and template matching. The evaluation of the proposed method considers both the use of three contact-less publicly available hand databases, and the comparison of the performance to two competitive pattern recognition techniques existing in literature: namely Support Vector Machines (SVM) and k-Nearest Neighbour (k-NN). Results highlight the fact that the proposed method outcomes existing approaches in literature in terms of computational cost, accuracy in human identification, number of extracted features and number of samples for template creation. The proposed method is a suitable solution for human identification in contact-less scenarios based on hand biometrics, providing a feasible solution to devices with limited hardware requirements like mobile devices. PMID:22346634

Biomedical Informatics on the Cloud: A Treasure Hunt for Advancing Cardiovascular Medicine.

PubMed

Ping, Peipei; Hermjakob, Henning; Polson, Jennifer S; Benos, Panagiotis V; Wang, Wei

2018-04-27

In the digital age of cardiovascular medicine, the rate of biomedical discovery can be greatly accelerated by the guidance and resources required to unearth potential collections of knowledge. A unified computational platform leverages metadata to not only provide direction but also empower researchers to mine a wealth of biomedical information and forge novel mechanistic insights. This review takes the opportunity to present an overview of the cloud-based computational environment, including the functional roles of metadata, the architecture schema of indexing and search, and the practical scenarios of machine learning-supported molecular signature extraction. By introducing several established resources and state-of-the-art workflows, we share with our readers a broadly defined informatics framework to phenotype cardiovascular health and disease. © 2018 American Heart Association, Inc.

Ambulatory REACT: real-time seizure detection with a DSP microprocessor.

PubMed

McEvoy, Robert P; Faul, Stephen; Marnane, William P

2010-01-01

REACT (Real-Time EEG Analysis for event deteCTion) is a Support Vector Machine based technology which, in recent years, has been successfully applied to the problem of automated seizure detection in both adults and neonates. This paper describes the implementation of REACT on a commercial DSP microprocessor; the Analog Devices Blackfin®. The primary aim of this work is to develop a prototype system for use in ambulatory or in-ward automated EEG analysis. Furthermore, the complexity of the various stages of the REACT algorithm on the Blackfin processor is analysed; in particular the EEG feature extraction stages. This hardware profile is used to select a reduced, platform-aware feature set, in order to evaluate the seizure classification accuracy of a lower-complexity, lower-power REACT system.

Exploring Characterizations of Learning Object Repositories Using Data Mining Techniques

NASA Astrophysics Data System (ADS)

Segura, Alejandra; Vidal, Christian; Menendez, Victor; Zapata, Alfredo; Prieto, Manuel

Learning object repositories provide a platform for the sharing of Web-based educational resources. As these repositories evolve independently, it is difficult for users to have a clear picture of the kind of contents they give access to. Metadata can be used to automatically extract a characterization of these resources by using machine learning techniques. This paper presents an exploratory study carried out in the contents of four public repositories that uses clustering and association rule mining algorithms to extract characterizations of repository contents. The results of the analysis include potential relationships between different attributes of learning objects that may be useful to gain an understanding of the kind of resources available and eventually develop search mechanisms that consider repository descriptions as a criteria in federated search.

Laser-boosted lightcraft technology demonstrator

NASA Technical Reports Server (NTRS)

Richard, J. C.; Morales, C.; Smith, W. L.; Myrabo, L. N.

1990-01-01

The detailed description and performance analysis of a 1.4 meter diameter Lightcraft Technology Demonstator (LTD) is presented. The launch system employs a 100 MW-class ground-based laser to transmit power directly to an advanced combined-cycle engine that propels the 120 kg LTD to orbit - with a mass ratio of two. The single-stage-to-orbit (SSTO) LTD machine then becomes an autonomous sensor satellite that can deliver precise, high quality information typical of today's large orbital platforms. The dominant motivation behind this study is to provide an example of how laser propulsion and its low launch costs can induce a comparable order-of-magnitude reduction in sensor satellite packaging costs. The issue is simply one of production technology for future, survivable SSTO aerospace vehicles that intimately share both laser propulsion engine and satellite functional hardware.

A Component-Based FPGA Design Framework for Neuronal Ion Channel Dynamics Simulations

PubMed Central

Mak, Terrence S. T.; Rachmuth, Guy; Lam, Kai-Pui; Poon, Chi-Sang

2008-01-01

Neuron-machine interfaces such as dynamic clamp and brain-implantable neuroprosthetic devices require real-time simulations of neuronal ion channel dynamics. Field Programmable Gate Array (FPGA) has emerged as a high-speed digital platform ideal for such application-specific computations. We propose an efficient and flexible component-based FPGA design framework for neuronal ion channel dynamics simulations, which overcomes certain limitations of the recently proposed memory-based approach. A parallel processing strategy is used to minimize computational delay, and a hardware-efficient factoring approach for calculating exponential and division functions in neuronal ion channel models is used to conserve resource consumption. Performances of the various FPGA design approaches are compared theoretically and experimentally in corresponding implementations of the AMPA and NMDA synaptic ion channel models. Our results suggest that the component-based design framework provides a more memory economic solution as well as more efficient logic utilization for large word lengths, whereas the memory-based approach may be suitable for time-critical applications where a higher throughput rate is desired. PMID:17190033

Integrated platform and API for electrophysiological data

PubMed Central

Sobolev, Andrey; Stoewer, Adrian; Leonhardt, Aljoscha; Rautenberg, Philipp L.; Kellner, Christian J.; Garbers, Christian; Wachtler, Thomas

2014-01-01

Recent advancements in technology and methodology have led to growing amounts of increasingly complex neuroscience data recorded from various species, modalities, and levels of study. The rapid data growth has made efficient data access and flexible, machine-readable data annotation a crucial requisite for neuroscientists. Clear and consistent annotation and organization of data is not only an important ingredient for reproducibility of results and re-use of data, but also essential for collaborative research and data sharing. In particular, efficient data management and interoperability requires a unified approach that integrates data and metadata and provides a common way of accessing this information. In this paper we describe GNData, a data management platform for neurophysiological data. GNData provides a storage system based on a data representation that is suitable to organize data and metadata from any electrophysiological experiment, with a functionality exposed via a common application programming interface (API). Data representation and API structure are compatible with existing approaches for data and metadata representation in neurophysiology. The API implementation is based on the Representational State Transfer (REST) pattern, which enables data access integration in software applications and facilitates the development of tools that communicate with the service. Client libraries that interact with the API provide direct data access from computing environments like Matlab or Python, enabling integration of data management into the scientist's experimental or analysis routines. PMID:24795616

Study on the Feasibility of RGB Substitute CIR for Automatic Removal Vegetation Occlusion Based on Ground Close-Range Building Images

NASA Astrophysics Data System (ADS)

Li, C.; Li, F.; Liu, Y.; Li, X.; Liu, P.; Xiao, B.

2012-07-01

Building 3D reconstruction based on ground remote sensing data (image, video and lidar) inevitably faces the problem that buildings are always occluded by vegetation, so how to automatically remove and repair vegetation occlusion is a very important preprocessing work for image understanding, compute vision and digital photogrammetry. In the traditional multispectral remote sensing which is achieved by aeronautics and space platforms, the Red and Near-infrared (NIR) bands, such as NDVI (Normalized Difference Vegetation Index), are useful to distinguish vegetation and clouds, amongst other targets. However, especially in the ground platform, CIR (Color Infra Red) is little utilized by compute vision and digital photogrammetry which usually only take true color RBG into account. Therefore whether CIR is necessary for vegetation segmentation or not has significance in that most of close-range cameras don't contain such NIR band. Moreover, the CIE L*a*b color space, which transform from RGB, seems not of much interest by photogrammetrists despite its powerfulness in image classification and analysis. So, CIE (L, a, b) feature and support vector machine (SVM) is suggested for vegetation segmentation to substitute for CIR. Finally, experimental results of visual effect and automation are given. The conclusion is that it's feasible to remove and segment vegetation occlusion without NIR band. This work should pave the way for texture reconstruction and repair for future 3D reconstruction.

Research and development of service robot platform based on artificial psychology

NASA Astrophysics Data System (ADS)

Zhang, Xueyuan; Wang, Zhiliang; Wang, Fenhua; Nagai, Masatake

2007-12-01

Some related works about the control architecture of robot system are briefly summarized. According to the discussions above, this paper proposes control architecture of service robot based on artificial psychology. In this control architecture, the robot can obtain the cognition of environment through sensors, and then be handled with intelligent model, affective model and learning model, and finally express the reaction to the outside stimulation through its behavior. For better understanding the architecture, hierarchical structure is also discussed. The control system of robot can be divided into five layers, namely physical layer, drives layer, information-processing and behavior-programming layer, application layer and system inspection and control layer. This paper shows how to achieve system integration from hardware modules, software interface and fault diagnosis. Embedded system GENE-8310 is selected as the PC platform of robot APROS-I, and its primary memory media is CF card. The arms and body of the robot are constituted by 13 motors and some connecting fittings. Besides, the robot has a robot head with emotional facial expression, and the head has 13 DOFs. The emotional and intelligent model is one of the most important parts in human-machine interaction. In order to better simulate human emotion, an emotional interaction model for robot is proposed according to the theory of need levels of Maslom and mood information of Siminov. This architecture has already been used in our intelligent service robot.

Integrated platform and API for electrophysiological data.

PubMed

Sobolev, Andrey; Stoewer, Adrian; Leonhardt, Aljoscha; Rautenberg, Philipp L; Kellner, Christian J; Garbers, Christian; Wachtler, Thomas

2014-01-01

Recent advancements in technology and methodology have led to growing amounts of increasingly complex neuroscience data recorded from various species, modalities, and levels of study. The rapid data growth has made efficient data access and flexible, machine-readable data annotation a crucial requisite for neuroscientists. Clear and consistent annotation and organization of data is not only an important ingredient for reproducibility of results and re-use of data, but also essential for collaborative research and data sharing. In particular, efficient data management and interoperability requires a unified approach that integrates data and metadata and provides a common way of accessing this information. In this paper we describe GNData, a data management platform for neurophysiological data. GNData provides a storage system based on a data representation that is suitable to organize data and metadata from any electrophysiological experiment, with a functionality exposed via a common application programming interface (API). Data representation and API structure are compatible with existing approaches for data and metadata representation in neurophysiology. The API implementation is based on the Representational State Transfer (REST) pattern, which enables data access integration in software applications and facilitates the development of tools that communicate with the service. Client libraries that interact with the API provide direct data access from computing environments like Matlab or Python, enabling integration of data management into the scientist's experimental or analysis routines.

76 FR 81518 - Notice of Issuance of Final Determination Concerning Laser-Based Multi-Function Office Machines

Federal Register 2010, 2011, 2012, 2013, 2014

2011-12-28

... Determination Concerning Laser-Based Multi-Function Office Machines AGENCY: U.S. Customs and Border Protection... country of origin of laser-based multi-function office machines. Based upon the facts presented, CBP has... essential character of the laser-based multi-function office machine, and it is at their assembly and...

Field portable mobile phone based fluorescence microscopy for detection of Giardia lamblia cysts in water samples

NASA Astrophysics Data System (ADS)

Ceylan Koydemir, Hatice; Gorocs, Zoltan; McLeod, Euan; Tseng, Derek; Ozcan, Aydogan

2015-03-01

Giardia lamblia is a waterborne parasite that causes an intestinal infection, known as giardiasis, and it is found not only in countries with inadequate sanitation and unsafe water but also streams and lakes of developed countries. Simple, sensitive, and rapid detection of this pathogen is important for monitoring of drinking water. Here we present a cost-effective and field portable mobile-phone based fluorescence microscopy platform designed for automated detection of Giardia lamblia cysts in large volume water samples (i.e., 10 ml) to be used in low-resource field settings. This fluorescence microscope is integrated with a disposable water-sampling cassette, which is based on a flow-through porous polycarbonate membrane and provides a wide surface area for fluorescence imaging and enumeration of the captured Giardia cysts on the membrane. Water sample of interest, containing fluorescently labeled Giardia cysts, is introduced into the absorbent pads that are in contact with the membrane in the cassette by capillary action, which eliminates the need for electrically driven flow for sample processing. Our fluorescence microscope weighs ~170 grams in total and has all the components of a regular microscope, capable of detecting individual fluorescently labeled cysts under light-emitting-diode (LED) based excitation. Including all the sample preparation, labeling and imaging steps, the entire measurement takes less than one hour for a sample volume of 10 ml. This mobile phone based compact and cost-effective fluorescent imaging platform together with its machine learning based cyst counting interface is easy to use and can even work in resource limited and field settings for spatio-temporal monitoring of water quality.

A microfabricated platform to form three-dimensional toroidal multicellular aggregate.

PubMed

Masuda, Taisuke; Takei, Natsuki; Nakano, Takuma; Anada, Takahisa; Suzuki, Osamu; Arai, Fumihito

2012-12-01

Techniques that allow cells to self-assemble into three-dimensional (3D) spheroid microtissues provide powerful in vitro models that are becoming increasingly popular in fields such as stem cell research, tissue engineering, and cancer biology. Appropriate simulation of the 3D environment in which tissues normally develop and function is crucial for the engineering of in vitro models that can be used for the formation of complex tissues. We have developed a unique multicellular aggregate formation platform that utilizes a maskless gray-scale photolithography. The cellular aggregate formed using this platform has a toroidal-like geometry and includes a micro lumen that facilitates the supply of oxygen and growth factors and the expulsion of waste products. As a result, this platform was capable of rapidly producing hundreds of multicellular aggregates at a time, and of regulating the diameter of aggregates with complex design. These toroidal multicellular aggregates can grow as long-term culture. In addition, the micro lumen can be used as a continuous channel and for the insertion of a vascular system or a nerve system into the assembled tissue. These platform characteristics highlight its potential to be used in a wide variety of applications, e.g. as a bioactuator, as a micro-machine component or in drug screening and tissue engineering.

Efficient Parallel Kernel Solvers for Computational Fluid Dynamics Applications

NASA Technical Reports Server (NTRS)

Sun, Xian-He

1997-01-01

Distributed-memory parallel computers dominate today's parallel computing arena. These machines, such as Intel Paragon, IBM SP2, and Cray Origin2OO, have successfully delivered high performance computing power for solving some of the so-called "grand-challenge" problems. Despite initial success, parallel machines have not been widely accepted in production engineering environments due to the complexity of parallel programming. On a parallel computing system, a task has to be partitioned and distributed appropriately among processors to reduce communication cost and to attain load balance. More importantly, even with careful partitioning and mapping, the performance of an algorithm may still be unsatisfactory, since conventional sequential algorithms may be serial in nature and may not be implemented efficiently on parallel machines. In many cases, new algorithms have to be introduced to increase parallel performance. In order to achieve optimal performance, in addition to partitioning and mapping, a careful performance study should be conducted for a given application to find a good algorithm-machine combination. This process, however, is usually painful and elusive. The goal of this project is to design and develop efficient parallel algorithms for highly accurate Computational Fluid Dynamics (CFD) simulations and other engineering applications. The work plan is 1) developing highly accurate parallel numerical algorithms, 2) conduct preliminary testing to verify the effectiveness and potential of these algorithms, 3) incorporate newly developed algorithms into actual simulation packages. The work plan has well achieved. Two highly accurate, efficient Poisson solvers have been developed and tested based on two different approaches: (1) Adopting a mathematical geometry which has a better capacity to describe the fluid, (2) Using compact scheme to gain high order accuracy in numerical discretization. The previously developed Parallel Diagonal Dominant (PDD) algorithm and Reduced Parallel Diagonal Dominant (RPDD) algorithm have been carefully studied on different parallel platforms for different applications, and a NASA simulation code developed by Man M. Rai and his colleagues has been parallelized and implemented based on data dependency analysis. These achievements are addressed in detail in the paper.

Method and system for fault accommodation of machines

NASA Technical Reports Server (NTRS)

Goebel, Kai Frank (Inventor); Subbu, Rajesh Venkat (Inventor); Rausch, Randal Thomas (Inventor); Frederick, Dean Kimball (Inventor)

2011-01-01

A method for multi-objective fault accommodation using predictive modeling is disclosed. The method includes using a simulated machine that simulates a faulted actual machine, and using a simulated controller that simulates an actual controller. A multi-objective optimization process is performed, based on specified control settings for the simulated controller and specified operational scenarios for the simulated machine controlled by the simulated controller, to generate a Pareto frontier-based solution space relating performance of the simulated machine to settings of the simulated controller, including adjustment to the operational scenarios to represent a fault condition of the simulated machine. Control settings of the actual controller are adjusted, represented by the simulated controller, for controlling the actual machine, represented by the simulated machine, in response to a fault condition of the actual machine, based on the Pareto frontier-based solution space, to maximize desirable operational conditions and minimize undesirable operational conditions while operating the actual machine in a region of the solution space defined by the Pareto frontier.

MC-GenomeKey: a multicloud system for the detection and annotation of genomic variants.

PubMed

Elshazly, Hatem; Souilmi, Yassine; Tonellato, Peter J; Wall, Dennis P; Abouelhoda, Mohamed

2017-01-20

Next Generation Genome sequencing techniques became affordable for massive sequencing efforts devoted to clinical characterization of human diseases. However, the cost of providing cloud-based data analysis of the mounting datasets remains a concerning bottleneck for providing cost-effective clinical services. To address this computational problem, it is important to optimize the variant analysis workflow and the used analysis tools to reduce the overall computational processing time, and concomitantly reduce the processing cost. Furthermore, it is important to capitalize on the use of the recent development in the cloud computing market, which have witnessed more providers competing in terms of products and prices. In this paper, we present a new package called MC-GenomeKey (Multi-Cloud GenomeKey) that efficiently executes the variant analysis workflow for detecting and annotating mutations using cloud resources from different commercial cloud providers. Our package supports Amazon, Google, and Azure clouds, as well as, any other cloud platform based on OpenStack. Our package allows different scenarios of execution with different levels of sophistication, up to the one where a workflow can be executed using a cluster whose nodes come from different clouds. MC-GenomeKey also supports scenarios to exploit the spot instance model of Amazon in combination with the use of other cloud platforms to provide significant cost reduction. To the best of our knowledge, this is the first solution that optimizes the execution of the workflow using computational resources from different cloud providers. MC-GenomeKey provides an efficient multicloud based solution to detect and annotate mutations. The package can run in different commercial cloud platforms, which enables the user to seize the best offers. The package also provides a reliable means to make use of the low-cost spot instance model of Amazon, as it provides an efficient solution to the sudden termination of spot machines as a result of a sudden price increase. The package has a web-interface and it is available for free for academic use.

ABA-Cloud: support for collaborative breath research

PubMed Central

Elsayed, Ibrahim; Ludescher, Thomas; King, Julian; Ager, Clemens; Trosin, Michael; Senocak, Uygar; Brezany, Peter; Feilhauer, Thomas; Amann, Anton

2016-01-01

This paper introduces the advanced breath analysis (ABA) platform, an innovative scientific research platform for the entire breath research domain. Within the ABA project, we are investigating novel data management concepts and semantic web technologies to document breath analysis studies for the long run as well as to enable their full automatic reproducibility. We propose several concept taxonomies (a hierarchical order of terms from a glossary of terms), which can be seen as a first step toward the definition of conceptualized terms commonly used by the international community of breath researchers. They build the basis for the development of an ontology (a concept from computer science used for communication between machines and/or humans and representation and reuse of knowledge) dedicated to breath research. PMID:23619467

ABA-Cloud: support for collaborative breath research.

PubMed

Elsayed, Ibrahim; Ludescher, Thomas; King, Julian; Ager, Clemens; Trosin, Michael; Senocak, Uygar; Brezany, Peter; Feilhauer, Thomas; Amann, Anton

2013-06-01

This paper introduces the advanced breath analysis (ABA) platform, an innovative scientific research platform for the entire breath research domain. Within the ABA project, we are investigating novel data management concepts and semantic web technologies to document breath analysis studies for the long run as well as to enable their full automatic reproducibility. We propose several concept taxonomies (a hierarchical order of terms from a glossary of terms), which can be seen as a first step toward the definition of conceptualized terms commonly used by the international community of breath researchers. They build the basis for the development of an ontology (a concept from computer science used for communication between machines and/or humans and representation and reuse of knowledge) dedicated to breath research.

Construction concept for erecting an offset-fed antenna

NASA Technical Reports Server (NTRS)

Rhodes, M. D.

1984-01-01

A design concept for the construction of an offset-fed antenna is discussed. Antennas of this type are of interest for space applications because the configuration eliminates the effects of feed and feed-support blockage. The proposed construction concept is developed around the assembly of a stiff truss substructure by pressure-suited astronauts operating in extravehicular activity (EVA) assisted by a mobile platform that moves along the structure to position the astronauts at joint locations where they can latch members in place. Construction can be accomplished from the shuttle cargo bay in the course of a normal flight or from a space station platform. The concepts demonstrates the versatility of machine assisted manned assembly and is only one of many potential applications.

Virtual hospital--a computer-aided platform to evaluate the sense of direction.

PubMed

Jiang, Ching-Fen; Li, Yuan-Shyi

2007-01-01

This paper presents a computer-aided platform, named Virtual Hospital (VH), to evaluate the wayfinding ability that is found impaired in senile people with early dementia. The development of the VH takes the advantage of virtual reality technology to make the evaluation of the sense of direction more convenient and accurate then the conventional way. A pilot study was carried out to test its feasibility in differentiating the sense of direction between different genders. The results with significant differences in the response time (p<0.05) and the pointing error (p<0.01) between genders suggest the potential of the VH for clinical uses. Further improvement on the human-machine interface is necessary to make it easy for geriatric people to use.

Rapid prototyping of an adaptive light-source for mobile manipulators with EasyKit and EasyLab

NASA Astrophysics Data System (ADS)

Wojtczyk, Martin; Barner, Simon; Geisinger, Michael; Knoll, Alois

2008-08-01

While still not common in day-to-day business, mobile robot platforms form a growing market in robotics. Mobile platforms equipped with a manipulator for increased flexibility have been used successfully in biotech laboratories for sample management as shown on the well-known ESACT meetings. Navigation and object recognition is carried out by the utilization of a mounted machine vision camera. To cope with the different illumination conditions in a large laboratory, development of an adaptive light source was indispensable. We present our approach of rapid developing a computer controlled, adaptive LED light within one single business day, by utilizing the hardware toolbox EasyKit and our appropriate software counterpart EasyLab.

Machine Learning Based Malware Detection

DTIC Science & Technology

2015-05-18

A TRIDENT SCHOLAR PROJECT REPORT NO. 440 Machine Learning Based Malware Detection by Midshipman 1/C Zane A. Markel, USN...COVERED (From - To) 4. TITLE AND SUBTITLE Machine Learning Based Malware Detection 5a. CONTRACT NUMBER 5b. GRANT NUMBER 5c. PROGRAM...suitably be projected into realistic performance. This work explores several aspects of machine learning based malware detection . First, we

Remote Sensing Information Sciences Research Group, year four

NASA Technical Reports Server (NTRS)

Estes, John E.; Smith, Terence; Star, Jeffrey L.

1987-01-01

The needs of the remote sensing research and application community which will be served by the Earth Observing System (EOS) and space station, including associated polar and co-orbiting platforms are examined. Research conducted was used to extend and expand existing remote sensing research activities in the areas of georeferenced information systems, machine assisted information extraction from image data, artificial intelligence, and vegetation analysis and modeling. Projects are discussed in detail.

Constructing and Classifying Email Networks from Raw Forensic Images

DTIC Science & Technology

2016-09-01

data mining for sequence and pattern mining ; in medical imaging for image segmentation; and in computer vision for object recognition” [28]. 2.3.1...machine learning and data mining suite that is written in Python. It provides a platform for experiment selection, recommendation systems, and...predictivemod- eling. The Orange library is a hierarchically-organized toolbox of data mining components. Data filtering and probability assessment are at the

New Abstractions for Mobile Connectivity and Resource Management

DTIC Science & Technology

2016-05-01

networked systems, con- sisting of replicated backend services and mobile , multi-homed clients. We derive a state machine for ECCP supporting migration...makes ECCP useful not only for mobility of client devices, but also for backend services which are increasingly run in VMs or containers on platforms...layers of the network stack, instead of the traditional IP/port, improve mobility for clients and backend services and reduce unnecessary coupling of

CAGE IIIA Distributed Simulation Design Methodology

DTIC Science & Technology

2014-05-01

2 VHF Very High Frequency VLC Video LAN Codec – an Open-source cross-platform multimedia player and framework VM Virtual Machine VOIP Voice Over...Implementing Defence Experimentation (GUIDEx). The key challenges for this methodology are with understanding how to: • design it o define the...operation and to be available in the other nation’s simulations. The challenge for the CAGE campaign of experiments is to continue to build upon this

Integrating Webtop Components with Thin-Client Web Applicators using WDK Tickets

NASA Technical Reports Server (NTRS)

Duley, Jason

2004-01-01

Contents include the folloving: Issues surrounding encryption/decryption of password strings when deploying on different machines and platforms. Security concerns when exposing docbases to internet users. Docbase Session management in Java Servlets. Customization of Webtop components. WDK Tickets as a silent login alternative. Encoding Tickets and Ticket syntax. Invoking Webtop components via an Action URL. Issues with accessing Webtop components on Mac OS X through SSL.

High-performance reconfigurable hardware architecture for restricted Boltzmann machines.

PubMed

Ly, Daniel Le; Chow, Paul

2010-11-01

Despite the popularity and success of neural networks in research, the number of resulting commercial or industrial applications has been limited. A primary cause for this lack of adoption is that neural networks are usually implemented as software running on general-purpose processors. Hence, a hardware implementation that can exploit the inherent parallelism in neural networks is desired. This paper investigates how the restricted Boltzmann machine (RBM), which is a popular type of neural network, can be mapped to a high-performance hardware architecture on field-programmable gate array (FPGA) platforms. The proposed modular framework is designed to reduce the time complexity of the computations through heavily customized hardware engines. A method to partition large RBMs into smaller congruent components is also presented, allowing the distribution of one RBM across multiple FPGA resources. The framework is tested on a platform of four Xilinx Virtex II-Pro XC2VP70 FPGAs running at 100 MHz through a variety of different configurations. The maximum performance was obtained by instantiating an RBM of 256 × 256 nodes distributed across four FPGAs, which resulted in a computational speed of 3.13 billion connection-updates-per-second and a speedup of 145-fold over an optimized C program running on a 2.8-GHz Intel processor.

1001 Ways to run AutoDock Vina for virtual screening

NASA Astrophysics Data System (ADS)

Jaghoori, Mohammad Mahdi; Bleijlevens, Boris; Olabarriaga, Silvia D.

2016-03-01

Large-scale computing technologies have enabled high-throughput virtual screening involving thousands to millions of drug candidates. It is not trivial, however, for biochemical scientists to evaluate the technical alternatives and their implications for running such large experiments. Besides experience with the molecular docking tool itself, the scientist needs to learn how to run it on high-performance computing (HPC) infrastructures, and understand the impact of the choices made. Here, we review such considerations for a specific tool, AutoDock Vina, and use experimental data to illustrate the following points: (1) an additional level of parallelization increases virtual screening throughput on a multi-core machine; (2) capturing of the random seed is not enough (though necessary) for reproducibility on heterogeneous distributed computing systems; (3) the overall time spent on the screening of a ligand library can be improved by analysis of factors affecting execution time per ligand, including number of active torsions, heavy atoms and exhaustiveness. We also illustrate differences among four common HPC infrastructures: grid, Hadoop, small cluster and multi-core (virtual machine on the cloud). Our analysis shows that these platforms are suitable for screening experiments of different sizes. These considerations can guide scientists when choosing the best computing platform and set-up for their future large virtual screening experiments.

1001 Ways to run AutoDock Vina for virtual screening.

PubMed

Jaghoori, Mohammad Mahdi; Bleijlevens, Boris; Olabarriaga, Silvia D

2016-03-01

Large-scale computing technologies have enabled high-throughput virtual screening involving thousands to millions of drug candidates. It is not trivial, however, for biochemical scientists to evaluate the technical alternatives and their implications for running such large experiments. Besides experience with the molecular docking tool itself, the scientist needs to learn how to run it on high-performance computing (HPC) infrastructures, and understand the impact of the choices made. Here, we review such considerations for a specific tool, AutoDock Vina, and use experimental data to illustrate the following points: (1) an additional level of parallelization increases virtual screening throughput on a multi-core machine; (2) capturing of the random seed is not enough (though necessary) for reproducibility on heterogeneous distributed computing systems; (3) the overall time spent on the screening of a ligand library can be improved by analysis of factors affecting execution time per ligand, including number of active torsions, heavy atoms and exhaustiveness. We also illustrate differences among four common HPC infrastructures: grid, Hadoop, small cluster and multi-core (virtual machine on the cloud). Our analysis shows that these platforms are suitable for screening experiments of different sizes. These considerations can guide scientists when choosing the best computing platform and set-up for their future large virtual screening experiments.

Wireless gyroscope platform enabled by a portable media device for quantifying wobble board therapy.

PubMed

LeMoyne, Robert; Mastroianni, Timothy

2017-07-01

The wobble board enables a therapy strategy for rehabilitation of the ankle foot complex. Quantification of therapy, such as through the use of a wobble board, can facilitate a therapist's acuity for advancing and optimizing the overall therapy strategy. The portable media device, such as an iPod, can be equipped with a software application to function as a wireless gyroscope platform. Integration of the wobble board with the portable media device functioning as a wireless gyroscope enables the potential for patient to therapist interaction through connectivity to the Internet. A patient can conduct wobble board therapy for the ankle foot complex from the convenient vantage point of a homebound setting with therapy data transmitted wirelessly as email attachments. The gyroscope signal of the wobble board therapy can be consolidated into a feature set for machine learning classification. Using a multilayer perceptron neural network considerable classification accuracy has been achieved for differentiating between a hemiplegic affected ankle and unaffected ankle while using a wobble board. The combination of machine learning, wireless systems, such as a portable media device functioning as a wireless gyroscope, and a conventional therapy device, such as a wobble board, are envisioned to advance the capability to optimally impact the rehabilitation experience.

Security Frameworks for Machine-to-Machine Devices and Networks

NASA Astrophysics Data System (ADS)

Demblewski, Michael

Attacks against mobile systems have escalated over the past decade. There have been increases of fraud, platform attacks, and malware. The Internet of Things (IoT) offers a new attack vector for Cybercriminals. M2M contributes to the growing number of devices that use wireless systems for Internet connection. As new applications and platforms are created, old vulnerabilities are transferred to next-generation systems. There is a research gap that exists between the current approaches for security framework development and the understanding of how these new technologies are different and how they are similar. This gap exists because system designers, security architects, and users are not fully aware of security risks and how next-generation devices can jeopardize safety and personal privacy. Current techniques, for developing security requirements, do not adequately consider the use of new technologies, and this weakens countermeasure implementations. These techniques rely on security frameworks for requirements development. These frameworks lack a method for identifying next generation security concerns and processes for comparing, contrasting and evaluating non-human device security protections. This research presents a solution for this problem by offering a novel security framework that is focused on the study of the "functions and capabilities" of M2M devices and improves the systems development life cycle for the overall IoT ecosystem.

Design of a portable electronic nose for real-fake detection of liquors

NASA Astrophysics Data System (ADS)

Qi, Pei-Feng; Zeng, Ming; Li, Zhi-Hua; Sun, Biao; Meng, Qing-Hao

2017-09-01

Portability is a major issue that influences the practical application of electronic noses (e-noses). For liquors detection, an e-nose must preprocess the liquid samples (e.g., using evaporation and thermal desorption), which makes the portable design even more difficult. To realize convenient and rapid detection of liquors, we designed a portable e-nose platform that consists of hardware and software systems. The hardware system contains an evaporation/sampling module, a reaction module, a control/data acquisition and analysis module, and a power module. The software system provides a user-friendly interface and can achieve automatic sampling and data processing. This e-nose platform has been applied to the real-fake recognition of Chinese liquors. Through parameter optimization of a one-class support vector machine classifier, the error rate of the negative samples is greatly reduced, and the overall recognition accuracy is improved. The results validated the feasibility of the designed portable e-nose platform.

Pick-up, transport and release of a molecular cargo using a small-molecule robotic arm

NASA Astrophysics Data System (ADS)

Kassem, Salma; Lee, Alan T. L.; Leigh, David A.; Markevicius, Augustinas; Solà, Jordi

2016-02-01

Modern-day factory assembly lines often feature robots that pick up, reposition and connect components in a programmed manner. The idea of manipulating molecular fragments in a similar way has to date only been explored using biological building blocks (specifically DNA). Here, we report on a wholly artificial small-molecule robotic arm capable of selectively transporting a molecular cargo in either direction between two spatially distinct, chemically similar, sites on a molecular platform. The arm picks up/releases a 3-mercaptopropanehydrazide cargo by formation/breakage of a disulfide bond, while dynamic hydrazone chemistry controls the cargo binding to the platform. Transport is controlled by selectively inducing conformational and configurational changes within an embedded hydrazone rotary switch that steers the robotic arm. In a three-stage operation, 79-85% of 3-mercaptopropanehydrazide molecules are transported in either (chosen) direction between the two platform sites, without the cargo at any time fully dissociating from the machine nor exchanging with other molecules in the bulk.

Pick-up, transport and release of a molecular cargo using a small-molecule robotic arm.

PubMed

Kassem, Salma; Lee, Alan T L; Leigh, David A; Markevicius, Augustinas; Solà, Jordi

2016-02-01

Modern-day factory assembly lines often feature robots that pick up, reposition and connect components in a programmed manner. The idea of manipulating molecular fragments in a similar way has to date only been explored using biological building blocks (specifically DNA). Here, we report on a wholly artificial small-molecule robotic arm capable of selectively transporting a molecular cargo in either direction between two spatially distinct, chemically similar, sites on a molecular platform. The arm picks up/releases a 3-mercaptopropanehydrazide cargo by formation/breakage of a disulfide bond, while dynamic hydrazone chemistry controls the cargo binding to the platform. Transport is controlled by selectively inducing conformational and configurational changes within an embedded hydrazone rotary switch that steers the robotic arm. In a three-stage operation, 79-85% of 3-mercaptopropanehydrazide molecules are transported in either (chosen) direction between the two platform sites, without the cargo at any time fully dissociating from the machine nor exchanging with other molecules in the bulk.

Medical Education Must Move from the Information Age to the Age of Artificial Intelligence.

PubMed

Wartman, Steven A; Combs, C Donald

2017-11-01

Changes to the medical profession require medical education reforms that will enable physicians to more effectively enter contemporary practice. Proposals for such reforms abound. Common themes include renewed emphasis on communication, teamwork, risk-management, and patient safety. These reforms are important but insufficient. They do not adequately address the most fundamental change--the practice of medicine is rapidly transitioning from the information age to the age of artificial intelligence. Employers need physicians who: work at the top of their license, have knowledge spanning the health professions and care continuum, effectively leverage data platforms, focus on analyzing outcomes and improving performance, and communicate the meaning of the probabilities generated by massive amounts of data to patients given their unique human complexities.Future medical practice will have four characteristics that must be addressed in medical education: care will be (1) provided in many locations; (2) provided by newly-constituted health care teams; and (3) based on a growing array of data from multiple sources and artificial intelligence applications; and (4) the interface between medicine and machines will need to be skillfully managed. Thus, medical education must make better use of the findings of cognitive psychology, pay more attention to the alignment of humans and machines in education, and increase the use of simulations. Medical education will need to evolve to include systematic curricular attention to the organization of professional effort among health professionals, the use of intelligence tools like machine learning and robots, and a relentless focus on improving performance and patient outcomes. [end of abstract].

High-Throughput Gene Expression Profiles to Define Drug Similarity and Predict Compound Activity.

PubMed

De Wolf, Hans; Cougnaud, Laure; Van Hoorde, Kirsten; De Bondt, An; Wegner, Joerg K; Ceulemans, Hugo; Göhlmann, Hinrich

2018-04-01

By adding biological information, beyond the chemical properties and desired effect of a compound, uncharted compound areas and connections can be explored. In this study, we add transcriptional information for 31K compounds of Janssen's primary screening deck, using the HT L1000 platform and assess (a) the transcriptional connection score for generating compound similarities, (b) machine learning algorithms for generating target activity predictions, and (c) the scaffold hopping potential of the resulting hits. We demonstrate that the transcriptional connection score is best computed from the significant genes only and should be interpreted within its confidence interval for which we provide the stats. These guidelines help to reduce noise, increase reproducibility, and enable the separation of specific and promiscuous compounds. The added value of machine learning is demonstrated for the NR3C1 and HSP90 targets. Support Vector Machine models yielded balanced accuracy values ≥80% when the expression values from DDIT4 & SERPINE1 and TMEM97 & SPR were used to predict the NR3C1 and HSP90 activity, respectively. Combining both models resulted in 22 new and confirmed HSP90-independent NR3C1 inhibitors, providing two scaffolds (i.e., pyrimidine and pyrazolo-pyrimidine), which could potentially be of interest in the treatment of depression (i.e., inhibiting the glucocorticoid receptor (i.e., NR3C1), while leaving its chaperone, HSP90, unaffected). As such, the initial hit rate increased by a factor 300, as less, but more specific chemistry could be screened, based on the upfront computed activity predictions.

A versatile nondestructive evaluation imaging workstation

NASA Technical Reports Server (NTRS)

Chern, E. James; Butler, David W.

1994-01-01

Ultrasonic C-scan and eddy current imaging systems are of the pointwise type evaluation systems that rely on a mechanical scanner to physically maneuver a probe relative to the specimen point by point in order to acquire data and generate images. Since the ultrasonic C-scan and eddy current imaging systems are based on the same mechanical scanning mechanisms, the two systems can be combined using the same PC platform with a common mechanical manipulation subsystem and integrated data acquisition software. Based on this concept, we have developed an IBM PC-based combined ultrasonic C-scan and eddy current imaging system. The system is modularized and provides capacity for future hardware and software expansions. Advantages associated with the combined system are: (1) eliminated duplication of the computer and mechanical hardware, (2) unified data acquisition, processing and storage software, (3) reduced setup time for repetitious ultrasonic and eddy current scans, and (4) improved system efficiency. The concept can be adapted to many engineering systems by integrating related PC-based instruments into one multipurpose workstation such as dispensing, machining, packaging, sorting, and other industrial applications.