Factorization and the synthesis of optimal feedback kernels for differential-delay systems

NASA Technical Reports Server (NTRS)

Milman, Mark M.; Scheid, Robert E.

1987-01-01

A combination of ideas from the theories of operator Riccati equations and Volterra factorizations leads to the derivation of a novel, relatively simple set of hyperbolic equations which characterize the optimal feedback kernel for the finite-time regulator problem for autonomous differential-delay systems. Analysis of these equations elucidates the underlying structure of the feedback kernel and leads to the development of fast and accurate numerical methods for its computation. Unlike traditional formulations based on the operator Riccati equation, the gain is characterized by means of classical solutions of the derived set of equations. This leads to the development of approximation schemes which are analogous to what has been accomplished for systems of ordinary differential equations with given initial conditions.

Towards smart energy systems: application of kernel machine regression for medium term electricity load forecasting.

PubMed

Alamaniotis, Miltiadis; Bargiotas, Dimitrios; Tsoukalas, Lefteri H

2016-01-01

Integration of energy systems with information technologies has facilitated the realization of smart energy systems that utilize information to optimize system operation. To that end, crucial in optimizing energy system operation is the accurate, ahead-of-time forecasting of load demand. In particular, load forecasting allows planning of system expansion, and decision making for enhancing system safety and reliability. In this paper, the application of two types of kernel machines for medium term load forecasting (MTLF) is presented and their performance is recorded based on a set of historical electricity load demand data. The two kernel machine models and more specifically Gaussian process regression (GPR) and relevance vector regression (RVR) are utilized for making predictions over future load demand. Both models, i.e., GPR and RVR, are equipped with a Gaussian kernel and are tested on daily predictions for a 30-day-ahead horizon taken from the New England Area. Furthermore, their performance is compared to the ARMA(2,2) model with respect to mean average percentage error and squared correlation coefficient. Results demonstrate the superiority of RVR over the other forecasting models in performing MTLF.

Fast Interrupt Priority Management in Operating System Kernels

DTIC Science & Technology

1993-05-01

We present results for the Mach 3.0 microkernel operating system, although the technique is applicable to other kernel architectures, both micro and...protection in the Mach 3.0 microkernel for several different processor architectures. For example, on the Omron Luna88k, we observed a 50% reduction in...general interrupt mask raise/lower pair within the Mach 3.0 microkernel on a variety of architectures. DTIC QUALM i.N1’R%.*1IMD 5 k81tltC Avail andl

Optimum-AIV: A planning and scheduling system for spacecraft AIV

NASA Technical Reports Server (NTRS)

Arentoft, M. M.; Fuchs, Jens J.; Parrod, Y.; Gasquet, Andre; Stader, J.; Stokes, I.; Vadon, H.

1991-01-01

A project undertaken for the European Space Agency (ESA) is presented. The project is developing a knowledge based software system for planning and scheduling of activities for spacecraft assembly, integration, and verification (AIV). The system extends into the monitoring of plan execution and the plan repair phase. The objectives are to develop an operational kernel of a planning, scheduling, and plan repair tool, called OPTIMUM-AIV, and to provide facilities which will allow individual projects to customize the kernel to suit its specific needs. The kernel shall consist of a set of software functionalities for assistance in initial specification of the AIV plan, in verification and generation of valid plans and schedules for the AIV activities, and in interactive monitoring and execution problem recovery for the detailed AIV plans. Embedded in OPTIMUM-AIV are external interfaces which allow integration with alternative scheduling systems and project databases. The current status of the OPTIMUM-AIV project, as of Jan. 1991, is that a further analysis of the AIV domain has taken place through interviews with satellite AIV experts, a software requirement document (SRD) for the full operational tool was approved, and an architectural design document (ADD) for the kernel excluding external interfaces is ready for review.

Architecture for removable media USB-ARM

DOEpatents

Shue, Craig A.; Lamb, Logan M.; Paul, Nathanael R.

2015-07-14

A storage device is coupled to a computing system comprising an operating system and application software. Access to the storage device is blocked by a kernel filter driver, except exclusive access is granted to a first anti-virus engine. The first anti-virus engine is directed to scan the storage device for malicious software and report results. Exclusive access may be granted to one or more other anti-virus engines and they may be directed to scan the storage device and report results. Approval of all or a portion of the information on the storage device is based on the results from the first anti-virus engine and the other anti-virus engines. The storage device is presented to the operating system and access is granted to the approved information. The operating system may be a Microsoft Windows operating system. The kernel filter driver and usage of anti-virus engines may be configurable by a user.

Linux Kernel Co-Scheduling and Bulk Synchronous Parallelism

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

Jones, Terry R

2012-01-01

This paper describes a kernel scheduling algorithm that is based on coscheduling principles and that is intended for parallel applications running on 1000 cores or more. Experimental results for a Linux implementation on a Cray XT5 machine are presented. The results indicate that Linux is a suitable operating system for this new scheduling scheme, and that this design provides a dramatic improvement in scaling performance for synchronizing collective operations at scale.

Multitasking kernel for the C and Fortran programming languages

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

Brooks, E.D. III

1984-09-01

A multitasking kernel for the C and Fortran programming languages which runs on the Unix operating system is presented. The kernel provides a multitasking environment which serves two purposes. The first is to provide an efficient portable environment for the coding, debugging and execution of production multiprocessor programs. The second is to provide a means of evaluating the performance of a multitasking program on model multiprocessors. The performance evaluation features require no changes in the source code of the application and are implemented as a set of compile and run time options in the kernel.

Kernel reconstruction methods for Doppler broadening - Temperature interpolation by linear combination of reference cross sections at optimally chosen temperatures

NASA Astrophysics Data System (ADS)

Ducru, Pablo; Josey, Colin; Dibert, Karia; Sobes, Vladimir; Forget, Benoit; Smith, Kord

2017-04-01

This article establishes a new family of methods to perform temperature interpolation of nuclear interactions cross sections, reaction rates, or cross sections times the energy. One of these quantities at temperature T is approximated as a linear combination of quantities at reference temperatures (Tj). The problem is formalized in a cross section independent fashion by considering the kernels of the different operators that convert cross section related quantities from a temperature T0 to a higher temperature T - namely the Doppler broadening operation. Doppler broadening interpolation of nuclear cross sections is thus here performed by reconstructing the kernel of the operation at a given temperature T by means of linear combination of kernels at reference temperatures (Tj). The choice of the L2 metric yields optimal linear interpolation coefficients in the form of the solutions of a linear algebraic system inversion. The optimization of the choice of reference temperatures (Tj) is then undertaken so as to best reconstruct, in the L∞ sense, the kernels over a given temperature range [Tmin ,Tmax ]. The performance of these kernel reconstruction methods is then assessed in light of previous temperature interpolation methods by testing them upon isotope 238U. Temperature-optimized free Doppler kernel reconstruction significantly outperforms all previous interpolation-based methods, achieving 0.1% relative error on temperature interpolation of 238U total cross section over the temperature range [ 300 K , 3000 K ] with only 9 reference temperatures.

Wavelet SVM in Reproducing Kernel Hilbert Space for hyperspectral remote sensing image classification

NASA Astrophysics Data System (ADS)

Du, Peijun; Tan, Kun; Xing, Xiaoshi

2010-12-01

Combining Support Vector Machine (SVM) with wavelet analysis, we constructed wavelet SVM (WSVM) classifier based on wavelet kernel functions in Reproducing Kernel Hilbert Space (RKHS). In conventional kernel theory, SVM is faced with the bottleneck of kernel parameter selection which further results in time-consuming and low classification accuracy. The wavelet kernel in RKHS is a kind of multidimensional wavelet function that can approximate arbitrary nonlinear functions. Implications on semiparametric estimation are proposed in this paper. Airborne Operational Modular Imaging Spectrometer II (OMIS II) hyperspectral remote sensing image with 64 bands and Reflective Optics System Imaging Spectrometer (ROSIS) data with 115 bands were used to experiment the performance and accuracy of the proposed WSVM classifier. The experimental results indicate that the WSVM classifier can obtain the highest accuracy when using the Coiflet Kernel function in wavelet transform. In contrast with some traditional classifiers, including Spectral Angle Mapping (SAM) and Minimum Distance Classification (MDC), and SVM classifier using Radial Basis Function kernel, the proposed wavelet SVM classifier using the wavelet kernel function in Reproducing Kernel Hilbert Space is capable of improving classification accuracy obviously.

A new discrete dipole kernel for quantitative susceptibility mapping.

PubMed

Milovic, Carlos; Acosta-Cabronero, Julio; Pinto, José Miguel; Mattern, Hendrik; Andia, Marcelo; Uribe, Sergio; Tejos, Cristian

2018-09-01

Most approaches for quantitative susceptibility mapping (QSM) are based on a forward model approximation that employs a continuous Fourier transform operator to solve a differential equation system. Such formulation, however, is prone to high-frequency aliasing. The aim of this study was to reduce such errors using an alternative dipole kernel formulation based on the discrete Fourier transform and discrete operators. The impact of such an approach on forward model calculation and susceptibility inversion was evaluated in contrast to the continuous formulation both with synthetic phantoms and in vivo MRI data. The discrete kernel demonstrated systematically better fits to analytic field solutions, and showed less over-oscillations and aliasing artifacts while preserving low- and medium-frequency responses relative to those obtained with the continuous kernel. In the context of QSM estimation, the use of the proposed discrete kernel resulted in error reduction and increased sharpness. This proof-of-concept study demonstrated that discretizing the dipole kernel is advantageous for QSM. The impact on small or narrow structures such as the venous vasculature might by particularly relevant to high-resolution QSM applications with ultra-high field MRI - a topic for future investigations. The proposed dipole kernel has a straightforward implementation to existing QSM routines. Copyright © 2018 Elsevier Inc. All rights reserved.

Distributed Sensor Networks

DTIC Science & Technology

1979-09-30

University, Pittsburgh, Pennsylvania (1976). 14. R. L. Kirby, "ULISP for PDP-11s with Memory Management ," Report MCS-76-23763, University of Maryland...teletVpe or 9 raphIc S output. The recor iuL, po , uitist il so mon itot its owvn ( Onmand queue and a( knowlede commands Sent to It hN the UsCtr interfa I...kernel. By a net- work kernel we mean a multicomputer distributed operating system kernel that includes proces- sor schedulers, "core" memory managers , and

Graph Theory Roots of Spatial Operators for Kinematics and Dynamics

NASA Technical Reports Server (NTRS)

Jain, Abhinandan

2011-01-01

Spatial operators have been used to analyze the dynamics of robotic multibody systems and to develop novel computational dynamics algorithms. Mass matrix factorization, inversion, diagonalization, and linearization are among several new insights obtained using such operators. While initially developed for serial rigid body manipulators, the spatial operators and the related mathematical analysis have been shown to extend very broadly including to tree and closed topology systems, to systems with flexible joints, links, etc. This work uses concepts from graph theory to explore the mathematical foundations of spatial operators. The goal is to study and characterize the properties of the spatial operators at an abstract level so that they can be applied to a broader range of dynamics problems. The rich mathematical properties of the kinematics and dynamics of robotic multibody systems has been an area of strong research interest for several decades. These properties are important to understand the inherent physical behavior of systems, for stability and control analysis, for the development of computational algorithms, and for model development of faithful models. Recurring patterns in spatial operators leads one to ask the more abstract question about the properties and characteristics of spatial operators that make them so broadly applicable. The idea is to step back from the specific application systems, and understand more deeply the generic requirements and properties of spatial operators, so that the insights and techniques are readily available across different kinematics and dynamics problems. In this work, techniques from graph theory were used to explore the abstract basis for the spatial operators. The close relationship between the mathematical properties of adjacency matrices for graphs and those of spatial operators and their kernels were established. The connections hold across very basic requirements on the system topology, the nature of the component bodies, the indexing schemes, etc. The relationship of the underlying structure is intimately connected with efficient, recursive computational algorithms. The results provide the foundational groundwork for a much broader look at the key problems in kinematics and dynamics. The properties of general graphs and trees of nodes and edge were examined, as well as the properties of adjacency matrices that are used to describe graph connectivity. The nilpotency property of such matrices for directed trees was reviewed, and the adjacency matrices were generalized to the notion of block weighted adjacency matrices that support block matrix elements. This leads us to the development of the notion of Spatial Kernel Operator SKO kernels. These kernels provide the basis for the development of SKO resolvent operators.

An Operating Environment for the Jellybean Machine

DTIC Science & Technology

1988-05-01

MODEL 48 5.4.4 Restarting a Context The operating system provides one primitive message (RESTART-CONTEXT) and two system calls (XFERID and XFER.ADDR) to...efficient, powerful services is reqired to support this "stem. To provide this supportive operating environment, I developed an operating system kernel that...serves many of the initial needs of our machine. This Jellybean Operating System Software provides an object- based storage model, where typed

Linux Kernel Co-Scheduling For Bulk Synchronous Parallel Applications

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

Jones, Terry R

2011-01-01

This paper describes a kernel scheduling algorithm that is based on co-scheduling principles and that is intended for parallel applications running on 1000 cores or more where inter-node scalability is key. Experimental results for a Linux implementation on a Cray XT5 machine are presented.1 The results indicate that Linux is a suitable operating system for this new scheduling scheme, and that this design provides a dramatic improvement in scaling performance for synchronizing collective operations at scale.

Kinetic Rate Kernels via Hierarchical Liouville-Space Projection Operator Approach.

PubMed

Zhang, Hou-Dao; Yan, YiJing

2016-05-19

Kinetic rate kernels in general multisite systems are formulated on the basis of a nonperturbative quantum dissipation theory, the hierarchical equations of motion (HEOM) formalism, together with the Nakajima-Zwanzig projection operator technique. The present approach exploits the HEOM-space linear algebra. The quantum non-Markovian site-to-site transfer rate can be faithfully evaluated via projected HEOM dynamics. The developed method is exact, as evident by the comparison to the direct HEOM evaluation results on the population evolution.

A prototype computer-aided modelling tool for life-support system models

NASA Technical Reports Server (NTRS)

Preisig, H. A.; Lee, Tae-Yeong; Little, Frank

1990-01-01

Based on the canonical decomposition of physical-chemical-biological systems, a prototype kernel has been developed to efficiently model alternative life-support systems. It supports (1) the work in an interdisciplinary group through an easy-to-use mostly graphical interface, (2) modularized object-oriented model representation, (3) reuse of models, (4) inheritance of structures from model object to model object, and (5) model data base. The kernel is implemented in Modula-II and presently operates on an IBM PC.

Robotic intelligence kernel

DOEpatents

Bruemmer, David J [Idaho Falls, ID

2009-11-17

A robot platform includes perceptors, locomotors, and a system controller. The system controller executes a robot intelligence kernel (RIK) that includes a multi-level architecture and a dynamic autonomy structure. The multi-level architecture includes a robot behavior level for defining robot behaviors, that incorporate robot attributes and a cognitive level for defining conduct modules that blend an adaptive interaction between predefined decision functions and the robot behaviors. The dynamic autonomy structure is configured for modifying a transaction capacity between an operator intervention and a robot initiative and may include multiple levels with at least a teleoperation mode configured to maximize the operator intervention and minimize the robot initiative and an autonomous mode configured to minimize the operator intervention and maximize the robot initiative. Within the RIK at least the cognitive level includes the dynamic autonomy structure.

Performance Evaluation of a Firm Real-Time DataBase System

DTIC Science & Technology

1995-01-01

after its deadline has passed. StarBase differs from previous real-time database work in that a) it relies on a real - time operating system which...StarBase, running on a real - time operating system kernel, RT-Mach. We discuss how performance was evaluated in StarBase using the StarBase workload

A Framework for Adaptable Operating and Runtime Systems

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

Sterling, Thomas

The emergence of new classes of HPC systems where performance improvement is enabled by Moore’s Law for technology is manifest through multi-core-based architectures including specialized GPU structures. Operating systems were originally designed for control of uniprocessor systems. By the 1980s multiprogramming, virtual memory, and network interconnection were integral services incorporated as part of most modern computers. HPC operating systems were primarily derivatives of the Unix model with Linux dominating the Top-500 list. The use of Linux for commodity clusters was first pioneered by the NASA Beowulf Project. However, the rapid increase in number of cores to achieve performance gain throughmore » technology advances has exposed the limitations of POSIX general-purpose operating systems in scaling and efficiency. This project was undertaken through the leadership of Sandia National Laboratories and in partnership of the University of New Mexico to investigate the alternative of composable lightweight kernels on scalable HPC architectures to achieve superior performance for a wide range of applications. The use of composable operating systems is intended to provide a minimalist set of services specifically required by a given application to preclude overheads and operational uncertainties (“OS noise”) that have been demonstrated to degrade efficiency and operational consistency. This project was undertaken as an exploration to investigate possible strategies and methods for composable lightweight kernel operating systems towards support for extreme scale systems.« less

Kernel reconstruction methods for Doppler broadening — Temperature interpolation by linear combination of reference cross sections at optimally chosen temperatures

DOE PAGES

Ducru, Pablo; Josey, Colin; Dibert, Karia; ...

2017-01-25

This paper establishes a new family of methods to perform temperature interpolation of nuclear interactions cross sections, reaction rates, or cross sections times the energy. One of these quantities at temperature T is approximated as a linear combination of quantities at reference temperatures (T j). The problem is formalized in a cross section independent fashion by considering the kernels of the different operators that convert cross section related quantities from a temperature T 0 to a higher temperature T — namely the Doppler broadening operation. Doppler broadening interpolation of nuclear cross sections is thus here performed by reconstructing the kernelmore » of the operation at a given temperature T by means of linear combination of kernels at reference temperatures (T j). The choice of the L 2 metric yields optimal linear interpolation coefficients in the form of the solutions of a linear algebraic system inversion. The optimization of the choice of reference temperatures (T j) is then undertaken so as to best reconstruct, in the L∞ sense, the kernels over a given temperature range [T min,T max]. The performance of these kernel reconstruction methods is then assessed in light of previous temperature interpolation methods by testing them upon isotope 238U. Temperature-optimized free Doppler kernel reconstruction significantly outperforms all previous interpolation-based methods, achieving 0.1% relative error on temperature interpolation of 238U total cross section over the temperature range [300 K,3000 K] with only 9 reference temperatures.« less

Secure Computer System: Unified Exposition and Multics Interpretation

DTIC Science & Technology

1976-03-01

prearranged code to semaphore critical information to an undercleared subject/process. Neither of these topics is directly addressed by the mathematical...FURTHER CONSIDERATIONS. RULES OF OPERATION FOR A SECURE MULTICS Kernel primitives for a secure Multics will be derived from a higher level user...the Multics architecture as little as possible; this will account to a large extent for radical differences in form between actual kernel primitives

SMOKE TOOL FOR MODELS-3 VERSION 4.1 STRUCTURE AND OPERATION DOCUMENTATION

EPA Science Inventory

The SMOKE Tool is a part of the Models-3 system, a flexible software system designed to simplify the development and use of air quality models and other environmental decision support tools. The SMOKE Tool is an input processor for SMOKE, (Sparse Matrix Operator Kernel Emissio...

Study on Energy Productivity Ratio (EPR) at palm kernel oil processing factory: case study on PT-X at Sumatera Utara Plantation

NASA Astrophysics Data System (ADS)

Haryanto, B.; Bukit, R. Br; Situmeang, E. M.; Christina, E. P.; Pandiangan, F.

2018-02-01

The purpose of this study was to determine the performance, productivity and feasibility of the operation of palm kernel processing plant based on Energy Productivity Ratio (EPR). EPR is expressed as the ratio of output to input energy and by-product. Palm Kernel plan is process in palm kernel to become palm kernel oil. The procedure started from collecting data needed as energy input such as: palm kernel prices, energy demand and depreciation of the factory. The energy output and its by-product comprise the whole production price such as: palm kernel oil price and the remaining products such as shells and pulp price. Calculation the equality of energy of palm kernel oil is to analyze the value of Energy Productivity Ratio (EPR) bases on processing capacity per year. The investigation has been done in Kernel Oil Processing Plant PT-X at Sumatera Utara plantation. The value of EPR was 1.54 (EPR > 1), which indicated that the processing of palm kernel into palm kernel oil is feasible to be operated based on the energy productivity.

A dynamic kernel modifier for linux

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

Minnich, R. G.

2002-09-03

Dynamic Kernel Modifier, or DKM, is a kernel module for Linux that allows user-mode programs to modify the execution of functions in the kernel without recompiling or modifying the kernel source in any way. Functions may be traced, either function entry only or function entry and exit; nullified; or replaced with some other function. For the tracing case, function execution results in the activation of a watchpoint. When the watchpoint is activated, the address of the function is logged in a FIFO buffer that is readable by external applications. The watchpoints are time-stamped with the resolution of the processor highmore » resolution timers, which on most modem processors are accurate to a single processor tick. DKM is very similar to earlier systems such as the SunOS trace device or Linux TT. Unlike these two systems, and other similar systems, DKM requires no kernel modifications. DKM allows users to do initial probing of the kernel to look for performance problems, or even to resolve potential problems by turning functions off or replacing them. DKM watchpoints are not without cost: it takes about 200 nanoseconds to make a log entry on an 800 Mhz Pentium-Ill. The overhead numbers are actually competitive with other hardware-based trace systems, although it has less 'Los Alamos National Laboratory is operated by the University of California for the National Nuclear Security Administration of the United States Department of Energy under contract W-7405-ENG-36. accuracy than an In-Circuit Emulator such as the American Arium. Once the user has zeroed in on a problem, other mechanisms with a higher degree of accuracy can be used.« less

Covert Android Rootkit Detection: Evaluating Linux Kernel Level Rootkits on the Android Operating System

DTIC Science & Technology

2012-06-14

the attacker . Thus, this race condition causes a privilege escalation . 2.2.5 Summary This section reviewed software exploitation of a Linux kernel...has led to increased targeting by malware writers. Android attacks have naturally sparked interest in researching protections for Android . This...release, Android 4.0 Ice Cream Sandwich. These rootkits focused on covert techniques to hide the presence of data used by an attacker to infect a

Numerical study of the ignition behavior of a post-discharge kernel injected into a turbulent stratified cross-flow

NASA Astrophysics Data System (ADS)

Jaravel, Thomas; Labahn, Jeffrey; Ihme, Matthias

2017-11-01

The reliable initiation of flame ignition by high-energy spark kernels is critical for the operability of aviation gas turbines. The evolution of a spark kernel ejected by an igniter into a turbulent stratified environment is investigated using detailed numerical simulations with complex chemistry. At early times post ejection, comparisons of simulation results with high-speed Schlieren data show that the initial trajectory of the kernel is well reproduced, with a significant amount of air entrainment from the surrounding flow that is induced by the kernel ejection. After transiting in a non-flammable mixture, the kernel reaches a second stream of flammable methane-air mixture, where the successful of the kernel ignition was found to depend on the local flow state and operating conditions. By performing parametric studies, the probability of kernel ignition was identified, and compared with experimental observations. The ignition behavior is characterized by analyzing the local chemical structure, and its stochastic variability is also investigated.

Application of Advanced Multi-Core Processor Technologies to Oceanographic Research

DTIC Science & Technology

2014-09-30

Jordan Stanway are taking on the work of analyzing their code, and we are working on the Robot Operating System (ROS) and MOOS-DB systems to evaluate...Linux/GNU operating system that should reduce the time required to build the kernel and userspace significantly. This part of the work is vital to...the platform to be used not only as a service, but also as a private deployable package. As much as possible, this system was built using operating

Attacking Time

DTIC Science & Technology

2015-06-01

version of the Bear operating system. The full system is depicted in Figure 3 and is composed of a minimalist micro-kernel with an associated...which are intended to support a general virtual machine execution environment, this minimalist hypervisor is designed to support only the operations...The use of a minimalist hypervisor in the Bear system opened the door to discovery of zero-day exploits. The approach leverages the hypervisors

A computer program to find the kernel of a polynomial operator

NASA Technical Reports Server (NTRS)

Gejji, R. R.

1976-01-01

This paper presents a FORTRAN program written to solve for the kernel of a matrix of polynomials with real coefficients. It is an implementation of Sain's free modular algorithm for solving the minimal design problem of linear multivariable systems. The structure of the program is discussed, together with some features as they relate to questions of implementing the above method. An example of the use of the program to solve a design problem is included.

The Design and Implementation of an Operating System for the IBM Personal Computer.

DTIC Science & Technology

1984-12-01

comprehensive study of an actual operating system in an effort to show students how theory has been put into action (Lions, 1978; McCharen, 1980). Another...Freedman, 1977). However, since it is easier to develop and maintain a program written in a high-order language (HOL), Pascal was chosen to be the primary...monolithic monitor approach and the kernel approach are strategies which can be used to structure operating systems ( Deitel , 1983; Holt, 1983

Managing a Real-Time Embedded Linux Platform with Buildroot

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

Diamond, J.; Martin, K.

2015-01-01

Developers of real-time embedded software often need to build the operating system, kernel, tools and supporting applications from source to work with the differences in their hardware configuration. The first attempts to introduce Linux-based real-time embedded systems into the Fermilab accelerator controls system used this approach but it was found to be time-consuming, difficult to maintain and difficult to adapt to different hardware configurations. Buildroot is an open source build system with a menu-driven configuration tool (similar to the Linux kernel build system) that automates this process. A customized Buildroot [1] system has been developed for use in the Fermilabmore » accelerator controls system that includes several hardware configuration profiles (including Intel, ARM and PowerPC) and packages for Fermilab support software. A bootable image file is produced containing the Linux kernel, shell and supporting software suite that varies from 3 to 20 megabytes large – ideal for network booting. The result is a platform that is easier to maintain and deploy in diverse hardware configurations« less

Systems and methods for autonomously controlling agricultural machinery

DOEpatents

Hoskinson, Reed L.; Bingham, Dennis N.; Svoboda, John M.; Hess, J. Richard

2003-07-08

Systems and methods for autonomously controlling agricultural machinery such as a grain combine. The operation components of a combine that function to harvest the grain have characteristics that are measured by sensors. For example, the combine speed, the fan speed, and the like can be measured. An important sensor is the grain loss sensor, which may be used to quantify the amount of grain expelled out of the combine. The grain loss sensor utilizes the fluorescence properties of the grain kernels and the plant residue to identify when the expelled plant material contains grain kernels. The sensor data, in combination with historical and current data stored in a database, is used to identify optimum operating conditions that will result in increased crop yield. After the optimum operating conditions are identified, an on-board computer can generate control signals that will adjust the operation of the components identified in the optimum operating conditions. The changes result in less grain loss and improved grain yield. Also, because new data is continually generated by the sensor, the system has the ability to continually learn such that the efficiency of the agricultural machinery is continually improved.

RTOS kernel in portable electrocardiograph

NASA Astrophysics Data System (ADS)

Centeno, C. A.; Voos, J. A.; Riva, G. G.; Zerbini, C.; Gonzalez, E. A.

2011-12-01

This paper presents the use of a Real Time Operating System (RTOS) on a portable electrocardiograph based on a microcontroller platform. All medical device digital functions are performed by the microcontroller. The electrocardiograph CPU is based on the 18F4550 microcontroller, in which an uCOS-II RTOS can be embedded. The decision associated with the kernel use is based on its benefits, the license for educational use and its intrinsic time control and peripherals management. The feasibility of its use on the electrocardiograph is evaluated based on the minimum memory requirements due to the kernel structure. The kernel's own tools were used for time estimation and evaluation of resources used by each process. After this feasibility analysis, the migration from cyclic code to a structure based on separate processes or tasks able to synchronize events is used; resulting in an electrocardiograph running on one Central Processing Unit (CPU) based on RTOS.

DARK Technology Transition Plan

DTIC Science & Technology

1990-04-01

or suspend/resume primitives , and bounded delays. Under the above circumstances, the extra language features, and the hidden system calls they... system , this implies that the mechanisms by which units interact - to synchronise , communicate, schedule one another, or alert one another - should be...the Kernel environment are: " The host system is a DEC VAX operating under VMS 5.0 (the specific host used at the SEI is a MicroVAX II operating under

Implementing an ADA Kernel on NEBULA.

DTIC Science & Technology

1983-08-01

physical address(es). No instruction supports directly semaphore operations , or spin-locks, or other entities used in the synchronisation of tasks...these operations It is found that NEBULA supports admirably the control structures oil Ada, but its Memory Mamagement system is not very suitable. Entry... operating system . With the advent of Ada, in theory at least, the whole program can be written in Ada in a manner that is independent of the computer and of

A Distributed Operating System Design and Dictionary/Directory for the Stock Point Logistics Integrated Communications Environment.

DTIC Science & Technology

1983-11-01

transmission, FM(R) will only have to hold one message. 3. Program Control Block (PCB) The PCB ( Deitel 82] will be maintained by the Executive in...and Use of Kernel to Process Interrupts 35 10. Layered Operating System Design 38 11. Program Control Block Table 43 12. Ready List Data Structure 45 13...examples of fully distributed systems in operation. An objective of the NPS research program for SPLICE is to advance our knowledge of distributed

Using a peanut drying monitoring system to estimate costs of nonbeneficial dryer operation

USDA-ARS?s Scientific Manuscript database

Presently, the peanut industry lacks a commercially available, industry-accepted solution for real-time kernel moisture content determination during peanut drying. Samples of unshelled peanuts are extracted from the semitrailer by an operator periodically, and the samples have to be cleaned and shel...

(EDMUNDS, WA) WILDLAND FIRE EMISSIONS MODELING: INTEGRATING BLUESKY AND SMOKE

EPA Science Inventory

This presentation is a status update of the BlueSky emissions modeling system. BlueSky-EM has been coupled with the Sparse Matrix Operational Kernel Emissions (SMOKE) system, and is now available as a tool for estimating emissions from wildland fires

ASIC-based architecture for the real-time computation of 2D convolution with large kernel size

NASA Astrophysics Data System (ADS)

Shao, Rui; Zhong, Sheng; Yan, Luxin

2015-12-01

Bidimensional convolution is a low-level processing algorithm of interest in many areas, but its high computational cost constrains the size of the kernels, especially in real-time embedded systems. This paper presents a hardware architecture for the ASIC-based implementation of 2-D convolution with medium-large kernels. Aiming to improve the efficiency of storage resources on-chip, reducing off-chip bandwidth of these two issues, proposed construction of a data cache reuse. Multi-block SPRAM to cross cached images and the on-chip ping-pong operation takes full advantage of the data convolution calculation reuse, design a new ASIC data scheduling scheme and overall architecture. Experimental results show that the structure can achieve 40× 32 size of template real-time convolution operations, and improve the utilization of on-chip memory bandwidth and on-chip memory resources, the experimental results show that the structure satisfies the conditions to maximize data throughput output , reducing the need for off-chip memory bandwidth.

JANUS: A Compilation System for Balancing Parallelism and Performance in OpenVX

NASA Astrophysics Data System (ADS)

Omidian, Hossein; Lemieux, Guy G. F.

2018-04-01

Embedded systems typically do not have enough on-chip memory for entire an image buffer. Programming systems like OpenCV operate on entire image frames at each step, making them use excessive memory bandwidth and power. In contrast, the paradigm used by OpenVX is much more efficient; it uses image tiling, and the compilation system is allowed to analyze and optimize the operation sequence, specified as a compute graph, before doing any pixel processing. In this work, we are building a compilation system for OpenVX that can analyze and optimize the compute graph to take advantage of parallel resources in many-core systems or FPGAs. Using a database of prewritten OpenVX kernels, it automatically adjusts the image tile size as well as using kernel duplication and coalescing to meet a defined area (resource) target, or to meet a specified throughput target. This allows a single compute graph to target implementations with a wide range of performance needs or capabilities, e.g. from handheld to datacenter, that use minimal resources and power to reach the performance target.

A Security Architecture for Fault-Tolerant Systems

DTIC Science & Technology

1993-06-03

aspect of our effort to achieve better performance is integrating the system into microkernel -based operating systems. 4 Summary and discussion In...135-171, June 1983. [vRBC+92] R. van Renesse, K. Birman, R. Cooper, B. Glade, and P. Stephenson. Reliable multicast between microkernels . In...Proceedings of the USENIX Microkernels and Other Kernel Architectures Workshop, April 1992. 29

Quality changes in macadamia kernel between harvest and farm-gate.

PubMed

Walton, David A; Wallace, Helen M

2011-02-01

Macadamia integrifolia, Macadamia tetraphylla and their hybrids are cultivated for their edible kernels. After harvest, nuts-in-shell are partially dried on-farm and sorted to eliminate poor-quality kernels before consignment to a processor. During these operations, kernel quality may be lost. In this study, macadamia nuts-in-shell were sampled at five points of an on-farm postharvest handling chain from dehusking to the final storage silo to assess quality loss prior to consignment. Shoulder damage, weight of pieces and unsound kernel were assessed for raw kernels, and colour, mottled colour and surface damage for roasted kernels. Shoulder damage, weight of pieces and unsound kernel for raw kernels increased significantly between the dehusker and the final silo. Roasted kernels displayed a significant increase in dark colour, mottled colour and surface damage during on-farm handling. Significant loss of macadamia kernel quality occurred on a commercial farm during sorting and storage of nuts-in-shell before nuts were consigned to a processor. Nuts-in-shell should be dried as quickly as possible and on-farm handling minimised to maintain optimum kernel quality. 2010 Society of Chemical Industry.

State Space Consistency and Differentiability Conditions for a Class of Causal Dynamical Input-Output Systems

DTIC Science & Technology

2008-01-01

1 0 MX i1; ;in=1 f i1; ; inp (1; ; n)ui1(t 1) uin(t n)d1; ; dn : The above sum is taken over all combinations without...repeating; hence, there are Mn terms. Such an operator is unchanged if the kernels f i1; ; inp , all p and n are symmetrized. The sym- metrized kernel...of f i1; ; inp , denoted by ef i1; ; inp , is de�ned by ef i1; ; inp (1; ; n) = 1n!X f i(1); ;i(n) p ((1); ; (n

Online polarimetry of the Nuclotron internal deuteron and proton beams

NASA Astrophysics Data System (ADS)

Isupov, A. Yu

2017-12-01

The spin studies at Nuclotron require fast and precise determination of the deuteron and proton beam polarization. For these purposes new powerful VME-based data acquisition (DAQ) system has been designed for the Deuteron Spin Structure setup placed at the Nuclotron Internal Target Station. The DAQ system is built using the netgraph-based data acquisition and processing framework ngdp. The software dealing with VME hardware is a set of netgraph nodes in form of the loadable kernel modules, so works in the operating system kernel context. The specific for current implementation nodes and user context utilities are described. The online events representation by ROOT classes allows us to generalize code for histograms filling and polarization calculations. The DAQ system was successfully used during 53rd and 54th Nuclotron runs, and their suitability for online polarimetry is demonstrated.

Fractional quantum integral operator with general kernels and applications

NASA Astrophysics Data System (ADS)

Babakhani, Azizollah; Neamaty, Abdolali; Yadollahzadeh, Milad; Agahi, Hamzeh

In this paper, we first introduce the concept of fractional quantum integral with general kernels, which generalizes several types of fractional integrals known from the literature. Then we give more general versions of some integral inequalities for this operator, thus generalizing some previous results obtained by many researchers.2,8,25,29,30,36

A new taxonomy for distributed computer systems based upon operating system structure

NASA Technical Reports Server (NTRS)

Foudriat, E. C.

1985-01-01

Characteristics of the resource structure found in the operating system are considered as a mechanism for classifying distributed computer systems. Since the operating system resources, themselves, are too diversified to provide a consistent classification, the structure upon which resources are built and shared are examined. The location and control character of this indivisibility provides the taxonomy for separating uniprocessors, computer networks, network computers (fully distributed processing systems or decentralized computers) and algorithm and/or data control multiprocessors. The taxonomy is important because it divides machines into a classification that is relevant or important to the client and not the hardware architect. It also defines the character of the kernel O/S structure needed for future computer systems. What constitutes an operating system for a fully distributed processor is discussed in detail.

A Fault-Oblivious Extreme-Scale Execution Environment (FOX)

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

Van Hensbergen, Eric; Speight, William; Xenidis, Jimi

IBM Research’s contribution to the Fault Oblivious Extreme-scale Execution Environment (FOX) revolved around three core research deliverables: • collaboration with Boston University around the Kittyhawk cloud infrastructure which both enabled a development and deployment platform for the project team and provided a fault-injection testbed to evaluate prototypes • operating systems research focused on exploring role-based operating system technologies through collaboration with Sandia National Labs on the NIX research operating system and collaboration with the broader IBM Research community around a hybrid operating system model which became known as FusedOS • IBM Research also participated in an advisory capacity with themore » Boston University SESA project, the core of which was derived from the K42 operating system research project funded in part by DARPA’s HPCS program. Both of these contributions were built on a foundation of previous operating systems research funding by the Department of Energy’s FastOS Program. Through the course of the X-stack funding we were able to develop prototypes, deploy them on production clusters at scale, and make them available to other researchers. As newer hardware, in the form of BlueGene/Q, came online, we were able to port the prototypes to the new hardware and release the source code for the resulting prototypes as open source to the community. In addition to the open source coded for the Kittyhawk and NIX prototypes, we were able to bring the BlueGene/Q Linux patches up to a more recent kernel and contribute them for inclusion by the broader Linux community. The lasting impact of the IBM Research work on FOX can be seen in its effect on the shift of IBM’s approach to HPC operating systems from Linux and Compute Node Kernels to role-based approaches as prototyped by the NIX and FusedOS work. This impact can be seen beyond IBM in follow-on ideas being incorporated into the proposals for the Exasacale Operating Systems/Runtime program.« less

Distributed smoothed tree kernel for protein-protein interaction extraction from the biomedical literature

PubMed Central

Murugesan, Gurusamy; Abdulkadhar, Sabenabanu; Natarajan, Jeyakumar

2017-01-01

Automatic extraction of protein-protein interaction (PPI) pairs from biomedical literature is a widely examined task in biological information extraction. Currently, many kernel based approaches such as linear kernel, tree kernel, graph kernel and combination of multiple kernels has achieved promising results in PPI task. However, most of these kernel methods fail to capture the semantic relation information between two entities. In this paper, we present a special type of tree kernel for PPI extraction which exploits both syntactic (structural) and semantic vectors information known as Distributed Smoothed Tree kernel (DSTK). DSTK comprises of distributed trees with syntactic information along with distributional semantic vectors representing semantic information of the sentences or phrases. To generate robust machine learning model composition of feature based kernel and DSTK were combined using ensemble support vector machine (SVM). Five different corpora (AIMed, BioInfer, HPRD50, IEPA, and LLL) were used for evaluating the performance of our system. Experimental results show that our system achieves better f-score with five different corpora compared to other state-of-the-art systems. PMID:29099838

Distributed smoothed tree kernel for protein-protein interaction extraction from the biomedical literature.

PubMed

Murugesan, Gurusamy; Abdulkadhar, Sabenabanu; Natarajan, Jeyakumar

2017-01-01

Automatic extraction of protein-protein interaction (PPI) pairs from biomedical literature is a widely examined task in biological information extraction. Currently, many kernel based approaches such as linear kernel, tree kernel, graph kernel and combination of multiple kernels has achieved promising results in PPI task. However, most of these kernel methods fail to capture the semantic relation information between two entities. In this paper, we present a special type of tree kernel for PPI extraction which exploits both syntactic (structural) and semantic vectors information known as Distributed Smoothed Tree kernel (DSTK). DSTK comprises of distributed trees with syntactic information along with distributional semantic vectors representing semantic information of the sentences or phrases. To generate robust machine learning model composition of feature based kernel and DSTK were combined using ensemble support vector machine (SVM). Five different corpora (AIMed, BioInfer, HPRD50, IEPA, and LLL) were used for evaluating the performance of our system. Experimental results show that our system achieves better f-score with five different corpora compared to other state-of-the-art systems.

Decomposition of the polynomial kernel of arbitrary higher spin Dirac operators

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

Eelbode, D., E-mail: David.Eelbode@ua.ac.be; Raeymaekers, T., E-mail: Tim.Raeymaekers@UGent.be; Van der Jeugt, J., E-mail: Joris.VanderJeugt@UGent.be

2015-10-15

In a series of recent papers, we have introduced higher spin Dirac operators, which are generalisations of the classical Dirac operator. Whereas the latter acts on spinor-valued functions, the former acts on functions taking values in arbitrary irreducible half-integer highest weight representations for the spin group. In this paper, we describe how the polynomial kernel spaces of such operators decompose in irreducible representations of the spin group. We will hereby make use of results from representation theory.

FAST TRACK COMMUNICATION: General approach to \\mathfrak {SU}(n) quasi-distribution functions

NASA Astrophysics Data System (ADS)

Klimov, Andrei B.; de Guise, Hubert

2010-10-01

We propose an operational form for the kernel of a mapping between an operator acting in a Hilbert space of a quantum system with an \\mathfrak {SU}(n) symmetry group and its symbol in the corresponding classical phase space. For symmetric irreps of \\mathfrak {SU}(n) , this mapping is bijective. We briefly discuss complications that will occur in the general case.

Tcl as a Software Environment for a TCS

NASA Astrophysics Data System (ADS)

Terrett, David L.

2002-12-01

This paper describes how the Tcl scripting language and C API has been used as the software environment for a telescope pointing kernel so that new pointing algorithms and software architectures can be developed and tested without needing a real-time operating system or real-time software environment. It has enabled development to continue outside the framework of a specific telescope project while continuing to build a system that is sufficiently complete to be capable of controlling real hardware but expending minimum effort on replacing the services that would normally by provided by a real-time software environment. Tcl is used as a scripting language for configuring the system at startup and then as the command interface for controlling the running system; the Tcl C language API is used to provided a system independent interface to file and socket I/O and other operating system services. The pointing algorithms themselves are implemented as a set of C++ objects calling C library functions that implement the algorithms described in [2]. Although originally designed as a test and development environment, the system, running as a soft real-time process on Linux, has been used to test the SOAR mount control system and will be used as the pointing kernel of the SOAR telescope control system

An O(N) and parallel approach to integral problems by a kernel-independent fast multipole method: Application to polarization and magnetization of interacting particles

NASA Astrophysics Data System (ADS)

Jiang, Xikai; Li, Jiyuan; Zhao, Xujun; Qin, Jian; Karpeev, Dmitry; Hernandez-Ortiz, Juan; de Pablo, Juan J.; Heinonen, Olle

2016-08-01

Large classes of materials systems in physics and engineering are governed by magnetic and electrostatic interactions. Continuum or mesoscale descriptions of such systems can be cast in terms of integral equations, whose direct computational evaluation requires O(N2) operations, where N is the number of unknowns. Such a scaling, which arises from the many-body nature of the relevant Green's function, has precluded wide-spread adoption of integral methods for solution of large-scale scientific and engineering problems. In this work, a parallel computational approach is presented that relies on using scalable open source libraries and utilizes a kernel-independent Fast Multipole Method (FMM) to evaluate the integrals in O(N) operations, with O(N) memory cost, thereby substantially improving the scalability and efficiency of computational integral methods. We demonstrate the accuracy, efficiency, and scalability of our approach in the context of two examples. In the first, we solve a boundary value problem for a ferroelectric/ferromagnetic volume in free space. In the second, we solve an electrostatic problem involving polarizable dielectric bodies in an unbounded dielectric medium. The results from these test cases show that our proposed parallel approach, which is built on a kernel-independent FMM, can enable highly efficient and accurate simulations and allow for considerable flexibility in a broad range of applications.

An O( N) and parallel approach to integral problems by a kernel-independent fast multipole method: Application to polarization and magnetization of interacting particles

DOE PAGES

Jiang, Xikai; Li, Jiyuan; Zhao, Xujun; ...

2016-08-10

Large classes of materials systems in physics and engineering are governed by magnetic and electrostatic interactions. Continuum or mesoscale descriptions of such systems can be cast in terms of integral equations, whose direct computational evaluation requires O( N 2) operations, where N is the number of unknowns. Such a scaling, which arises from the many-body nature of the relevant Green's function, has precluded wide-spread adoption of integral methods for solution of large-scale scientific and engineering problems. In this work, a parallel computational approach is presented that relies on using scalable open source libraries and utilizes a kernel-independent Fast Multipole Methodmore » (FMM) to evaluate the integrals in O( N) operations, with O( N) memory cost, thereby substantially improving the scalability and efficiency of computational integral methods. We demonstrate the accuracy, efficiency, and scalability of our approach in the context of two examples. In the first, we solve a boundary value problem for a ferroelectric/ferromagnetic volume in free space. In the second, we solve an electrostatic problem involving polarizable dielectric bodies in an unbounded dielectric medium. Lastly, the results from these test cases show that our proposed parallel approach, which is built on a kernel-independent FMM, can enable highly efficient and accurate simulations and allow for considerable flexibility in a broad range of applications.« less

Flexibly imposing periodicity in kernel independent FMM: A multipole-to-local operator approach

NASA Astrophysics Data System (ADS)

Yan, Wen; Shelley, Michael

2018-02-01

An important but missing component in the application of the kernel independent fast multipole method (KIFMM) is the capability for flexibly and efficiently imposing singly, doubly, and triply periodic boundary conditions. In most popular packages such periodicities are imposed with the hierarchical repetition of periodic boxes, which may give an incorrect answer due to the conditional convergence of some kernel sums. Here we present an efficient method to properly impose periodic boundary conditions using a near-far splitting scheme. The near-field contribution is directly calculated with the KIFMM method, while the far-field contribution is calculated with a multipole-to-local (M2L) operator which is independent of the source and target point distribution. The M2L operator is constructed with the far-field portion of the kernel function to generate the far-field contribution with the downward equivalent source points in KIFMM. This method guarantees the sum of the near-field & far-field converge pointwise to results satisfying periodicity and compatibility conditions. The computational cost of the far-field calculation observes the same O (N) complexity as FMM and is designed to be small by reusing the data computed by KIFMM for the near-field. The far-field calculations require no additional control parameters, and observes the same theoretical error bound as KIFMM. We present accuracy and timing test results for the Laplace kernel in singly periodic domains and the Stokes velocity kernel in doubly and triply periodic domains.

Exploiting graph kernels for high performance biomedical relation extraction.

PubMed

Panyam, Nagesh C; Verspoor, Karin; Cohn, Trevor; Ramamohanarao, Kotagiri

2018-01-30

Relation extraction from biomedical publications is an important task in the area of semantic mining of text. Kernel methods for supervised relation extraction are often preferred over manual feature engineering methods, when classifying highly ordered structures such as trees and graphs obtained from syntactic parsing of a sentence. Tree kernels such as the Subset Tree Kernel and Partial Tree Kernel have been shown to be effective for classifying constituency parse trees and basic dependency parse graphs of a sentence. Graph kernels such as the All Path Graph kernel (APG) and Approximate Subgraph Matching (ASM) kernel have been shown to be suitable for classifying general graphs with cycles, such as the enhanced dependency parse graph of a sentence. In this work, we present a high performance Chemical-Induced Disease (CID) relation extraction system. We present a comparative study of kernel methods for the CID task and also extend our study to the Protein-Protein Interaction (PPI) extraction task, an important biomedical relation extraction task. We discuss novel modifications to the ASM kernel to boost its performance and a method to apply graph kernels for extracting relations expressed in multiple sentences. Our system for CID relation extraction attains an F-score of 60%, without using external knowledge sources or task specific heuristic or rules. In comparison, the state of the art Chemical-Disease Relation Extraction system achieves an F-score of 56% using an ensemble of multiple machine learning methods, which is then boosted to 61% with a rule based system employing task specific post processing rules. For the CID task, graph kernels outperform tree kernels substantially, and the best performance is obtained with APG kernel that attains an F-score of 60%, followed by the ASM kernel at 57%. The performance difference between the ASM and APG kernels for CID sentence level relation extraction is not significant. In our evaluation of ASM for the PPI task, ASM performed better than APG kernel for the BioInfer dataset, in the Area Under Curve (AUC) measure (74% vs 69%). However, for all the other PPI datasets, namely AIMed, HPRD50, IEPA and LLL, ASM is substantially outperformed by the APG kernel in F-score and AUC measures. We demonstrate a high performance Chemical Induced Disease relation extraction, without employing external knowledge sources or task specific heuristics. Our work shows that graph kernels are effective in extracting relations that are expressed in multiple sentences. We also show that the graph kernels, namely the ASM and APG kernels, substantially outperform the tree kernels. Among the graph kernels, we showed the ASM kernel as effective for biomedical relation extraction, with comparable performance to the APG kernel for datasets such as the CID-sentence level relation extraction and BioInfer in PPI. Overall, the APG kernel is shown to be significantly more accurate than the ASM kernel, achieving better performance on most datasets.

Kernel-Based Approximate Dynamic Programming Using Bellman Residual Elimination

DTIC Science & Technology

2010-02-01

framework is the ability to utilize stochastic system models, thereby allowing the system to make sound decisions even if there is randomness in the system ...approximate policy when a system model is unavailable. We present theoretical analysis of all BRE algorithms proving convergence to the optimal policy in...policies based on MDPs is that there may be parameters of the system model that are poorly known and/or vary with time as the system operates. System

A Kernel-based Lagrangian method for imperfectly-mixed chemical reactions

NASA Astrophysics Data System (ADS)

Schmidt, Michael J.; Pankavich, Stephen; Benson, David A.

2017-05-01

Current Lagrangian (particle-tracking) algorithms used to simulate diffusion-reaction equations must employ a certain number of particles to properly emulate the system dynamics-particularly for imperfectly-mixed systems. The number of particles is tied to the statistics of the initial concentration fields of the system at hand. Systems with shorter-range correlation and/or smaller concentration variance require more particles, potentially limiting the computational feasibility of the method. For the well-known problem of bimolecular reaction, we show that using kernel-based, rather than Dirac delta, particles can significantly reduce the required number of particles. We derive the fixed width of a Gaussian kernel for a given reduced number of particles that analytically eliminates the error between kernel and Dirac solutions at any specified time. We also show how to solve for the fixed kernel size by minimizing the squared differences between solutions over any given time interval. Numerical results show that the width of the kernel should be kept below about 12% of the domain size, and that the analytic equations used to derive kernel width suffer significantly from the neglect of higher-order moments. The simulations with a kernel width given by least squares minimization perform better than those made to match at one specific time. A heuristic time-variable kernel size, based on the previous results, performs on par with the least squares fixed kernel size.

Anisotropic hydrodynamics with a scalar collisional kernel

NASA Astrophysics Data System (ADS)

Almaalol, Dekrayat; Strickland, Michael

2018-04-01

Prior studies of nonequilibrium dynamics using anisotropic hydrodynamics have used the relativistic Anderson-Witting scattering kernel or some variant thereof. In this paper, we make the first study of the impact of using a more realistic scattering kernel. For this purpose, we consider a conformal system undergoing transversally homogenous and boost-invariant Bjorken expansion and take the collisional kernel to be given by the leading order 2 ↔2 scattering kernel in scalar λ ϕ4 . We consider both classical and quantum statistics to assess the impact of Bose enhancement on the dynamics. We also determine the anisotropic nonequilibrium attractor of a system subject to this collisional kernel. We find that, when the near-equilibrium relaxation-times in the Anderson-Witting and scalar collisional kernels are matched, the scalar kernel results in a higher degree of momentum-space anisotropy during the system's evolution, given the same initial conditions. Additionally, we find that taking into account Bose enhancement further increases the dynamically generated momentum-space anisotropy.

An Evaluation of Kernel Equating: Parallel Equating with Classical Methods in the SAT Subject Tests[TM] Program. Research Report. ETS RR-09-06

ERIC Educational Resources Information Center

Grant, Mary C.; Zhang, Lilly; Damiano, Michele

2009-01-01

This study investigated kernel equating methods by comparing these methods to operational equatings for two tests in the SAT Subject Tests[TM] program. GENASYS (ETS, 2007) was used for all equating methods and scaled score kernel equating results were compared to Tucker, Levine observed score, chained linear, and chained equipercentile equating…

Parallel-aware, dedicated job co-scheduling within/across symmetric multiprocessing nodes

DOEpatents

Jones, Terry R.; Watson, Pythagoras C.; Tuel, William; Brenner, Larry; ,Caffrey, Patrick; Fier, Jeffrey

2010-10-05

In a parallel computing environment comprising a network of SMP nodes each having at least one processor, a parallel-aware co-scheduling method and system for improving the performance and scalability of a dedicated parallel job having synchronizing collective operations. The method and system uses a global co-scheduler and an operating system kernel dispatcher adapted to coordinate interfering system and daemon activities on a node and across nodes to promote intra-node and inter-node overlap of said interfering system and daemon activities as well as intra-node and inter-node overlap of said synchronizing collective operations. In this manner, the impact of random short-lived interruptions, such as timer-decrement processing and periodic daemon activity, on synchronizing collective operations is minimized on large processor-count SPMD bulk-synchronous programming styles.

Multiscale asymmetric orthogonal wavelet kernel for linear programming support vector learning and nonlinear dynamic systems identification.

PubMed

Lu, Zhao; Sun, Jing; Butts, Kenneth

2014-05-01

Support vector regression for approximating nonlinear dynamic systems is more delicate than the approximation of indicator functions in support vector classification, particularly for systems that involve multitudes of time scales in their sampled data. The kernel used for support vector learning determines the class of functions from which a support vector machine can draw its solution, and the choice of kernel significantly influences the performance of a support vector machine. In this paper, to bridge the gap between wavelet multiresolution analysis and kernel learning, the closed-form orthogonal wavelet is exploited to construct new multiscale asymmetric orthogonal wavelet kernels for linear programming support vector learning. The closed-form multiscale orthogonal wavelet kernel provides a systematic framework to implement multiscale kernel learning via dyadic dilations and also enables us to represent complex nonlinear dynamics effectively. To demonstrate the superiority of the proposed multiscale wavelet kernel in identifying complex nonlinear dynamic systems, two case studies are presented that aim at building parallel models on benchmark datasets. The development of parallel models that address the long-term/mid-term prediction issue is more intricate and challenging than the identification of series-parallel models where only one-step ahead prediction is required. Simulation results illustrate the effectiveness of the proposed multiscale kernel learning.

Green operators for low regularity spacetimes

NASA Astrophysics Data System (ADS)

Sanchez Sanchez, Yafet; Vickers, James

2018-02-01

In this paper we define and construct advanced and retarded Green operators for the wave operator on spacetimes with low regularity. In order to do so we require that the spacetime satisfies the condition of generalised hyperbolicity which is equivalent to well-posedness of the classical inhomogeneous problem with zero initial data where weak solutions are properly supported. Moreover, we provide an explicit formula for the kernel of the Green operators in terms of an arbitrary eigenbasis of H 1 and a suitable Green matrix that solves a system of second order ODEs.

Volterra series truncation and kernel estimation of nonlinear systems in the frequency domain

NASA Astrophysics Data System (ADS)

Zhang, B.; Billings, S. A.

2017-02-01

The Volterra series model is a direct generalisation of the linear convolution integral and is capable of displaying the intrinsic features of a nonlinear system in a simple and easy to apply way. Nonlinear system analysis using Volterra series is normally based on the analysis of its frequency-domain kernels and a truncated description. But the estimation of Volterra kernels and the truncation of Volterra series are coupled with each other. In this paper, a novel complex-valued orthogonal least squares algorithm is developed. The new algorithm provides a powerful tool to determine which terms should be included in the Volterra series expansion and to estimate the kernels and thus solves the two problems all together. The estimated results are compared with those determined using the analytical expressions of the kernels to validate the method. To further evaluate the effectiveness of the method, the physical parameters of the system are also extracted from the measured kernels. Simulation studies demonstrates that the new approach not only can truncate the Volterra series expansion and estimate the kernels of a weakly nonlinear system, but also can indicate the applicability of the Volterra series analysis in a severely nonlinear system case.

Optimal algorithm for automatic detection of microaneurysms based on receiver operating characteristic curve

NASA Astrophysics Data System (ADS)

Xu, Lili; Luo, Shuqian

2010-11-01

Microaneurysms (MAs) are the first manifestations of the diabetic retinopathy (DR) as well as an indicator for its progression. Their automatic detection plays a key role for both mass screening and monitoring and is therefore in the core of any system for computer-assisted diagnosis of DR. The algorithm basically comprises the following stages: candidate detection aiming at extracting the patterns possibly corresponding to MAs based on mathematical morphological black top hat, feature extraction to characterize these candidates, and classification based on support vector machine (SVM), to validate MAs. Feature vector and kernel function of SVM selection is very important to the algorithm. We use the receiver operating characteristic (ROC) curve to evaluate the distinguishing performance of different feature vectors and different kernel functions of SVM. The ROC analysis indicates the quadratic polynomial SVM with a combination of features as the input shows the best discriminating performance.

Optimal algorithm for automatic detection of microaneurysms based on receiver operating characteristic curve.

PubMed

Xu, Lili; Luo, Shuqian

2010-01-01

Microaneurysms (MAs) are the first manifestations of the diabetic retinopathy (DR) as well as an indicator for its progression. Their automatic detection plays a key role for both mass screening and monitoring and is therefore in the core of any system for computer-assisted diagnosis of DR. The algorithm basically comprises the following stages: candidate detection aiming at extracting the patterns possibly corresponding to MAs based on mathematical morphological black top hat, feature extraction to characterize these candidates, and classification based on support vector machine (SVM), to validate MAs. Feature vector and kernel function of SVM selection is very important to the algorithm. We use the receiver operating characteristic (ROC) curve to evaluate the distinguishing performance of different feature vectors and different kernel functions of SVM. The ROC analysis indicates the quadratic polynomial SVM with a combination of features as the input shows the best discriminating performance.

STATISTICAL PROPERTIES OF SOLAR ACTIVE REGIONS OBTAINED FROM AN AUTOMATIC DETECTION SYSTEM AND THE COMPUTATIONAL BIASES

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

Zhang Jie; Wang Yuming; Liu Yang, E-mail: jzhang7@gmu.ed

We have developed a computational software system to automate the process of identifying solar active regions (ARs) and quantifying their physical properties based on high-resolution synoptic magnetograms constructed from Michelson Doppler Imager (MDI; on board the SOHO spacecraft) images from 1996 to 2008. The system, based on morphological analysis and intensity thresholding, has four functional modules: (1) intensity segmentation to obtain kernel pixels, (2) a morphological opening operation to erase small kernels, which effectively remove ephemeral regions and magnetic fragments in decayed ARs, (3) region growing to extend kernels to full AR size, and (4) the morphological closing operation tomore » merge/group regions with a small spatial gap. We calculate the basic physical parameters of the 1730 ARs identified by the auto system. The mean and maximum magnetic flux of individual ARs are 1.67 x 10{sup 22} Mx and 1.97 x 10{sup 23} Mx, while that per Carrington rotation are 1.83 x 10{sup 23} Mx and 6.96 x 10{sup 23} Mx, respectively. The frequency distributions of ARs with respect to both area size and magnetic flux follow a log-normal function. However, when we decrease the detection thresholds and thus increase the number of detected ARs, the frequency distribution largely follows a power-law function. We also find that the equatorward drifting motion of the AR bands with solar cycle can be described by a linear function superposed with intermittent reverse driftings. The average drifting speed over one solar cycle is 1{sup o}.83{+-}0{sup o}.04 yr{sup -1} or 0.708 {+-} 0.015 m s{sup -1}.« less

Dependable Real-Time Systems

DTIC Science & Technology

1991-09-30

0196 or 413 545-0720 PI E-mail Address: krithi@nirvan.cs.umass.edu, stankovic(ocs.umass.edu Grant or Contract Title: Dependable Real - Time Systems Grant...Dependable Real - Time Systems " Grant or Contract Number: N00014-85-k-0398 L " Reporting Period: 1 Oct 87 - 30 Sep 91 , 2. Summary of Accomplishments ’ 2.1 Our...in developing a sound approach to scheduling tasks in complex real - time systems , (2) developed a real-time operating system kernel, a preliminary

Research on Separation of Three Powers Architecture for Trusted OS

NASA Astrophysics Data System (ADS)

Li, Yu; Zhao, Yong; Xin, Siyuan

The privilege in the operating system (OS) often results in the break of confidentiality and integrity of the system. To solve this problem, several security mechanisms are proposed, such as Role-based Access Control, Separation of Duty. However, these mechanisms can not eliminate the privilege in OS kernel layer. This paper proposes a Separation of Three Powers Architecture (STPA). The authorizations in OS are divided into three parts: System Management Subsystem (SMS), Security Management Subsystem (SEMS) and Audit Subsystem (AS). Mutual support and mutual checks and balances which are the design principles of STPA eliminate the administrator in the kernel layer. Furthermore, the paper gives the formal description for authorization division using the graph theory. Finally, the implementation of STPA is given. Proved by experiments, the Separation of Three Powers Architecture we proposed can provide reliable protection for the OS through authorization division.

Kernel functions and Baecklund transformations for relativistic Calogero-Moser and Toda systems

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

Hallnaes, Martin; Ruijsenaars, Simon

We obtain kernel functions associated with the quantum relativistic Toda systems, both for the periodic version and for the nonperiodic version with its dual. This involves taking limits of previously known results concerning kernel functions for the elliptic and hyperbolic relativistic Calogero-Moser systems. We show that the special kernel functions at issue admit a limit that yields generating functions of Baecklund transformations for the classical relativistic Calogero-Moser and Toda systems. We also obtain the nonrelativistic counterparts of our results, which tie in with previous results in the literature.

FOS: A Factored Operating Systems for High Assurance and Scalability on Multicores

DTIC Science & Technology

2012-08-01

computing. It builds on previous work in distributed and microkernel OSes by factoring services out of the kernel, and then further distributing each...2 3.0 Methods, Assumptions, and Procedures (System Design) .................................................. 4 3.1 Microkernel ...cooperating servers. We term such a service a fleet. Figure 2 shows the high-level architecture of fos. A small microkernel runs on every core

Ada 9X Project Report, A Study of Implementation-Dependent Pragmas and Attributes in Ada

DTIC Science & Technology

1989-11-01

here communicatons with the vendor were often required to firmly establish the behavior of some implementation-dependent features CMU-SEI-SR-89-19 3 2.2...compilers), by potential market penetration (percent coverage of all surveyed implementations), and by cross-compiler influence (percentage of cross...operations in the context of a tightly integrated development environment, specific underlying operating system services (beneath the Ada run- time kernel

Electromagnetics. Volume 1, Number 4, October-December 1981.

DTIC Science & Technology

1981-01-01

terms. 1.6 Matrix and Operator Theory Integral equations have been cast in approximate numerical form by the moment method (MoM). In this numerical...introduced the eigenmode expansion method to find more properties of the SEM [3.4]. One defines eigenvalues and eigenmodes for the integral operator (kernel...exterior surface of the system. Mechanisms that play a role in the penetration are (1) diffusion through metal skins , (2) field leakage through

Exact calculation of the time convolutionless master equation generator: Application to the nonequilibrium resonant level model

NASA Astrophysics Data System (ADS)

Kidon, Lyran; Wilner, Eli Y.; Rabani, Eran

2015-12-01

The generalized quantum master equation provides a powerful tool to describe the dynamics in quantum impurity models driven away from equilibrium. Two complementary approaches, one based on Nakajima-Zwanzig-Mori time-convolution (TC) and the other on the Tokuyama-Mori time-convolutionless (TCL) formulations provide a starting point to describe the time-evolution of the reduced density matrix. A key in both approaches is to obtain the so called "memory kernel" or "generator," going beyond second or fourth order perturbation techniques. While numerically converged techniques are available for the TC memory kernel, the canonical approach to obtain the TCL generator is based on inverting a super-operator in the full Hilbert space, which is difficult to perform and thus, nearly all applications of the TCL approach rely on a perturbative scheme of some sort. Here, the TCL generator is expressed using a reduced system propagator which can be obtained from system observables alone and requires the calculation of super-operators and their inverse in the reduced Hilbert space rather than the full one. This makes the formulation amenable to quantum impurity solvers or to diagrammatic techniques, such as the nonequilibrium Green's function. We implement the TCL approach for the resonant level model driven away from equilibrium and compare the time scales for the decay of the generator with that of the memory kernel in the TC approach. Furthermore, the effects of temperature, source-drain bias, and gate potential on the TCL/TC generators are discussed.

A customizable system for real-time image processing using the Blackfin DSProcessor and the MicroC/OS-II real-time kernel

NASA Astrophysics Data System (ADS)

Coffey, Stephen; Connell, Joseph

2005-06-01

This paper presents a development platform for real-time image processing based on the ADSP-BF533 Blackfin processor and the MicroC/OS-II real-time operating system (RTOS). MicroC/OS-II is a completely portable, ROMable, pre-emptive, real-time kernel. The Blackfin Digital Signal Processors (DSPs), incorporating the Analog Devices/Intel Micro Signal Architecture (MSA), are a broad family of 16-bit fixed-point products with a dual Multiply Accumulate (MAC) core. In addition, they have a rich instruction set with variable instruction length and both DSP and MCU functionality thus making them ideal for media based applications. Using the MicroC/OS-II for task scheduling and management, the proposed system can capture and process raw RGB data from any standard 8-bit greyscale image sensor in soft real-time and then display the processed result using a simple PC graphical user interface (GUI). Additionally, the GUI allows configuration of the image capture rate and the system and core DSP clock rates thereby allowing connectivity to a selection of image sensors and memory devices. The GUI also allows selection from a set of image processing algorithms based in the embedded operating system.

A High Performance Block Eigensolver for Nuclear Configuration Interaction Calculations

DOE PAGES

Aktulga, Hasan Metin; Afibuzzaman, Md.; Williams, Samuel; ...

2017-06-01

As on-node parallelism increases and the performance gap between the processor and the memory system widens, achieving high performance in large-scale scientific applications requires an architecture-aware design of algorithms and solvers. We focus on the eigenvalue problem arising in nuclear Configuration Interaction (CI) calculations, where a few extreme eigenpairs of a sparse symmetric matrix are needed. Here, we consider a block iterative eigensolver whose main computational kernels are the multiplication of a sparse matrix with multiple vectors (SpMM), and tall-skinny matrix operations. We then present techniques to significantly improve the SpMM and the transpose operation SpMM T by using themore » compressed sparse blocks (CSB) format. We achieve 3-4× speedup on the requisite operations over good implementations with the commonly used compressed sparse row (CSR) format. We develop a performance model that allows us to correctly estimate the performance of our SpMM kernel implementations, and we identify cache bandwidth as a potential performance bottleneck beyond DRAM. We also analyze and optimize the performance of LOBPCG kernels (inner product and linear combinations on multiple vectors) and show up to 15× speedup over using high performance BLAS libraries for these operations. The resulting high performance LOBPCG solver achieves 1.4× to 1.8× speedup over the existing Lanczos solver on a series of CI computations on high-end multicore architectures (Intel Xeons). We also analyze the performance of our techniques on an Intel Xeon Phi Knights Corner (KNC) processor.« less

A High Performance Block Eigensolver for Nuclear Configuration Interaction Calculations

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

Aktulga, Hasan Metin; Afibuzzaman, Md.; Williams, Samuel

As on-node parallelism increases and the performance gap between the processor and the memory system widens, achieving high performance in large-scale scientific applications requires an architecture-aware design of algorithms and solvers. We focus on the eigenvalue problem arising in nuclear Configuration Interaction (CI) calculations, where a few extreme eigenpairs of a sparse symmetric matrix are needed. Here, we consider a block iterative eigensolver whose main computational kernels are the multiplication of a sparse matrix with multiple vectors (SpMM), and tall-skinny matrix operations. We then present techniques to significantly improve the SpMM and the transpose operation SpMM T by using themore » compressed sparse blocks (CSB) format. We achieve 3-4× speedup on the requisite operations over good implementations with the commonly used compressed sparse row (CSR) format. We develop a performance model that allows us to correctly estimate the performance of our SpMM kernel implementations, and we identify cache bandwidth as a potential performance bottleneck beyond DRAM. We also analyze and optimize the performance of LOBPCG kernels (inner product and linear combinations on multiple vectors) and show up to 15× speedup over using high performance BLAS libraries for these operations. The resulting high performance LOBPCG solver achieves 1.4× to 1.8× speedup over the existing Lanczos solver on a series of CI computations on high-end multicore architectures (Intel Xeons). We also analyze the performance of our techniques on an Intel Xeon Phi Knights Corner (KNC) processor.« less

Technical Note: Image filtering to make computer-aided detection robust to image reconstruction kernel choice in lung cancer CT screening

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

Ohkubo, Masaki, E-mail: mook@clg.niigata-u.ac.jp

Purpose: In lung cancer computed tomography (CT) screening, the performance of a computer-aided detection (CAD) system depends on the selection of the image reconstruction kernel. To reduce this dependence on reconstruction kernels, the authors propose a novel application of an image filtering method previously proposed by their group. Methods: The proposed filtering process uses the ratio of modulation transfer functions (MTFs) of two reconstruction kernels as a filtering function in the spatial-frequency domain. This method is referred to as MTF{sub ratio} filtering. Test image data were obtained from CT screening scans of 67 subjects who each had one nodule. Imagesmore » were reconstructed using two kernels: f{sub STD} (for standard lung imaging) and f{sub SHARP} (for sharp edge-enhancement lung imaging). The MTF{sub ratio} filtering was implemented using the MTFs measured for those kernels and was applied to the reconstructed f{sub SHARP} images to obtain images that were similar to the f{sub STD} images. A mean filter and a median filter were applied (separately) for comparison. All reconstructed and filtered images were processed using their prototype CAD system. Results: The MTF{sub ratio} filtered images showed excellent agreement with the f{sub STD} images. The standard deviation for the difference between these images was very small, ∼6.0 Hounsfield units (HU). However, the mean and median filtered images showed larger differences of ∼48.1 and ∼57.9 HU from the f{sub STD} images, respectively. The free-response receiver operating characteristic (FROC) curve for the f{sub SHARP} images indicated poorer performance compared with the FROC curve for the f{sub STD} images. The FROC curve for the MTF{sub ratio} filtered images was equivalent to the curve for the f{sub STD} images. However, this similarity was not achieved by using the mean filter or median filter. Conclusions: The accuracy of MTF{sub ratio} image filtering was verified and the method was demonstrated to be effective for reducing the kernel dependence of CAD performance.« less

WebGeocalc and Cosmographia: Modern Tools to Access SPICE Archives

NASA Astrophysics Data System (ADS)

Semenov, B. V.; Acton, C. H.; Bachman, N. J.; Ferguson, E. W.; Rose, M. E.; Wright, E. D.

2017-06-01

The WebGeocalc (WGC) web client-server tool and the SPICE-enhanced Cosmographia visualization program are two new ways for accessing space mission geometry data provided in the PDS SPICE kernel archives and by mission operational SPICE kernel sets.

Quantum entanglement in photoactive prebiotic systems.

PubMed

Tamulis, Arvydas; Grigalavicius, Mantas

2014-06-01

This paper contains the review of quantum entanglement investigations in living systems, and in the quantum mechanically modelled photoactive prebiotic kernel systems. We define our modelled self-assembled supramolecular photoactive centres, composed of one or more sensitizer molecules, precursors of fatty acids and a number of water molecules, as a photoactive prebiotic kernel systems. We propose that life first emerged in the form of such minimal photoactive prebiotic kernel systems and later in the process of evolution these photoactive prebiotic kernel systems would have produced fatty acids and covered themselves with fatty acid envelopes to become the minimal cells of the Fatty Acid World. Specifically, we model self-assembling of photoactive prebiotic systems with observed quantum entanglement phenomena. We address the idea that quantum entanglement was important in the first stages of origins of life and evolution of the biospheres because simultaneously excite two prebiotic kernels in the system by appearance of two additional quantum entangled excited states, leading to faster growth and self-replication of minimal living cells. The quantum mechanically modelled possibility of synthesizing artificial self-reproducing quantum entangled prebiotic kernel systems and minimal cells also impacts the possibility of the most probable path of emergence of protocells on the Earth or elsewhere. We also examine the quantum entangled logic gates discovered in the modelled systems composed of two prebiotic kernels. Such logic gates may have application in the destruction of cancer cells or becoming building blocks of new forms of artificial cells including magnetically active ones.

HARE: Final Report

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

Mckie, Jim

2012-01-09

This report documents the results of work done over a 6 year period under the FAST-OS programs. The first effort was called Right-Weight Kernels, (RWK) and was concerned with improving measurements of OS noise so it could be treated quantitatively; and evaluating the use of two operating systems, Linux and Plan 9, on HPC systems and determining how these operating systems needed to be extended or changed for HPC, while still retaining their general-purpose nature. The second program, HARE, explored the creation of alternative runtime models, building on RWK. All of the HARE work was done on Plan 9. Themore » HARE researchers were mindful of the very good Linux and LWK work being done at other labs and saw no need to recreate it. Even given this limited funding, the two efforts had outsized impact: _ Helped Cray decide to use Linux, instead of a custom kernel, and provided the tools needed to make Linux perform well _ Created a successor operating system to Plan 9, NIX, which has been taken in by Bell Labs for further development _ Created a standard system measurement tool, Fixed Time Quantum or FTQ, which is widely used for measuring operating systems impact on applications _ Spurred the use of the 9p protocol in several organizations, including IBM _ Built software in use at many companies, including IBM, Cray, and Google _ Spurred the creation of alternative runtimes for use on HPC systems _ Demonstrated that, with proper modifications, a general purpose operating systems can provide communications up to 3 times as effective as user-level libraries Open source was a key part of this work. The code developed for this project is in wide use and available at many places. The core Blue Gene code is available at https://bitbucket.org/ericvh/hare. We describe details of these impacts in the following sections. The rest of this report is organized as follows: First, we describe commercial impact; next, we describe the FTQ benchmark and its impact in more detail; operating systems and runtime research follows; we discuss infrastructure software; and close with a description of the new NIX operating system, future work, and conclusions.« less

Racing to learn: statistical inference and learning in a single spiking neuron with adaptive kernels

PubMed Central

Afshar, Saeed; George, Libin; Tapson, Jonathan; van Schaik, André; Hamilton, Tara J.

2014-01-01

This paper describes the Synapto-dendritic Kernel Adapting Neuron (SKAN), a simple spiking neuron model that performs statistical inference and unsupervised learning of spatiotemporal spike patterns. SKAN is the first proposed neuron model to investigate the effects of dynamic synapto-dendritic kernels and demonstrate their computational power even at the single neuron scale. The rule-set defining the neuron is simple: there are no complex mathematical operations such as normalization, exponentiation or even multiplication. The functionalities of SKAN emerge from the real-time interaction of simple additive and binary processes. Like a biological neuron, SKAN is robust to signal and parameter noise, and can utilize both in its operations. At the network scale neurons are locked in a race with each other with the fastest neuron to spike effectively “hiding” its learnt pattern from its neighbors. The robustness to noise, high speed, and simple building blocks not only make SKAN an interesting neuron model in computational neuroscience, but also make it ideal for implementation in digital and analog neuromorphic systems which is demonstrated through an implementation in a Field Programmable Gate Array (FPGA). Matlab, Python, and Verilog implementations of SKAN are available at: http://www.uws.edu.au/bioelectronics_neuroscience/bens/reproducible_research. PMID:25505378

Racing to learn: statistical inference and learning in a single spiking neuron with adaptive kernels.

PubMed

Afshar, Saeed; George, Libin; Tapson, Jonathan; van Schaik, André; Hamilton, Tara J

2014-01-01

This paper describes the Synapto-dendritic Kernel Adapting Neuron (SKAN), a simple spiking neuron model that performs statistical inference and unsupervised learning of spatiotemporal spike patterns. SKAN is the first proposed neuron model to investigate the effects of dynamic synapto-dendritic kernels and demonstrate their computational power even at the single neuron scale. The rule-set defining the neuron is simple: there are no complex mathematical operations such as normalization, exponentiation or even multiplication. The functionalities of SKAN emerge from the real-time interaction of simple additive and binary processes. Like a biological neuron, SKAN is robust to signal and parameter noise, and can utilize both in its operations. At the network scale neurons are locked in a race with each other with the fastest neuron to spike effectively "hiding" its learnt pattern from its neighbors. The robustness to noise, high speed, and simple building blocks not only make SKAN an interesting neuron model in computational neuroscience, but also make it ideal for implementation in digital and analog neuromorphic systems which is demonstrated through an implementation in a Field Programmable Gate Array (FPGA). Matlab, Python, and Verilog implementations of SKAN are available at: http://www.uws.edu.au/bioelectronics_neuroscience/bens/reproducible_research.

From a Proven Correct Microkernel to Trustworthy Large Systems

NASA Astrophysics Data System (ADS)

Andronick, June

The seL4 microkernel was the world's first general-purpose operating system kernel with a formal, machine-checked proof of correctness. The next big step in the challenge of building truly trustworthy systems is to provide a framework for developing secure systems on top of seL4. This paper first gives an overview of seL4's correctness proof, together with its main implications and assumptions, and then describes our approach to provide formal security guarantees for large, complex systems.

An SVM-based solution for fault detection in wind turbines.

PubMed

Santos, Pedro; Villa, Luisa F; Reñones, Aníbal; Bustillo, Andres; Maudes, Jesús

2015-03-09

Research into fault diagnosis in machines with a wide range of variable loads and speeds, such as wind turbines, is of great industrial interest. Analysis of the power signals emitted by wind turbines for the diagnosis of mechanical faults in their mechanical transmission chain is insufficient. A successful diagnosis requires the inclusion of accelerometers to evaluate vibrations. This work presents a multi-sensory system for fault diagnosis in wind turbines, combined with a data-mining solution for the classification of the operational state of the turbine. The selected sensors are accelerometers, in which vibration signals are processed using angular resampling techniques and electrical, torque and speed measurements. Support vector machines (SVMs) are selected for the classification task, including two traditional and two promising new kernels. This multi-sensory system has been validated on a test-bed that simulates the real conditions of wind turbines with two fault typologies: misalignment and imbalance. Comparison of SVM performance with the results of artificial neural networks (ANNs) shows that linear kernel SVM outperforms other kernels and ANNs in terms of accuracy, training and tuning times. The suitability and superior performance of linear SVM is also experimentally analyzed, to conclude that this data acquisition technique generates linearly separable datasets.

Unified connected theory of few-body reaction mechanisms in N-body scattering theory

NASA Technical Reports Server (NTRS)

Polyzou, W. N.; Redish, E. F.

1978-01-01

A unified treatment of different reaction mechanisms in nonrelativistic N-body scattering is presented. The theory is based on connected kernel integral equations that are expected to become compact for reasonable constraints on the potentials. The operators T/sub +-//sup ab/(A) are approximate transition operators that describe the scattering proceeding through an arbitrary reaction mechanism A. These operators are uniquely determined by a connected kernel equation and satisfy an optical theorem consistent with the choice of reaction mechanism. Connected kernel equations relating T/sub +-//sup ab/(A) to the full T/sub +-//sup ab/ allow correction of the approximate solutions for any ignored process to any order. This theory gives a unified treatment of all few-body reaction mechanisms with the same dynamic simplicity of a model calculation, but can include complicated reaction mechanisms involving overlapping configurations where it is difficult to formulate models.

Time-frequency distributions for propulsion-system diagnostics

NASA Astrophysics Data System (ADS)

Griffin, Michael E.; Tulpule, Sharayu

1991-12-01

The Wigner distribution and its smoothed versions, i.e., Choi-Williams and Gaussian kernels, are evaluated for propulsion system diagnostics. The approach is intended for off-line kernel design by using the ambiguity domain to select the appropriate Gaussian kernel. The features produced by the Wigner distribution and its smoothed versions correlate remarkably well with documented failure indications. The selection of the kernel on the other hand is very subjective for our unstructured data.

A New Generation of Real-Time Systems in the JET Tokamak

NASA Astrophysics Data System (ADS)

Alves, Diogo; Neto, Andre C.; Valcarcel, Daniel F.; Felton, Robert; Lopez, Juan M.; Barbalace, Antonio; Boncagni, Luca; Card, Peter; De Tommasi, Gianmaria; Goodyear, Alex; Jachmich, Stefan; Lomas, Peter J.; Maviglia, Francesco; McCullen, Paul; Murari, Andrea; Rainford, Mark; Reux, Cedric; Rimini, Fernanda; Sartori, Filippo; Stephen, Adam V.; Vega, Jesus; Vitelli, Riccardo; Zabeo, Luca; Zastrow, Klaus-Dieter

2014-04-01

Recently, a new recipe for developing and deploying real-time systems has become increasingly adopted in the JET tokamak. Powered by the advent of x86 multi-core technology and the reliability of JET's well established Real-Time Data Network (RTDN) to handle all real-time I/O, an official Linux vanilla kernel has been demonstrated to be able to provide real-time performance to user-space applications that are required to meet stringent timing constraints. In particular, a careful rearrangement of the Interrupt ReQuests' (IRQs) affinities together with the kernel's CPU isolation mechanism allows one to obtain either soft or hard real-time behavior depending on the synchronization mechanism adopted. Finally, the Multithreaded Application Real-Time executor (MARTe) framework is used for building applications particularly optimised for exploring multi-core architectures. In the past year, four new systems based on this philosophy have been installed and are now part of JET's routine operation. The focus of the present work is on the configuration aspects that enable these new systems' real-time capability. Details are given about the common real-time configuration of these systems, followed by a brief description of each system together with results regarding their real-time performance. A cycle time jitter analysis of a user-space MARTe based application synchronizing over a network is also presented. The goal is to compare its deterministic performance while running on a vanilla and on a Messaging Real time Grid (MRG) Linux kernel.

Hardware, Languages, and Architectures for Defense Against Hostile Operating Systems

DTIC Science & Technology

2015-05-14

Executive Service Directorate (0704-0188). Respondents should be aware that notwithstanding any other provision of law, no person shall be subject to...in privileged system services . ExpressOS requires only a modest annotation burden (annotations were about 3% of code of the kernel), modest performance...INT benchmarks . In addition to enabling the development of an architecture- neutral instrumentation framework, our approach can take advantage of the

Real-Time, General-Purpose, High-Speed Signal Processing Systems for Underwater Research. Proceedings of a Working Level Conference held at Supreme Allied Commander, Atlantic Anti-Submarine Warfare Research Center (SACLANTCEN) on 18-21 September 1979. Part 2. Sessions IV to VI.

DTIC Science & Technology

1979-12-01

ACTIVATED, SYSTEM OPERATION AND TESTING MASCOT PROVIDES: 1. SYSTEM BUILD SOFTWARE COMPILE-TIME CHECKS,a. 2. RUN-TIME SUPERVISOR KERNEL, 3, MONITOR AND...p AD-AOBI 851 SACLANT ASW RESEARCH CENTRE LA SPEZIA 11ITALY) F/B 1711 REAL-TIME, GENERAL-PURPOSE, HIGH-SPEED SIGNAL PROCESSING SYSTEM -- ETC (U) DEC 79...Table of Contents Table of Contents (Cont’d) Page Signal processing language and operating system (w) 23-1 to 23-12 by S. Weinstein A modular signal

Gnuastro: GNU Astronomy Utilities

NASA Astrophysics Data System (ADS)

Akhlaghi, Mohammad

2018-01-01

Gnuastro (GNU Astronomy Utilities) manipulates and analyzes astronomical data. It is an official GNU package of a large collection of programs and C/C++ library functions. Command-line programs perform arithmetic operations on images, convert FITS images to common types like JPG or PDF, convolve an image with a given kernel or matching of kernels, perform cosmological calculations, crop parts of large images (possibly in multiple files), manipulate FITS extensions and keywords, and perform statistical operations. In addition, it contains programs to make catalogs from detection maps, add noise, make mock profiles with a variety of radial functions using monte-carlo integration for their centers, match catalogs, and detect objects in an image among many other operations. The command-line programs share the same basic command-line user interface for the comfort of both the users and developers. Gnuastro is written to comply fully with the GNU coding standards and integrates well with all Unix-like operating systems. This enables astronomers to expect a fully familiar experience in the source code, building, installing and command-line user interaction that they have seen in all the other GNU software that they use. Gnuastro's extensive library is included for users who want to build their own unique programs.

Kernel-based Linux emulation for Plan 9.

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

Minnich, Ronald G.

2010-09-01

CNKemu is a kernel-based system for the 9k variant of the Plan 9 kernel. It is designed to provide transparent binary support for programs compiled for IBM's Compute Node Kernel (CNK) on the Blue Gene series of supercomputers. This support allows users to build applications with the standard Blue Gene toolchain, including C++ and Fortran compilers. While the CNK is not Linux, IBM designed the CNK so that the user interface has much in common with the Linux 2.0 system call interface. The Plan 9 CNK emulator hence provides the foundation of kernel-based Linux system call support on Plan 9.more » In this paper we discuss cnkemu's implementation and some of its more interesting features, such as the ability to easily intermix Plan 9 and Linux system calls.« less

Small convolution kernels for high-fidelity image restoration

NASA Technical Reports Server (NTRS)

Reichenbach, Stephen E.; Park, Stephen K.

1991-01-01

An algorithm is developed for computing the mean-square-optimal values for small, image-restoration kernels. The algorithm is based on a comprehensive, end-to-end imaging system model that accounts for the important components of the imaging process: the statistics of the scene, the point-spread function of the image-gathering device, sampling effects, noise, and display reconstruction. Subject to constraints on the spatial support of the kernel, the algorithm generates the kernel values that restore the image with maximum fidelity, that is, the kernel minimizes the expected mean-square restoration error. The algorithm is consistent with the derivation of the spatially unconstrained Wiener filter, but leads to a small, spatially constrained kernel that, unlike the unconstrained filter, can be efficiently implemented by convolution. Simulation experiments demonstrate that for a wide range of imaging systems these small kernels can restore images with fidelity comparable to images restored with the unconstrained Wiener filter.

Occurrence of 'super soft' wheat kernel texture in hexaploid and tetraploid wheats

USDA-ARS?s Scientific Manuscript database

Wheat kernel texture is a key trait that governs milling performance, flour starch damage, flour particle size, flour hydration properties, and baking quality. Kernel texture is commonly measured using the Perten Single Kernel Characterization System (SKCS). The SKCS returns texture values (Hardness...

Contribution of regional-scale fire events to ozone and PM2.5 air quality estimated by photochemical modeling approaches

EPA Science Inventory

Two specific fires from 2011 are tracked for local to regional scale contribution to ozone (O3) and fine particulate matter (PM2.5) using a freely available regulatory modeling system that includes the BlueSky wildland fire emissions tool, Spare Matrix Operator Kernel Emissions (...

A Frequency-Domain Implementation of a Sliding-Window Traffic Sign Detector for Large Scale Panoramic Datasets

NASA Astrophysics Data System (ADS)

Creusen, I. M.; Hazelhoff, L.; De With, P. H. N.

2013-10-01

In large-scale automatic traffic sign surveying systems, the primary computational effort is concentrated at the traffic sign detection stage. This paper focuses on reducing the computational load of particularly the sliding window object detection algorithm which is employed for traffic sign detection. Sliding-window object detectors often use a linear SVM to classify the features in a window. In this case, the classification can be seen as a convolution of the feature maps with the SVM kernel. It is well known that convolution can be efficiently implemented in the frequency domain, for kernels larger than a certain size. We show that by careful reordering of sliding-window operations, most of the frequency-domain transformations can be eliminated, leading to a substantial increase in efficiency. Additionally, we suggest to use the overlap-add method to keep the memory use within reasonable bounds. This allows us to keep all the transformed kernels in memory, thereby eliminating even more domain transformations, and allows all scales in a multiscale pyramid to be processed using the same set of transformed kernels. For a typical sliding-window implementation, we have found that the detector execution performance improves with a factor of 5.3. As a bonus, many of the detector improvements from literature, e.g. chi-squared kernel approximations, sub-class splitting algorithms etc., can be more easily applied at a lower performance penalty because of an improved scalability.

Applications of discrete element method in modeling of grain postharvest operations

USDA-ARS?s Scientific Manuscript database

Grain kernels are finite and discrete materials. Although flowing grain can behave like a continuum fluid at times, the discontinuous behavior exhibited by grain kernels cannot be simulated solely with conventional continuum-based computer modeling such as finite-element or finite-difference methods...

Parametric output-only identification of time-varying structures using a kernel recursive extended least squares TARMA approach

NASA Astrophysics Data System (ADS)

Ma, Zhi-Sai; Liu, Li; Zhou, Si-Da; Yu, Lei; Naets, Frank; Heylen, Ward; Desmet, Wim

2018-01-01

The problem of parametric output-only identification of time-varying structures in a recursive manner is considered. A kernelized time-dependent autoregressive moving average (TARMA) model is proposed by expanding the time-varying model parameters onto the basis set of kernel functions in a reproducing kernel Hilbert space. An exponentially weighted kernel recursive extended least squares TARMA identification scheme is proposed, and a sliding-window technique is subsequently applied to fix the computational complexity for each consecutive update, allowing the method to operate online in time-varying environments. The proposed sliding-window exponentially weighted kernel recursive extended least squares TARMA method is employed for the identification of a laboratory time-varying structure consisting of a simply supported beam and a moving mass sliding on it. The proposed method is comparatively assessed against an existing recursive pseudo-linear regression TARMA method via Monte Carlo experiments and shown to be capable of accurately tracking the time-varying dynamics. Furthermore, the comparisons demonstrate the superior achievable accuracy, lower computational complexity and enhanced online identification capability of the proposed kernel recursive extended least squares TARMA approach.

CLAss-Specific Subspace Kernel Representations and Adaptive Margin Slack Minimization for Large Scale Classification.

PubMed

Yu, Yinan; Diamantaras, Konstantinos I; McKelvey, Tomas; Kung, Sun-Yuan

2018-02-01

In kernel-based classification models, given limited computational power and storage capacity, operations over the full kernel matrix becomes prohibitive. In this paper, we propose a new supervised learning framework using kernel models for sequential data processing. The framework is based on two components that both aim at enhancing the classification capability with a subset selection scheme. The first part is a subspace projection technique in the reproducing kernel Hilbert space using a CLAss-specific Subspace Kernel representation for kernel approximation. In the second part, we propose a novel structural risk minimization algorithm called the adaptive margin slack minimization to iteratively improve the classification accuracy by an adaptive data selection. We motivate each part separately, and then integrate them into learning frameworks for large scale data. We propose two such frameworks: the memory efficient sequential processing for sequential data processing and the parallelized sequential processing for distributed computing with sequential data acquisition. We test our methods on several benchmark data sets and compared with the state-of-the-art techniques to verify the validity of the proposed techniques.

Dynamic experiment design regularization approach to adaptive imaging with array radar/SAR sensor systems.

PubMed

Shkvarko, Yuriy; Tuxpan, José; Santos, Stewart

2011-01-01

We consider a problem of high-resolution array radar/SAR imaging formalized in terms of a nonlinear ill-posed inverse problem of nonparametric estimation of the power spatial spectrum pattern (SSP) of the random wavefield scattered from a remotely sensed scene observed through a kernel signal formation operator and contaminated with random Gaussian noise. First, the Sobolev-type solution space is constructed to specify the class of consistent kernel SSP estimators with the reproducing kernel structures adapted to the metrics in such the solution space. Next, the "model-free" variational analysis (VA)-based image enhancement approach and the "model-based" descriptive experiment design (DEED) regularization paradigm are unified into a new dynamic experiment design (DYED) regularization framework. Application of the proposed DYED framework to the adaptive array radar/SAR imaging problem leads to a class of two-level (DEED-VA) regularized SSP reconstruction techniques that aggregate the kernel adaptive anisotropic windowing with the projections onto convex sets to enforce the consistency and robustness of the overall iterative SSP estimators. We also show how the proposed DYED regularization method may be considered as a generalization of the MVDR, APES and other high-resolution nonparametric adaptive radar sensing techniques. A family of the DYED-related algorithms is constructed and their effectiveness is finally illustrated via numerical simulations.

Fusion PIC code performance analysis on the Cori KNL system

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

Koskela, Tuomas S.; Deslippe, Jack; Friesen, Brian

We study the attainable performance of Particle-In-Cell codes on the Cori KNL system by analyzing a miniature particle push application based on the fusion PIC code XGC1. We start from the most basic building blocks of a PIC code and build up the complexity to identify the kernels that cost the most in performance and focus optimization efforts there. Particle push kernels operate at high AI and are not likely to be memory bandwidth or even cache bandwidth bound on KNL. Therefore, we see only minor benefits from the high bandwidth memory available on KNL, and achieving good vectorization ismore » shown to be the most beneficial optimization path with theoretical yield of up to 8x speedup on KNL. In practice we are able to obtain up to a 4x gain from vectorization due to limitations set by the data layout and memory latency.« less

Fredholm-Volterra Integral Equation with a Generalized Singular Kernel and its Numerical Solutions

NASA Astrophysics Data System (ADS)

El-Kalla, I. L.; Al-Bugami, A. M.

2010-11-01

In this paper, the existence and uniqueness of solution of the Fredholm-Volterra integral equation (F-VIE), with a generalized singular kernel, are discussed and proved in the spaceL2(Ω)×C(0,T). The Fredholm integral term (FIT) is considered in position while the Volterra integral term (VIT) is considered in time. Using a numerical technique we have a system of Fredholm integral equations (SFIEs). This system of integral equations can be reduced to a linear algebraic system (LAS) of equations by using two different methods. These methods are: Toeplitz matrix method and Product Nyström method. A numerical examples are considered when the generalized kernel takes the following forms: Carleman function, logarithmic form, Cauchy kernel, and Hilbert kernel.

The Chimera II Real-Time Operating System for advanced sensor-based control applications

NASA Technical Reports Server (NTRS)

Stewart, David B.; Schmitz, Donald E.; Khosla, Pradeep K.

1992-01-01

Attention is given to the Chimera II Real-Time Operating System, which has been developed for advanced sensor-based control applications. The Chimera II provides a high-performance real-time kernel and a variety of IPC features. The hardware platform required to run Chimera II consists of commercially available hardware, and allows custom hardware to be easily integrated. The design allows it to be used with almost any type of VMEbus-based processors and devices. It allows radially differing hardware to be programmed using a common system, thus providing a first and necessary step towards the standardization of reconfigurable systems that results in a reduction of development time and cost.

Influence of wheat kernel physical properties on the pulverizing process.

PubMed

Dziki, Dariusz; Cacak-Pietrzak, Grażyna; Miś, Antoni; Jończyk, Krzysztof; Gawlik-Dziki, Urszula

2014-10-01

The physical properties of wheat kernel were determined and related to pulverizing performance by correlation analysis. Nineteen samples of wheat cultivars about similar level of protein content (11.2-12.8 % w.b.) and obtained from organic farming system were used for analysis. The kernel (moisture content 10 % w.b.) was pulverized by using the laboratory hammer mill equipped with round holes 1.0 mm screen. The specific grinding energy ranged from 120 kJkg(-1) to 159 kJkg(-1). On the basis of data obtained many of significant correlations (p < 0.05) were found between wheat kernel physical properties and pulverizing process of wheat kernel, especially wheat kernel hardness index (obtained on the basis of Single Kernel Characterization System) and vitreousness significantly and positively correlated with the grinding energy indices and the mass fraction of coarse particles (> 0.5 mm). Among the kernel mechanical properties determined on the basis of uniaxial compression test only the rapture force was correlated with the impact grinding results. The results showed also positive and significant relationships between kernel ash content and grinding energy requirements. On the basis of wheat physical properties the multiple linear regression was proposed for predicting the average particle size of pulverized kernel.

Fully-Automated High-Throughput NMR System for Screening of Haploid Kernels of Maize (Corn) by Measurement of Oil Content

PubMed Central

Xu, Xiaoping; Huang, Qingming; Chen, Shanshan; Yang, Peiqiang; Chen, Shaojiang; Song, Yiqiao

2016-01-01

One of the modern crop breeding techniques uses doubled haploid plants that contain an identical pair of chromosomes in order to accelerate the breeding process. Rapid haploid identification method is critical for large-scale selections of double haploids. The conventional methods based on the color of the endosperm and embryo seeds are slow, manual and prone to error. On the other hand, there exists a significant difference between diploid and haploid seeds generated by high oil inducer, which makes it possible to use oil content to identify the haploid. This paper describes a fully-automated high-throughput NMR screening system for maize haploid kernel identification. The system is comprised of a sampler unit to select a single kernel to feed for measurement of NMR and weight, and a kernel sorter to distribute the kernel according to the measurement result. Tests of the system show a consistent accuracy of 94% with an average screening time of 4 seconds per kernel. Field test result is described and the directions for future improvement are discussed. PMID:27454427

Discrete element method as an approach to model the wheat milling process

USDA-ARS?s Scientific Manuscript database

It is a well-known phenomenon that break-release, particle size, and size distribution of wheat milling are functions of machine operational parameters and grain properties. Due to the non-uniformity of characteristics and properties of wheat kernels, the kernel physical and mechanical properties af...

Multiple Kernel Learning for Heterogeneous Anomaly Detection: Algorithm and Aviation Safety Case Study

NASA Technical Reports Server (NTRS)

Das, Santanu; Srivastava, Ashok N.; Matthews, Bryan L.; Oza, Nikunj C.

2010-01-01

The world-wide aviation system is one of the most complex dynamical systems ever developed and is generating data at an extremely rapid rate. Most modern commercial aircraft record several hundred flight parameters including information from the guidance, navigation, and control systems, the avionics and propulsion systems, and the pilot inputs into the aircraft. These parameters may be continuous measurements or binary or categorical measurements recorded in one second intervals for the duration of the flight. Currently, most approaches to aviation safety are reactive, meaning that they are designed to react to an aviation safety incident or accident. In this paper, we discuss a novel approach based on the theory of multiple kernel learning to detect potential safety anomalies in very large data bases of discrete and continuous data from world-wide operations of commercial fleets. We pose a general anomaly detection problem which includes both discrete and continuous data streams, where we assume that the discrete streams have a causal influence on the continuous streams. We also assume that atypical sequence of events in the discrete streams can lead to off-nominal system performance. We discuss the application domain, novel algorithms, and also discuss results on real-world data sets. Our algorithm uncovers operationally significant events in high dimensional data streams in the aviation industry which are not detectable using state of the art methods

Invited Review. Combustion instability in spray-guided stratified-charge engines. A review

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

Fansler, Todd D.; Reuss, D. L.; Sick, V.

2015-02-02

Our article reviews systematic research on combustion instabilities (principally rare, random misfires and partial burns) in spray-guided stratified-charge (SGSC) engines operated at part load with highly stratified fuel -air -residual mixtures. Results from high-speed optical imaging diagnostics and numerical simulation provide a conceptual framework and quantify the sensitivity of ignition and flame propagation to strong, cyclically varying temporal and spatial gradients in the flow field and in the fuel -air -residual distribution. For SGSC engines using multi-hole injectors, spark stretching and locally rich ignition are beneficial. Moreover, combustion instability is dominated by convective flow fluctuations that impede motion of themore » spark or flame kernel toward the bulk of the fuel, coupled with low flame speeds due to locally lean mixtures surrounding the kernel. In SGSC engines using outwardly opening piezo-electric injectors, ignition and early flame growth are strongly influenced by the spray's characteristic recirculation vortex. For both injection systems, the spray and the intake/compression-generated flow field influence each other. Factors underlying the benefits of multi-pulse injection are identified. Finally, some unresolved questions include (1) the extent to which piezo-SGSC misfires are caused by failure to form a flame kernel rather than by flame-kernel extinction (as in multi-hole SGSC engines); (2) the relative contributions of partially premixed flame propagation and mixing-controlled combustion under the exceptionally late-injection conditions that permit SGSC operation on E85-like fuels with very low NO x and soot emissions; and (3) the effects of flow-field variability on later combustion, where fuel-air-residual mixing within the piston bowl becomes important.« less

An ensemble method for extracting adverse drug events from social media.

PubMed

Liu, Jing; Zhao, Songzheng; Zhang, Xiaodi

2016-06-01

Because adverse drug events (ADEs) are a serious health problem and a leading cause of death, it is of vital importance to identify them correctly and in a timely manner. With the development of Web 2.0, social media has become a large data source for information on ADEs. The objective of this study is to develop a relation extraction system that uses natural language processing techniques to effectively distinguish between ADEs and non-ADEs in informal text on social media. We develop a feature-based approach that utilizes various lexical, syntactic, and semantic features. Information-gain-based feature selection is performed to address high-dimensional features. Then, we evaluate the effectiveness of four well-known kernel-based approaches (i.e., subset tree kernel, tree kernel, shortest dependency path kernel, and all-paths graph kernel) and several ensembles that are generated by adopting different combination methods (i.e., majority voting, weighted averaging, and stacked generalization). All of the approaches are tested using three data sets: two health-related discussion forums and one general social networking site (i.e., Twitter). When investigating the contribution of each feature subset, the feature-based approach attains the best area under the receiver operating characteristics curve (AUC) values, which are 78.6%, 72.2%, and 79.2% on the three data sets. When individual methods are used, we attain the best AUC values of 82.1%, 73.2%, and 77.0% using the subset tree kernel, shortest dependency path kernel, and feature-based approach on the three data sets, respectively. When using classifier ensembles, we achieve the best AUC values of 84.5%, 77.3%, and 84.5% on the three data sets, outperforming the baselines. Our experimental results indicate that ADE extraction from social media can benefit from feature selection. With respect to the effectiveness of different feature subsets, lexical features and semantic features can enhance the ADE extraction capability. Kernel-based approaches, which can stay away from the feature sparsity issue, are qualified to address the ADE extraction problem. Combining different individual classifiers using suitable combination methods can further enhance the ADE extraction effectiveness. Copyright © 2016 Elsevier B.V. All rights reserved.

Image preprocessing study on KPCA-based face recognition

NASA Astrophysics Data System (ADS)

Li, Xuan; Li, Dehua

2015-12-01

Face recognition as an important biometric identification method, with its friendly, natural, convenient advantages, has obtained more and more attention. This paper intends to research a face recognition system including face detection, feature extraction and face recognition, mainly through researching on related theory and the key technology of various preprocessing methods in face detection process, using KPCA method, focuses on the different recognition results in different preprocessing methods. In this paper, we choose YCbCr color space for skin segmentation and choose integral projection for face location. We use erosion and dilation of the opening and closing operation and illumination compensation method to preprocess face images, and then use the face recognition method based on kernel principal component analysis method for analysis and research, and the experiments were carried out using the typical face database. The algorithms experiment on MATLAB platform. Experimental results show that integration of the kernel method based on PCA algorithm under certain conditions make the extracted features represent the original image information better for using nonlinear feature extraction method, which can obtain higher recognition rate. In the image preprocessing stage, we found that images under various operations may appear different results, so as to obtain different recognition rate in recognition stage. At the same time, in the process of the kernel principal component analysis, the value of the power of the polynomial function can affect the recognition result.

Resummed memory kernels in generalized system-bath master equations

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

Mavros, Michael G.; Van Voorhis, Troy, E-mail: tvan@mit.edu

2014-08-07

Generalized master equations provide a concise formalism for studying reduced population dynamics. Usually, these master equations require a perturbative expansion of the memory kernels governing the dynamics; in order to prevent divergences, these expansions must be resummed. Resummation techniques of perturbation series are ubiquitous in physics, but they have not been readily studied for the time-dependent memory kernels used in generalized master equations. In this paper, we present a comparison of different resummation techniques for such memory kernels up to fourth order. We study specifically the spin-boson Hamiltonian as a model system bath Hamiltonian, treating the diabatic coupling between themore » two states as a perturbation. A novel derivation of the fourth-order memory kernel for the spin-boson problem is presented; then, the second- and fourth-order kernels are evaluated numerically for a variety of spin-boson parameter regimes. We find that resumming the kernels through fourth order using a Padé approximant results in divergent populations in the strong electronic coupling regime due to a singularity introduced by the nature of the resummation, and thus recommend a non-divergent exponential resummation (the “Landau-Zener resummation” of previous work). The inclusion of fourth-order effects in a Landau-Zener-resummed kernel is shown to improve both the dephasing rate and the obedience of detailed balance over simpler prescriptions like the non-interacting blip approximation, showing a relatively quick convergence on the exact answer. The results suggest that including higher-order contributions to the memory kernel of a generalized master equation and performing an appropriate resummation can provide a numerically-exact solution to system-bath dynamics for a general spectral density, opening the way to a new class of methods for treating system-bath dynamics.« less

Using the Intel Math Kernel Library on Peregrine | High-Performance

Science.gov Websites

Computing | NREL the Intel Math Kernel Library on Peregrine Using the Intel Math Kernel Library on Peregrine Learn how to use the Intel Math Kernel Library (MKL) with Peregrine system software. MKL architectures. Core math functions in MKL include BLAS, LAPACK, ScaLAPACK, sparse solvers, fast Fourier

An SVM-Based Solution for Fault Detection in Wind Turbines

PubMed Central

Santos, Pedro; Villa, Luisa F.; Reñones, Aníbal; Bustillo, Andres; Maudes, Jesús

2015-01-01

Research into fault diagnosis in machines with a wide range of variable loads and speeds, such as wind turbines, is of great industrial interest. Analysis of the power signals emitted by wind turbines for the diagnosis of mechanical faults in their mechanical transmission chain is insufficient. A successful diagnosis requires the inclusion of accelerometers to evaluate vibrations. This work presents a multi-sensory system for fault diagnosis in wind turbines, combined with a data-mining solution for the classification of the operational state of the turbine. The selected sensors are accelerometers, in which vibration signals are processed using angular resampling techniques and electrical, torque and speed measurements. Support vector machines (SVMs) are selected for the classification task, including two traditional and two promising new kernels. This multi-sensory system has been validated on a test-bed that simulates the real conditions of wind turbines with two fault typologies: misalignment and imbalance. Comparison of SVM performance with the results of artificial neural networks (ANNs) shows that linear kernel SVM outperforms other kernels and ANNs in terms of accuracy, training and tuning times. The suitability and superior performance of linear SVM is also experimentally analyzed, to conclude that this data acquisition technique generates linearly separable datasets. PMID:25760051

Design and Analysis of Architectures for Structural Health Monitoring Systems

NASA Technical Reports Server (NTRS)

Mukkamala, Ravi; Sixto, S. L. (Technical Monitor)

2002-01-01

During the two-year project period, we have worked on several aspects of Health Usage and Monitoring Systems for structural health monitoring. In particular, we have made contributions in the following areas. 1. Reference HUMS architecture: We developed a high-level architecture for health monitoring and usage systems (HUMS). The proposed reference architecture is shown. It is compatible with the Generic Open Architecture (GOA) proposed as a standard for avionics systems. 2. HUMS kernel: One of the critical layers of HUMS reference architecture is the HUMS kernel. We developed a detailed design of a kernel to implement the high level architecture.3. Prototype implementation of HUMS kernel: We have implemented a preliminary version of the HUMS kernel on a Unix platform.We have implemented both a centralized system version and a distributed version. 4. SCRAMNet and HUMS: SCRAMNet (Shared Common Random Access Memory Network) is a system that is found to be suitable to implement HUMS. For this reason, we have conducted a simulation study to determine its stability in handling the input data rates in HUMS. 5. Architectural specification.

An Internal Data Non-hiding Type Real-time Kernel and its Application to the Mechatronics Controller

NASA Astrophysics Data System (ADS)

Yoshida, Toshio

For the mechatronics equipment controller that controls robots and machine tools, high-speed motion control processing is essential. The software system of the controller like other embedded systems is composed of three layers software such as real-time kernel layer, middleware layer, and application software layer on the dedicated hardware. The application layer in the top layer is composed of many numbers of tasks, and application function of the system is realized by the cooperation between these tasks. In this paper we propose an internal data non-hiding type real-time kernel in which customizing the task control is possible only by change in the program code of the task side without any changes in the program code of real-time kernel. It is necessary to reduce the overhead caused by the real-time kernel task control for the speed-up of the motion control of the mechatronics equipment. For this, customizing the task control function is needed. We developed internal data non-cryptic type real-time kernel ZRK to evaluate this method, and applied to the control of the multi system automatic lathe. The effect of the speed-up of the task cooperation processing was able to be confirmed by combined task control processing on the task side program code using an internal data non-hiding type real-time kernel ZRK.

Reconstruction of noisy and blurred images using blur kernel

NASA Astrophysics Data System (ADS)

Ellappan, Vijayan; Chopra, Vishal

2017-11-01

Blur is a common in so many digital images. Blur can be caused by motion of the camera and scene object. In this work we proposed a new method for deblurring images. This work uses sparse representation to identify the blur kernel. By analyzing the image coordinates Using coarse and fine, we fetch the kernel based image coordinates and according to that observation we get the motion angle of the shaken or blurred image. Then we calculate the length of the motion kernel using radon transformation and Fourier for the length calculation of the image and we use Lucy Richardson algorithm which is also called NON-Blind(NBID) Algorithm for more clean and less noisy image output. All these operation will be performed in MATLAB IDE.

A message passing kernel for the hypercluster parallel processing test bed

NASA Technical Reports Server (NTRS)

Blech, Richard A.; Quealy, Angela; Cole, Gary L.

1989-01-01

A Message-Passing Kernel (MPK) for the Hypercluster parallel-processing test bed is described. The Hypercluster is being developed at the NASA Lewis Research Center to support investigations of parallel algorithms and architectures for computational fluid and structural mechanics applications. The Hypercluster resembles the hypercube architecture except that each node consists of multiple processors communicating through shared memory. The MPK efficiently routes information through the Hypercluster, using a message-passing protocol when necessary and faster shared-memory communication whenever possible. The MPK also interfaces all of the processors with the Hypercluster operating system (HYCLOPS), which runs on a Front-End Processor (FEP). This approach distributes many of the I/O tasks to the Hypercluster processors and eliminates the need for a separate I/O support program on the FEP.

Learn the Lagrangian: A Vector-Valued RKHS Approach to Identifying Lagrangian Systems.

PubMed

Cheng, Ching-An; Huang, Han-Pang

2016-12-01

We study the modeling of Lagrangian systems with multiple degrees of freedom. Based on system dynamics, canonical parametric models require ad hoc derivations and sometimes simplification for a computable solution; on the other hand, due to the lack of prior knowledge in the system's structure, modern nonparametric models in machine learning face the curse of dimensionality, especially in learning large systems. In this paper, we bridge this gap by unifying the theories of Lagrangian systems and vector-valued reproducing kernel Hilbert space. We reformulate Lagrangian systems with kernels that embed the governing Euler-Lagrange equation-the Lagrangian kernels-and show that these kernels span a subspace capturing the Lagrangian's projection as inverse dynamics. By such property, our model uses only inputs and outputs as in machine learning and inherits the structured form as in system dynamics, thereby removing the need for the mundane derivations for new systems as well as the generalization problem in learning from scratches. In effect, it learns the system's Lagrangian, a simpler task than directly learning the dynamics. To demonstrate, we applied the proposed kernel to identify the robot inverse dynamics in simulations and experiments. Our results present a competitive novel approach to identifying Lagrangian systems, despite using only inputs and outputs.

Provably trustworthy systems.

PubMed

Klein, Gerwin; Andronick, June; Keller, Gabriele; Matichuk, Daniel; Murray, Toby; O'Connor, Liam

2017-10-13

We present recent work on building and scaling trustworthy systems with formal, machine-checkable proof from the ground up, including the operating system kernel, at the level of binary machine code. We first give a brief overview of the seL4 microkernel verification and how it can be used to build verified systems. We then show two complementary techniques for scaling these methods to larger systems: proof engineering, to estimate verification effort; and code/proof co-generation, for scalable development of provably trustworthy applications.This article is part of the themed issue 'Verified trustworthy software systems'. © 2017 The Author(s).

Dynamic Experiment Design Regularization Approach to Adaptive Imaging with Array Radar/SAR Sensor Systems

PubMed Central

Shkvarko, Yuriy; Tuxpan, José; Santos, Stewart

2011-01-01

We consider a problem of high-resolution array radar/SAR imaging formalized in terms of a nonlinear ill-posed inverse problem of nonparametric estimation of the power spatial spectrum pattern (SSP) of the random wavefield scattered from a remotely sensed scene observed through a kernel signal formation operator and contaminated with random Gaussian noise. First, the Sobolev-type solution space is constructed to specify the class of consistent kernel SSP estimators with the reproducing kernel structures adapted to the metrics in such the solution space. Next, the “model-free” variational analysis (VA)-based image enhancement approach and the “model-based” descriptive experiment design (DEED) regularization paradigm are unified into a new dynamic experiment design (DYED) regularization framework. Application of the proposed DYED framework to the adaptive array radar/SAR imaging problem leads to a class of two-level (DEED-VA) regularized SSP reconstruction techniques that aggregate the kernel adaptive anisotropic windowing with the projections onto convex sets to enforce the consistency and robustness of the overall iterative SSP estimators. We also show how the proposed DYED regularization method may be considered as a generalization of the MVDR, APES and other high-resolution nonparametric adaptive radar sensing techniques. A family of the DYED-related algorithms is constructed and their effectiveness is finally illustrated via numerical simulations. PMID:22163859

Nowcasting Cloud Fields for U.S. Air Force Special Operations

DTIC Science & Technology

2017-03-01

application of Bayes’ Rule offers many advantages over Kernel Density Estimation (KDE) and other commonly used statistical post-processing methods...reflectance and probability of cloud. A statistical post-processing technique is applied using Bayesian estimation to train the system from a set of past...nowcasting, low cloud forecasting, cloud reflectance, ISR, Bayesian estimation, statistical post-processing, machine learning 15. NUMBER OF PAGES

DOEDEF Software System, Version 2. 2: Operational instructions

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

Meirans, L.

The DOEDEF (Department of Energy Data Exchange Format) Software System is a collection of software routines written to facilitate the manipulation of IGES (Initial Graphics Exchange Specification) data. Typically, the IGES data has been produced by the IGES processors for a Computer-Aided Design (CAD) system, and the data manipulations are user-defined ''flavoring'' operations. The DOEDEF Software System is used in conjunction with the RIM (Relational Information Management) DBMS from Boeing Computer Services (Version 7, UD18 or higher). The three major pieces of the software system are: Parser, reads an ASCII IGES file and converts it to the RIM database equivalent;more » Kernel, provides the user with IGES-oriented interface routines to the database; and Filewriter, writes the RIM database to an IGES file.« less

Interlaboratory study of DNA extraction from multiple ground samples, multiplex real-time PCR, and multiplex qualitative PCR for individual kernel detection system of genetically modified maize.

PubMed

Akiyama, Hiroshi; Sakata, Kozue; Makiyma, Daiki; Nakamura, Kosuke; Teshima, Reiko; Nakashima, Akie; Ogawa, Asako; Yamagishi, Toru; Futo, Satoshi; Oguchi, Taichi; Mano, Junichi; Kitta, Kazumi

2011-01-01

In many countries, the labeling of grains, feed, and foodstuff is mandatory if the genetically modified (GM) organism content exceeds a certain level of approved GM varieties. We previously developed an individual kernel detection system consisting of grinding individual kernels, DNA extraction from the individually ground kernels, GM detection using multiplex real-time PCR, and GM event detection using multiplex qualitative PCR to analyze the precise commingling level and varieties of GM maize in real sample grains. We performed the interlaboratory study of the DNA extraction with multiple ground samples, multiplex real-time PCR detection, and multiplex qualitative PCR detection to evaluate its applicability, practicality, and ruggedness for the individual kernel detection system of GM maize. DNA extraction with multiple ground samples, multiplex real-time PCR, and multiplex qualitative PCR were evaluated by five laboratories in Japan, and all results from these laboratories were consistent with the expected results in terms of the commingling level and event analysis. Thus, the DNA extraction with multiple ground samples, multiplex real-time PCR, and multiplex qualitative PCR for the individual kernel detection system is applicable and practicable in a laboratory to regulate the commingling level of GM maize grain for GM samples, including stacked GM maize.

Linked-cluster formulation of electron-hole interaction kernel in real-space representation without using unoccupied states.

PubMed

Bayne, Michael G; Scher, Jeremy A; Ellis, Benjamin H; Chakraborty, Arindam

2018-05-21

Electron-hole or quasiparticle representation plays a central role in describing electronic excitations in many-electron systems. For charge-neutral excitation, the electron-hole interaction kernel is the quantity of interest for calculating important excitation properties such as optical gap, optical spectra, electron-hole recombination and electron-hole binding energies. The electron-hole interaction kernel can be formally derived from the density-density correlation function using both Green's function and TDDFT formalism. The accurate determination of the electron-hole interaction kernel remains a significant challenge for precise calculations of optical properties in the GW+BSE formalism. From the TDDFT perspective, the electron-hole interaction kernel has been viewed as a path to systematic development of frequency-dependent exchange-correlation functionals. Traditional approaches, such as MBPT formalism, use unoccupied states (which are defined with respect to Fermi vacuum) to construct the electron-hole interaction kernel. However, the inclusion of unoccupied states has long been recognized as the leading computational bottleneck that limits the application of this approach for larger finite systems. In this work, an alternative derivation that avoids using unoccupied states to construct the electron-hole interaction kernel is presented. The central idea of this approach is to use explicitly correlated geminal functions for treating electron-electron correlation for both ground and excited state wave functions. Using this ansatz, it is derived using both diagrammatic and algebraic techniques that the electron-hole interaction kernel can be expressed only in terms of linked closed-loop diagrams. It is proved that the cancellation of unlinked diagrams is a consequence of linked-cluster theorem in real-space representation. The electron-hole interaction kernel derived in this work was used to calculate excitation energies in many-electron systems and results were found to be in good agreement with the EOM-CCSD and GW+BSE methods. The numerical results highlight the effectiveness of the developed method for overcoming the computational barrier of accurately determining the electron-hole interaction kernel to applications of large finite systems such as quantum dots and nanorods.

Heat kernel for the elliptic system of linear elasticity with boundary conditions

NASA Astrophysics Data System (ADS)

Taylor, Justin; Kim, Seick; Brown, Russell

2014-10-01

We consider the elliptic system of linear elasticity with bounded measurable coefficients in a domain where the second Korn inequality holds. We construct heat kernel of the system subject to Dirichlet, Neumann, or mixed boundary condition under the assumption that weak solutions of the elliptic system are Hölder continuous in the interior. Moreover, we show that if weak solutions of the mixed problem are Hölder continuous up to the boundary, then the corresponding heat kernel has a Gaussian bound. In particular, if the domain is a two dimensional Lipschitz domain satisfying a corkscrew or non-tangential accessibility condition on the set where we specify Dirichlet boundary condition, then we show that the heat kernel has a Gaussian bound. As an application, we construct Green's function for elliptic mixed problem in such a domain.

A Unified and Comprehensible View of Parametric and Kernel Methods for Genomic Prediction with Application to Rice.

PubMed

Jacquin, Laval; Cao, Tuong-Vi; Ahmadi, Nourollah

2016-01-01

One objective of this study was to provide readers with a clear and unified understanding of parametric statistical and kernel methods, used for genomic prediction, and to compare some of these in the context of rice breeding for quantitative traits. Furthermore, another objective was to provide a simple and user-friendly R package, named KRMM, which allows users to perform RKHS regression with several kernels. After introducing the concept of regularized empirical risk minimization, the connections between well-known parametric and kernel methods such as Ridge regression [i.e., genomic best linear unbiased predictor (GBLUP)] and reproducing kernel Hilbert space (RKHS) regression were reviewed. Ridge regression was then reformulated so as to show and emphasize the advantage of the kernel "trick" concept, exploited by kernel methods in the context of epistatic genetic architectures, over parametric frameworks used by conventional methods. Some parametric and kernel methods; least absolute shrinkage and selection operator (LASSO), GBLUP, support vector machine regression (SVR) and RKHS regression were thereupon compared for their genomic predictive ability in the context of rice breeding using three real data sets. Among the compared methods, RKHS regression and SVR were often the most accurate methods for prediction followed by GBLUP and LASSO. An R function which allows users to perform RR-BLUP of marker effects, GBLUP and RKHS regression, with a Gaussian, Laplacian, polynomial or ANOVA kernel, in a reasonable computation time has been developed. Moreover, a modified version of this function, which allows users to tune kernels for RKHS regression, has also been developed and parallelized for HPC Linux clusters. The corresponding KRMM package and all scripts have been made publicly available.

The role of simulation in space operations training

NASA Astrophysics Data System (ADS)

Ocasio, Frank; Atkins, Dana

The expanding use of computer simulation to train aerospace personnel is reviewed emphasizing the increasing complexity of responsibilities in the operations segment. The inefficiency of on-the-job training is discussed, and the simulation technologies employed by the USAF Combat Crew Training Squadron are described. The Mission Control Complex-Kernel is employed to simulate an operational Satellite Control Squadron (SCS) and a downscaled SCS. A system for telemetry simulation is incorporated into the launch and early-orbit segments of the training, and the training emphasizes time-critical actions, schedule adherence, and the interaction with external organizations. Hands-on training is required to supplement the simulator training which cannot be used to simulate anomalies in satellites and ground systems. The use of a centralized simulator as an instructional tool facilitates and expedites the transition of the student to operational levels.

Software implementation of the SKIPSM paradigm under PIP

NASA Astrophysics Data System (ADS)

Hack, Ralf; Waltz, Frederick M.; Batchelor, Bruce G.

1997-09-01

SKIPSM (separated-kernel image processing using finite state machines) is a technique for implementing large-kernel binary- morphology operators and many other operations. While earlier papers on SKIPSM concentrated mainly on implementations using pipelined hardware, there is considerable scope for achieving major speed improvements in software systems. Using identical control software, one-pass binary erosion and dilation structuring elements (SEs) ranging from the trivial (3 by 3) to the gigantic (51 by 51, or even larger), are readily available. Processing speed is independent of the size of the SE, making the SKIPSM approach practical for work with very large SEs on ordinary desktop computers. PIP (prolog image processing) is an interactive machine vision prototyping environment developed at the University of Wales Cardiff. It consists of a large number of image processing operators embedded within the standard AI language Prolog. This paper describes the SKIPSM implementation of binary morphology operators within PIP. A large set of binary erosion and dilation operations (circles, squares, diamonds, octagons, etc.) is available to the user through a command-line driven dialogue, via pull-down menus, or incorporated into standard (Prolog) programs. Little has been done thus far to optimize speed on this first software implementation of SKIPSM. Nevertheless, the results are impressive. The paper describes sample applications and presents timing figures. Readers have the opportunity to try out these operations on demonstration software written by the University of Wales, or via their WWW home page at http://bruce.cs.cf.ac.uk/bruce/index.html .

On the interpretation of kernels - Computer simulation of responses to impulse pairs

NASA Technical Reports Server (NTRS)

Hung, G.; Stark, L.; Eykhoff, P.

1983-01-01

A method is presented for the use of a unit impulse response and responses to impulse pairs of variable separation in the calculation of the second-degree kernels of a quadratic system. A quadratic system may be built from simple linear terms of known dynamics and a multiplier. Computer simulation results on quadratic systems with building elements of various time constants indicate reasonably that the larger time constant term before multiplication dominates in the envelope of the off-diagonal kernel curves as these move perpendicular to and away from the main diagonal. The smaller time constant term before multiplication combines with the effect of the time constant after multiplication to dominate in the kernel curves in the direction of the second-degree impulse response, i.e., parallel to the main diagonal. Such types of insight may be helpful in recognizing essential aspects of (second-degree) kernels; they may be used in simplifying the model structure and, perhaps, add to the physical/physiological understanding of the underlying processes.

Experimental analysis of performance and emission on DI diesel engine fueled with diesel-palm kernel methyl ester-triacetin blends: a Taguchi fuzzy-based optimization.

PubMed

Panda, Jibitesh Kumar; Sastry, Gadepalli Ravi Kiran; Rai, Ram Naresh

2018-05-25

The energy situation and the concerns about global warming nowadays have ignited research interest in non-conventional and alternative fuel resources to decrease the emission and the continuous dependency on fossil fuels, particularly for various sectors like power generation, transportation, and agriculture. In the present work, the research is focused on evaluating the performance, emission characteristics, and combustion of biodiesel such as palm kernel methyl ester with the addition of diesel additive "triacetin" in it. A timed manifold injection (TMI) system was taken up to examine the influence of durations of several blends induced on the emission and performance characteristics as compared to normal diesel operation. This experimental study shows better performance and releases less emission as compared with mineral diesel and in turn, indicates that high performance and low emission is promising in PKME-triacetin fuel operation. This analysis also attempts to describe the application of the fuzzy logic-based Taguchi analysis to optimize the emission and performance parameters.

Sepsis mortality prediction with the Quotient Basis Kernel.

PubMed

Ribas Ripoll, Vicent J; Vellido, Alfredo; Romero, Enrique; Ruiz-Rodríguez, Juan Carlos

2014-05-01

This paper presents an algorithm to assess the risk of death in patients with sepsis. Sepsis is a common clinical syndrome in the intensive care unit (ICU) that can lead to severe sepsis, a severe state of septic shock or multi-organ failure. The proposed algorithm may be implemented as part of a clinical decision support system that can be used in combination with the scores deployed in the ICU to improve the accuracy, sensitivity and specificity of mortality prediction for patients with sepsis. In this paper, we used the Simplified Acute Physiology Score (SAPS) for ICU patients and the Sequential Organ Failure Assessment (SOFA) to build our kernels and algorithms. In the proposed method, we embed the available data in a suitable feature space and use algorithms based on linear algebra, geometry and statistics for inference. We present a simplified version of the Fisher kernel (practical Fisher kernel for multinomial distributions), as well as a novel kernel that we named the Quotient Basis Kernel (QBK). These kernels are used as the basis for mortality prediction using soft-margin support vector machines. The two new kernels presented are compared against other generative kernels based on the Jensen-Shannon metric (centred, exponential and inverse) and other widely used kernels (linear, polynomial and Gaussian). Clinical relevance is also evaluated by comparing these results with logistic regression and the standard clinical prediction method based on the initial SAPS score. As described in this paper, we tested the new methods via cross-validation with a cohort of 400 test patients. The results obtained using our methods compare favourably with those obtained using alternative kernels (80.18% accuracy for the QBK) and the standard clinical prediction method, which are based on the basal SAPS score or logistic regression (71.32% and 71.55%, respectively). The QBK presented a sensitivity and specificity of 79.34% and 83.24%, which outperformed the other kernels analysed, logistic regression and the standard clinical prediction method based on the basal SAPS score. Several scoring systems for patients with sepsis have been introduced and developed over the last 30 years. They allow for the assessment of the severity of disease and provide an estimate of in-hospital mortality. Physiology-based scoring systems are applied to critically ill patients and have a number of advantages over diagnosis-based systems. Severity score systems are often used to stratify critically ill patients for possible inclusion in clinical trials. In this paper, we present an effective algorithm that combines both scoring methodologies for the assessment of death in patients with sepsis that can be used to improve the sensitivity and specificity of the currently available methods. Copyright © 2014 Elsevier B.V. All rights reserved.

Dielectric relaxation measurement and analysis of restricted water structure in rice kernels

NASA Astrophysics Data System (ADS)

Yagihara, Shin; Oyama, Mikio; Inoue, Akio; Asano, Megumi; Sudo, Seiichi; Shinyashiki, Naoki

2007-04-01

Dielectric relaxation measurements were performed for rice kernels by time domain reflectometry (TDR) with flat-end coaxial electrodes. Difficulties in good contact between the surfaces of the electrodes and the kernels are eliminated by a TDR set-up with a sample holder for a kernel, and the water content could be evaluated from relaxation curves. Dielectric measurements were performed for rice kernels, rice flour and boiled rice with various water contents, and the water amount and dynamic behaviour of water molecules were explained from restricted dynamics of water molecules and also from the τ-β (relaxation time versus the relaxation-time distribution parameter of the Cole-Cole equation) diagram. In comparison with other aqueous systems, the dynamic structure of water in moist rice is more similar to aqueous dispersion systems than to aqueous solutions.

Advanced Free Flight Planner and Dispatcher's Workstation: Preliminary Design Specification

NASA Technical Reports Server (NTRS)

Wilson, J.; Wright, C.; Couluris, G. J.

1997-01-01

The National Aeronautics and Space Administration (NASA) has implemented the Advanced Air Transportation Technology (AATT) program to investigate future improvements to the national and international air traffic management systems. This research, as part of the AATT program, developed preliminary design requirements for an advanced Airline Operations Control (AOC) dispatcher's workstation, with emphasis on flight planning. This design will support the implementation of an experimental workstation in NASA laboratories that would emulate AOC dispatch operations. The work developed an airline flight plan data base and specified requirements for: a computer tool for generation and evaluation of free flight, user preferred trajectories (UPT); the kernel of an advanced flight planning system to be incorporated into the UPT-generation tool; and an AOC workstation to house the UPT-generation tool and to provide a real-time testing environment. A prototype for the advanced flight plan optimization kernel was developed and demonstrated. The flight planner uses dynamic programming to search a four-dimensional wind and temperature grid to identify the optimal route, altitude and speed for successive segments of a flight. An iterative process is employed in which a series of trajectories are successively refined until the LTPT is identified. The flight planner is designed to function in the current operational environment as well as in free flight. The free flight environment would enable greater flexibility in UPT selection based on alleviation of current procedural constraints. The prototype also takes advantage of advanced computer processing capabilities to implement more powerful optimization routines than would be possible with older computer systems.

Convergence of high order memory kernels in the Nakajima-Zwanzig generalized master equation and rate constants: Case study of the spin-boson model.

PubMed

Xu, Meng; Yan, Yaming; Liu, Yanying; Shi, Qiang

2018-04-28

The Nakajima-Zwanzig generalized master equation provides a formally exact framework to simulate quantum dynamics in condensed phases. Yet, the exact memory kernel is hard to obtain and calculations based on perturbative expansions are often employed. By using the spin-boson model as an example, we assess the convergence of high order memory kernels in the Nakajima-Zwanzig generalized master equation. The exact memory kernels are calculated by combining the hierarchical equation of motion approach and the Dyson expansion of the exact memory kernel. High order expansions of the memory kernels are obtained by extending our previous work to calculate perturbative expansions of open system quantum dynamics [M. Xu et al., J. Chem. Phys. 146, 064102 (2017)]. It is found that the high order expansions do not necessarily converge in certain parameter regimes where the exact kernel show a long memory time, especially in cases of slow bath, weak system-bath coupling, and low temperature. Effectiveness of the Padé and Landau-Zener resummation approaches is tested, and the convergence of higher order rate constants beyond Fermi's golden rule is investigated.

Convergence of high order memory kernels in the Nakajima-Zwanzig generalized master equation and rate constants: Case study of the spin-boson model

NASA Astrophysics Data System (ADS)

Xu, Meng; Yan, Yaming; Liu, Yanying; Shi, Qiang

2018-04-01

The Nakajima-Zwanzig generalized master equation provides a formally exact framework to simulate quantum dynamics in condensed phases. Yet, the exact memory kernel is hard to obtain and calculations based on perturbative expansions are often employed. By using the spin-boson model as an example, we assess the convergence of high order memory kernels in the Nakajima-Zwanzig generalized master equation. The exact memory kernels are calculated by combining the hierarchical equation of motion approach and the Dyson expansion of the exact memory kernel. High order expansions of the memory kernels are obtained by extending our previous work to calculate perturbative expansions of open system quantum dynamics [M. Xu et al., J. Chem. Phys. 146, 064102 (2017)]. It is found that the high order expansions do not necessarily converge in certain parameter regimes where the exact kernel show a long memory time, especially in cases of slow bath, weak system-bath coupling, and low temperature. Effectiveness of the Padé and Landau-Zener resummation approaches is tested, and the convergence of higher order rate constants beyond Fermi's golden rule is investigated.

Extending Mondrian Memory Protection

DTIC Science & Technology

2010-11-01

a kernel semaphore is locked or unlocked. In addition, we extended the system call interface to receive notifications about user-land locking...operations (such as calls to the mutex and semaphore code provided by the C library). By patching the dynamically loadable GLibC5, we are able to test... semaphores , and spinlocks. RTO-MP-IST-091 10- 9 Extending Mondrian Memory Protection to loading extension plugins. This prevents any untrusted code

Prostate cancer detection using machine learning techniques by employing combination of features extracting strategies.

PubMed

Hussain, Lal; Ahmed, Adeel; Saeed, Sharjil; Rathore, Saima; Awan, Imtiaz Ahmed; Shah, Saeed Arif; Majid, Abdul; Idris, Adnan; Awan, Anees Ahmed

2018-02-06

Prostate is a second leading causes of cancer deaths among men. Early detection of cancer can effectively reduce the rate of mortality caused by Prostate cancer. Due to high and multiresolution of MRIs from prostate cancer require a proper diagnostic systems and tools. In the past researchers developed Computer aided diagnosis (CAD) systems that help the radiologist to detect the abnormalities. In this research paper, we have employed novel Machine learning techniques such as Bayesian approach, Support vector machine (SVM) kernels: polynomial, radial base function (RBF) and Gaussian and Decision Tree for detecting prostate cancer. Moreover, different features extracting strategies are proposed to improve the detection performance. The features extracting strategies are based on texture, morphological, scale invariant feature transform (SIFT), and elliptic Fourier descriptors (EFDs) features. The performance was evaluated based on single as well as combination of features using Machine Learning Classification techniques. The Cross validation (Jack-knife k-fold) was performed and performance was evaluated in term of receiver operating curve (ROC) and specificity, sensitivity, Positive predictive value (PPV), negative predictive value (NPV), false positive rate (FPR). Based on single features extracting strategies, SVM Gaussian Kernel gives the highest accuracy of 98.34% with AUC of 0.999. While, using combination of features extracting strategies, SVM Gaussian kernel with texture + morphological, and EFDs + morphological features give the highest accuracy of 99.71% and AUC of 1.00.

Stochastic multiresonance for a fractional linear oscillator with time-delayed kernel and quadratic noise

NASA Astrophysics Data System (ADS)

Guo, Feng; Wang, Xue-Yuan; Zhu, Cheng-Yin; Cheng, Xiao-Feng; Zhang, Zheng-Yu; Huang, Xu-Hui

2017-12-01

The stochastic resonance for a fractional oscillator with time-delayed kernel and quadratic trichotomous noise is investigated. Applying linear system theory and Laplace transform, the system output amplitude (SPA) for the fractional oscillator is obtained. It is found that the SPA is a periodical function of the kernel delayed-time. Stochastic multiplicative phenomenon appears on the SPA versus the driving frequency, versus the noise amplitude, and versus the fractional exponent. The non-monotonous dependence of the SPA on the system parameters is also discussed.

Quantization with maximally degenerate Poisson brackets: the harmonic oscillator!

NASA Astrophysics Data System (ADS)

Nutku, Yavuz

2003-07-01

Nambu's construction of multi-linear brackets for super-integrable systems can be thought of as degenerate Poisson brackets with a maximal set of Casimirs in their kernel. By introducing privileged coordinates in phase space these degenerate Poisson brackets are brought to the form of Heisenberg's equations. We propose a definition for constructing quantum operators for classical functions, which enables us to turn the maximally degenerate Poisson brackets into operators. They pose a set of eigenvalue problems for a new state vector. The requirement of the single-valuedness of this eigenfunction leads to quantization. The example of the harmonic oscillator is used to illustrate this general procedure for quantizing a class of maximally super-integrable systems.

Real time UNIX in embedded control-a case study within the context of LynxOS

NASA Astrophysics Data System (ADS)

Kleines, H.; Zwoll, K.

1996-02-01

Intelligent communication controllers for a layered protocol profile are a typical example of an embedded control application, where the classical approach for the software development is based on a proprietary real-time operating system kernel under which the individual layers are implemented as tasks. Based on the exemplary implementation of a derivative of MAP 3.0, an unusual and innovative approach is presented, where the protocol software is implemented under the UNIX-compatible real-time operating system LynxOS. The overall design of the embedded control application is presented under a more general view and economical implications as well as aspects of the development environment and performance are discussed

Stochastic subset selection for learning with kernel machines.

PubMed

Rhinelander, Jason; Liu, Xiaoping P

2012-06-01

Kernel machines have gained much popularity in applications of machine learning. Support vector machines (SVMs) are a subset of kernel machines and generalize well for classification, regression, and anomaly detection tasks. The training procedure for traditional SVMs involves solving a quadratic programming (QP) problem. The QP problem scales super linearly in computational effort with the number of training samples and is often used for the offline batch processing of data. Kernel machines operate by retaining a subset of observed data during training. The data vectors contained within this subset are referred to as support vectors (SVs). The work presented in this paper introduces a subset selection method for the use of kernel machines in online, changing environments. Our algorithm works by using a stochastic indexing technique when selecting a subset of SVs when computing the kernel expansion. The work described here is novel because it separates the selection of kernel basis functions from the training algorithm used. The subset selection algorithm presented here can be used in conjunction with any online training technique. It is important for online kernel machines to be computationally efficient due to the real-time requirements of online environments. Our algorithm is an important contribution because it scales linearly with the number of training samples and is compatible with current training techniques. Our algorithm outperforms standard techniques in terms of computational efficiency and provides increased recognition accuracy in our experiments. We provide results from experiments using both simulated and real-world data sets to verify our algorithm.

Bose–Einstein condensation temperature of finite systems

NASA Astrophysics Data System (ADS)

Xie, Mi

2018-05-01

In studies of the Bose–Einstein condensation of ideal gases in finite systems, the divergence problem usually arises in the equation of state. In this paper, we present a technique based on the heat kernel expansion and zeta function regularization to solve the divergence problem, and obtain the analytical expression of the Bose–Einstein condensation temperature for general finite systems. The result is represented by the heat kernel coefficients, where the asymptotic energy spectrum of the system is used. Besides the general case, for systems with exact spectra, e.g. ideal gases in an infinite slab or in a three-sphere, the sums of the spectra can be obtained exactly and the calculation of corrections to the critical temperatures is more direct. For a system confined in a bounded potential, the form of the heat kernel is different from the usual heat kernel expansion. We show that as long as the asymptotic form of the global heat kernel can be found, our method works. For Bose gases confined in three- and two-dimensional isotropic harmonic potentials, we obtain the higher-order corrections to the usual results of the critical temperatures. Our method can also be applied to the problem of generalized condensation, and we give the correction of the boundary on the second critical temperature in a highly anisotropic slab.

Indigenous knowledge of shea processing and quality perception of shea products in Benin.

PubMed

Honfo, Fernande G; Linnemann, Anita R; Akissoe, Noël H; Soumanou, Mohamed M; van Boekel, Martinus A J S

2012-01-01

A survey among 246 people belonging to 14 ethnic groups and living in 5 different parklands in Benin revealed different practices to process shea kernels (namely boiling followed sun drying and smoking) and extract shea butter. A relation between parklands, gathering period, and sun-drying conditions was established. Moisture content and appearance of kernels were the selection criteria for users of shea kernels; color was the main characteristic to buy butter. Constraints to be solved are long processing times, lack of milling equipment and high water requirements. Best practices for smoking, sun drying, and roasting operations need to be established for further improvement.

Modular Affective Reasoning-Based Versatile Introspective Architecture (MARVIN)

DTIC Science & Technology

2008-08-14

monolithic kernels found in most mass market OSs, where these kinds of system processes run within the kernel , and thus need to be highly optimized as well as...without modifying pre- existing process management elements, we expect the process of transitioning this component from MINIX to monolithic kernels to...necessary to incorporate them into a monolithic kernel . To demonstrate how the APMM would work in practice, we used it as the basis for building a simulated

Adaptive Shape Kernel-Based Mean Shift Tracker in Robot Vision System

PubMed Central

2016-01-01

This paper proposes an adaptive shape kernel-based mean shift tracker using a single static camera for the robot vision system. The question that we address in this paper is how to construct such a kernel shape that is adaptive to the object shape. We perform nonlinear manifold learning technique to obtain the low-dimensional shape space which is trained by training data with the same view as the tracking video. The proposed kernel searches the shape in the low-dimensional shape space obtained by nonlinear manifold learning technique and constructs the adaptive kernel shape in the high-dimensional shape space. It can improve mean shift tracker performance to track object position and object contour and avoid the background clutter. In the experimental part, we take the walking human as example to validate that our method is accurate and robust to track human position and describe human contour. PMID:27379165

Ranking Support Vector Machine with Kernel Approximation

PubMed Central

Dou, Yong

2017-01-01

Learning to rank algorithm has become important in recent years due to its successful application in information retrieval, recommender system, and computational biology, and so forth. Ranking support vector machine (RankSVM) is one of the state-of-art ranking models and has been favorably used. Nonlinear RankSVM (RankSVM with nonlinear kernels) can give higher accuracy than linear RankSVM (RankSVM with a linear kernel) for complex nonlinear ranking problem. However, the learning methods for nonlinear RankSVM are still time-consuming because of the calculation of kernel matrix. In this paper, we propose a fast ranking algorithm based on kernel approximation to avoid computing the kernel matrix. We explore two types of kernel approximation methods, namely, the Nyström method and random Fourier features. Primal truncated Newton method is used to optimize the pairwise L2-loss (squared Hinge-loss) objective function of the ranking model after the nonlinear kernel approximation. Experimental results demonstrate that our proposed method gets a much faster training speed than kernel RankSVM and achieves comparable or better performance over state-of-the-art ranking algorithms. PMID:28293256

Ranking Support Vector Machine with Kernel Approximation.

PubMed

Chen, Kai; Li, Rongchun; Dou, Yong; Liang, Zhengfa; Lv, Qi

2017-01-01

Learning to rank algorithm has become important in recent years due to its successful application in information retrieval, recommender system, and computational biology, and so forth. Ranking support vector machine (RankSVM) is one of the state-of-art ranking models and has been favorably used. Nonlinear RankSVM (RankSVM with nonlinear kernels) can give higher accuracy than linear RankSVM (RankSVM with a linear kernel) for complex nonlinear ranking problem. However, the learning methods for nonlinear RankSVM are still time-consuming because of the calculation of kernel matrix. In this paper, we propose a fast ranking algorithm based on kernel approximation to avoid computing the kernel matrix. We explore two types of kernel approximation methods, namely, the Nyström method and random Fourier features. Primal truncated Newton method is used to optimize the pairwise L2-loss (squared Hinge-loss) objective function of the ranking model after the nonlinear kernel approximation. Experimental results demonstrate that our proposed method gets a much faster training speed than kernel RankSVM and achieves comparable or better performance over state-of-the-art ranking algorithms.

Deploy Nalu/Kokkos algorithmic infrastructure with performance benchmarking.

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

Domino, Stefan P.; Ananthan, Shreyas; Knaus, Robert C.

The former Nalu interior heterogeneous algorithm design, which was originally designed to manage matrix assembly operations over all elemental topology types, has been modified to operate over homogeneous collections of mesh entities. This newly templated kernel design allows for removal of workset variable resize operations that were formerly required at each loop over a Sierra ToolKit (STK) bucket (nominally, 512 entities in size). Extensive usage of the Standard Template Library (STL) std::vector has been removed in favor of intrinsic Kokkos memory views. In this milestone effort, the transition to Kokkos as the underlying infrastructure to support performance and portability onmore » many-core architectures has been deployed for key matrix algorithmic kernels. A unit-test driven design effort has developed a homogeneous entity algorithm that employs a team-based thread parallelism construct. The STK Single Instruction Multiple Data (SIMD) infrastructure is used to interleave data for improved vectorization. The collective algorithm design, which allows for concurrent threading and SIMD management, has been deployed for the core low-Mach element- based algorithm. Several tests to ascertain SIMD performance on Intel KNL and Haswell architectures have been carried out. The performance test matrix includes evaluation of both low- and higher-order methods. The higher-order low-Mach methodology builds on polynomial promotion of the core low-order control volume nite element method (CVFEM). Performance testing of the Kokkos-view/SIMD design indicates low-order matrix assembly kernel speed-up ranging between two and four times depending on mesh loading and node count. Better speedups are observed for higher-order meshes (currently only P=2 has been tested) especially on KNL. The increased workload per element on higher-order meshes bene ts from the wide SIMD width on KNL machines. Combining multiple threads with SIMD on KNL achieves a 4.6x speedup over the baseline, with assembly timings faster than that observed on Haswell architecture. The computational workload of higher-order meshes, therefore, seems ideally suited for the many-core architecture and justi es further exploration of higher-order on NGP platforms. A Trilinos/Tpetra-based multi-threaded GMRES preconditioned by symmetric Gauss Seidel (SGS) represents the core solver infrastructure for the low-Mach advection/diffusion implicit solves. The threaded solver stack has been tested on small problems on NREL's Peregrine system using the newly developed and deployed Kokkos-view/SIMD kernels. fforts are underway to deploy the Tpetra-based solver stack on NERSC Cori system to benchmark its performance at scale on KNL machines.« less

Acceleration of GPU-based Krylov solvers via data transfer reduction

DOE PAGES

Anzt, Hartwig; Tomov, Stanimire; Luszczek, Piotr; ...

2015-04-08

Krylov subspace iterative solvers are often the method of choice when solving large sparse linear systems. At the same time, hardware accelerators such as graphics processing units continue to offer significant floating point performance gains for matrix and vector computations through easy-to-use libraries of computational kernels. However, as these libraries are usually composed of a well optimized but limited set of linear algebra operations, applications that use them often fail to reduce certain data communications, and hence fail to leverage the full potential of the accelerator. In this study, we target the acceleration of Krylov subspace iterative methods for graphicsmore » processing units, and in particular the Biconjugate Gradient Stabilized solver that significant improvement can be achieved by reformulating the method to reduce data-communications through application-specific kernels instead of using the generic BLAS kernels, e.g. as provided by NVIDIA’s cuBLAS library, and by designing a graphics processing unit specific sparse matrix-vector product kernel that is able to more efficiently use the graphics processing unit’s computing power. Furthermore, we derive a model estimating the performance improvement, and use experimental data to validate the expected runtime savings. Finally, considering that the derived implementation achieves significantly higher performance, we assert that similar optimizations addressing algorithm structure, as well as sparse matrix-vector, are crucial for the subsequent development of high-performance graphics processing units accelerated Krylov subspace iterative methods.« less

Discontinuous functional for linear-response time-dependent density-functional theory: The exact-exchange kernel and approximate forms

NASA Astrophysics Data System (ADS)

Hellgren, Maria; Gross, E. K. U.

2013-11-01

We present a detailed study of the exact-exchange (EXX) kernel of time-dependent density-functional theory with an emphasis on its discontinuity at integer particle numbers. It was recently found that this exact property leads to sharp peaks and step features in the kernel that diverge in the dissociation limit of diatomic systems [Hellgren and Gross, Phys. Rev. APLRAAN1050-294710.1103/PhysRevA.85.022514 85, 022514 (2012)]. To further analyze the discontinuity of the kernel, we here make use of two different approximations to the EXX kernel: the Petersilka Gossmann Gross (PGG) approximation and a common energy denominator approximation (CEDA). It is demonstrated that whereas the PGG approximation neglects the discontinuity, the CEDA includes it explicitly. By studying model molecular systems it is shown that the so-called field-counteracting effect in the density-functional description of molecular chains can be viewed in terms of the discontinuity of the static kernel. The role of the frequency dependence is also investigated, highlighting its importance for long-range charge-transfer excitations as well as inner-shell excitations.

Derived virtual devices: a secure distributed file system mechanism

NASA Technical Reports Server (NTRS)

VanMeter, Rodney; Hotz, Steve; Finn, Gregory

1996-01-01

This paper presents the design of derived virtual devices (DVDs). DVDs are the mechanism used by the Netstation Project to provide secure shared access to network-attached peripherals distributed in an untrusted network environment. DVDs improve Input/Output efficiency by allowing user processes to perform I/O operations directly from devices without intermediate transfer through the controlling operating system kernel. The security enforced at the device through the DVD mechanism includes resource boundary checking, user authentication, and restricted operations, e.g., read-only access. To illustrate the application of DVDs, we present the interactions between a network-attached disk and a file system designed to exploit the DVD abstraction. We further discuss third-party transfer as a mechanism intended to provide for efficient data transfer in a typical NAP environment. We show how DVDs facilitate third-party transfer, and provide the security required in a more open network environment.

A method of smoothed particle hydrodynamics using spheroidal kernels

NASA Technical Reports Server (NTRS)

Fulbright, Michael S.; Benz, Willy; Davies, Melvyn B.

1995-01-01

We present a new method of three-dimensional smoothed particle hydrodynamics (SPH) designed to model systems dominated by deformation along a preferential axis. These systems cause severe problems for SPH codes using spherical kernels, which are best suited for modeling systems which retain rough spherical symmetry. Our method allows the smoothing length in the direction of the deformation to evolve independently of the smoothing length in the perpendicular plane, resulting in a kernel with a spheroidal shape. As a result the spatial resolution in the direction of deformation is significantly improved. As a test case we present the one-dimensional homologous collapse of a zero-temperature, uniform-density cloud, which serves to demonstrate the advantages of spheroidal kernels. We also present new results on the problem of the tidal disruption of a star by a massive black hole.

Multiple kernel learning in protein-protein interaction extraction from biomedical literature.

PubMed

Yang, Zhihao; Tang, Nan; Zhang, Xiao; Lin, Hongfei; Li, Yanpeng; Yang, Zhiwei

2011-03-01

Knowledge about protein-protein interactions (PPIs) unveils the molecular mechanisms of biological processes. The volume and content of published biomedical literature on protein interactions is expanding rapidly, making it increasingly difficult for interaction database administrators, responsible for content input and maintenance to detect and manually update protein interaction information. The objective of this work is to develop an effective approach to automatic extraction of PPI information from biomedical literature. We present a weighted multiple kernel learning-based approach for automatic PPI extraction from biomedical literature. The approach combines the following kernels: feature-based, tree, graph and part-of-speech (POS) path. In particular, we extend the shortest path-enclosed tree (SPT) and dependency path tree to capture richer contextual information. Our experimental results show that the combination of SPT and dependency path tree extensions contributes to the improvement of performance by almost 0.7 percentage units in F-score and 2 percentage units in area under the receiver operating characteristics curve (AUC). Combining two or more appropriately weighed individual will further improve the performance. Both on the individual corpus and cross-corpus evaluation our combined kernel can achieve state-of-the-art performance with respect to comparable evaluations, with 64.41% F-score and 88.46% AUC on the AImed corpus. As different kernels calculate the similarity between two sentences from different aspects. Our combined kernel can reduce the risk of missing important features. More specifically, we use a weighted linear combination of individual kernels instead of assigning the same weight to each individual kernel, thus allowing the introduction of each kernel to incrementally contribute to the performance improvement. In addition, SPT and dependency path tree extensions can improve the performance by including richer context information. Copyright © 2010 Elsevier B.V. All rights reserved.

Quasi-kernel polynomials and convergence results for quasi-minimal residual iterations

NASA Technical Reports Server (NTRS)

Freund, Roland W.

1992-01-01

Recently, Freund and Nachtigal have proposed a novel polynominal-based iteration, the quasi-minimal residual algorithm (QMR), for solving general nonsingular non-Hermitian linear systems. Motivated by the QMR method, we have introduced the general concept of quasi-kernel polynomials, and we have shown that the QMR algorithm is based on a particular instance of quasi-kernel polynomials. In this paper, we continue our study of quasi-kernel polynomials. In particular, we derive bounds for the norms of quasi-kernel polynomials. These results are then applied to obtain convergence theorems both for the QMR method and for a transpose-free variant of QMR, the TFQMR algorithm.

Detection and Prevention of Android Malware Attempting to Root the Device

DTIC Science & Technology

2014-03-01

detect the operation of malware trying to root the phone. This research aims to detect the Exploid, RageAgainstTheCage, and Gingerbreak exploits on...attackers can use malware to root the system. By placing sensors inside the critical paths, the research detected all 379 malware samples trying the root...zero false positive results. Unlike static signature detection at the application level, this research provides dynamic detection at the kernel level

Vis-NIR spectrometric determination of Brix and sucrose in sugar production samples using kernel partial least squares with interval selection based on the successive projections algorithm.

PubMed

de Almeida, Valber Elias; de Araújo Gomes, Adriano; de Sousa Fernandes, David Douglas; Goicoechea, Héctor Casimiro; Galvão, Roberto Kawakami Harrop; Araújo, Mario Cesar Ugulino

2018-05-01

This paper proposes a new variable selection method for nonlinear multivariate calibration, combining the Successive Projections Algorithm for interval selection (iSPA) with the Kernel Partial Least Squares (Kernel-PLS) modelling technique. The proposed iSPA-Kernel-PLS algorithm is employed in a case study involving a Vis-NIR spectrometric dataset with complex nonlinear features. The analytical problem consists of determining Brix and sucrose content in samples from a sugar production system, on the basis of transflectance spectra. As compared to full-spectrum Kernel-PLS, the iSPA-Kernel-PLS models involve a smaller number of variables and display statistically significant superiority in terms of accuracy and/or bias in the predictions. Published by Elsevier B.V.

Time and Space Partitioning the EagleEye Reference Misson

NASA Astrophysics Data System (ADS)

Bos, Victor; Mendham, Peter; Kauppinen, Panu; Holsti, Niklas; Crespo, Alfons; Masmano, Miguel; de la Puente, Juan A.; Zamorano, Juan

2013-08-01

We discuss experiences gained by porting a Software Validation Facility (SVF) and a satellite Central Software (CSW) to a platform with support for Time and Space Partitioning (TSP). The SVF and CSW are part of the EagleEye Reference mission of the European Space Agency (ESA). As a reference mission, EagleEye is a perfect candidate to evaluate practical aspects of developing satellite CSW for and on TSP platforms. The specific TSP platform we used consists of a simulated LEON3 CPU controlled by the XtratuM separation micro-kernel. On top of this, we run five separate partitions. Each partition runs its own real-time operating system or Ada run-time kernel, which in turn are running the application software of the CSW. We describe issues related to partitioning; inter-partition communication; scheduling; I/O; and fault-detection, isolation, and recovery (FDIR).

Automatic plankton image classification combining multiple view features via multiple kernel learning.

PubMed

Zheng, Haiyong; Wang, Ruchen; Yu, Zhibin; Wang, Nan; Gu, Zhaorui; Zheng, Bing

2017-12-28

Plankton, including phytoplankton and zooplankton, are the main source of food for organisms in the ocean and form the base of marine food chain. As the fundamental components of marine ecosystems, plankton is very sensitive to environment changes, and the study of plankton abundance and distribution is crucial, in order to understand environment changes and protect marine ecosystems. This study was carried out to develop an extensive applicable plankton classification system with high accuracy for the increasing number of various imaging devices. Literature shows that most plankton image classification systems were limited to only one specific imaging device and a relatively narrow taxonomic scope. The real practical system for automatic plankton classification is even non-existent and this study is partly to fill this gap. Inspired by the analysis of literature and development of technology, we focused on the requirements of practical application and proposed an automatic system for plankton image classification combining multiple view features via multiple kernel learning (MKL). For one thing, in order to describe the biomorphic characteristics of plankton more completely and comprehensively, we combined general features with robust features, especially by adding features like Inner-Distance Shape Context for morphological representation. For another, we divided all the features into different types from multiple views and feed them to multiple classifiers instead of only one by combining different kernel matrices computed from different types of features optimally via multiple kernel learning. Moreover, we also applied feature selection method to choose the optimal feature subsets from redundant features for satisfying different datasets from different imaging devices. We implemented our proposed classification system on three different datasets across more than 20 categories from phytoplankton to zooplankton. The experimental results validated that our system outperforms state-of-the-art plankton image classification systems in terms of accuracy and robustness. This study demonstrated automatic plankton image classification system combining multiple view features using multiple kernel learning. The results indicated that multiple view features combined by NLMKL using three kernel functions (linear, polynomial and Gaussian kernel functions) can describe and use information of features better so that achieve a higher classification accuracy.

Robotic Intelligence Kernel: Driver

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

The INL Robotic Intelligence Kernel-Driver is built on top of the RIK-A and implements a dynamic autonomy structure. The RIK-D is used to orchestrate hardware for sensing and action as well as software components for perception, communication, behavior and world modeling into a single cognitive behavior kernel that provides intrinsic intelligence for a wide variety of unmanned ground vehicle systems.

Feasibility of detecting aflatoxin B1 on inoculated maize kernels surface using Vis/NIR hyperspectral imaging

USDA-ARS?s Scientific Manuscript database

The feasibility of using a visible/near-infrared hyperspectral imaging system with a wavelength range between 400 and 1000 nm to detect and differentiate different levels of aflatoxin B1 (AFB1) artificially titrated on maize kernel surface was examined. To reduce the color effects of maize kernels, ...

SMART (Sandia's Modular Architecture for Robotics and Teleoperation) Ver. 1.0

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

Anderson, Robert

"SMART Ver. 0.8 Beta" provides a system developer with software tools to create a telerobotic control system, i.e., a system whereby an end-user can interact with mechatronic equipment. It consists of three main components: the SMART Editor (tsmed), the SMART Real-time kernel (rtos), and the SMART Supervisor (gui). The SMART Editor is a graphical icon-based code generation tool for creating end-user systems, given descriptions of SMART modules. The SMART real-time kernel implements behaviors that combine modules representing input devices, sensors, constraints, filters, and robotic devices. Included with this software release is a number of core modules, which can be combinedmore » with additional project and device specific modules to create a telerobotic controller. The SMART Supervisor is a graphical front-end for running a SMART system. It is an optional component of the SMART Environment and utilizes the TeVTk windowing and scripting environment. Although the code contained within this release is complete, and can be utilized for defining, running, and interfacing to a sample end-user SMART system, most systems will include additional project and hardware specific modules developed either by the system developer or obtained independently from a SMART module developer. SMART is a software system designed to integrate the different robots, input devices, sensors and dynamic elements required for advanced modes of telerobotic control. "SMART Ver. 0.8 Beta" defines and implements a telerobotic controller. A telerobotic system consists of combinations of modules that implement behaviors. Each real-time module represents an input device, robot device, sensor, constraint, connection or filter. The underlying theory utilizes non-linear discretized multidimensional network elements to model each individual module, and guarantees that upon a valid connection, the resulting system will perform in a stable fashion. Different combinations of modules implement different behaviors. Each module must have at a minimum an initialization routine, a parameter adjustment routine, and an update routine. The SMART runtime kernel runs continuously within a real-time embedded system. Each module is first set-up by the kernel, initialized, and then updated at a fixed rate whenever it is in context. The kernel responds to operator directed commands by changing the state of the system, changing parameters on individual modules, and switching behavioral modes. The SMART Editor is a tool used to define, verify, configure and generate source code for a SMART control system. It uses icon representations of the modules, code patches from valid configurations of the modules, and configuration files describing how a module can be connected into a system to lead the end-user in through the steps needed to create a final system. The SMART Supervisor serves as an interface to a SMART run-time system. It provides an interface on a host computer that connects to the embedded system via TCPIIP ASCII commands. It utilizes a scripting language (Tel) and a graphics windowing environment (Tk). This system can either be customized to fit an end-user's needs or completely replaced as needed.« less

Detection of ochratoxin A contamination in stored wheat using near-infrared hyperspectral imaging

NASA Astrophysics Data System (ADS)

Senthilkumar, T.; Jayas, D. S.; White, N. D. G.; Fields, P. G.; Gräfenhan, T.

2017-03-01

Near-infrared (NIR) hyperspectral imaging system was used to detect five concentration levels of ochratoxin A (OTA) in contaminated wheat kernels. The wheat kernels artificially inoculated with two different OTA producing Penicillium verrucosum strains, two different non-toxigenic P. verrucosum strains, and sterile control wheat kernels were subjected to NIR hyperspectral imaging. The acquired three-dimensional data were reshaped into readable two-dimensional data. Principal Component Analysis (PCA) was applied to the two dimensional data to identify the key wavelengths which had greater significance in detecting OTA contamination in wheat. Statistical and histogram features extracted at the key wavelengths were used in the linear, quadratic and Mahalanobis statistical discriminant models to differentiate between sterile control, five concentration levels of OTA contamination in wheat kernels, and five infection levels of non-OTA producing P. verrucosum inoculated wheat kernels. The classification models differentiated sterile control samples from OTA contaminated wheat kernels and non-OTA producing P. verrucosum inoculated wheat kernels with a 100% accuracy. The classification models also differentiated between five concentration levels of OTA contaminated wheat kernels and between five infection levels of non-OTA producing P. verrucosum inoculated wheat kernels with a correct classification of more than 98%. The non-OTA producing P. verrucosum inoculated wheat kernels and OTA contaminated wheat kernels subjected to hyperspectral imaging provided different spectral patterns.

Application of kernel method in fluorescence molecular tomography

NASA Astrophysics Data System (ADS)

Zhao, Yue; Baikejiang, Reheman; Li, Changqing

2017-02-01

Reconstruction of fluorescence molecular tomography (FMT) is an ill-posed inverse problem. Anatomical guidance in the FMT reconstruction can improve FMT reconstruction efficiently. We have developed a kernel method to introduce the anatomical guidance into FMT robustly and easily. The kernel method is from machine learning for pattern analysis and is an efficient way to represent anatomical features. For the finite element method based FMT reconstruction, we calculate a kernel function for each finite element node from an anatomical image, such as a micro-CT image. Then the fluorophore concentration at each node is represented by a kernel coefficient vector and the corresponding kernel function. In the FMT forward model, we have a new system matrix by multiplying the sensitivity matrix with the kernel matrix. Thus, the kernel coefficient vector is the unknown to be reconstructed following a standard iterative reconstruction process. We convert the FMT reconstruction problem into the kernel coefficient reconstruction problem. The desired fluorophore concentration at each node can be calculated accordingly. Numerical simulation studies have demonstrated that the proposed kernel-based algorithm can improve the spatial resolution of the reconstructed FMT images. In the proposed kernel method, the anatomical guidance can be obtained directly from the anatomical image and is included in the forward modeling. One of the advantages is that we do not need to segment the anatomical image for the targets and background.

Online learning control using adaptive critic designs with sparse kernel machines.

PubMed

Xu, Xin; Hou, Zhongsheng; Lian, Chuanqiang; He, Haibo

2013-05-01

In the past decade, adaptive critic designs (ACDs), including heuristic dynamic programming (HDP), dual heuristic programming (DHP), and their action-dependent ones, have been widely studied to realize online learning control of dynamical systems. However, because neural networks with manually designed features are commonly used to deal with continuous state and action spaces, the generalization capability and learning efficiency of previous ACDs still need to be improved. In this paper, a novel framework of ACDs with sparse kernel machines is presented by integrating kernel methods into the critic of ACDs. To improve the generalization capability as well as the computational efficiency of kernel machines, a sparsification method based on the approximately linear dependence analysis is used. Using the sparse kernel machines, two kernel-based ACD algorithms, that is, kernel HDP (KHDP) and kernel DHP (KDHP), are proposed and their performance is analyzed both theoretically and empirically. Because of the representation learning and generalization capability of sparse kernel machines, KHDP and KDHP can obtain much better performance than previous HDP and DHP with manually designed neural networks. Simulation and experimental results of two nonlinear control problems, that is, a continuous-action inverted pendulum problem and a ball and plate control problem, demonstrate the effectiveness of the proposed kernel ACD methods.

KNBD: A Remote Kernel Block Server for Linux

NASA Technical Reports Server (NTRS)

Becker, Jeff

1999-01-01

I am developing a prototype of a Linux remote disk block server whose purpose is to serve as a lower level component of a parallel file system. Parallel file systems are an important component of high performance supercomputers and clusters. Although supercomputer vendors such as SGI and IBM have their own custom solutions, there has been a void and hence a demand for such a system on Beowulf-type PC Clusters. Recently, the Parallel Virtual File System (PVFS) project at Clemson University has begun to address this need (1). Although their system provides much of the functionality of (and indeed was inspired by) the equivalent file systems in the commercial supercomputer market, their system is all in user-space. Migrating their 10 services to the kernel could provide a performance boost, by obviating the need for expensive system calls. Thanks to Pavel Machek, the Linux kernel has provided the network block device (2) with kernels 2.1.101 and later. You can configure this block device to redirect reads and writes to a remote machine's disk. This can be used as a building block for constructing a striped file system across several nodes.

Registering Cortical Surfaces Based on Whole-Brain Structural Connectivity and Continuous Connectivity Analysis

PubMed Central

Gutman, Boris; Leonardo, Cassandra; Jahanshad, Neda; Hibar, Derrek; Eschen-burg, Kristian; Nir, Talia; Villalon, Julio; Thompson, Paul

2014-01-01

We present a framework for registering cortical surfaces based on tractography-informed structural connectivity. We define connectivity as a continuous kernel on the product space of the cortex, and develop a method for estimating this kernel from tractography fiber models. Next, we formulate the kernel registration problem, and present a means to non-linearly register two brains’ continuous connectivity profiles. We apply theoretical results from operator theory to develop an algorithm for decomposing the connectome into its shared and individual components. Lastly, we extend two discrete connectivity measures to the continuous case, and apply our framework to 98 Alzheimer’s patients and controls. Our measures show significant differences between the two groups. PMID:25320795

Large Scale Portability of Hospital Information System Software

PubMed Central

Munnecke, Thomas H.; Kuhn, Ingeborg M.

1986-01-01

As part of its Decentralized Hospital Computer Program (DHCP) the Veterans Administration installed new hospital information systems in 169 of its facilities during 1984 and 1985. The application software for these systems is based on the ANS MUMPS language, is public domain, and is designed to be operating system and hardware independent. The software, developed by VA employees, is built upon a layered approach, where application packages layer on a common data dictionary which is supported by a Kernel of software. Communications between facilities are based on public domain Department of Defense ARPA net standards for domain naming, mail transfer protocols, and message formats, layered on a variety of communications technologies.

Generalized and efficient algorithm for computing multipole energies and gradients based on Cartesian tensors

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

Lin, Dejun, E-mail: dejun.lin@gmail.com

2015-09-21

Accurate representation of intermolecular forces has been the central task of classical atomic simulations, known as molecular mechanics. Recent advancements in molecular mechanics models have put forward the explicit representation of permanent and/or induced electric multipole (EMP) moments. The formulas developed so far to calculate EMP interactions tend to have complicated expressions, especially in Cartesian coordinates, which can only be applied to a specific kernel potential function. For example, one needs to develop a new formula each time a new kernel function is encountered. The complication of these formalisms arises from an intriguing and yet obscured mathematical relation between themore » kernel functions and the gradient operators. Here, I uncover this relation via rigorous derivation and find that the formula to calculate EMP interactions is basically invariant to the potential kernel functions as long as they are of the form f(r), i.e., any Green’s function that depends on inter-particle distance. I provide an algorithm for efficient evaluation of EMP interaction energies, forces, and torques for any kernel f(r) up to any arbitrary rank of EMP moments in Cartesian coordinates. The working equations of this algorithm are essentially the same for any kernel f(r). Recently, a few recursive algorithms were proposed to calculate EMP interactions. Depending on the kernel functions, the algorithm here is about 4–16 times faster than these algorithms in terms of the required number of floating point operations and is much more memory efficient. I show that it is even faster than a theoretically ideal recursion scheme, i.e., one that requires 1 floating point multiplication and 1 addition per recursion step. This algorithm has a compact vector-based expression that is optimal for computer programming. The Cartesian nature of this algorithm makes it fit easily into modern molecular simulation packages as compared with spherical coordinate-based algorithms. A software library based on this algorithm has been implemented in C++11 and has been released.« less

Real time UNIX in embedded control -- A case study within context of LynxOS

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

Kleines, H.; Zwoll, K.

1996-02-01

Intelligent communication controllers for a layered protocol profile are a typical example of an embedded control application, where the classical approach for the software development is based on a proprietary real-time operating system kernel under which the individual layers are implemented as tasks. Based on the exemplary implementation of a derivative of MAP 3.0, an unusual and innovative approach is presented, where the protocol software is implemented under the UNIX-compatible real-time operating system LynxOS. The overall design of the embedded control application is presented under a more general view and economical implications as well as aspects of the development environmentmore » and performance are discussed.« less

CMMAD Usability Case Study in Support of Countermine and Hazard Sensing

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

Victor G. Walker; David I. Gertman

2010-04-01

During field trials, operator usability data were collected in support of lane clearing missions and hazard sensing for two robot platforms with Robot Intelligence Kernel (RIK) software and sensor scanning payloads onboard. The tests featured autonomous and shared robot autonomy levels where tasking of the robot used a graphical interface featuring mine location and sensor readings. The goal of this work was to provide insights that could be used to further technology development. The efficacy of countermine systems in terms of mobility, search, path planning, detection, and localization were assessed. Findings from objective and subjective operator interaction measures are reviewedmore » along with commentary from soldiers having taken part in the study who strongly endorse the system.« less

Insights into the spurious long-range nature of local rs-dependent non-local exchange-correlation kernels

DOE PAGES

Lu, Deyu

2016-08-05

A systematic route to go beyond the exact exchange plus random phase approximation (RPA) is to include a physical exchange-correlation kernel in the adiabatic-connection fluctuation-dissipation theorem. Previously, [D. Lu, J. Chem. Phys. 140, 18A520 (2014)], we found that non-local kernels with a screening length depending on the local Wigner-Seitz radius, r s(r), suffer an error associated with a spurious long-range repulsion in van der Waals bounded systems, which deteriorates the binding energy curve as compared to RPA. Here, we analyze the source of the error and propose to replace r s(r) by a global, average r s in the kernel.more » Exemplary studies with the Corradini, del Sole, Onida, and Palummo kernel show that while this change does not affect the already outstanding performance in crystalline solids, using an average r s significantly reduces the spurious long-range tail in the exchange-correlation kernel in van der Waals bounded systems. Finally, when this method is combined with further corrections using local dielectric response theory, the binding energy of the Kr dimer is improved three times as compared to RPA.« less

Integrating semantic information into multiple kernels for protein-protein interaction extraction from biomedical literatures.

PubMed

Li, Lishuang; Zhang, Panpan; Zheng, Tianfu; Zhang, Hongying; Jiang, Zhenchao; Huang, Degen

2014-01-01

Protein-Protein Interaction (PPI) extraction is an important task in the biomedical information extraction. Presently, many machine learning methods for PPI extraction have achieved promising results. However, the performance is still not satisfactory. One reason is that the semantic resources were basically ignored. In this paper, we propose a multiple-kernel learning-based approach to extract PPIs, combining the feature-based kernel, tree kernel and semantic kernel. Particularly, we extend the shortest path-enclosed tree kernel (SPT) by a dynamic extended strategy to retrieve the richer syntactic information. Our semantic kernel calculates the protein-protein pair similarity and the context similarity based on two semantic resources: WordNet and Medical Subject Heading (MeSH). We evaluate our method with Support Vector Machine (SVM) and achieve an F-score of 69.40% and an AUC of 92.00%, which show that our method outperforms most of the state-of-the-art systems by integrating semantic information.

A machine vision system for high speed sorting of small spots on grains

USDA-ARS?s Scientific Manuscript database

A sorting system was developed to detect and remove individual grain kernels with small localized blemishes or defects. The system uses a color VGA sensor to capture images of the kernels at high speed as the grain drops off an inclined chute. The image data are directly input into a field-programma...

The construction of a two-dimensional reproducing kernel function and its application in a biomedical model.

PubMed

Guo, Qi; Shen, Shu-Ting

2016-04-29

There are two major classes of cardiac tissue models: the ionic model and the FitzHugh-Nagumo model. During computer simulation, each model entails solving a system of complex ordinary differential equations and a partial differential equation with non-flux boundary conditions. The reproducing kernel method possesses significant applications in solving partial differential equations. The derivative of the reproducing kernel function is a wavelet function, which has local properties and sensitivities to singularity. Therefore, study on the application of reproducing kernel would be advantageous. Applying new mathematical theory to the numerical solution of the ventricular muscle model so as to improve its precision in comparison with other methods at present. A two-dimensional reproducing kernel function inspace is constructed and applied in computing the solution of two-dimensional cardiac tissue model by means of the difference method through time and the reproducing kernel method through space. Compared with other methods, this method holds several advantages such as high accuracy in computing solutions, insensitivity to different time steps and a slow propagation speed of error. It is suitable for disorderly scattered node systems without meshing, and can arbitrarily change the location and density of the solution on different time layers. The reproducing kernel method has higher solution accuracy and stability in the solutions of the two-dimensional cardiac tissue model.

Multiple Scale Landscape Pattern Index Interpretation for the Persistent Monitoring of Land-Cover and Land-Use

NASA Astrophysics Data System (ADS)

Spivey, Alvin J.

Mapping land-cover land-use change (LCLUC) over regional and continental scales, and long time scales (years and decades), can be accomplished using thematically identified classification maps of a landscape---a LCLU class map. Observations of a landscape's LCLU class map pattern can indicate the most relevant process, like hydrologic or ecologic function, causing landscape scale environmental change. Quantified as Landscape Pattern Metrics (LPM), emergent landscape patterns act as Landscape Indicators (LI) when physically interpreted. The common mathematical approach to quantifying observed landscape scale pattern is to have LPM measure how connected a class exists within the landscape, through nonlinear local kernel operations of edges and gradients in class maps. Commonly applied kernel-based LPM that consistently reveal causal processes are Dominance, Contagion, and Fractal Dimension. These kernel-based LPM can be difficult to interpret. The emphasis on an image pixel's edge by gradient operations and dependence on an image pixel's existence according to classification accuracy limit the interpretation of LPM. For example, the Dominance and Contagion kernel-based LPM very similarly measure how connected a landscape is. Because of this, their reported edge measurements of connected pattern correlate strongly, making their results ambiguous. Additionally, each of these kernel-based LPM are unscalable when comparing class maps from separate imaging system sensor scenarios that change the image pixel's edge position (i.e. changes in landscape extent, changes in pixel size, changes in orientation, etc), and can only interpret landscape pattern as accurately as the LCLU map classification will allow. This dissertation discusses the reliability of common LPM in light of imaging system effects such as: algorithm classification likelihoods, LCLU classification accuracy due to random image sensor noise, and image scale. A description of an approach to generating well behaved LPM through a Fourier system analysis of the entire class map, or any subset of the class map (e.g. the watershed) is the focus of this work. The Fourier approach provides four improvements for LPM. First, the approach reduces any correlation between metrics by developing them within an independent (i.e. orthogonal) Fourier vector space; a Fourier vector space that includes relevant physically representative parameters ( i.e. between class Euclidean distance). Second, accounting for LCLU classification accuracy the LPM measurement precision and measurement accuracy are reported. Third, the mathematics of this approach makes it possible to compare image data captured at separate pixel resolutions or even from separate landscape scenes. Fourth, Fourier interpreted landscape pattern measurement can be a measure of the entire landscape shape, of individual landscape cover change, or as exchanges between class map subsets by operating on the entire class map, subset of class map, or separate subsets of class map[s] respectively. These LCLUC LPM are examined along the 1991-1992 and 2000-2001 records of National Land Cover Database Landsat data products. Those LPM results are used in a predictive fecal coliform model at the South Carolina watershed level in the context of past (validation study) change. Finally, the proposed LPM ability to be used as ecologically relevant environmental indicators is tested by correlating metrics with other, well known LI that consistently reveal causal processes in the literature.

Energy scaling advantages of resistive memory crossbar based computation and its application to sparse coding

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

Agarwal, Sapan; Quach, Tu -Thach; Parekh, Ojas

In this study, the exponential increase in data over the last decade presents a significant challenge to analytics efforts that seek to process and interpret such data for various applications. Neural-inspired computing approaches are being developed in order to leverage the computational properties of the analog, low-power data processing observed in biological systems. Analog resistive memory crossbars can perform a parallel read or a vector-matrix multiplication as well as a parallel write or a rank-1 update with high computational efficiency. For an N × N crossbar, these two kernels can be O(N) more energy efficient than a conventional digital memory-basedmore » architecture. If the read operation is noise limited, the energy to read a column can be independent of the crossbar size (O(1)). These two kernels form the basis of many neuromorphic algorithms such as image, text, and speech recognition. For instance, these kernels can be applied to a neural sparse coding algorithm to give an O(N) reduction in energy for the entire algorithm when run with finite precision. Sparse coding is a rich problem with a host of applications including computer vision, object tracking, and more generally unsupervised learning.« less

Energy scaling advantages of resistive memory crossbar based computation and its application to sparse coding

DOE PAGES

Agarwal, Sapan; Quach, Tu -Thach; Parekh, Ojas; ...

2016-01-06

In this study, the exponential increase in data over the last decade presents a significant challenge to analytics efforts that seek to process and interpret such data for various applications. Neural-inspired computing approaches are being developed in order to leverage the computational properties of the analog, low-power data processing observed in biological systems. Analog resistive memory crossbars can perform a parallel read or a vector-matrix multiplication as well as a parallel write or a rank-1 update with high computational efficiency. For an N × N crossbar, these two kernels can be O(N) more energy efficient than a conventional digital memory-basedmore » architecture. If the read operation is noise limited, the energy to read a column can be independent of the crossbar size (O(1)). These two kernels form the basis of many neuromorphic algorithms such as image, text, and speech recognition. For instance, these kernels can be applied to a neural sparse coding algorithm to give an O(N) reduction in energy for the entire algorithm when run with finite precision. Sparse coding is a rich problem with a host of applications including computer vision, object tracking, and more generally unsupervised learning.« less

Adaptive Fault-Resistant Systems

DTIC Science & Technology

1994-10-01

An Architectural Overview of the Alpha Real-Time Distributed Kernel . In Proceeding., of the USEN[X Workshop on Microkernels and Other Kernel ...system and the controller are monolithic . We have noted earlier some of the problems of distributed systems-for exam- ple, the need to bound the...are monolithic . In practice, designers employ a layered structuring for their systems in order to manage complexity, and we expect that practical

Some operational tools for solving fractional and higher integer order differential equations: A survey on their mutual relations

NASA Astrophysics Data System (ADS)

Kiryakova, Virginia S.

2012-11-01

The Laplace Transform (LT) serves as a basis of the Operational Calculus (OC), widely explored by engineers and applied scientists in solving mathematical models for their practical needs. This transform is closely related to the exponential and trigonometric functions (exp, cos, sin) and to the classical differentiation and integration operators, reducing them to simple algebraic operations. Thus, the classical LT and the OC give useful tool to handle differential equations and systems with constant coefficients. Several generalizations of the LT have been introduced to allow solving, in a similar way, of differential equations with variable coefficients and of higher integer orders, as well as of fractional (arbitrary non-integer) orders. Note that fractional order mathematical models are recently widely used to describe better various systems and phenomena of the real world. This paper surveys briefly some of our results on classes of such integral transforms, that can be obtained from the LT by means of "transmutations" which are operators of the generalized fractional calculus (GFC). On the list of these Laplace-type integral transforms, we consider the Borel-Dzrbashjan, Meijer, Krätzel, Obrechkoff, generalized Obrechkoff (multi-index Borel-Dzrbashjan) transforms, etc. All of them are G- and H-integral transforms of convolutional type, having as kernels Meijer's G- or Fox's H-functions. Besides, some special functions (also being G- and H-functions), among them - the generalized Bessel-type and Mittag-Leffler (M-L) type functions, are generating Gel'fond-Leontiev (G-L) operators of generalized differentiation and integration, which happen to be also operators of GFC. Our integral transforms have operational properties analogous to those of the LT - they do algebrize the G-L generalized integrations and differentiations, and thus can serve for solving wide classes of differential equations with variable coefficients of arbitrary, including non-integer order. Throughout the survey, we illustrate the parallels in the relationships: Laplace type integral transforms - special functions as kernels - operators of generalized integration and differentiation generated by special functions - special functions as solutions of related differential equations. The role of the so-called Special Functions of Fractional Calculus is emphasized.

Preparation of uranium fuel kernels with silicon carbide nanoparticles using the internal gelation process

NASA Astrophysics Data System (ADS)

Hunt, R. D.; Silva, G. W. C. M.; Lindemer, T. B.; Anderson, K. K.; Collins, J. L.

2012-08-01

The US Department of Energy continues to use the internal gelation process in its preparation of tristructural isotropic coated fuel particles. The focus of this work is to develop uranium fuel kernels with adequately dispersed silicon carbide (SiC) nanoparticles, high crush strengths, uniform particle diameter, and good sphericity. During irradiation to high burnup, the SiC in the uranium kernels will serve as getters for excess oxygen and help control the oxygen potential in order to minimize the potential for kernel migration. The hardness of SiC required modifications to the gelation system that was used to make uranium kernels. Suitable processing conditions and potential equipment changes were identified so that the SiC could be homogeneously dispersed in gel spheres. Finally, dilute hydrogen rather than argon should be used to sinter the uranium kernels with SiC.

NARMER-1: a photon point-kernel code with build-up factors

NASA Astrophysics Data System (ADS)

Visonneau, Thierry; Pangault, Laurence; Malouch, Fadhel; Malvagi, Fausto; Dolci, Florence

2017-09-01

This paper presents an overview of NARMER-1, the new generation of photon point-kernel code developed by the Reactor Studies and Applied Mathematics Unit (SERMA) at CEA Saclay Center. After a short introduction giving some history points and the current context of development of the code, the paper exposes the principles implemented in the calculation, the physical quantities computed and surveys the generic features: programming language, computer platforms, geometry package, sources description, etc. Moreover, specific and recent features are also detailed: exclusion sphere, tetrahedral meshes, parallel operations. Then some points about verification and validation are presented. Finally we present some tools that can help the user for operations like visualization and pre-treatment.

Digital logic optimization using selection operators

NASA Technical Reports Server (NTRS)

Whitaker, Sterling R. (Inventor); Miles, Lowell H. (Inventor); Cameron, Eric G. (Inventor); Gambles, Jody W. (Inventor)

2004-01-01

According to the invention, a digital design method for manipulating a digital circuit netlist is disclosed. In one step, a first netlist is loaded. The first netlist is comprised of first basic cells that are comprised of first kernel cells. The first netlist is manipulated to create a second netlist. The second netlist is comprised of second basic cells that are comprised of second kernel cells. A percentage of the first and second kernel cells are selection circuits. There is less chip area consumed in the second basic cells than in the first basic cells. The second netlist is stored. In various embodiments, the percentage could be 2% or more, 5% or more, 10% or more, 20% or more, 30% or more, or 40% or more.

Equation for the Nakanishi Weight Function Using the Inverse Stieltjes Transform

NASA Astrophysics Data System (ADS)

Karmanov, V. A.; Carbonell, J.; Frederico, T.

2018-05-01

The bound state Bethe-Salpeter amplitude was expressed by Nakanishi in terms of a smooth weight function g. By using the generalized Stieltjes transform, we derive an integral equation for the Nakanishi function g for a bound state case. It has the standard form g= \\hat{V} g, where \\hat{V} is a two-dimensional integral operator. The prescription for obtaining the kernel V starting with the kernel K of the Bethe-Salpeter equation is given.

Predicting drug-target interactions by dual-network integrated logistic matrix factorization

NASA Astrophysics Data System (ADS)

Hao, Ming; Bryant, Stephen H.; Wang, Yanli

2017-01-01

In this work, we propose a dual-network integrated logistic matrix factorization (DNILMF) algorithm to predict potential drug-target interactions (DTI). The prediction procedure consists of four steps: (1) inferring new drug/target profiles and constructing profile kernel matrix; (2) diffusing drug profile kernel matrix with drug structure kernel matrix; (3) diffusing target profile kernel matrix with target sequence kernel matrix; and (4) building DNILMF model and smoothing new drug/target predictions based on their neighbors. We compare our algorithm with the state-of-the-art method based on the benchmark dataset. Results indicate that the DNILMF algorithm outperforms the previously reported approaches in terms of AUPR (area under precision-recall curve) and AUC (area under curve of receiver operating characteristic) based on the 5 trials of 10-fold cross-validation. We conclude that the performance improvement depends on not only the proposed objective function, but also the used nonlinear diffusion technique which is important but under studied in the DTI prediction field. In addition, we also compile a new DTI dataset for increasing the diversity of currently available benchmark datasets. The top prediction results for the new dataset are confirmed by experimental studies or supported by other computational research.

Kernel Wiener filter and its application to pattern recognition.

PubMed

Yoshino, Hirokazu; Dong, Chen; Washizawa, Yoshikazu; Yamashita, Yukihiko

2010-11-01

The Wiener filter (WF) is widely used for inverse problems. From an observed signal, it provides the best estimated signal with respect to the squared error averaged over the original and the observed signals among linear operators. The kernel WF (KWF), extended directly from WF, has a problem that an additive noise has to be handled by samples. Since the computational complexity of kernel methods depends on the number of samples, a huge computational cost is necessary for the case. By using the first-order approximation of kernel functions, we realize KWF that can handle such a noise not by samples but as a random variable. We also propose the error estimation method for kernel filters by using the approximations. In order to show the advantages of the proposed methods, we conducted the experiments to denoise images and estimate errors. We also apply KWF to classification since KWF can provide an approximated result of the maximum a posteriori classifier that provides the best recognition accuracy. The noise term in the criterion can be used for the classification in the presence of noise or a new regularization to suppress changes in the input space, whereas the ordinary regularization for the kernel method suppresses changes in the feature space. In order to show the advantages of the proposed methods, we conducted experiments of binary and multiclass classifications and classification in the presence of noise.

An Improved B+ Tree for Flash File Systems

NASA Astrophysics Data System (ADS)

Havasi, Ferenc

Nowadays mobile devices such as mobile phones, mp3 players and PDAs are becoming evermore common. Most of them use flash chips as storage. To store data efficiently on flash, it is necessary to adapt ordinary file systems because they are designed for use on hard disks. Most of the file systems use some kind of search tree to store index information, which is very important from a performance aspect. Here we improved the B+ search tree algorithm so as to make flash devices more efficient. Our implementation of this solution saves 98%-99% of the flash operations, and is now the part of the Linux kernel.

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

Hornung, Richard D.; Hones, Holger E.

The RAJA Performance Suite is designed to evaluate performance of the RAJA performance portability library on a wide variety of important high performance computing (HPC) algorithmic lulmels. These kernels assess compiler optimizations and various parallel programming model backends accessible through RAJA, such as OpenMP, CUDA, etc. The Initial version of the suite contains 25 computational kernels, each of which appears in 6 variants: Baseline SequcntiaJ, RAJA SequentiaJ, Baseline OpenMP, RAJA OpenMP, Baseline CUDA, RAJA CUDA. All variants of each kernel perform essentially the same mathematical operations and the loop body code for each kernel is identical across all variants. Theremore » are a few kernels, such as those that contain reduction operations, that require CUDA-specific coding for their CUDA variants. ActuaJ computer instructions executed and how they run in parallel differs depending on the parallel programming model backend used and which optimizations are perfonned by the compiler used to build the Perfonnance Suite executable. The Suite will be used primarily by RAJA developers to perform regular assessments of RAJA performance across a range of hardware platforms and compilers as RAJA features are being developed. It will also be used by LLNL hardware and software vendor panners for new defining requirements for future computing platform procurements and acceptance testing. In particular, the RAJA Performance Suite will be used for compiler acceptance testing of the upcoming CORAUSierra machine {initial LLNL delivery expected in late-2017/early 2018) and the CORAL-2 procurement. The Suite will aJso be used to generate concise source code reproducers of compiler and runtime issues we uncover so that we may provide them to relevant vendors to be fixed.« less

Interaction with Machine Improvisation

NASA Astrophysics Data System (ADS)

Assayag, Gerard; Bloch, George; Cont, Arshia; Dubnov, Shlomo

We describe two multi-agent architectures for an improvisation oriented musician-machine interaction systems that learn in real time from human performers. The improvisation kernel is based on sequence modeling and statistical learning. We present two frameworks of interaction with this kernel. In the first, the stylistic interaction is guided by a human operator in front of an interactive computer environment. In the second framework, the stylistic interaction is delegated to machine intelligence and therefore, knowledge propagation and decision are taken care of by the computer alone. The first framework involves a hybrid architecture using two popular composition/performance environments, Max and OpenMusic, that are put to work and communicate together, each one handling the process at a different time/memory scale. The second framework shares the same representational schemes with the first but uses an Active Learning architecture based on collaborative, competitive and memory-based learning to handle stylistic interactions. Both systems are capable of processing real-time audio/video as well as MIDI. After discussing the general cognitive background of improvisation practices, the statistical modelling tools and the concurrent agent architecture are presented. Then, an Active Learning scheme is described and considered in terms of using different improvisation regimes for improvisation planning. Finally, we provide more details about the different system implementations and describe several performances with the system.

Asymptotic formula for the Riesz means of the spectral functions of Laplace-Beltrami operator on unit sphere

NASA Astrophysics Data System (ADS)

Fadly Nurullah Rasedee, Ahmad; Ahmedov, Anvarjon; Sathar, Mohammad Hasan Abdul

2017-09-01

The mathematical models of the heat and mass transfer processes on the ball type solids can be solved using the theory of convergence of Fourier-Laplace series on unit sphere. Many interesting models have divergent Fourier-Laplace series, which can be made convergent by introducing Riesz and Cesaro means of the series. Partial sums of the Fourier-Laplace series summed by Riesz method are integral operators with the kernel known as Riesz means of the spectral function. In order to obtain the convergence results for the partial sums by Riesz means we need to know an asymptotic behavior of the latter kernel. In this work the estimations for Riesz means of spectral function of Laplace-Beltrami operator which guarantees the convergence of the Fourier-Laplace series by Riesz method are obtained.

Kernel Recursive Least-Squares Temporal Difference Algorithms with Sparsification and Regularization

PubMed Central

Zhu, Qingxin; Niu, Xinzheng

2016-01-01

By combining with sparse kernel methods, least-squares temporal difference (LSTD) algorithms can construct the feature dictionary automatically and obtain a better generalization ability. However, the previous kernel-based LSTD algorithms do not consider regularization and their sparsification processes are batch or offline, which hinder their widespread applications in online learning problems. In this paper, we combine the following five techniques and propose two novel kernel recursive LSTD algorithms: (i) online sparsification, which can cope with unknown state regions and be used for online learning, (ii) L 2 and L 1 regularization, which can avoid overfitting and eliminate the influence of noise, (iii) recursive least squares, which can eliminate matrix-inversion operations and reduce computational complexity, (iv) a sliding-window approach, which can avoid caching all history samples and reduce the computational cost, and (v) the fixed-point subiteration and online pruning, which can make L 1 regularization easy to implement. Finally, simulation results on two 50-state chain problems demonstrate the effectiveness of our algorithms. PMID:27436996

Kernel Recursive Least-Squares Temporal Difference Algorithms with Sparsification and Regularization.

PubMed

Zhang, Chunyuan; Zhu, Qingxin; Niu, Xinzheng

2016-01-01

By combining with sparse kernel methods, least-squares temporal difference (LSTD) algorithms can construct the feature dictionary automatically and obtain a better generalization ability. However, the previous kernel-based LSTD algorithms do not consider regularization and their sparsification processes are batch or offline, which hinder their widespread applications in online learning problems. In this paper, we combine the following five techniques and propose two novel kernel recursive LSTD algorithms: (i) online sparsification, which can cope with unknown state regions and be used for online learning, (ii) L 2 and L 1 regularization, which can avoid overfitting and eliminate the influence of noise, (iii) recursive least squares, which can eliminate matrix-inversion operations and reduce computational complexity, (iv) a sliding-window approach, which can avoid caching all history samples and reduce the computational cost, and (v) the fixed-point subiteration and online pruning, which can make L 1 regularization easy to implement. Finally, simulation results on two 50-state chain problems demonstrate the effectiveness of our algorithms.

Adaptive Multilevel Middleware for Object Systems

DTIC Science & Technology

2006-12-01

the system at the system-call level or using the CORBA-standard Extensible Transport Framework ( ETF ). Transparent insertion is highly desirable from an...often as it needs to. This is remedied by using the real-time scheduling class in a stock Linux kernel. We used schedsetscheduler system call (with...real-time scheduling class (SCHEDFIFO) for all the ML-NFD programs, later experiments with CPU load indicate that a stock Linux kernel is not

Extreme Performance Scalable Operating Systems Final Progress Report (July 1, 2008 - October 31, 2011)

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

Malony, Allen D; Shende, Sameer

This is the final progress report for the FastOS (Phase 2) (FastOS-2) project with Argonne National Laboratory and the University of Oregon (UO). The project started at UO on July 1, 2008 and ran until April 30, 2010, at which time a six-month no-cost extension began. The FastOS-2 work at UO delivered excellent results in all research work areas: * scalable parallel monitoring * kernel-level performance measurement * parallel I/0 system measurement * large-scale and hybrid application performance measurement * onlne scalable performance data reduction and analysis * binary instrumentation

BSD Portals for LINUX 2.0

NASA Technical Reports Server (NTRS)

McNab, A. David; woo, Alex (Technical Monitor)

1999-01-01

Portals, an experimental feature of 4.4BSD, extend the file system name space by exporting certain open () requests to a user-space daemon. A portal daemon is mounted into the file name space as if it were a standard file system. When the kernel resolves a pathname and encounters a portal mount point, the remainder of the path is passed to the portal daemon. Depending on the portal "pathname" and the daemon's configuration, some type of open (2) is performed. The resulting file descriptor is passed back to the kernel which eventually returns it to the user, to whom it appears that a "normal" open has occurred. A proxy portalfs file system is responsible for kernel interaction with the daemon. The overall effect is that the portal daemon performs an open (2) on behalf of the kernel, possibly hiding substantial complexity from the calling process. One particularly useful application is implementing a connection service that allows simple scripts to open network sockets. This paper describes the implementation of portals for LINUX 2.0.

Burrower bugs (Heteroptera: Cydnidae) in peanut: seasonal species abundance, tillage effects, grade reduction effects, insecticide efficacy, and management.

PubMed

Chapin, Jay W; Thomas, James S

2003-08-01

Pitfall traps placed in South Carolina peanut, Arachis hypogaea (L.), fields collected three species of burrower bugs (Cydnidae): Cyrtomenus ciliatus (Palisot de Beauvois), Sehirus cinctus cinctus (Palisot de Beauvois), and Pangaeus bilineatus (Say). Cyrtomenus ciliatus was rarely collected. Sehirus cinctus produced a nymphal cohort in peanut during May and June, probably because of abundant henbit seeds, Lamium amplexicaule L., in strip-till production systems. No S. cinctus were present during peanut pod formation. Pangaeus bilineatus was the most abundant species collected and the only species associated with peanut kernel feeding injury. Overwintering P. bilineatus adults were present in a conservation tillage peanut field before planting and two to three subsequent generations were observed. Few nymphs were collected until the R6 (full seed) growth stage. Tillage and choice of cover crop affected P. bilineatus populations. Peanuts strip-tilled into corn or wheat residue had greater P. bilineatus populations and kernel-feeding than conventional tillage or strip-tillage into rye residue. Fall tillage before planting a wheat cover crop also reduced burrower bug feeding on peanut. At-pegging (early July) granular chlorpyrifos treatments were most consistent in suppressing kernel feeding. Kernels fed on by P. bilineatus were on average 10% lighter than unfed on kernels. Pangaeus bilineatus feeding reduced peanut grade by reducing individual kernel weight, and increasing the percentage damaged kernels. Each 10% increase in kernels fed on by P. bilineatus was associated with a 1.7% decrease in total sound mature kernels, and kernel feeding levels above 30% increase the risk of damaged kernel grade penalties.

Adiabatic-connection fluctuation-dissipation DFT for the structural properties of solids—The renormalized ALDA and electron gas kernels

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

Patrick, Christopher E., E-mail: chripa@fysik.dtu.dk; Thygesen, Kristian S., E-mail: thygesen@fysik.dtu.dk

2015-09-14

We present calculations of the correlation energies of crystalline solids and isolated systems within the adiabatic-connection fluctuation-dissipation formulation of density-functional theory. We perform a quantitative comparison of a set of model exchange-correlation kernels originally derived for the homogeneous electron gas (HEG), including the recently introduced renormalized adiabatic local-density approximation (rALDA) and also kernels which (a) satisfy known exact limits of the HEG, (b) carry a frequency dependence, or (c) display a 1/k{sup 2} divergence for small wavevectors. After generalizing the kernels to inhomogeneous systems through a reciprocal-space averaging procedure, we calculate the lattice constants and bulk moduli of a testmore » set of 10 solids consisting of tetrahedrally bonded semiconductors (C, Si, SiC), ionic compounds (MgO, LiCl, LiF), and metals (Al, Na, Cu, Pd). We also consider the atomization energy of the H{sub 2} molecule. We compare the results calculated with different kernels to those obtained from the random-phase approximation (RPA) and to experimental measurements. We demonstrate that the model kernels correct the RPA’s tendency to overestimate the magnitude of the correlation energy whilst maintaining a high-accuracy description of structural properties.« less

A new approach to approximating the linear quadratic optimal control law for hereditary systems with control delays

NASA Technical Reports Server (NTRS)

Milman, M. H.

1985-01-01

A factorization approach is presented for deriving approximations to the optimal feedback gain for the linear regulator-quadratic cost problem associated with time-varying functional differential equations with control delays. The approach is based on a discretization of the state penalty which leads to a simple structure for the feedback control law. General properties of the Volterra factors of Hilbert-Schmidt operators are then used to obtain convergence results for the feedback kernels.

KSOS Secure Unix Verification Plan (Kernelized Secure Operating System).

DTIC Science & Technology

1980-12-01

shall be handled as proprietary information untii 5 Apri 1978. After that time, the Government m-. distribute the document as it sees fit. UNIX and PWB...Accession For P-’(’ T.’i3 :- NTI G.;:’... &I : " \\ " Y: Codes mdlc/or 71!O lii WDL-TR7809 KSOS VERIFICATION PLAN SECTION I INTRODUCTION "’The purpose...funding, additional tools may be available by the time they are needed for FSOS verification. We intend to use the best available technology in

KAPSE (Kernel ADA Programming Support Environment) Interface Team Public Report. Volume 5.

DTIC Science & Technology

1985-08-01

Computer Nons , April 1964. e The K Ma policy statent mde the Language Control Fcility Newsletter for the Ada Jovial Wmkng Group. e There will be a CRIS...cre-ed, the initial access control inA-mIion my be splied by the ACCSS parmeter. If non -nll, this parumter speci fi the initia access tolicontr olti...assigned, or controlled by the operating system to &,.sure consistent and non -conflicting usage by programs under execution. Examples of resources

Fouling mechanism in ultrafiltration of vegetable oil

NASA Astrophysics Data System (ADS)

Ariono, D.; Wardani, A. K.; Widodo, S.; Aryanti, Putu T. P.; Wenten, I. G.

2018-03-01

Energy efficient and cost-effective separation of impurities from vegetable oil is a great challenge for vegetable oil processing. Several technologies have been developed, including pressurized membrane, chemical treatment, and chemical free separation methods. Among those technologies, ultrafiltration membrane is one of the most attractive processes with low operating pressure and temperature. In this work, hydrophobic polypropylene ultrafiltration membrane was used to remove impurities such as non-dissolved solids from palm kernel oil. Unfortunately, the hydrophobicity of polypropylene membrane leads to significant impact on the reduction of permeate flux due to membrane fouling. This fouling is associated with the accumulation of substances on the membrane surface or within the membrane pores. For better understanding, fouling mechanism that occurred during palm kernel oil ultrafiltration using hydrophobic polypropylene membrane was investigated. The effect of trans-membrane pressure and feed temperature on fouling mechanism was also studied. The result showed that cake formation became the dominant fouling mechanism up to 50 min operation of palm kernel oil ultrafiltration. Furthermore, the fouling mechanism was not affected by the increase of trans-membrane pressure and feed temperature.

PERI - Auto-tuning Memory Intensive Kernels for Multicore

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

Bailey, David H; Williams, Samuel; Datta, Kaushik

2008-06-24

We present an auto-tuning approach to optimize application performance on emerging multicore architectures. The methodology extends the idea of search-based performance optimizations, popular in linear algebra and FFT libraries, to application-specific computational kernels. Our work applies this strategy to Sparse Matrix Vector Multiplication (SpMV), the explicit heat equation PDE on a regular grid (Stencil), and a lattice Boltzmann application (LBMHD). We explore one of the broadest sets of multicore architectures in the HPC literature, including the Intel Xeon Clovertown, AMD Opteron Barcelona, Sun Victoria Falls, and the Sony-Toshiba-IBM (STI) Cell. Rather than hand-tuning each kernel for each system, we developmore » a code generator for each kernel that allows us to identify a highly optimized version for each platform, while amortizing the human programming effort. Results show that our auto-tuned kernel applications often achieve a better than 4X improvement compared with the original code. Additionally, we analyze a Roofline performance model for each platform to reveal hardware bottlenecks and software challenges for future multicore systems and applications.« less

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

Lu, Deyu

A systematic route to go beyond the exact exchange plus random phase approximation (RPA) is to include a physical exchange-correlation kernel in the adiabatic-connection fluctuation-dissipation theorem. Previously, [D. Lu, J. Chem. Phys. 140, 18A520 (2014)], we found that non-local kernels with a screening length depending on the local Wigner-Seitz radius, r s(r), suffer an error associated with a spurious long-range repulsion in van der Waals bounded systems, which deteriorates the binding energy curve as compared to RPA. Here, we analyze the source of the error and propose to replace r s(r) by a global, average r s in the kernel.more » Exemplary studies with the Corradini, del Sole, Onida, and Palummo kernel show that while this change does not affect the already outstanding performance in crystalline solids, using an average r s significantly reduces the spurious long-range tail in the exchange-correlation kernel in van der Waals bounded systems. Finally, when this method is combined with further corrections using local dielectric response theory, the binding energy of the Kr dimer is improved three times as compared to RPA.« less

Higher-order kinetic expansion of quantum dissipative dynamics: mapping quantum networks to kinetic networks.

PubMed

Wu, Jianlan; Cao, Jianshu

2013-07-28

We apply a new formalism to derive the higher-order quantum kinetic expansion (QKE) for studying dissipative dynamics in a general quantum network coupled with an arbitrary thermal bath. The dynamics of system population is described by a time-convoluted kinetic equation, where the time-nonlocal rate kernel is systematically expanded of the order of off-diagonal elements of the system Hamiltonian. In the second order, the rate kernel recovers the expression of the noninteracting-blip approximation method. The higher-order corrections in the rate kernel account for the effects of the multi-site quantum coherence and the bath relaxation. In a quantum harmonic bath, the rate kernels of different orders are analytically derived. As demonstrated by four examples, the higher-order QKE can reliably predict quantum dissipative dynamics, comparing well with the hierarchic equation approach. More importantly, the higher-order rate kernels can distinguish and quantify distinct nontrivial quantum coherent effects, such as long-range energy transfer from quantum tunneling and quantum interference arising from the phase accumulation of interactions.

A Study of Umbilical Communication Interface of Simulator Kernel to Enhance Visibility and Controllability

NASA Astrophysics Data System (ADS)

Koo, Cheol Hea; Lee, Hoon Hee; Moon, Sung Tae; Han, Sang Hyuck; Ju, Gwang Hyeok

2013-08-01

In aerospace research and practical development area, increasing the usage of simulation in software development, component design and system operation has been maintained and the increasing speed getting faster. This phenomenon can be found from the easiness of handling of simulation and the powerfulness of the output from the simulation. Simulation brings lots of benefit from the several characteristics of it as following, - easy to handle ; it is never broken or damaged by mistake - never wear out ; it is never getting old - cost effective ; once it is built, it can be distributed over 100 ~ 1000 people GenSim (Generic Simulator) which is developing by KARI and compatible with ESA SMP standard provides such a simulation platform to support flight software validation and mission operation verification. User interface of GenSim is shown in Figure 1 [1,2]. As shown in Figure 1, as most simulation platform typically has, GenSim has GRD (Graphical Display) and AND (Alpha Numeric Display). But frequently more complex and powerful handling of the simulated data is required at the actual system validation for example mission operation. In Figure 2, system simulation result of COMS (Communication, Ocean, and Meteorological Satellite, launched at June 28 2008) is being drawn by Celestia 3D program. In this case, the needed data from Celestia is given by one of the simulation model resident in system simulator through UDP network connection in this case. But the requirement of displaying format, data size, and communication rate is variable so developer has to manage the connection protocol manually at each time and each case. It brings a chaos in the simulation model design and development, also to the performance issue at last. Performance issue is happen when the required data magnitude is higher than the capacity of simulation kernel to process the required data safely. The problem is that the sending data to a visualization tool such as celestia is given by a simulation model not kernel. Because the simulation model has no way to know about the status of simulation kernel load to process simulation events, as the result the simulation model sends the data as frequent as needed. This story may make many potential problems like lack of response, failure of meeting deadline and data integrity problem with the model data during the simulation. SIMSAT and EuroSim gives a warning message if the user request event such as printing log can't be processed as planned or requested. As the consequence the requested event will be delayed or not be able to be processed, and it means that this phenomenon may violate the planned deadline. In most soft real time simulation, this can be neglected and just make a little inconvenience of users. But it shall be noted that if the user request is not managed properly at some critical situation, the simulation results may be ended with a mess and chaos. As we traced the disadvantages of what simulation model provide the user request, simulation model is not appropriate to provide a service for such user request. This kind of work shall be minimized as much as possible.

Design of an EEG-based brain-computer interface (BCI) from standard components running in real-time under Windows.

PubMed

Guger, C; Schlögl, A; Walterspacher, D; Pfurtscheller, G

1999-01-01

An EEG-based brain-computer interface (BCI) is a direct connection between the human brain and the computer. Such a communication system is needed by patients with severe motor impairments (e.g. late stage of Amyotrophic Lateral Sclerosis) and has to operate in real-time. This paper describes the selection of the appropriate components to construct such a BCI and focuses also on the selection of a suitable programming language and operating system. The multichannel system runs under Windows 95, equipped with a real-time Kernel expansion to obtain reasonable real-time operations on a standard PC. Matlab controls the data acquisition and the presentation of the experimental paradigm, while Simulink is used to calculate the recursive least square (RLS) algorithm that describes the current state of the EEG in real-time. First results of the new low-cost BCI show that the accuracy of differentiating imagination of left and right hand movement is around 95%.

Correlation and classification of single kernel fluorescence hyperspectral data with aflatoxin concentration in corn kernels inoculated with Aspergillus flavus spores.

PubMed

Yao, H; Hruska, Z; Kincaid, R; Brown, R; Cleveland, T; Bhatnagar, D

2010-05-01

The objective of this study was to examine the relationship between fluorescence emissions of corn kernels inoculated with Aspergillus flavus and aflatoxin contamination levels within the kernels. Aflatoxin contamination in corn has been a long-standing problem plaguing the grain industry with potentially devastating consequences to corn growers. In this study, aflatoxin-contaminated corn kernels were produced through artificial inoculation of corn ears in the field with toxigenic A. flavus spores. The kernel fluorescence emission data were taken with a fluorescence hyperspectral imaging system when corn kernels were excited with ultraviolet light. Raw fluorescence image data were preprocessed and regions of interest in each image were created for all kernels. The regions of interest were used to extract spectral signatures and statistical information. The aflatoxin contamination level of single corn kernels was then chemically measured using affinity column chromatography. A fluorescence peak shift phenomenon was noted among different groups of kernels with different aflatoxin contamination levels. The fluorescence peak shift was found to move more toward the longer wavelength in the blue region for the highly contaminated kernels and toward the shorter wavelengths for the clean kernels. Highly contaminated kernels were also found to have a lower fluorescence peak magnitude compared with the less contaminated kernels. It was also noted that a general negative correlation exists between measured aflatoxin and the fluorescence image bands in the blue and green regions. The correlation coefficients of determination, r(2), was 0.72 for the multiple linear regression model. The multivariate analysis of variance found that the fluorescence means of four aflatoxin groups, <1, 1-20, 20-100, and >or=100 ng g(-1) (parts per billion), were significantly different from each other at the 0.01 level of alpha. Classification accuracy under a two-class schema ranged from 0.84 to 0.91 when a threshold of either 20 or 100 ng g(-1) was used. Overall, the results indicate that fluorescence hyperspectral imaging may be applicable in estimating aflatoxin content in individual corn kernels.

Height system unification based on the Fixed Geodetic Boundary Value Problem with limited availability of gravity data

NASA Astrophysics Data System (ADS)

Porz, Lucas; Grombein, Thomas; Seitz, Kurt; Heck, Bernhard; Wenzel, Friedemann

2017-04-01

Regional height reference systems are generally related to individual vertical datums defined by specific tide gauges. The discrepancies of these vertical datums with respect to a unified global datum cause height system biases that range in an order of 1-2 m at a global scale. One approach for unification of height systems relates to the solution of a Geodetic Boundary Value Problem (GBVP). In particular, the fixed GBVP, using gravity disturbances as boundary values, is solved at GNSS/leveling benchmarks, whereupon height datum offsets can be estimated by least squares adjustment. In spherical approximation, the solution of the fixed GBVP is obtained by Hotine's spherical integral formula. However, this method relies on the global availability of gravity data. In practice, gravity data of the necessary resolution and accuracy is not accessible globally. Thus, the integration is restricted to an area within the vicinity of the computation points. The resulting truncation error can reach several meters in height, making height system unification without further consideration of this effect unfeasible. This study analyzes methods for reducing the truncation error by combining terrestrial gravity data with satellite-based global geopotential models and by modifying the integral kernel in order to accelerate the convergence of the resulting potential. For this purpose, EGM2008-derived gravity functionals are used as pseudo-observations to be integrated numerically. Geopotential models of different spectral degrees are implemented using a remove-restore-scheme. Three types of modification are applied to the Hotine-kernel and the convergence of the resulting potential is analyzed. In a further step, the impact of these operations on the estimation of height datum offsets is investigated within a closed loop simulation. A minimum integration radius in combination with a specific modification of the Hotine-kernel is suggested in order to achieve sub-cm accuracy for the estimation of height datum offsets.

Security Tagged Architecture Co-Design (STACD)

DTIC Science & Technology

2015-09-01

components have access to all other system components whether they need it or not. Microkernels [8, 9, 10] seek to reduce the kernel size to improve...does not provide the fine-grained control to allow for formal verification. Microkernels reduce the size of the kernel enough to allow for a formal...verification of the kernel. Tanenbaum [14] documents many of the security virtues of microkernels and argues that the Ring 3 Ring 2 Ring 1

Bose-Einstein condensation on a manifold with non-negative Ricci curvature

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

Akant, Levent, E-mail: levent.akant@boun.edu.tr; Ertuğrul, Emine, E-mail: emine.ertugrul@boun.edu.tr; Tapramaz, Ferzan, E-mail: waskhez@gmail.com

The Bose-Einstein condensation for an ideal Bose gas and for a dilute weakly interacting Bose gas in a manifold with non-negative Ricci curvature is investigated using the heat kernel and eigenvalue estimates of the Laplace operator. The main focus is on the nonrelativistic gas. However, special relativistic ideal gas is also discussed. The thermodynamic limit of the heat kernel and eigenvalue estimates is taken and the results are used to derive bounds for the depletion coefficient. In the case of a weakly interacting gas, Bogoliubov approximation is employed. The ground state is analyzed using heat kernel methods and finite sizemore » effects on the ground state energy are proposed. The justification of the c-number substitution on a manifold is given.« less

Non-parametric wall model and methods of identifying boundary conditions for moments in gas flow equations

NASA Astrophysics Data System (ADS)

Liao, Meng; To, Quy-Dong; Léonard, Céline; Monchiet, Vincent

2018-03-01

In this paper, we use the molecular dynamics simulation method to study gas-wall boundary conditions. Discrete scattering information of gas molecules at the wall surface is obtained from collision simulations. The collision data can be used to identify the accommodation coefficients for parametric wall models such as Maxwell and Cercignani-Lampis scattering kernels. Since these scattering kernels are based on a limited number of accommodation coefficients, we adopt non-parametric statistical methods to construct the kernel to overcome these issues. Different from parametric kernels, the non-parametric kernels require no parameter (i.e. accommodation coefficients) and no predefined distribution. We also propose approaches to derive directly the Navier friction and Kapitza thermal resistance coefficients as well as other interface coefficients associated with moment equations from the non-parametric kernels. The methods are applied successfully to systems composed of CH4 or CO2 and graphite, which are of interest to the petroleum industry.

Classification of Astrocytomas and Oligodendrogliomas from Mass Spectrometry Data Using Sparse Kernel Machines

PubMed Central

Huang, Jacob; Gholami, Behnood; Agar, Nathalie Y. R.; Norton, Isaiah; Haddad, Wassim M.; Tannenbaum, Allen R.

2013-01-01

Glioma histologies are the primary factor in prognostic estimates and are used in determining the proper course of treatment. Furthermore, due to the sensitivity of cranial environments, real-time tumor-cell classification and boundary detection can aid in the precision and completeness of tumor resection. A recent improvement to mass spectrometry known as desorption electrospray ionization operates in an ambient environment without the application of a preparation compound. This allows for a real-time acquisition of mass spectra during surgeries and other live operations. In this paper, we present a framework using sparse kernel machines to determine a glioma sample’s histopathological subtype by analyzing its chemical composition acquired by desorption electrospray ionization mass spectrometry. PMID:22256188

Optimal focal-plane restoration

NASA Technical Reports Server (NTRS)

Reichenbach, Stephen E.; Park, Stephen K.

1989-01-01

Image restoration can be implemented efficiently by calculating the convolution of the digital image and a small kernel during image acquisition. Processing the image in the focal-plane in this way requires less computation than traditional Fourier-transform-based techniques such as the Wiener filter and constrained least-squares filter. Here, the values of the convolution kernel that yield the restoration with minimum expected mean-square error are determined using a frequency analysis of the end-to-end imaging system. This development accounts for constraints on the size and shape of the spatial kernel and all the components of the imaging system. Simulation results indicate the technique is effective and efficient.

Study of heterogeneous and reconfigurable architectures in the communication domain

NASA Astrophysics Data System (ADS)

Feldkaemper, H. T.; Blume, H.; Noll, T. G.

2003-05-01

One of the most challenging design issues for next generations of (mobile) communication systems is fulfilling the computational demands while finding an appropriate trade-off between flexibility and implementation aspects, especially power consumption. Flexibility of modern architectures is desirable, e.g. concerning adaptation to new standards and reduction of time-to-market of a new product. Typical target architectures for future communication systems include embedded FPGAs, dedicated macros as well as programmable digital signal and control oriented processor cores as each of these has its specific advantages. These will be integrated as a System-on-Chip (SoC). For such a heterogeneous architecture a design space exploration and an appropriate partitioning plays a crucial role. On the exemplary vehicle of a Viterbi decoder as frequently used in communication systems we show which costs in terms of ATE complexity arise implementing typical components on different types of architecture blocks. A factor of about seven orders of magnitude spans between a physically optimised implementation and an implementation on a programmable DSP kernel. An implementation on an embedded FPGA kernel is in between these two representing an attractive compromise with high flexibility and low power consumption. Extending this comparison to further components, it is shown quantitatively that the cost ratio between different implementation alternatives is closely related to the operation to be performed. This information is essential for the appropriate partitioning of heterogeneous systems.

Analysis of the power flow in nonlinear oscillators driven by random excitation using the first Wiener kernel

NASA Astrophysics Data System (ADS)

Hawes, D. H.; Langley, R. S.

2018-01-01

Random excitation of mechanical systems occurs in a wide variety of structures and, in some applications, calculation of the power dissipated by such a system will be of interest. In this paper, using the Wiener series, a general methodology is developed for calculating the power dissipated by a general nonlinear multi-degree-of freedom oscillatory system excited by random Gaussian base motion of any spectrum. The Wiener series method is most commonly applied to systems with white noise inputs, but can be extended to encompass a general non-white input. From the extended series a simple expression for the power dissipated can be derived in terms of the first term, or kernel, of the series and the spectrum of the input. Calculation of the first kernel can be performed either via numerical simulations or from experimental data and a useful property of the kernel, namely that the integral over its frequency domain representation is proportional to the oscillating mass, is derived. The resulting equations offer a simple conceptual analysis of the power flow in nonlinear randomly excited systems and hence assist the design of any system where power dissipation is a consideration. The results are validated both numerically and experimentally using a base-excited cantilever beam with a nonlinear restoring force produced by magnets.

Kernel structures for Clouds

NASA Technical Reports Server (NTRS)

Spafford, Eugene H.; Mckendry, Martin S.

1986-01-01

An overview of the internal structure of the Clouds kernel was presented. An indication of how these structures will interact in the prototype Clouds implementation is given. Many specific details have yet to be determined and await experimentation with an actual working system.

Effect of Local TOF Kernel Miscalibrations on Contrast-Noise in TOF PET

NASA Astrophysics Data System (ADS)

Clementel, Enrico; Mollet, Pieter; Vandenberghe, Stefaan

2013-06-01

TOF PET imaging requires specific calibrations: accurate characterization of the system timing resolution and timing offset is required to achieve the full potential image quality. Current system models used in image reconstruction assume a spatially uniform timing resolution kernel. Furthermore, although the timing offset errors are often pre-corrected, this correction becomes less accurate with the time since, especially in older scanners, the timing offsets are often calibrated only during the installation, as the procedure is time-consuming. In this study, we investigate and compare the effects of local mismatch of timing resolution when a uniform kernel is applied to systems with local variations in timing resolution and the effects of uncorrected time offset errors on image quality. A ring-like phantom was acquired on a Philips Gemini TF scanner and timing histograms were obtained from coincidence events to measure timing resolution along all sets of LORs crossing the scanner center. In addition, multiple acquisitions of a cylindrical phantom, 20 cm in diameter with spherical inserts, and a point source were simulated. A location-dependent timing resolution was simulated, with a median value of 500 ps and increasingly large local variations, and timing offset errors ranging from 0 to 350 ps were also simulated. Images were reconstructed with TOF MLEM with a uniform kernel corresponding to the effective timing resolution of the data, as well as with purposefully mismatched kernels. To CRC vs noise curves were measured over the simulated cylinder realizations, while the simulated point source was processed to generate timing histograms of the data. Results show that timing resolution is not uniform over the FOV of the considered scanner. The simulated phantom data indicate that CRC is moderately reduced in data sets with locally varying timing resolution reconstructed with a uniform kernel, while still performing better than non-TOF reconstruction. On the other hand, uncorrected offset errors in our setup have a larger potential for decreasing image quality and can lead to a reduction of CRC of up to 15% and an increase in the measured timing resolution kernel up to 40%. However, in realistic conditions in frequently calibrated systems, using a larger effective timing kernel in image reconstruction can compensate uncorrected offset errors.

Intelligent Design of Metal Oxide Gas Sensor Arrays Using Reciprocal Kernel Support Vector Regression

NASA Astrophysics Data System (ADS)

Dougherty, Andrew W.

Metal oxides are a staple of the sensor industry. The combination of their sensitivity to a number of gases, and the electrical nature of their sensing mechanism, make the particularly attractive in solid state devices. The high temperature stability of the ceramic material also make them ideal for detecting combustion byproducts where exhaust temperatures can be high. However, problems do exist with metal oxide sensors. They are not very selective as they all tend to be sensitive to a number of reduction and oxidation reactions on the oxide's surface. This makes sensors with large numbers of sensors interesting to study as a method for introducing orthogonality to the system. Also, the sensors tend to suffer from long term drift for a number of reasons. In this thesis I will develop a system for intelligently modeling metal oxide sensors and determining their suitability for use in large arrays designed to analyze exhaust gas streams. It will introduce prior knowledge of the metal oxide sensors' response mechanisms in order to produce a response function for each sensor from sparse training data. The system will use the same technique to model and remove any long term drift from the sensor response. It will also provide an efficient means for determining the orthogonality of the sensor to determine whether they are useful in gas sensing arrays. The system is based on least squares support vector regression using the reciprocal kernel. The reciprocal kernel is introduced along with a method of optimizing the free parameters of the reciprocal kernel support vector machine. The reciprocal kernel is shown to be simpler and to perform better than an earlier kernel, the modified reciprocal kernel. Least squares support vector regression is chosen as it uses all of the training points and an emphasis was placed throughout this research for extracting the maximum information from very sparse data. The reciprocal kernel is shown to be effective in modeling the sensor responses in the time, gas and temperature domains, and the dual representation of the support vector regression solution is shown to provide insight into the sensor's sensitivity and potential orthogonality. Finally, the dual weights of the support vector regression solution to the sensor's response are suggested as a fitness function for a genetic algorithm, or some other method for efficiently searching large parameter spaces.

BP network identification technology of infrared polarization based on fuzzy c-means clustering

NASA Astrophysics Data System (ADS)

Zeng, Haifang; Gu, Guohua; He, Weiji; Chen, Qian; Yang, Wei

2011-08-01

Infrared detection system is frequently employed on surveillance operations and reconnaissance mission to detect particular targets of interest in both civilian and military communities. By incorporating the polarization of light as supplementary information, the target discrimination performance could be enhanced. So this paper proposed an infrared target identification method which is based on fuzzy theory and neural network with polarization properties of targets. The paper utilizes polarization degree and light intensity to advance the unsupervised KFCM (kernel fuzzy C-Means) clustering method. And establish different material pol1arization properties database. In the built network, the system can feedback output corresponding material types of probability distribution toward any input polarized degree such as 10° 15°, 20°, 25°, 30°. KFCM, which has stronger robustness and accuracy than FCM, introduces kernel idea and gives the noise points and invalid value different but intuitively reasonable weights. Because of differences in characterization of material properties, there will be some conflicts in classification results. And D - S evidence theory was used in the combination of the polarization and intensity information. Related results show KFCM clustering precision and operation rate are higher than that of the FCM clustering method. The artificial neural network method realizes material identification, which reasonable solved the problems of complexity in environmental information of infrared polarization, and improperness of background knowledge and inference rule. This method of polarization identification is fast in speed, good in self-adaption and high in resolution.

Multi-kernel aggregation of local and global features in long-wave infrared for detection of SWAT teams in challenging environments

NASA Astrophysics Data System (ADS)

Arya, Ankit S.; Anderson, Derek T.; Bethel, Cindy L.; Carruth, Daniel

2013-05-01

A vision system was designed for people detection to provide support to SWAT team members operating in challenging environments such as low-to-no light, smoke, etc. When the vision system is mounted on a mobile robot platform: it will enable the robot to function as an effective member of the SWAT team; to provide surveillance information; to make first contact with suspects; and provide safe entry for team members. The vision task is challenging because SWAT team members are typically concealed, carry various equipment such as shields, and perform tactical and stealthy maneuvers. Occlusion is a particular challenge because team members operate in close proximity to one another. An uncooled electro-opticaljlong wav e infrared (EO/ LWIR) camera, 7.5 to 13.5 m, was used. A unique thermal dataset was collected of SWAT team members from multiple teams performing tactical maneuvers during monthly training exercises. Our approach consisted of two stages: an object detector trained on people to find candidate windows, and a secondary feature extraction, multi-kernel (MK) aggregation and classification step to distinguish between SWAT team members and civilians. Two types of thermal features, local and global, are presented based on ma ximally stable extremal region (MSER) blob detection. Support vector machine (SVM) classification results of approximately [70, 93]% for SWAT team member detection are reported based on the exploration of different combinations of visual information in terms of training data.

Sliding Window Generalized Kernel Affine Projection Algorithm Using Projection Mappings

NASA Astrophysics Data System (ADS)

Slavakis, Konstantinos; Theodoridis, Sergios

2008-12-01

Very recently, a solution to the kernel-based online classification problem has been given by the adaptive projected subgradient method (APSM). The developed algorithm can be considered as a generalization of a kernel affine projection algorithm (APA) and the kernel normalized least mean squares (NLMS). Furthermore, sparsification of the resulting kernel series expansion was achieved by imposing a closed ball (convex set) constraint on the norm of the classifiers. This paper presents another sparsification method for the APSM approach to the online classification task by generating a sequence of linear subspaces in a reproducing kernel Hilbert space (RKHS). To cope with the inherent memory limitations of online systems and to embed tracking capabilities to the design, an upper bound on the dimension of the linear subspaces is imposed. The underlying principle of the design is the notion of projection mappings. Classification is performed by metric projection mappings, sparsification is achieved by orthogonal projections, while the online system's memory requirements and tracking are attained by oblique projections. The resulting sparsification scheme shows strong similarities with the classical sliding window adaptive schemes. The proposed design is validated by the adaptive equalization problem of a nonlinear communication channel, and is compared with classical and recent stochastic gradient descent techniques, as well as with the APSM's solution where sparsification is performed by a closed ball constraint on the norm of the classifiers.

Power weighted L p -inequalities for Laguerre-Riesz transforms

NASA Astrophysics Data System (ADS)

Harboure, Eleonor; Segovia, Carlos; Torrea, José L.; Viviani, Beatriz

2008-10-01

In this paper we give a complete description of the power weighted inequalities, of strong, weak and restricted weak type for the pair of Riesz transforms associated with the Laguerre function system \\{mathcal{L}_k^{α}\\}, for any given α>-1. We achieve these results by a careful estimate of the kernels: near the diagonal we show that they are local Calderón-Zygmund operators while in the complement they are majorized by Hardy type operators and the maximal heat-diffusion operator. We also show that in all the cases our results are sharp.

An efficient diagnosis system for Parkinson's disease using kernel-based extreme learning machine with subtractive clustering features weighting approach.

PubMed

Ma, Chao; Ouyang, Jihong; Chen, Hui-Ling; Zhao, Xue-Hua

2014-01-01

A novel hybrid method named SCFW-KELM, which integrates effective subtractive clustering features weighting and a fast classifier kernel-based extreme learning machine (KELM), has been introduced for the diagnosis of PD. In the proposed method, SCFW is used as a data preprocessing tool, which aims at decreasing the variance in features of the PD dataset, in order to further improve the diagnostic accuracy of the KELM classifier. The impact of the type of kernel functions on the performance of KELM has been investigated in detail. The efficiency and effectiveness of the proposed method have been rigorously evaluated against the PD dataset in terms of classification accuracy, sensitivity, specificity, area under the receiver operating characteristic (ROC) curve (AUC), f-measure, and kappa statistics value. Experimental results have demonstrated that the proposed SCFW-KELM significantly outperforms SVM-based, KNN-based, and ELM-based approaches and other methods in the literature and achieved highest classification results reported so far via 10-fold cross validation scheme, with the classification accuracy of 99.49%, the sensitivity of 100%, the specificity of 99.39%, AUC of 99.69%, the f-measure value of 0.9964, and kappa value of 0.9867. Promisingly, the proposed method might serve as a new candidate of powerful methods for the diagnosis of PD with excellent performance.

An Efficient Diagnosis System for Parkinson's Disease Using Kernel-Based Extreme Learning Machine with Subtractive Clustering Features Weighting Approach

PubMed Central

Ma, Chao; Ouyang, Jihong; Chen, Hui-Ling; Zhao, Xue-Hua

2014-01-01

A novel hybrid method named SCFW-KELM, which integrates effective subtractive clustering features weighting and a fast classifier kernel-based extreme learning machine (KELM), has been introduced for the diagnosis of PD. In the proposed method, SCFW is used as a data preprocessing tool, which aims at decreasing the variance in features of the PD dataset, in order to further improve the diagnostic accuracy of the KELM classifier. The impact of the type of kernel functions on the performance of KELM has been investigated in detail. The efficiency and effectiveness of the proposed method have been rigorously evaluated against the PD dataset in terms of classification accuracy, sensitivity, specificity, area under the receiver operating characteristic (ROC) curve (AUC), f-measure, and kappa statistics value. Experimental results have demonstrated that the proposed SCFW-KELM significantly outperforms SVM-based, KNN-based, and ELM-based approaches and other methods in the literature and achieved highest classification results reported so far via 10-fold cross validation scheme, with the classification accuracy of 99.49%, the sensitivity of 100%, the specificity of 99.39%, AUC of 99.69%, the f-measure value of 0.9964, and kappa value of 0.9867. Promisingly, the proposed method might serve as a new candidate of powerful methods for the diagnosis of PD with excellent performance. PMID:25484912

A Non-Local, Energy-Optimized Kernel: Recovering Second-Order Exchange and Beyond in Extended Systems

NASA Astrophysics Data System (ADS)

Bates, Jefferson; Laricchia, Savio; Ruzsinszky, Adrienn

The Random Phase Approximation (RPA) is quickly becoming a standard method beyond semi-local Density Functional Theory that naturally incorporates weak interactions and eliminates self-interaction error. RPA is not perfect, however, and suffers from self-correlation error as well as an incorrect description of short-ranged correlation typically leading to underbinding. To improve upon RPA we introduce a short-ranged, exchange-like kernel that is one-electron self-correlation free for one and two electron systems in the high-density limit. By tuning the one free parameter in our model to recover an exact limit of the homogeneous electron gas correlation energy we obtain a non-local, energy-optimized kernel that reduces the errors of RPA for both homogeneous and inhomogeneous solids. To reduce the computational cost of the standard kernel-corrected RPA, we also implement RPA renormalized perturbation theory for extended systems, and demonstrate its capability to describe the dominant correlation effects with a low-order expansion in both metallic and non-metallic systems. Furthermore we stress that for norm-conserving implementations the accuracy of RPA and beyond RPA structural properties compared to experiment is inherently limited by the choice of pseudopotential. Current affiliation: King's College London.

Efficient nonparametric n -body force fields from machine learning

NASA Astrophysics Data System (ADS)

Glielmo, Aldo; Zeni, Claudio; De Vita, Alessandro

2018-05-01

We provide a definition and explicit expressions for n -body Gaussian process (GP) kernels, which can learn any interatomic interaction occurring in a physical system, up to n -body contributions, for any value of n . The series is complete, as it can be shown that the "universal approximator" squared exponential kernel can be written as a sum of n -body kernels. These recipes enable the choice of optimally efficient force models for each target system, as confirmed by extensive testing on various materials. We furthermore describe how the n -body kernels can be "mapped" on equivalent representations that provide database-size-independent predictions and are thus crucially more efficient. We explicitly carry out this mapping procedure for the first nontrivial (three-body) kernel of the series, and we show that this reproduces the GP-predicted forces with meV /Å accuracy while being orders of magnitude faster. These results pave the way to using novel force models (here named "M-FFs") that are computationally as fast as their corresponding standard parametrized n -body force fields, while retaining the nonparametric character, the ease of training and validation, and the accuracy of the best recently proposed machine-learning potentials.

A Novel Mittag-Leffler Kernel Based Hybrid Fault Diagnosis Method for Wheeled Robot Driving System.

PubMed

Yuan, Xianfeng; Song, Mumin; Zhou, Fengyu; Chen, Zhumin; Li, Yan

2015-01-01

The wheeled robots have been successfully applied in many aspects, such as industrial handling vehicles, and wheeled service robots. To improve the safety and reliability of wheeled robots, this paper presents a novel hybrid fault diagnosis framework based on Mittag-Leffler kernel (ML-kernel) support vector machine (SVM) and Dempster-Shafer (D-S) fusion. Using sensor data sampled under different running conditions, the proposed approach initially establishes multiple principal component analysis (PCA) models for fault feature extraction. The fault feature vectors are then applied to train the probabilistic SVM (PSVM) classifiers that arrive at a preliminary fault diagnosis. To improve the accuracy of preliminary results, a novel ML-kernel based PSVM classifier is proposed in this paper, and the positive definiteness of the ML-kernel is proved as well. The basic probability assignments (BPAs) are defined based on the preliminary fault diagnosis results and their confidence values. Eventually, the final fault diagnosis result is archived by the fusion of the BPAs. Experimental results show that the proposed framework not only is capable of detecting and identifying the faults in the robot driving system, but also has better performance in stability and diagnosis accuracy compared with the traditional methods.

A Novel Mittag-Leffler Kernel Based Hybrid Fault Diagnosis Method for Wheeled Robot Driving System

PubMed Central

Yuan, Xianfeng; Song, Mumin; Chen, Zhumin; Li, Yan

2015-01-01

The wheeled robots have been successfully applied in many aspects, such as industrial handling vehicles, and wheeled service robots. To improve the safety and reliability of wheeled robots, this paper presents a novel hybrid fault diagnosis framework based on Mittag-Leffler kernel (ML-kernel) support vector machine (SVM) and Dempster-Shafer (D-S) fusion. Using sensor data sampled under different running conditions, the proposed approach initially establishes multiple principal component analysis (PCA) models for fault feature extraction. The fault feature vectors are then applied to train the probabilistic SVM (PSVM) classifiers that arrive at a preliminary fault diagnosis. To improve the accuracy of preliminary results, a novel ML-kernel based PSVM classifier is proposed in this paper, and the positive definiteness of the ML-kernel is proved as well. The basic probability assignments (BPAs) are defined based on the preliminary fault diagnosis results and their confidence values. Eventually, the final fault diagnosis result is archived by the fusion of the BPAs. Experimental results show that the proposed framework not only is capable of detecting and identifying the faults in the robot driving system, but also has better performance in stability and diagnosis accuracy compared with the traditional methods. PMID:26229526

Source Code Analysis Laboratory (SCALe) for Energy Delivery Systems

DTIC Science & Technology

2010-12-01

the software for reevaluation. Once the ree- valuation process is completed, CERT provides the client a report detailing the software’s con - formance...Flagged Nonconformities (FNC) Software System TP/FNC Ratio Mozilla Firefox version 2.0 6/12 50% Linux kernel version 2.6.15 10/126 8% Wine...inappropriately tuned for analysis of the Linux kernel, which has anomalous results. Customizing SCALe to work with energy system software will help

Compiler-Driven Performance Optimization and Tuning for Multicore Architectures

DTIC Science & Technology

2015-04-10

develop a powerful system for auto-tuning of library routines and compute-intensive kernels, driven by the Pluto system for multicores that we are...kernels, driven by the Pluto system for multicores that we are developing. The work here is motivated by recent advances in two major areas of...automatic C-to-CUDA code generator using a polyhedral compiler transformation framework. We have used and adapted PLUTO (our state-of-the-art tool

Long-Term file activity patterns in a UNIX workstation environment

NASA Technical Reports Server (NTRS)

Gibson, Timothy J.; Miller, Ethan L.

1998-01-01

As mass storage technology becomes more affordable for sites smaller than supercomputer centers, understanding their file access patterns becomes crucial for developing systems to store rarely used data on tertiary storage devices such as tapes and optical disks. This paper presents a new way to collect and analyze file system statistics for UNIX-based file systems. The collection system runs in user-space and requires no modification of the operating system kernel. The statistics package provides details about file system operations at the file level: creations, deletions, modifications, etc. The paper analyzes four months of file system activity on a university file system. The results confirm previously published results gathered from supercomputer file systems, but differ in several important areas. Files in this study were considerably smaller than those at supercomputer centers, and they were accessed less frequently. Additionally, the long-term creation rate on workstation file systems is sufficiently low so that all data more than a day old could be cheaply saved on a mass storage device, allowing the integration of time travel into every file system.

Preparation of Simulated LBL Defects for Round Robin Experiment

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

Gerczak, Tyler J.; Baldwin, Charles A.; Hunn, John D.

2016-01-01

A critical characteristic of the TRISO fuel design is its ability to retain fission products. During reactor operation, the TRISO layers act as barriers to release of fission products not stabilized in the kernel. Each component of the TRISO particle and compact construction plays a unique role in retaining select fission products, and layer performance is often interrelated. The IPyC, SiC, and OPyC layers are barriers to the release of fission product gases such as Kr and Xe. The SiC layer provides the primary barrier to release of metallic fission products not retained in the kernel, as transport across themore » SiC layer is rate limiting due to the greater permeability of the IPyC and OPyC layers to many metallic fission products. These attributes allow intact TRISO coatings to successfully retain most fission products released from the kernel, with the majority of released fission products during operation being due to defective, damaged, or failed coatings. This dominant release of fission products from compromised particles contributes to the overall source term in reactor; causing safety and maintenance concerns and limiting the lifetime of the fuel. Under these considerations, an understanding of the nature and frequency of compromised particles is an important part of predicting the expected fission product release and ensuring safe and efficient operation.« less

Discriminant analysis for fast multiclass data classification through regularized kernel function approximation.

PubMed

Ghorai, Santanu; Mukherjee, Anirban; Dutta, Pranab K

2010-06-01

In this brief we have proposed the multiclass data classification by computationally inexpensive discriminant analysis through vector-valued regularized kernel function approximation (VVRKFA). VVRKFA being an extension of fast regularized kernel function approximation (FRKFA), provides the vector-valued response at single step. The VVRKFA finds a linear operator and a bias vector by using a reduced kernel that maps a pattern from feature space into the low dimensional label space. The classification of patterns is carried out in this low dimensional label subspace. A test pattern is classified depending on its proximity to class centroids. The effectiveness of the proposed method is experimentally verified and compared with multiclass support vector machine (SVM) on several benchmark data sets as well as on gene microarray data for multi-category cancer classification. The results indicate the significant improvement in both training and testing time compared to that of multiclass SVM with comparable testing accuracy principally in large data sets. Experiments in this brief also serve as comparison of performance of VVRKFA with stratified random sampling and sub-sampling.

An orthogonal oriented quadrature hexagonal image pyramid

NASA Technical Reports Server (NTRS)

Watson, Andrew B.; Ahumada, Albert J., Jr.

1987-01-01

An image pyramid has been developed with basis functions that are orthogonal, self-similar, and localized in space, spatial frequency, orientation, and phase. The pyramid operates on a hexagonal sample lattice. The set of seven basis functions consist of three even high-pass kernels, three odd high-pass kernels, and one low-pass kernel. The three even kernels are identified when rotated by 60 or 120 deg, and likewise for the odd. The seven basis functions occupy a point and a hexagon of six nearest neighbors on a hexagonal sample lattice. At the lowest level of the pyramid, the input lattice is the image sample lattice. At each higher level, the input lattice is provided by the low-pass coefficients computed at the previous level. At each level, the output is subsampled in such a way as to yield a new hexagonal lattice with a spacing sq rt 7 larger than the previous level, so that the number of coefficients is reduced by a factor of 7 at each level. The relationship between this image code and the processing architecture of the primate visual cortex is discussed.

Research in Parallel Computing: 1987-1990

DTIC Science & Technology

1994-08-05

emulation, we layered UNIX BSD 4.3 functionality above the kernel primitives, but packaged both as a monolithic unit running in privileged state. This...further, so that only a "pure kernel " or " microkernel " runs in privileged mode, while the other components of the environment execute as one or more client... kernel DTIC TAB 24 2.2.2 Nectar’s communication software Unannounced 0 25 2.2.3 A Nectar programming interface Justification 25 2.3 System evaluation 26

Kernel analysis in TeV gamma-ray selection

NASA Astrophysics Data System (ADS)

Moriarty, P.; Samuelson, F. W.

2000-06-01

We discuss the use of kernel analysis as a technique for selecting gamma-ray candidates in Atmospheric Cherenkov astronomy. The method is applied to observations of the Crab Nebula and Markarian 501 recorded with the Whipple 10 m Atmospheric Cherenkov imaging system, and the results are compared with the standard Supercuts analysis. Since kernel analysis is computationally intensive, we examine approaches to reducing the computational load. Extension of the technique to estimate the energy of the gamma-ray primary is considered. .

The Flux OSKit: A Substrate for Kernel and Language Research

DTIC Science & Technology

1997-10-01

unclassified Standard Form 298 (Rev. 8-98) Prescribed by ANSI Std Z39-18 tions. Our own microkernel -based OS, Fluke [17], puts almost all of the OSKit to use...kernels distance the language from the hardware; even microkernels and other extensible kernels enforce some default policy which often conflicts with a...be particu- larly useful in these research projects. 6.1.1 The Fluke OS In 1996 we developed an entirely new microkernel - based system called Fluke

Putting Priors in Mixture Density Mercer Kernels

NASA Technical Reports Server (NTRS)

Srivastava, Ashok N.; Schumann, Johann; Fischer, Bernd

2004-01-01

This paper presents a new methodology for automatic knowledge driven data mining based on the theory of Mercer Kernels, which are highly nonlinear symmetric positive definite mappings from the original image space to a very high, possibly infinite dimensional feature space. We describe a new method called Mixture Density Mercer Kernels to learn kernel function directly from data, rather than using predefined kernels. These data adaptive kernels can en- code prior knowledge in the kernel using a Bayesian formulation, thus allowing for physical information to be encoded in the model. We compare the results with existing algorithms on data from the Sloan Digital Sky Survey (SDSS). The code for these experiments has been generated with the AUTOBAYES tool, which automatically generates efficient and documented C/C++ code from abstract statistical model specifications. The core of the system is a schema library which contains template for learning and knowledge discovery algorithms like different versions of EM, or numeric optimization methods like conjugate gradient methods. The template instantiation is supported by symbolic- algebraic computations, which allows AUTOBAYES to find closed-form solutions and, where possible, to integrate them into the code. The results show that the Mixture Density Mercer-Kernel described here outperforms tree-based classification in distinguishing high-redshift galaxies from low- redshift galaxies by approximately 16% on test data, bagged trees by approximately 7%, and bagged trees built on a much larger sample of data by approximately 2%.

The Unified Floating Point Vector Coprocessor for Reconfigurable Hardware

NASA Astrophysics Data System (ADS)

Kathiara, Jainik

There has been an increased interest recently in using embedded cores on FPGAs. Many of the applications that make use of these cores have floating point operations. Due to the complexity and expense of floating point hardware, these algorithms are usually converted to fixed point operations or implemented using floating-point emulation in software. As the technology advances, more and more homogeneous computational resources and fixed function embedded blocks are added to FPGAs and hence implementation of floating point hardware becomes a feasible option. In this research we have implemented a high performance, autonomous floating point vector Coprocessor (FPVC) that works independently within an embedded processor system. We have presented a unified approach to vector and scalar computation, using a single register file for both scalar operands and vector elements. The Hybrid vector/SIMD computational model of FPVC results in greater overall performance for most applications along with improved peak performance compared to other approaches. By parameterizing vector length and the number of vector lanes, we can design an application specific FPVC and take optimal advantage of the FPGA fabric. For this research we have also initiated designing a software library for various computational kernels, each of which adapts FPVC's configuration and provide maximal performance. The kernels implemented are from the area of linear algebra and include matrix multiplication and QR and Cholesky decomposition. We have demonstrated the operation of FPVC on a Xilinx Virtex 5 using the embedded PowerPC.

Individual detection of genetically modified maize varieties in non-identity-preserved maize samples.

PubMed

Akiyama, Hiroshi; Sakata, Kozue; Kondo, Kazunari; Tanaka, Asako; Liu, Ming S; Oguchi, Taichi; Furui, Satoshi; Kitta, Kazumi; Hino, Akihiro; Teshima, Reiko

2008-03-26

In many countries, the labeling of grains and feed- and foodstuffs is mandatory if the genetically modified organism (GMO) content exceeds a certain level of approved GM varieties. The GMO content in a maize sample containing the combined-trait (stacked) GM maize as determined by the currently available methodology is likely to be overestimated. However, there has been little information in the literature on the mixing level and varieties of stacked GM maize in real sample grains. For the first time, the GMO content of non-identity-preserved (non-IP) maize samples imported from the United States has been successfully determined by using a previously developed individual kernel detection system coupled to a multiplex qualitative PCR method followed by multichannel capillary gel electrophoresis system analysis. To clarify the GMO content in the maize samples imported from the United States, determine how many stacked GM traits are contained therein, and which GM trait varieties frequently appeared in 2005, the GMO content (percent) on a kernel basis and the varieties of the GM kernels in the non-IP maize samples imported from the United States were investigated using the individual kernel analysis system. The average (+/-standard deviation) of the GMO contents on a kernel basis in five non-IP sample lots was determined to be 51.0+/-21.6%, the percentage of a single GM trait grains was 39%, and the percentage of the stacked GM trait grains was 12%. The MON810 grains and NK603 grains were the most frequent varieties in the single GM traits. The most frequent stacked GM traits were the MON810xNK603 grains. In addition, the present study would provide the answer and impact for the quantification of GM maize content in the GM maize kernels on labeling regulation.

Conormal distributions in the Shubin calculus of pseudodifferential operators

NASA Astrophysics Data System (ADS)

Cappiello, Marco; Schulz, René; Wahlberg, Patrik

2018-02-01

We characterize the Schwartz kernels of pseudodifferential operators of Shubin type by means of a Fourier-Bros-Iagolnitzer transform. Based on this, we introduce as a generalization a new class of tempered distributions called Shubin conormal distributions. We study their transformation behavior, normal forms, and microlocal properties.

Load balancing and closed chain multiple arm control

NASA Technical Reports Server (NTRS)

Kreutz, Kenneth; Lokshin, Anatole

1988-01-01

The authors give the general dynamical equations for several rigid link manipulators rigidly grasping a commonly held rigid object. It is shown that the number of arm-configuration degrees of freedom lost due to imposing the closed-loop kinematic constraints is the same as the number of degrees of freedom gained for controlling the internal forces of the closed-chain system. This number is equal to the dimension of the kernel of the Jacobian operator which transforms contact forces to the net forces acting on the held object, and it is shown that this kernel can be identified with the subspace of controllable internal forces of the closed-chain system. Control of these forces makes it possible to regulate the grasping forces imparted to the held object or to control the load taken by each arm. It is shown that the internal forces can be influenced without affecting the control of the configuration degrees of freedom. Control laws of the feedback linearization type are shown to be useful for controlling the location and attitude of a frame fixed with respect to the held object, while simultaneously controlling the internal forces of the closed-chain system. Force feedback can be used to linearize and control the system even when the held object has unknown mass properties. If saturation effects are ignored, an unconstrained quadratic optimization can be performed to distribute the load optimally among the joint actuators.

Testing an Open Source installation and server provisioning tool for the INFN CNAF Tierl Storage system

NASA Astrophysics Data System (ADS)

Pezzi, M.; Favaro, M.; Gregori, D.; Ricci, P. P.; Sapunenko, V.

2014-06-01

In large computing centers, such as the INFN CNAF Tier1 [1], is essential to be able to configure all the machines, depending on use, in an automated way. For several years at the Tier1 has been used Quattor[2], a server provisioning tool, which is currently used in production. Nevertheless we have recently started a comparison study involving other tools able to provide specific server installation and configuration features and also offer a proper full customizable solution as an alternative to Quattor. Our choice at the moment fell on integration between two tools: Cobbler [3] for the installation phase and Puppet [4] for the server provisioning and management operation. The tool should provide the following properties in order to replicate and gradually improve the current system features: implement a system check for storage specific constraints such as kernel modules black list at boot time to avoid undesired SAN (Storage Area Network) access during disk partitioning; a simple and effective mechanism for kernel upgrade and downgrade; the ability of setting package provider using yum, rpm or apt; easy to use Virtual Machine installation support including bonding and specific Ethernet configuration; scalability for managing thousands of nodes and parallel installations. This paper describes the results of the comparison and the tests carried out to verify the requirements and the new system suitability in the INFN-T1 environment.

Unsupervised multiple kernel learning for heterogeneous data integration.

PubMed

Mariette, Jérôme; Villa-Vialaneix, Nathalie

2018-03-15

Recent high-throughput sequencing advances have expanded the breadth of available omics datasets and the integrated analysis of multiple datasets obtained on the same samples has allowed to gain important insights in a wide range of applications. However, the integration of various sources of information remains a challenge for systems biology since produced datasets are often of heterogeneous types, with the need of developing generic methods to take their different specificities into account. We propose a multiple kernel framework that allows to integrate multiple datasets of various types into a single exploratory analysis. Several solutions are provided to learn either a consensus meta-kernel or a meta-kernel that preserves the original topology of the datasets. We applied our framework to analyse two public multi-omics datasets. First, the multiple metagenomic datasets, collected during the TARA Oceans expedition, was explored to demonstrate that our method is able to retrieve previous findings in a single kernel PCA as well as to provide a new image of the sample structures when a larger number of datasets are included in the analysis. To perform this analysis, a generic procedure is also proposed to improve the interpretability of the kernel PCA in regards with the original data. Second, the multi-omics breast cancer datasets, provided by The Cancer Genome Atlas, is analysed using a kernel Self-Organizing Maps with both single and multi-omics strategies. The comparison of these two approaches demonstrates the benefit of our integration method to improve the representation of the studied biological system. Proposed methods are available in the R package mixKernel, released on CRAN. It is fully compatible with the mixOmics package and a tutorial describing the approach can be found on mixOmics web site http://mixomics.org/mixkernel/. jerome.mariette@inra.fr or nathalie.villa-vialaneix@inra.fr. Supplementary data are available at Bioinformatics online.

Bands selection and classification of hyperspectral images based on hybrid kernels SVM by evolutionary algorithm

NASA Astrophysics Data System (ADS)

Hu, Yan-Yan; Li, Dong-Sheng

2016-01-01

The hyperspectral images(HSI) consist of many closely spaced bands carrying the most object information. While due to its high dimensionality and high volume nature, it is hard to get satisfactory classification performance. In order to reduce HSI data dimensionality preparation for high classification accuracy, it is proposed to combine a band selection method of artificial immune systems (AIS) with a hybrid kernels support vector machine (SVM-HK) algorithm. In fact, after comparing different kernels for hyperspectral analysis, the approach mixed radial basis function kernel (RBF-K) with sigmoid kernel (Sig-K) and applied the optimized hybrid kernels in SVM classifiers. Then the SVM-HK algorithm used to induce the bands selection of an improved version of AIS. The AIS was composed of clonal selection and elite antibody mutation, including evaluation process with optional index factor (OIF). Experimental classification performance was on a San Diego Naval Base acquired by AVIRIS, the HRS dataset shows that the method is able to efficiently achieve bands redundancy removal while outperforming the traditional SVM classifier.

Computer Program Development Specification for Ada Integrated Environment: KAPSE (Kernel Ada Programming Support Environment)/Database, Type B5, B5-AIE(1).KAPSE(1).

DTIC Science & Technology

1982-11-12

File 1/0 Prgram Invocation Other Access M and Control Services KAPSE/Host Interface most Operating System Peripherals/ 01 su ?eetworks 6282318-2 Figure 3...3.2.4.3.8.5 Transitory Windows The TRANSITORY flag is used to prevent permanent dependence on temporary windows created simply for focusing on a part of the...KAPSE/Tool interfaces in terms of these low-level host-independent interfaces. In addition, the KAPSE/Host interface packages prevent the application

Modeling of thin-walled structures interacting with acoustic media as constrained two-dimensional continua

NASA Astrophysics Data System (ADS)

Rabinskiy, L. N.; Zhavoronok, S. I.

2018-04-01

The transient interaction of acoustic media and elastic shells is considered on the basis of the transition function approach. The three-dimensional hyperbolic initial boundary-value problem is reduced to a two-dimensional problem of shell theory with integral operators approximating the acoustic medium effect on the shell dynamics. The kernels of these integral operators are determined by the elementary solution of the problem of acoustic waves diffraction at a rigid obstacle with the same boundary shape as the wetted shell surface. The closed-form elementary solution for arbitrary convex obstacles can be obtained at the initial interaction stages on the background of the so-called “thin layer hypothesis”. Thus, the shell–wave interaction model defined by integro-differential dynamic equations with analytically determined kernels of integral operators becomes hence two-dimensional but nonlocal in time. On the other hand, the initial interaction stage results in localized dynamic loadings and consequently in complex strain and stress states that require higher-order shell theories. Here the modified theory of I.N.Vekua–A.A.Amosov-type is formulated in terms of analytical continuum dynamics. The shell model is constructed on a two-dimensional manifold within a set of field variables, Lagrangian density, and constraint equations following from the boundary conditions “shifted” from the shell faces to its base surface. Such an approach allows one to construct consistent low-order shell models within a unified formal hierarchy. The equations of the N th-order shell theory are singularly perturbed and contain second-order partial derivatives with respect to time and surface coordinates whereas the numerical integration of systems of first-order equations is more efficient. Such systems can be obtained as Hamilton–de Donder–Weyl-type equations for the Lagrangian dynamical system. The Hamiltonian formulation of the elementary N th-order shell theory is here briefly described.

Operator based integration of information in multimodal radiological search mission with applications to anomaly detection

NASA Astrophysics Data System (ADS)

Benedetto, J.; Cloninger, A.; Czaja, W.; Doster, T.; Kochersberger, K.; Manning, B.; McCullough, T.; McLane, M.

2014-05-01

Successful performance of radiological search mission is dependent on effective utilization of mixture of signals. Examples of modalities include, e.g., EO imagery and gamma radiation data, or radiation data collected during multiple events. In addition, elevation data or spatial proximity can be used to enhance the performance of acquisition systems. State of the art techniques in processing and exploitation of complex information manifolds rely on diffusion operators. Our approach involves machine learning techniques based on analysis of joint data- dependent graphs and their associated diffusion kernels. Then, the significant eigenvectors of the derived fused graph Laplace and Schroedinger operators form the new representation, which provides integrated features from the heterogeneous input data. The families of data-dependent Laplace and Schroedinger operators on joint data graphs, shall be integrated by means of appropriately designed fusion metrics. These fused representations are used for target and anomaly detection.

Accurate interatomic force fields via machine learning with covariant kernels

NASA Astrophysics Data System (ADS)

Glielmo, Aldo; Sollich, Peter; De Vita, Alessandro

2017-06-01

We present a novel scheme to accurately predict atomic forces as vector quantities, rather than sets of scalar components, by Gaussian process (GP) regression. This is based on matrix-valued kernel functions, on which we impose the requirements that the predicted force rotates with the target configuration and is independent of any rotations applied to the configuration database entries. We show that such covariant GP kernels can be obtained by integration over the elements of the rotation group SO (d ) for the relevant dimensionality d . Remarkably, in specific cases the integration can be carried out analytically and yields a conservative force field that can be recast into a pair interaction form. Finally, we show that restricting the integration to a summation over the elements of a finite point group relevant to the target system is sufficient to recover an accurate GP. The accuracy of our kernels in predicting quantum-mechanical forces in real materials is investigated by tests on pure and defective Ni, Fe, and Si crystalline systems.

Feasibility of detecting aflatoxin B1 on inoculated maize kernels surface using Vis/NIR hyperspectral imaging.

PubMed

Wang, Wei; Heitschmidt, Gerald W; Windham, William R; Feldner, Peggy; Ni, Xinzhi; Chu, Xuan

2015-01-01

The feasibility of using a visible/near-infrared hyperspectral imaging system with a wavelength range between 400 and 1000 nm to detect and differentiate different levels of aflatoxin B1 (AFB1 ) artificially titrated on maize kernel surface was examined. To reduce the color effects of maize kernels, image analysis was limited to a subset of original spectra (600 to 1000 nm). Residual staining from the AFB1 on the kernels surface was selected as regions of interest for analysis. Principal components analysis (PCA) was applied to reduce the dimensionality of hyperspectral image data, and then a stepwise factorial discriminant analysis (FDA) was performed on latent PCA variables. The results indicated that discriminant factors F2 can be used to separate control samples from all of the other groups of kernels with AFB1 inoculated, whereas the discriminant factors F1 can be used to identify maize kernels with levels of AFB1 as low as 10 ppb. An overall classification accuracy of 98% was achieved. Finally, the peaks of β coefficients of the discrimination factors F1 and F2 were analyzed and several key wavelengths identified for differentiating maize kernels with and without AFB1 , as well as those with differing levels of AFB1 inoculation. Results indicated that Vis/NIR hyperspectral imaging technology combined with the PCA-FDA was a practical method to detect and differentiate different levels of AFB1 artificially inoculated on the maize kernels surface. However, indicated the potential to detect and differentiate naturally occurring toxins in maize kernel. © 2014 Institute of Food Technologists®

Application of SWIR hyperspectral imaging and chemometrics for identification of aflatoxin B1 contaminated maize kernels

NASA Astrophysics Data System (ADS)

Kimuli, Daniel; Wang, Wei; Wang, Wei; Jiang, Hongzhe; Zhao, Xin; Chu, Xuan

2018-03-01

A short-wave infrared (SWIR) hyperspectral imaging system (1000-2500 nm) combined with chemometric data analysis was used to detect aflatoxin B1 (AFB1) on surfaces of 600 kernels of four yellow maize varieties from different States of the USA (Georgia, Illinois, Indiana and Nebraska). For each variety, four AFB1 solutions (10, 20, 100 and 500 ppb) were artificially deposited on kernels and a control group was generated from kernels treated with methanol solution. Principal component analysis (PCA), partial least squares discriminant analysis (PLSDA) and factorial discriminant analysis (FDA) were applied to explore and classify maize kernels according to AFB1 contamination. PCA results revealed partial separation of control kernels from AFB1 contaminated kernels for each variety while no pattern of separation was observed among pooled samples. A combination of standard normal variate and first derivative pre-treatments produced the best PLSDA classification model with accuracy of 100% and 96% in calibration and validation, respectively, from Illinois variety. The best AFB1 classification results came from FDA on raw spectra with accuracy of 100% in calibration and validation for Illinois and Nebraska varieties. However, for both PLSDA and FDA models, poor AFB1 classification results were obtained for pooled samples relative to individual varieties. SWIR spectra combined with chemometrics and spectra pre-treatments showed the possibility of detecting maize kernels of different varieties coated with AFB1. The study further suggests that increase of maize kernel constituents like water, protein, starch and lipid in a pooled sample may have influence on detection accuracy of AFB1 contamination.

A Experimental Study of the Growth of Laser Spark and Electric Spark Ignited Flame Kernels.

NASA Astrophysics Data System (ADS)

Ho, Chi Ming

1995-01-01

Better ignition sources are constantly in demand for enhancing the spark ignition in practical applications such as automotive and liquid rocket engines. In response to this practical challenge, the present experimental study was conducted with the major objective to obtain a better understanding on how spark formation and hence spark characteristics affect the flame kernel growth. Two laser sparks and one electric spark were studied in air, propane-air, propane -air-nitrogen, methane-air, and methane-oxygen mixtures that were initially at ambient pressure and temperature. The growth of the kernels was monitored by imaging the kernels with shadowgraph systems, and by imaging the planar laser -induced fluorescence of the hydroxyl radicals inside the kernels. Characteristic dimensions and kernel structures were obtained from these images. Since different energy transfer mechanisms are involved in the formation of a laser spark as compared to that of an electric spark; a laser spark is insensitive to changes in mixture ratio and mixture type, while an electric spark is sensitive to changes in both. The detailed structures of the kernels in air and propane-air mixtures primarily depend on the spark characteristics. But the combustion heat released rapidly in methane-oxygen mixtures significantly modifies the kernel structure. Uneven spark energy distribution causes remarkably asymmetric kernel structure. The breakdown energy of a spark creates a blast wave that shows good agreement with the numerical point blast solution, and a succeeding complex spark-induced flow that agrees reasonably well with a simple puff model. The transient growth rates of the propane-air, propane-air -nitrogen, and methane-air flame kernels can be interpreted in terms of spark effects, flame stretch, and preferential diffusion. For a given mixture, a spark with higher breakdown energy produces a greater and longer-lasting enhancing effect on the kernel growth rate. By comparing the growth rates of the appropriate mixtures, the positive and negative effects of preferential diffusion and flame stretch on the developing flame are clearly demonstrated.

Adaptive kernel regression for freehand 3D ultrasound reconstruction

NASA Astrophysics Data System (ADS)

Alshalalfah, Abdel-Latif; Daoud, Mohammad I.; Al-Najar, Mahasen

2017-03-01

Freehand three-dimensional (3D) ultrasound imaging enables low-cost and flexible 3D scanning of arbitrary-shaped organs, where the operator can freely move a two-dimensional (2D) ultrasound probe to acquire a sequence of tracked cross-sectional images of the anatomy. Often, the acquired 2D ultrasound images are irregularly and sparsely distributed in the 3D space. Several 3D reconstruction algorithms have been proposed to synthesize 3D ultrasound volumes based on the acquired 2D images. A challenging task during the reconstruction process is to preserve the texture patterns in the synthesized volume and ensure that all gaps in the volume are correctly filled. This paper presents an adaptive kernel regression algorithm that can effectively reconstruct high-quality freehand 3D ultrasound volumes. The algorithm employs a kernel regression model that enables nonparametric interpolation of the voxel gray-level values. The kernel size of the regression model is adaptively adjusted based on the characteristics of the voxel that is being interpolated. In particular, when the algorithm is employed to interpolate a voxel located in a region with dense ultrasound data samples, the size of the kernel is reduced to preserve the texture patterns. On the other hand, the size of the kernel is increased in areas that include large gaps to enable effective gap filling. The performance of the proposed algorithm was compared with seven previous interpolation approaches by synthesizing freehand 3D ultrasound volumes of a benign breast tumor. The experimental results show that the proposed algorithm outperforms the other interpolation approaches.

An Ensemble Approach to Building Mercer Kernels with Prior Information

NASA Technical Reports Server (NTRS)

Srivastava, Ashok N.; Schumann, Johann; Fischer, Bernd

2005-01-01

This paper presents a new methodology for automatic knowledge driven data mining based on the theory of Mercer Kernels, which are highly nonlinear symmetric positive definite mappings from the original image space to a very high, possibly dimensional feature space. we describe a new method called Mixture Density Mercer Kernels to learn kernel function directly from data, rather than using pre-defined kernels. These data adaptive kernels can encode prior knowledge in the kernel using a Bayesian formulation, thus allowing for physical information to be encoded in the model. Specifically, we demonstrate the use of the algorithm in situations with extremely small samples of data. We compare the results with existing algorithms on data from the Sloan Digital Sky Survey (SDSS) and demonstrate the method's superior performance against standard methods. The code for these experiments has been generated with the AUTOBAYES tool, which automatically generates efficient and documented C/C++ code from abstract statistical model specifications. The core of the system is a schema library which contains templates for learning and knowledge discovery algorithms like different versions of EM, or numeric optimization methods like conjugate gradient methods. The template instantiation is supported by symbolic-algebraic computations, which allows AUTOBAYES to find closed-form solutions and, where possible, to integrate them into the code.

Increasing the Size of Microwave Popcorn

NASA Astrophysics Data System (ADS)

Smoyer, Justin

2005-03-01

Each year Americans consume approximately 17 billion quarts of popcorn. Since the 1940s, microwaves have been the heating source of choice for most. By treating the popcorn mechanism as a thermodynamic system, it has been shown mathematically and experimentally that reducing the surrounding pressure of the unpopped kernels, results in an increased volume of the kernels [Quinn et al, http://xxx.lanl.gov/abs/cond-mat/0409434 v1 2004]. In this project an alternate method of popping with the microwave was used to further test and confirm this hypothesis. Numerous experimental trials where run to test the validity of the theory. The results show that there is a significant increase in the average kernel size as well as a reduction in the number of unpopped kernels.

Reformulation of Possio's kernel with application to unsteady wind tunnel interference

NASA Technical Reports Server (NTRS)

Fromme, J. A.; Golberg, M. A.

1980-01-01

An efficient method for computing the Possio kernel has remained elusive up to the present time. In this paper the Possio is reformulated so that it can be computed accurately using existing high precision numerical quadrature techniques. Convergence to the correct values is demonstrated and optimization of the integration procedures is discussed. Since more general kernels such as those associated with unsteady flows in ventilated wind tunnels are analytic perturbations of the Possio free air kernel, a more accurate evaluation of their collocation matrices results with an exponential improvement in convergence. An application to predicting frequency response of an airfoil-trailing edge control system in a wind tunnel compared with that in free air is given showing strong interference effects.

Hi-Res scan mode in clinical MDCT systems: Experimental assessment of spatial resolution performance

PubMed Central

Cruz-Bastida, Juan P.; Gomez-Cardona, Daniel; Li, Ke; Sun, Heyi; Hsieh, Jiang; Szczykutowicz, Timothy P.; Chen, Guang-Hong

2016-01-01

Purpose: The introduction of a High-Resolution (Hi-Res) scan mode and another associated option that combines Hi-Res mode with the so-called High Definition (HD) reconstruction kernels (referred to as a Hi-Res/HD mode in this paper) in some multi-detector CT (MDCT) systems offers new opportunities to increase spatial resolution for some clinical applications that demand high spatial resolution. The purpose of this work was to quantify the in-plane spatial resolution along both the radial direction and tangential direction for the Hi-Res and Hi-Res/HD scan modes at different off-center positions. Methods: A technique was introduced and validated to address the signal saturation problem encountered in the attempt to quantify spatial resolution for the Hi-Res and Hi-Res/HD scan modes. Using the proposed method, the modulation transfer functions (MTFs) of a 64-slice MDCT system (Discovery CT750 HD, GE Healthcare) equipped with both Hi-Res and Hi-Res/HD modes were measured using a metal bead at nine different off-centered positions (0–16 cm with a step size of 2 cm); at each position, both conventional scans and Hi-Res scans were performed. For each type of scan and position, 80 repeated acquisitions were performed to reduce noise induced uncertainties in the MTF measurements. A total of 15 reconstruction kernels, including eight conventional kernels and seven HD kernels, were used to reconstruct CT images of the bead. An ex vivo animal study consisting of a bone fracture model was performed to corroborate the MTF results, as the detection of this high-contrast and high frequency task is predominantly determined by spatial resolution. Images of this animal model generated by different scan modes and reconstruction kernels were qualitatively compared with the MTF results. Results: At the centered position, the use of Hi-Res mode resulted in a slight improvement in the MTF; each HD kernel generated higher spatial resolution than its counterpart conventional kernel. However, the MTF along the tangential direction of the scan field of view (SFOV) was significantly degraded at off-centered positions, yet the combined Hi-Res/HD mode reduced this azimuthal MTF degradation. Images of the animal bone fracture model confirmed the improved spatial resolution at the off-centered positions through the use of the Hi-Res mode and HD kernels. Conclusions: The Hi-Res/HD scan improve spatial resolution of MDCT systems at both centered and off-centered positions. PMID:27147351

Hi-Res scan mode in clinical MDCT systems: Experimental assessment of spatial resolution performance.

PubMed

Cruz-Bastida, Juan P; Gomez-Cardona, Daniel; Li, Ke; Sun, Heyi; Hsieh, Jiang; Szczykutowicz, Timothy P; Chen, Guang-Hong

2016-05-01

The introduction of a High-Resolution (Hi-Res) scan mode and another associated option that combines Hi-Res mode with the so-called High Definition (HD) reconstruction kernels (referred to as a Hi-Res/HD mode in this paper) in some multi-detector CT (MDCT) systems offers new opportunities to increase spatial resolution for some clinical applications that demand high spatial resolution. The purpose of this work was to quantify the in-plane spatial resolution along both the radial direction and tangential direction for the Hi-Res and Hi-Res/HD scan modes at different off-center positions. A technique was introduced and validated to address the signal saturation problem encountered in the attempt to quantify spatial resolution for the Hi-Res and Hi-Res/HD scan modes. Using the proposed method, the modulation transfer functions (MTFs) of a 64-slice MDCT system (Discovery CT750 HD, GE Healthcare) equipped with both Hi-Res and Hi-Res/HD modes were measured using a metal bead at nine different off-centered positions (0-16 cm with a step size of 2 cm); at each position, both conventional scans and Hi-Res scans were performed. For each type of scan and position, 80 repeated acquisitions were performed to reduce noise induced uncertainties in the MTF measurements. A total of 15 reconstruction kernels, including eight conventional kernels and seven HD kernels, were used to reconstruct CT images of the bead. An ex vivo animal study consisting of a bone fracture model was performed to corroborate the MTF results, as the detection of this high-contrast and high frequency task is predominantly determined by spatial resolution. Images of this animal model generated by different scan modes and reconstruction kernels were qualitatively compared with the MTF results. At the centered position, the use of Hi-Res mode resulted in a slight improvement in the MTF; each HD kernel generated higher spatial resolution than its counterpart conventional kernel. However, the MTF along the tangential direction of the scan field of view (SFOV) was significantly degraded at off-centered positions, yet the combined Hi-Res/HD mode reduced this azimuthal MTF degradation. Images of the animal bone fracture model confirmed the improved spatial resolution at the off-centered positions through the use of the Hi-Res mode and HD kernels. The Hi-Res/HD scan improve spatial resolution of MDCT systems at both centered and off-centered positions.

Carbon partitioning between oil and carbohydrates in developing oat (Avena sativa L.) seeds.

PubMed

Ekman, Asa; Hayden, Daniel M; Dehesh, Katayoon; Bülow, Leif; Stymne, Sten

2008-01-01

Cereals accumulate starch in the endosperm as their major energy reserve in the grain. In most cereals the embryo, scutellum, and aleurone layer are high in oil, but these tissues constitute a very small part of the total seed weight. However, in oat (Avena sativa L.) most of the oil in kernels is deposited in the same endosperm cells that accumulate starch. Thus oat endosperm is a desirable model system to study the metabolic switches responsible for carbon partitioning between oil and starch synthesis. A prerequisite for such investigations is the development of an experimental system for oat that allows for metabolic flux analysis using stable and radioactive isotope labelling. An in vitro liquid culture system, developed for detached oat panicles and optimized to mimic kernel composition during different developmental stages in planta, is presented here. This system was subsequently used in analyses of carbon partitioning between lipids and carbohydrates by the administration of 14C-labelled sucrose to two cultivars having different amounts of kernel oil. The data presented in this study clearly show that a higher amount of oil in the high-oil cultivar compared with the medium-oil cultivar was due to a higher proportion of carbon partitioning into oil during seed filling, predominantly at the earlier stages of kernel development.

Tuning Linux to meet real time requirements

NASA Astrophysics Data System (ADS)

Herbel, Richard S.; Le, Dang N.

2007-04-01

There is a desire to use Linux in military systems. Customers are requesting contractors to use open source to the maximal possible extent in contracts. Linux is probably the best operating system of choice to meet this need. It is widely used. It is free. It is royalty free, and, best of all, it is completely open source. However, there is a problem. Linux was not originally built to be a real time operating system. There are many places where interrupts can and will be blocked for an indeterminate amount of time. There have been several attempts to bridge this gap. One of them is from RTLinux, which attempts to build a microkernel underneath Linux. The microkernel will handle all interrupts and then pass it up to the Linux operating system. This does insure good interrupt latency; however, it is not free [1]. Another is RTAI, which provides a similar typed interface; however, the PowerPC platform, which is used widely in real time embedded community, was stated as "recovering" [2]. Thus this is not suited for military usage. This paper provides a method for tuning a standard Linux kernel so it can meet the real time requirement of an embedded system.

Quantitative comparison of noise texture across CT scanners from different manufacturers

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

Solomon, Justin B.; Christianson, Olav; Samei, Ehsan

2012-10-15

Purpose: To quantitatively compare noise texture across computed tomography (CT) scanners from different manufacturers using the noise power spectrum (NPS). Methods: The American College of Radiology CT accreditation phantom (Gammex 464, Gammex, Inc., Middleton, WI) was imaged on two scanners: Discovery CT 750HD (GE Healthcare, Waukesha, WI), and SOMATOM Definition Flash (Siemens Healthcare, Germany), using a consistent acquisition protocol (120 kVp, 0.625/0.6 mm slice thickness, 250 mAs, and 22 cm field of view). Images were reconstructed using filtered backprojection and a wide selection of reconstruction kernels. For each image set, the 2D NPS were estimated from the uniform section ofmore » the phantom. The 2D spectra were normalized by their integral value, radially averaged, and filtered by the human visual response function. A systematic kernel-by-kernel comparison across manufacturers was performed by computing the root mean square difference (RMSD) and the peak frequency difference (PFD) between the NPS from different kernels. GE and Siemens kernels were compared and kernel pairs that minimized the RMSD and |PFD| were identified. Results: The RMSD (|PFD|) values between the NPS of GE and Siemens kernels varied from 0.01 mm{sup 2} (0.002 mm{sup -1}) to 0.29 mm{sup 2} (0.74 mm{sup -1}). The GE kernels 'Soft,''Standard,''Chest,' and 'Lung' closely matched the Siemens kernels 'B35f,''B43f,''B41f,' and 'B80f' (RMSD < 0.05 mm{sup 2}, |PFD| < 0.02 mm{sup -1}, respectively). The GE 'Bone,''Bone+,' and 'Edge' kernels all matched most closely with Siemens 'B75f' kernel but with sizeable RMSD and |PFD| values up to 0.18 mm{sup 2} and 0.41 mm{sup -1}, respectively. These sizeable RMSD and |PFD| values corresponded to visually perceivable differences in the noise texture of the images. Conclusions: It is possible to use the NPS to quantitatively compare noise texture across CT systems. The degree to which similar texture across scanners could be achieved varies and is limited by the kernels available on each scanner.« less

Quantitative comparison of noise texture across CT scanners from different manufacturers.

PubMed

Solomon, Justin B; Christianson, Olav; Samei, Ehsan

2012-10-01

To quantitatively compare noise texture across computed tomography (CT) scanners from different manufacturers using the noise power spectrum (NPS). The American College of Radiology CT accreditation phantom (Gammex 464, Gammex, Inc., Middleton, WI) was imaged on two scanners: Discovery CT 750HD (GE Healthcare, Waukesha, WI), and SOMATOM Definition Flash (Siemens Healthcare, Germany), using a consistent acquisition protocol (120 kVp, 0.625∕0.6 mm slice thickness, 250 mAs, and 22 cm field of view). Images were reconstructed using filtered backprojection and a wide selection of reconstruction kernels. For each image set, the 2D NPS were estimated from the uniform section of the phantom. The 2D spectra were normalized by their integral value, radially averaged, and filtered by the human visual response function. A systematic kernel-by-kernel comparison across manufacturers was performed by computing the root mean square difference (RMSD) and the peak frequency difference (PFD) between the NPS from different kernels. GE and Siemens kernels were compared and kernel pairs that minimized the RMSD and |PFD| were identified. The RMSD (|PFD|) values between the NPS of GE and Siemens kernels varied from 0.01 mm(2) (0.002 mm(-1)) to 0.29 mm(2) (0.74 mm(-1)). The GE kernels "Soft," "Standard," "Chest," and "Lung" closely matched the Siemens kernels "B35f," "B43f," "B41f," and "B80f" (RMSD < 0.05 mm(2), |PFD| < 0.02 mm(-1), respectively). The GE "Bone," "Bone+," and "Edge" kernels all matched most closely with Siemens "B75f" kernel but with sizeable RMSD and |PFD| values up to 0.18 mm(2) and 0.41 mm(-1), respectively. These sizeable RMSD and |PFD| values corresponded to visually perceivable differences in the noise texture of the images. It is possible to use the NPS to quantitatively compare noise texture across CT systems. The degree to which similar texture across scanners could be achieved varies and is limited by the kernels available on each scanner.

FRIT characterized hierarchical kernel memory arrangement for multiband palmprint recognition

NASA Astrophysics Data System (ADS)

Kisku, Dakshina R.; Gupta, Phalguni; Sing, Jamuna K.

2015-10-01

In this paper, we present a hierarchical kernel associative memory (H-KAM) based computational model with Finite Ridgelet Transform (FRIT) representation for multispectral palmprint recognition. To characterize a multispectral palmprint image, the Finite Ridgelet Transform is used to achieve a very compact and distinctive representation of linear singularities while it also captures the singularities along lines and edges. The proposed system makes use of Finite Ridgelet Transform to represent multispectral palmprint image and it is then modeled by Kernel Associative Memories. Finally, the recognition scheme is thoroughly tested with a benchmarking multispectral palmprint database CASIA. For recognition purpose a Bayesian classifier is used. The experimental results exhibit robustness of the proposed system under different wavelengths of palm image.

A low cost implementation of multi-parameter patient monitor using intersection kernel support vector machine classifier

NASA Astrophysics Data System (ADS)

Mohan, Dhanya; Kumar, C. Santhosh

2016-03-01

Predicting the physiological condition (normal/abnormal) of a patient is highly desirable to enhance the quality of health care. Multi-parameter patient monitors (MPMs) using heart rate, arterial blood pressure, respiration rate and oxygen saturation (S pO2) as input parameters were developed to monitor the condition of patients, with minimum human resource utilization. The Support vector machine (SVM), an advanced machine learning approach popularly used for classification and regression is used for the realization of MPMs. For making MPMs cost effective, we experiment on the hardware implementation of the MPM using support vector machine classifier. The training of the system is done using the matlab environment and the detection of the alarm/noalarm condition is implemented in hardware. We used different kernels for SVM classification and note that the best performance was obtained using intersection kernel SVM (IKSVM). The intersection kernel support vector machine classifier MPM has outperformed the best known MPM using radial basis function kernel by an absoute improvement of 2.74% in accuracy, 1.86% in sensitivity and 3.01% in specificity. The hardware model was developed based on the improved performance system using Verilog Hardware Description Language and was implemented on Altera cyclone-II development board.

Classification of Microarray Data Using Kernel Fuzzy Inference System

PubMed Central

Kumar Rath, Santanu

2014-01-01

The DNA microarray classification technique has gained more popularity in both research and practice. In real data analysis, such as microarray data, the dataset contains a huge number of insignificant and irrelevant features that tend to lose useful information. Classes with high relevance and feature sets with high significance are generally referred for the selected features, which determine the samples classification into their respective classes. In this paper, kernel fuzzy inference system (K-FIS) algorithm is applied to classify the microarray data (leukemia) using t-test as a feature selection method. Kernel functions are used to map original data points into a higher-dimensional (possibly infinite-dimensional) feature space defined by a (usually nonlinear) function ϕ through a mathematical process called the kernel trick. This paper also presents a comparative study for classification using K-FIS along with support vector machine (SVM) for different set of features (genes). Performance parameters available in the literature such as precision, recall, specificity, F-measure, ROC curve, and accuracy are considered to analyze the efficiency of the classification model. From the proposed approach, it is apparent that K-FIS model obtains similar results when compared with SVM model. This is an indication that the proposed approach relies on kernel function. PMID:27433543

Combining neural networks and signed particles to simulate quantum systems more efficiently

NASA Astrophysics Data System (ADS)

Sellier, Jean Michel

2018-04-01

Recently a new formulation of quantum mechanics has been suggested which describes systems by means of ensembles of classical particles provided with a sign. This novel approach mainly consists of two steps: the computation of the Wigner kernel, a multi-dimensional function describing the effects of the potential over the system, and the field-less evolution of the particles which eventually create new signed particles in the process. Although this method has proved to be extremely advantageous in terms of computational resources - as a matter of fact it is able to simulate in a time-dependent fashion many-body systems on relatively small machines - the Wigner kernel can represent the bottleneck of simulations of certain systems. Moreover, storing the kernel can be another issue as the amount of memory needed is cursed by the dimensionality of the system. In this work, we introduce a new technique which drastically reduces the computation time and memory requirement to simulate time-dependent quantum systems which is based on the use of an appropriately tailored neural network combined with the signed particle formalism. In particular, the suggested neural network is able to compute efficiently and reliably the Wigner kernel without any training as its entire set of weights and biases is specified by analytical formulas. As a consequence, the amount of memory for quantum simulations radically drops since the kernel does not need to be stored anymore as it is now computed by the neural network itself, only on the cells of the (discretized) phase-space which are occupied by particles. As its is clearly shown in the final part of this paper, not only this novel approach drastically reduces the computational time, it also remains accurate. The author believes this work opens the way towards effective design of quantum devices, with incredible practical implications.

General methodology for nonlinear modeling of neural systems with Poisson point-process inputs.

PubMed

Marmarelis, V Z; Berger, T W

2005-07-01

This paper presents a general methodological framework for the practical modeling of neural systems with point-process inputs (sequences of action potentials or, more broadly, identical events) based on the Volterra and Wiener theories of functional expansions and system identification. The paper clarifies the distinctions between Volterra and Wiener kernels obtained from Poisson point-process inputs. It shows that only the Wiener kernels can be estimated via cross-correlation, but must be defined as zero along the diagonals. The Volterra kernels can be estimated far more accurately (and from shorter data-records) by use of the Laguerre expansion technique adapted to point-process inputs, and they are independent of the mean rate of stimulation (unlike their P-W counterparts that depend on it). The Volterra kernels can also be estimated for broadband point-process inputs that are not Poisson. Useful applications of this modeling approach include cases where we seek to determine (model) the transfer characteristics between one neuronal axon (a point-process 'input') and another axon (a point-process 'output') or some other measure of neuronal activity (a continuous 'output', such as population activity) with which a causal link exists.

An information theoretic approach of designing sparse kernel adaptive filters.

PubMed

Liu, Weifeng; Park, Il; Principe, José C

2009-12-01

This paper discusses an information theoretic approach of designing sparse kernel adaptive filters. To determine useful data to be learned and remove redundant ones, a subjective information measure called surprise is introduced. Surprise captures the amount of information a datum contains which is transferable to a learning system. Based on this concept, we propose a systematic sparsification scheme, which can drastically reduce the time and space complexity without harming the performance of kernel adaptive filters. Nonlinear regression, short term chaotic time-series prediction, and long term time-series forecasting examples are presented.

Jdpd: an open java simulation kernel for molecular fragment dissipative particle dynamics.

PubMed

van den Broek, Karina; Kuhn, Hubert; Zielesny, Achim

2018-05-21

Jdpd is an open Java simulation kernel for Molecular Fragment Dissipative Particle Dynamics with parallelizable force calculation, efficient caching options and fast property calculations. It is characterized by an interface and factory-pattern driven design for simple code changes and may help to avoid problems of polyglot programming. Detailed input/output communication, parallelization and process control as well as internal logging capabilities for debugging purposes are supported. The new kernel may be utilized in different simulation environments ranging from flexible scripting solutions up to fully integrated "all-in-one" simulation systems.

Frequency Domain Analysis of Narx Neural Networks

NASA Astrophysics Data System (ADS)

Chance, J. E.; Worden, K.; Tomlinson, G. R.

1998-06-01

A method is proposed for interpreting the behaviour of NARX neural networks. The correspondence between time-delay neural networks and Volterra series is extended to the NARX class of networks. The Volterra kernels, or rather, their Fourier transforms, are obtained via harmonic probing. In the same way that the Volterra kernels generalize the impulse response to non-linear systems, the Volterra kernel transforms can be viewed as higher-order analogues of the Frequency Response Functions commonly used in Engineering dynamics; they can be interpreted in much the same way.

Grey Language Hesitant Fuzzy Group Decision Making Method Based on Kernel and Grey Scale

PubMed Central

Diao, Yuzhu; Hu, Aqin

2018-01-01

Based on grey language multi-attribute group decision making, a kernel and grey scale scoring function is put forward according to the definition of grey language and the meaning of the kernel and grey scale. The function introduces grey scale into the decision-making method to avoid information distortion. This method is applied to the grey language hesitant fuzzy group decision making, and the grey correlation degree is used to sort the schemes. The effectiveness and practicability of the decision-making method are further verified by the industry chain sustainable development ability evaluation example of a circular economy. Moreover, its simplicity and feasibility are verified by comparing it with the traditional grey language decision-making method and the grey language hesitant fuzzy weighted arithmetic averaging (GLHWAA) operator integration method after determining the index weight based on the grey correlation. PMID:29498699

Grey Language Hesitant Fuzzy Group Decision Making Method Based on Kernel and Grey Scale.

PubMed

Li, Qingsheng; Diao, Yuzhu; Gong, Zaiwu; Hu, Aqin

2018-03-02

Based on grey language multi-attribute group decision making, a kernel and grey scale scoring function is put forward according to the definition of grey language and the meaning of the kernel and grey scale. The function introduces grey scale into the decision-making method to avoid information distortion. This method is applied to the grey language hesitant fuzzy group decision making, and the grey correlation degree is used to sort the schemes. The effectiveness and practicability of the decision-making method are further verified by the industry chain sustainable development ability evaluation example of a circular economy. Moreover, its simplicity and feasibility are verified by comparing it with the traditional grey language decision-making method and the grey language hesitant fuzzy weighted arithmetic averaging (GLHWAA) operator integration method after determining the index weight based on the grey correlation.

Oscillatory singular integrals and harmonic analysis on nilpotent groups

PubMed Central

Ricci, F.; Stein, E. M.

1986-01-01

Several related classes of operators on nilpotent Lie groups are considered. These operators involve the following features: (i) oscillatory factors that are exponentials of imaginary polynomials, (ii) convolutions with singular kernels supported on lower-dimensional submanifolds, (iii) validity in the general context not requiring the existence of dilations that are automorphisms. PMID:16593640

Decolonisation of fractional calculus rules: Breaking commutativity and associativity to capture more natural phenomena

NASA Astrophysics Data System (ADS)

Atangana, Abdon; Gómez-Aguilar, J. F.

2018-04-01

To answer some issues raised about the concept of fractional differentiation and integration based on the exponential and Mittag-Leffler laws, we present, in this paper, fundamental differences between the power law, exponential decay, Mittag-Leffler law and their possible applications in nature. We demonstrate the failure of the semi-group principle in modeling real-world problems. We use natural phenomena to illustrate the importance of non-commutative and non-associative operators under which the Caputo-Fabrizio and Atangana-Baleanu fractional operators fall. We present statistical properties of generator for each fractional derivative, including Riemann-Liouville, Caputo-Fabrizio and Atangana-Baleanu ones. The Atangana-Baleanu and Caputo-Fabrizio fractional derivatives show crossover properties for the mean-square displacement, while the Riemann-Liouville is scale invariant. Their probability distributions are also a Gaussian to non-Gaussian crossover, with the difference that the Caputo Fabrizio kernel has a steady state between the transition. Only the Atangana-Baleanu kernel is a crossover for the waiting time distribution from stretched exponential to power law. A new criterion was suggested, namely the Atangana-Gómez fractional bracket, that helps describe the energy needed by a fractional derivative to characterize a 2-pletic manifold. Based on these properties, we classified fractional derivatives in three categories: weak, mild and strong fractional differential and integral operators. We presented some applications of fractional differential operators to describe real-world problems and we proved, with numerical simulations, that the Riemann-Liouville power-law derivative provides a description of real-world problems with much additional information, that can be seen as noise or error due to specific memory properties of its power-law kernel. The Caputo-Fabrizio derivative is less noisy while the Atangana-Baleanu fractional derivative provides an excellent description, due to its Mittag-Leffler memory, able to distinguish between dynamical systems taking place at different scales without steady state. The study suggests that the properties of associativity and commutativity or the semi-group principle are just irrelevant in fractional calculus. Properties of classical derivatives were established for the ordinary calculus with no memory effect and it is a failure of mathematical investigation to attempt to describe more complex natural phenomena using the same notions.

HyspIRI Intelligent Payload Module(IPM) and Benchmarking Algorithms for Upload

NASA Technical Reports Server (NTRS)

Mandl, Daniel

2010-01-01

Features: Hardware: a) Xilinx Virtex-5 (GSFC Space Cube 2); b) 2 x 400MHz PPC; c) 100MHz Bus; d) 2 x 512MB SDRAM; e) Dual Gigabit Ethernet. Support Linux kernel 2.6.31 (gcc version 4.2.2). Support software running in stand alone mode for better performance. Can stream raw data up to 800 Mbps. Ready for operations. Software Application Examples: Band-stripping Algiotrhmsl:cloud, sulfur, flood, thermal, SWIL, NDVI, NDWI, SIWI, oil spills, algae blooms, etc. Corrections: geometric, radiometric, atmospheric. Core Flight System/dynamic software bus. CCSDS File Delivery Protocol. Delay Tolerant Network. CASPER /onboard planning. Fault monitoring/recovery software. S/C command and telemetry software. Data compression. Sensor Web for Autonomous Mission Operations.

On the heat trace of Schroedinger operators

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

Banuelos, R.; Sa Barreto, A.

1995-12-31

Trace formulae for heat kernels of Schroedinger operators have been widely studied in connection with spectral and scattering theory. They have been used to obtain information about a potential from its spectrum, or from its scattering data, and vice-versa. Using elementary Fourier transform methods we obtain a formula for the general coefficient in the asymptotic expansion of the trace of the heat kernel of the Schroedinger operator {minus}{Delta} + V, as t {down_arrow} 0, with V {element_of} S(R{sup n}), the class of functions with rapid decay at infinity. In dimension n = 1 a recurrent formula for the general coefficientmore » in the expansion is obtained in [6]. However the KdV methods used there do not seem to generalize to higher dimension. Using the formula of [6] and the symmetry of some integrals, Y. Colin de Verdiere has computed the first four coefficients for potentials in three space dimensions. Also in [1] a different method is used to compute heat coefficients for differential operators on manifolds. 14 refs.« less

A fast and well-conditioned spectral method for singular integral equations

NASA Astrophysics Data System (ADS)

Slevinsky, Richard Mikael; Olver, Sheehan

2017-03-01

We develop a spectral method for solving univariate singular integral equations over unions of intervals by utilizing Chebyshev and ultraspherical polynomials to reformulate the equations as almost-banded infinite-dimensional systems. This is accomplished by utilizing low rank approximations for sparse representations of the bivariate kernels. The resulting system can be solved in O (m2 n) operations using an adaptive QR factorization, where m is the bandwidth and n is the optimal number of unknowns needed to resolve the true solution. The complexity is reduced to O (mn) operations by pre-caching the QR factorization when the same operator is used for multiple right-hand sides. Stability is proved by showing that the resulting linear operator can be diagonally preconditioned to be a compact perturbation of the identity. Applications considered include the Faraday cage, and acoustic scattering for the Helmholtz and gravity Helmholtz equations, including spectrally accurate numerical evaluation of the far- and near-field solution. The JULIA software package SingularIntegralEquations.jl implements our method with a convenient, user-friendly interface.

Protein subcellular localization prediction using multiple kernel learning based support vector machine.

PubMed

Hasan, Md Al Mehedi; Ahmad, Shamim; Molla, Md Khademul Islam

2017-03-28

Predicting the subcellular locations of proteins can provide useful hints that reveal their functions, increase our understanding of the mechanisms of some diseases, and finally aid in the development of novel drugs. As the number of newly discovered proteins has been growing exponentially, which in turns, makes the subcellular localization prediction by purely laboratory tests prohibitively laborious and expensive. In this context, to tackle the challenges, computational methods are being developed as an alternative choice to aid biologists in selecting target proteins and designing related experiments. However, the success of protein subcellular localization prediction is still a complicated and challenging issue, particularly, when query proteins have multi-label characteristics, i.e., if they exist simultaneously in more than one subcellular location or if they move between two or more different subcellular locations. To date, to address this problem, several types of subcellular localization prediction methods with different levels of accuracy have been proposed. The support vector machine (SVM) has been employed to provide potential solutions to the protein subcellular localization prediction problem. However, the practicability of an SVM is affected by the challenges of selecting an appropriate kernel and selecting the parameters of the selected kernel. To address this difficulty, in this study, we aimed to develop an efficient multi-label protein subcellular localization prediction system, named as MKLoc, by introducing multiple kernel learning (MKL) based SVM. We evaluated MKLoc using a combined dataset containing 5447 single-localized proteins (originally published as part of the Höglund dataset) and 3056 multi-localized proteins (originally published as part of the DBMLoc set). Note that this dataset was used by Briesemeister et al. in their extensive comparison of multi-localization prediction systems. Finally, our experimental results indicate that MKLoc not only achieves higher accuracy than a single kernel based SVM system but also shows significantly better results than those obtained from other top systems (MDLoc, BNCs, YLoc+). Moreover, MKLoc requires less computation time to tune and train the system than that required for BNCs and single kernel based SVM.

Adaptive wiener image restoration kernel

DOEpatents

Yuan, Ding [Henderson, NV

2007-06-05

A method and device for restoration of electro-optical image data using an adaptive Wiener filter begins with constructing imaging system Optical Transfer Function, and the Fourier Transformations of the noise and the image. A spatial representation of the imaged object is restored by spatial convolution of the image using a Wiener restoration kernel.

Effect of finite sample size on feature selection and classification: a simulation study.

PubMed

Way, Ted W; Sahiner, Berkman; Hadjiiski, Lubomir M; Chan, Heang-Ping

2010-02-01

The small number of samples available for training and testing is often the limiting factor in finding the most effective features and designing an optimal computer-aided diagnosis (CAD) system. Training on a limited set of samples introduces bias and variance in the performance of a CAD system relative to that trained with an infinite sample size. In this work, the authors conducted a simulation study to evaluate the performances of various combinations of classifiers and feature selection techniques and their dependence on the class distribution, dimensionality, and the training sample size. The understanding of these relationships will facilitate development of effective CAD systems under the constraint of limited available samples. Three feature selection techniques, the stepwise feature selection (SFS), sequential floating forward search (SFFS), and principal component analysis (PCA), and two commonly used classifiers, Fisher's linear discriminant analysis (LDA) and support vector machine (SVM), were investigated. Samples were drawn from multidimensional feature spaces of multivariate Gaussian distributions with equal or unequal covariance matrices and unequal means, and with equal covariance matrices and unequal means estimated from a clinical data set. Classifier performance was quantified by the area under the receiver operating characteristic curve Az. The mean Az values obtained by resubstitution and hold-out methods were evaluated for training sample sizes ranging from 15 to 100 per class. The number of simulated features available for selection was chosen to be 50, 100, and 200. It was found that the relative performance of the different combinations of classifier and feature selection method depends on the feature space distributions, the dimensionality, and the available training sample sizes. The LDA and SVM with radial kernel performed similarly for most of the conditions evaluated in this study, although the SVM classifier showed a slightly higher hold-out performance than LDA for some conditions and vice versa for other conditions. PCA was comparable to or better than SFS and SFFS for LDA at small samples sizes, but inferior for SVM with polynomial kernel. For the class distributions simulated from clinical data, PCA did not show advantages over the other two feature selection methods. Under this condition, the SVM with radial kernel performed better than the LDA when few training samples were available, while LDA performed better when a large number of training samples were available. None of the investigated feature selection-classifier combinations provided consistently superior performance under the studied conditions for different sample sizes and feature space distributions. In general, the SFFS method was comparable to the SFS method while PCA may have an advantage for Gaussian feature spaces with unequal covariance matrices. The performance of the SVM with radial kernel was better than, or comparable to, that of the SVM with polynomial kernel under most conditions studied.

Ford Motor Company NDE facility shielding design.

PubMed

Metzger, Robert L; Van Riper, Kenneth A; Jones, Martin H

2005-01-01

Ford Motor Company proposed the construction of a large non-destructive evaluation laboratory for radiography of automotive power train components. The authors were commissioned to design the shielding and to survey the completed facility for compliance with radiation doses for occupationally and non-occupationally exposed personnel. The two X-ray sources are Varian Linatron 3000 accelerators operating at 9-11 MV. One performs computed tomography of automotive transmissions, while the other does real-time radiography of operating engines and transmissions. The shield thickness for the primary barrier and all secondary barriers were determined by point-kernel techniques. Point-kernel techniques did not work well for skyshine calculations and locations where multiple sources (e.g. tube head leakage and various scatter fields) impacted doses. Shielding for these areas was determined using transport calculations. A number of MCNP [Briesmeister, J. F. MCNPCA general Monte Carlo N-particle transport code version 4B. Los Alamos National Laboratory Manual (1997)] calculations focused on skyshine estimates and the office areas. Measurements on the operational facility confirmed the shielding calculations.

A robust, high-throughput method for computing maize ear, cob, and kernel attributes automatically from images.

PubMed

Miller, Nathan D; Haase, Nicholas J; Lee, Jonghyun; Kaeppler, Shawn M; de Leon, Natalia; Spalding, Edgar P

2017-01-01

Grain yield of the maize plant depends on the sizes, shapes, and numbers of ears and the kernels they bear. An automated pipeline that can measure these components of yield from easily-obtained digital images is needed to advance our understanding of this globally important crop. Here we present three custom algorithms designed to compute such yield components automatically from digital images acquired by a low-cost platform. One algorithm determines the average space each kernel occupies along the cob axis using a sliding-window Fourier transform analysis of image intensity features. A second counts individual kernels removed from ears, including those in clusters. A third measures each kernel's major and minor axis after a Bayesian analysis of contour points identifies the kernel tip. Dimensionless ear and kernel shape traits that may interrelate yield components are measured by principal components analysis of contour point sets. Increased objectivity and speed compared to typical manual methods are achieved without loss of accuracy as evidenced by high correlations with ground truth measurements and simulated data. Millimeter-scale differences among ear, cob, and kernel traits that ranged more than 2.5-fold across a diverse group of inbred maize lines were resolved. This system for measuring maize ear, cob, and kernel attributes is being used by multiple research groups as an automated Web service running on community high-throughput computing and distributed data storage infrastructure. Users may create their own workflow using the source code that is staged for download on a public repository. © 2016 The Authors. The Plant Journal published by Society for Experimental Biology and John Wiley & Sons Ltd.

User's Guide for a Modular Flutter Analysis Software System (Fast Version 1.0)

NASA Technical Reports Server (NTRS)

Desmarais, R. N.; Bennett, R. M.

1978-01-01

The use and operation of a group of computer programs to perform a flutter analysis of a single planar wing are described. This system of programs is called FAST for Flutter Analysis System, and consists of five programs. Each program performs certain portions of a flutter analysis and can be run sequentially as a job step or individually. FAST uses natural vibration modes as input data and performs a conventional V-g type of solution. The unsteady aerodynamics programs in FAST are based on the subsonic kernel function lifting-surface theory although other aerodynamic programs can be used. Application of the programs is illustrated by a sample case of a complete flutter calculation that exercises each program.

SPLICER - A GENETIC ALGORITHM TOOL FOR SEARCH AND OPTIMIZATION, VERSION 1.0 (MACINTOSH VERSION)

NASA Technical Reports Server (NTRS)

Wang, L.

1994-01-01

SPLICER is a genetic algorithm tool which can be used to solve search and optimization problems. Genetic algorithms are adaptive search procedures (i.e. problem solving methods) based loosely on the processes of natural selection and Darwinian "survival of the fittest." SPLICER provides the underlying framework and structure for building a genetic algorithm application. These algorithms apply genetically-inspired operators to populations of potential solutions in an iterative fashion, creating new populations while searching for an optimal or near-optimal solution to the problem at hand. SPLICER 1.0 was created using a modular architecture that includes a Genetic Algorithm Kernel, interchangeable Representation Libraries, Fitness Modules and User Interface Libraries, and well-defined interfaces between these components. The architecture supports portability, flexibility, and extensibility. SPLICER comes with all source code and several examples. For instance, a "traveling salesperson" example searches for the minimum distance through a number of cities visiting each city only once. Stand-alone SPLICER applications can be used without any programming knowledge. However, to fully utilize SPLICER within new problem domains, familiarity with C language programming is essential. SPLICER's genetic algorithm (GA) kernel was developed independent of representation (i.e. problem encoding), fitness function or user interface type. The GA kernel comprises all functions necessary for the manipulation of populations. These functions include the creation of populations and population members, the iterative population model, fitness scaling, parent selection and sampling, and the generation of population statistics. In addition, miscellaneous functions are included in the kernel (e.g., random number generators). Different problem-encoding schemes and functions are defined and stored in interchangeable representation libraries. This allows the GA kernel to be used with any representation scheme. The SPLICER tool provides representation libraries for binary strings and for permutations. These libraries contain functions for the definition, creation, and decoding of genetic strings, as well as multiple crossover and mutation operators. Furthermore, the SPLICER tool defines the appropriate interfaces to allow users to create new representation libraries. Fitness modules are the only component of the SPLICER system a user will normally need to create or alter to solve a particular problem. Fitness functions are defined and stored in interchangeable fitness modules which must be created using C language. Within a fitness module, a user can create a fitness (or scoring) function, set the initial values for various SPLICER control parameters (e.g., population size), create a function which graphically displays the best solutions as they are found, and provide descriptive information about the problem. The tool comes with several example fitness modules, while the process of developing a fitness module is fully discussed in the accompanying documentation. The user interface is event-driven and provides graphic output in windows. SPLICER is written in Think C for Apple Macintosh computers running System 6.0.3 or later and Sun series workstations running SunOS. The UNIX version is easily ported to other UNIX platforms and requires MIT's X Window System, Version 11 Revision 4 or 5, MIT's Athena Widget Set, and the Xw Widget Set. Example executables and source code are included for each machine version. The standard distribution media for the Macintosh version is a set of three 3.5 inch Macintosh format diskettes. The standard distribution medium for the UNIX version is a .25 inch streaming magnetic tape cartridge in UNIX tar format. For the UNIX version, alternate distribution media and formats are available upon request. SPLICER was developed in 1991.

Continuation of research into software for space operations support: Conversion of the display manager to X Windows/Motif, volume 2

NASA Technical Reports Server (NTRS)

Collier, Mark D.; Killough, Ronnie; Martin, Nancy L.

1990-01-01

NASA is currently using a set of applications called the Display Builder and Display Manager. They run on Concurrent systems and heavily depend on the Graphic Kernel System (GKS). At this time however, these two applications would more appropriately be developed in X Windows, in which a low X is used for all actual text and graphics display and a standard widget set (such as Motif) is used for the user interface. Use of the X Windows will increase performance, improve the user interface, enhance portability, and improve reliability. Prototype of X Window/Motif based Display Manager provides the following advantages over a GKS based application: improved performance by using a low level X Windows, display of graphic and text will be more efficient; improved user interface by using Motif; Improved portability by operating on both Concurrent and Sun workstations; and Improved reliability.

A Tropospheric Emission Spectrometer HDO/H2O Retrieval Simulator for Climate Models

NASA Technical Reports Server (NTRS)

Field, R. D.; Risi, C.; Schmidt, G. A.; Worden, J.; Voulgarakis, A.; LeGrande, A. N.; Sobel, A. H.; Healy, R. J.

2012-01-01

Retrievals of the isotopic composition of water vapor from the Aura Tropospheric Emission Spectrometer (TES) have unique value in constraining moist processes in climate models. Accurate comparison between simulated and retrieved values requires that model profiles that would be poorly retrieved are excluded, and that an instrument operator be applied to the remaining profiles. Typically, this is done by sampling model output at satellite measurement points and using the quality flags and averaging kernels from individual retrievals at specific places and times. This approach is not reliable when the model meteorological conditions influencing retrieval sensitivity are different from those observed by the instrument at short time scales, which will be the case for free-running climate simulations. In this study, we describe an alternative, categorical approach to applying the instrument operator, implemented within the NASA GISS ModelE general circulation model. Retrieval quality and averaging kernel structure are predicted empirically from model conditions, rather than obtained from collocated satellite observations. This approach can be used for arbitrary model configurations, and requires no agreement between satellite-retrieved and model meteorology at short time scales. To test this approach, nudged simUlations were conducted using both the retrieval-based and categorical operators. Cloud cover, surface temperature and free-tropospheric moisture content were the most important predictors of retrieval quality and averaging kernel structure. There was good agreement between the D fields after applying the retrieval-based and more detailed categorical operators, with increases of up to 30 over the ocean and decreases of up to 40 over land relative to the raw model fields. The categorical operator performed better over the ocean than over land, and requires further refinement for use outside of the tropics. After applying the TES operator, ModelE had D biases of 8 over ocean and 34 over land compared to TES D, which were less than the biases using raw model D fields.

Bi-cubic interpolation for shift-free pan-sharpening

NASA Astrophysics Data System (ADS)

Aiazzi, Bruno; Baronti, Stefano; Selva, Massimo; Alparone, Luciano

2013-12-01

Most of pan-sharpening techniques require the re-sampling of the multi-spectral (MS) image for matching the size of the panchromatic (Pan) image, before the geometric details of Pan are injected into the MS image. This operation is usually performed in a separable fashion by means of symmetric digital low-pass filtering kernels with odd lengths that utilize piecewise local polynomials, typically implementing linear or cubic interpolation functions. Conversely, constant, i.e. nearest-neighbour, and quadratic kernels, implementing zero and two degree polynomials, respectively, introduce shifts in the magnified images, that are sub-pixel in the case of interpolation by an even factor, as it is the most usual case. However, in standard satellite systems, the point spread functions (PSF) of the MS and Pan instruments are centered in the middle of each pixel. Hence, commercial MS and Pan data products, whose scale ratio is an even number, are relatively shifted by an odd number of half pixels. Filters of even lengths may be exploited to compensate the half-pixel shifts between the MS and Pan sampling grids. In this paper, it is shown that separable polynomial interpolations of odd degrees are feasible with linear-phase kernels of even lengths. The major benefit is that bi-cubic interpolation, which is known to represent the best trade-off between performances and computational complexity, can be applied to commercial MS + Pan datasets, without the need of performing a further half-pixel registration after interpolation, to align the expanded MS with the Pan image.

The architecture and conservation pattern of whole-cell control circuitry.

PubMed

McAdams, Harley H; Shapiro, Lucy

2011-05-27

The control circuitry that directs and paces Caulobacter cell cycle progression involves the entire cell operating as an integrated system. This control circuitry monitors the environment and the internal state of the cell, including the cell topology, as it orchestrates orderly activation of cell cycle subsystems and Caulobacter's asymmetric cell division. The proteins of the Caulobacter cell cycle control system and its internal organization are co-conserved across many alphaproteobacteria species, but there are great differences in the regulatory apparatus' functionality and peripheral connectivity to other cellular subsystems from species to species. This pattern is similar to that observed for the "kernels" of the regulatory networks that regulate development of metazoan body plans. The Caulobacter cell cycle control system has been exquisitely optimized as a total system for robust operation in the face of internal stochastic noise and environmental uncertainty. When sufficient details accumulate, as for Caulobacter cell cycle regulation, the system design has been found to be eminently rational and indeed consistent with good design practices for human-designed asynchronous control systems. Copyright © 2011 Elsevier Ltd. All rights reserved.

Product demand forecasts using wavelet kernel support vector machine and particle swarm optimization in manufacture system

NASA Astrophysics Data System (ADS)

Wu, Qi

2010-03-01

Demand forecasts play a crucial role in supply chain management. The future demand for a certain product is the basis for the respective replenishment systems. Aiming at demand series with small samples, seasonal character, nonlinearity, randomicity and fuzziness, the existing support vector kernel does not approach the random curve of the sales time series in the space (quadratic continuous integral space). In this paper, we present a hybrid intelligent system combining the wavelet kernel support vector machine and particle swarm optimization for demand forecasting. The results of application in car sale series forecasting show that the forecasting approach based on the hybrid PSOWv-SVM model is effective and feasible, the comparison between the method proposed in this paper and other ones is also given, which proves that this method is, for the discussed example, better than hybrid PSOv-SVM and other traditional methods.

Fission Product Release and Survivability of UN-Kernel LWR TRISO Fuel

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

Besmann, Theodore M; Ferber, Mattison K; Lin, Hua-Tay

2014-01-01

A thermomechanical assessment of the LWR application of TRISO fuel with UN kernels was performed. Fission product release under operational and transient temperature conditions was determined by extrapolation from range calculations and limited data from irradiated UN pellets. Both fission recoil and diffusive release were considered and internal particle pressures computed for both 650 and 800 m diameter kernels as a function of buffer layer thickness. These pressures were used in conjunction with a finite element program to compute the radial and tangential stresses generated with a TRISO particle as a function of fluence. Creep and swelling of the innermore » and outer pyrolytic carbon layers were included in the analyses. A measure of reliability of the TRISO particle was obtained by measuring the probability of survival of the SiC barrier layer and the maximum tensile stress generated in the pyrolytic carbon layers as a function of fluence. These reliability estimates were obtained as functions of the kernel diameter, buffer layer thickness, and pyrolytic carbon layer thickness. The value of the probability of survival at the end of irradiation was inversely proportional to the maximum pressure.« less

Fission product release and survivability of UN-kernel LWR TRISO fuel

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

T. M. Besmann; M. K. Ferber; H.-T. Lin

2014-05-01

A thermomechanical assessment of the LWR application of TRISO fuel with UN kernels was performed. Fission product release under operational and transient temperature conditions was determined by extrapolation from fission product recoil calculations and limited data from irradiated UN pellets. Both fission recoil and diffusive release were considered and internal particle pressures computed for both 650 and 800 um diameter kernels as a function of buffer layer thickness. These pressures were used in conjunction with a finite element program to compute the radial and tangential stresses generated within a TRISO particle undergoing burnup. Creep and swelling of the inner andmore » outer pyrolytic carbon layers were included in the analyses. A measure of reliability of the TRISO particle was obtained by computing the probability of survival of the SiC barrier layer and the maximum tensile stress generated in the pyrolytic carbon layers from internal pressure and thermomechanics of the layers. These reliability estimates were obtained as functions of the kernel diameter, buffer layer thickness, and pyrolytic carbon layer thickness. The value of the probability of survival at the end of irradiation was inversely proportional to the maximum pressure.« less

A fast non-local means algorithm based on integral image and reconstructed similar kernel

NASA Astrophysics Data System (ADS)

Lin, Zheng; Song, Enmin

2018-03-01

Image denoising is one of the essential methods in digital image processing. The non-local means (NLM) denoising approach is a remarkable denoising technique. However, its time complexity of the computation is high. In this paper, we design a fast NLM algorithm based on integral image and reconstructed similar kernel. First, the integral image is introduced in the traditional NLM algorithm. In doing so, it reduces a great deal of repetitive operations in the parallel processing, which will greatly improves the running speed of the algorithm. Secondly, in order to amend the error of the integral image, we construct a similar window resembling the Gaussian kernel in the pyramidal stacking pattern. Finally, in order to eliminate the influence produced by replacing the Gaussian weighted Euclidean distance with Euclidean distance, we propose a scheme to construct a similar kernel with a size of 3 x 3 in a neighborhood window which will reduce the effect of noise on a single pixel. Experimental results demonstrate that the proposed algorithm is about seventeen times faster than the traditional NLM algorithm, yet produce comparable results in terms of Peak Signal-to- Noise Ratio (the PSNR increased 2.9% in average) and perceptual image quality.

Fast pyrolysis of palm kernel shells: influence of operation parameters on the bio-oil yield and the yield of phenol and phenolic compounds.

PubMed

Kim, Seon-Jin; Jung, Su-Hwa; Kim, Joo-Sik

2010-12-01

Palm kernel shells were pyrolyzed in a pyrolysis plant equipped with a fluidized-bed reactor and a char-separation system. The influence of reaction temperature, feed size and feed rate on the product spectrum was also investigated. In addition, the effect of reaction temperature on the yields of phenol and phenolic compounds in the bio-oil was examined. The maximum bio-oil yield was 48.7 wt.% of the product at 490 degrees C. The maximum yield of phenol plus phenolic compounds amounted to about 70 area percentage at 475 degrees C. The yield of pyrolytic lignin after its isolation from the bio-oil was approximately 46 wt.% based on the water and ash free oil. The pyrolytic lignin was mainly composed of phenol, phenolic compounds and oligomers of coniferyl, sinapyl and p-coumaryl alcohols. From the result of a GPC analysis, the number average molecular weight and the weight average molecular weight were 325 and 463 g/mol, respectively. 2010 Elsevier Ltd. All rights reserved.

The effect of relatedness and pack size on territory overlap in African wild dogs.

PubMed

Jackson, Craig R; Groom, Rosemary J; Jordan, Neil R; McNutt, J Weldon

2017-01-01

Spacing patterns mediate competitive interactions between conspecifics, ultimately increasing fitness. The degree of territorial overlap between neighbouring African wild dog ( Lycaon pictus ) packs varies greatly, yet the role of factors potentially affecting the degree of overlap, such as relatedness and pack size, remain unclear. We used movement data from 21 wild dog packs to calculate the extent of territory overlap (20 dyads). On average, unrelated neighbouring packs had low levels of overlap restricted to the peripheral regions of their 95% utilisation kernels. Related neighbours had significantly greater levels of peripheral overlap. Only one unrelated dyad included overlap between 75%-75% kernels, but no 50%-50% kernels overlapped. However, eight of 12 related dyads overlapped between their respective 75% kernels and six between the frequented 50% kernels. Overlap between these more frequented kernels confers a heightened likelihood of encounter, as the mean utilisation intensity per unit area within the 50% kernels was 4.93 times greater than in the 95% kernels, and 2.34 times greater than in the 75% kernels. Related packs spent significantly more time in their 95% kernel overlap zones than did unrelated packs. Pack size appeared to have little effect on overlap between related dyads, yet among unrelated neighbours larger packs tended to overlap more onto smaller packs' territories. However, the true effect is unclear given that the model's confidence intervals overlapped zero. Evidence suggests that costly intraspecific aggression is greatly reduced between related packs. Consequently, the tendency for dispersing individuals to establish territories alongside relatives, where intensively utilised portions of ranges regularly overlap, may extend kin selection and inclusive fitness benefits from the intra-pack to inter-pack level. This natural spacing system can affect survival parameters and the carrying capacity of protected areas, having important management implications for intensively managed populations of this endangered species.

QTL detection for wheat kernel size and quality and the responses of these traits to low nitrogen stress.

PubMed

Cui, Fa; Fan, Xiaoli; Chen, Mei; Zhang, Na; Zhao, Chunhua; Zhang, Wei; Han, Jie; Ji, Jun; Zhao, Xueqiang; Yang, Lijuan; Zhao, Zongwu; Tong, Yiping; Wang, Tao; Li, Junming

2016-03-01

QTLs for kernel characteristics and tolerance to N stress were identified, and the functions of ten known genes with regard to these traits were specified. Kernel size and quality characteristics in wheat (Triticum aestivum L.) ultimately determine the end use of the grain and affect its commodity price, both of which are influenced by the application of nitrogen (N) fertilizer. This study characterized quantitative trait loci (QTLs) for kernel size and quality and examined the responses of these traits to low-N stress using a recombinant inbred line population derived from Kenong 9204 × Jing 411. Phenotypic analyses were conducted in five trials that each included low- and high-N treatments. We identified 109 putative additive QTLs for 11 kernel size and quality characteristics and 49 QTLs for tolerance to N stress, 27 and 14 of which were stable across the tested environments, respectively. These QTLs were distributed across all wheat chromosomes except for chromosomes 3A, 4D, 6D, and 7B. Eleven QTL clusters that simultaneously affected kernel size- and quality-related traits were identified. At nine locations, 25 of the 49 QTLs for N deficiency tolerance coincided with the QTLs for kernel characteristics, indicating their genetic independence. The feasibility of indirect selection of a superior genotype for kernel size and quality under high-N conditions in breeding programs designed for a lower input management system are discussed. In addition, we specified the functions of Glu-A1, Glu-B1, Glu-A3, Glu-B3, TaCwi-A1, TaSus2, TaGS2-D1, PPO-D1, Rht-B1, and Ha with regard to kernel characteristics and the sensitivities of these characteristics to N stress. This study provides useful information for the genetic improvement of wheat kernel size, quality, and resistance to N stress.

Assessment of the microbiological safety of edible roasted nut kernels on retail sale in England, with a focus on Salmonella.

PubMed

Little, C L; Jemmott, W; Surman-Lee, S; Hucklesby, L; de Pinnal, E

2009-04-01

There is little published information on the prevalence of Salmonella in edible nut kernels. A study in early 2008 of edible roasted nut kernels on retail sale in England was undertaken to assess the microbiological safety of this product. A total of 727 nut kernel samples of different varieties were examined. Overall, Salmonella and Escherichia coli were detected from 0.2 and 0.4% of edible roasted nut kernels. Of the nut varieties examined, Salmonella Havana was detected from 1 (4.0%) sample of pistachio nuts, indicating a risk to health. The United Kingdom Food Standards Agency was immediately informed, and full investigations were undertaken. Further examination established the contamination to be associated with the pistachio kernels and not the partly opened shells. Salmonella was not detected in other varieties tested (almonds, Brazils, cashews, hazelnuts, macadamia, peanuts, pecans, pine nuts, and walnuts). E. coli was found at low levels (range of 3.6 to 4/g) in walnuts (1.4%), almonds (1.2%), and Brazils (0.5%). The presence of Salmonella is unacceptable in edible nut kernels. Prevention of microbial contamination in these products lies in the application of good agricultural, manufacturing, and storage practices together with a hazard analysis and critical control points system that encompass all stages of production, processing, and distribution.

The Raid distributed database system

NASA Technical Reports Server (NTRS)

Bhargava, Bharat; Riedl, John

1989-01-01

Raid, a robust and adaptable distributed database system for transaction processing (TP), is described. Raid is a message-passing system, with server processes on each site to manage concurrent processing, consistent replicated copies during site failures, and atomic distributed commitment. A high-level layered communications package provides a clean location-independent interface between servers. The latest design of the package delivers messages via shared memory in a configuration with several servers linked into a single process. Raid provides the infrastructure to investigate various methods for supporting reliable distributed TP. Measurements on TP and server CPU time are presented, along with data from experiments on communications software, consistent replicated copy control during site failures, and concurrent distributed checkpointing. A software tool for evaluating the implementation of TP algorithms in an operating-system kernel is proposed.

Lesion classification using clinical and visual data fusion by multiple kernel learning

NASA Astrophysics Data System (ADS)

Kisilev, Pavel; Hashoul, Sharbell; Walach, Eugene; Tzadok, Asaf

2014-03-01

To overcome operator dependency and to increase diagnosis accuracy in breast ultrasound (US), a lot of effort has been devoted to developing computer-aided diagnosis (CAD) systems for breast cancer detection and classification. Unfortunately, the efficacy of such CAD systems is limited since they rely on correct automatic lesions detection and localization, and on robustness of features computed based on the detected areas. In this paper we propose a new approach to boost the performance of a Machine Learning based CAD system, by combining visual and clinical data from patient files. We compute a set of visual features from breast ultrasound images, and construct the textual descriptor of patients by extracting relevant keywords from patients' clinical data files. We then use the Multiple Kernel Learning (MKL) framework to train SVM based classifier to discriminate between benign and malignant cases. We investigate different types of data fusion methods, namely, early, late, and intermediate (MKL-based) fusion. Our database consists of 408 patient cases, each containing US images, textual description of complaints and symptoms filled by physicians, and confirmed diagnoses. We show experimentally that the proposed MKL-based approach is superior to other classification methods. Even though the clinical data is very sparse and noisy, its MKL-based fusion with visual features yields significant improvement of the classification accuracy, as compared to the image features only based classifier.

The spatial sensitivity of Sp converted waves-kernels and their applications

NASA Astrophysics Data System (ADS)

Mancinelli, N. J.; Fischer, K. M.

2017-12-01

We have developed a framework for improved imaging of strong lateral variations in crust and upper mantle seismic discontinuity structure using teleseismic S-to-P (Sp) scattered waves. In our framework, we rapidly compute scattered wave sensitivities to velocity perturbations in a one-dimensional background model using ray-theoretical methods to account for timing, scattering, and geometrical spreading effects. The kernels accurately describe the amplitude and phase information of a scattered waveform, which we confirm by benchmarking against kernels derived from numerical solutions of the wave equation. The kernels demonstrate that the amplitude of an Sp converted wave at a given time is sensitive to structure along a quasi-hyperbolic curve, such that structure far from the direct ray path can influence the measurements. We use synthetic datasets to explore two potential applications of the scattered wave sensitivity kernels. First, we back-project scattered energy back to its origin using the kernel adjoint operator. This approach successfully images mantle interfaces at depths of 120-180 km with up to 20 km of vertical relief over lateral distances of 100 km (i.e., undulations with a maximal 20% grade) when station spacing is 10 km. Adjacent measurements sum coherently at nodes where gradients in seismic properties occur, and destructively interfere at nodes lacking gradients. In cases where the station spacing is greater than 10 km, the destructive interference can be incomplete, and smearing along the isochrons can occur. We demonstrate, however, that model smoothing can dampen these artifacts. This method is relatively fast, and accurately retrieves the positions of the interfaces, but it generally does not retrieve the strength of the velocity perturbations. Therefore, in our second approach, we attempt to invert directly for velocity perturbations from our reference model using an iterative conjugate-directions scheme.

Background field removal technique using regularization enabled sophisticated harmonic artifact reduction for phase data with varying kernel sizes.

PubMed

Kan, Hirohito; Kasai, Harumasa; Arai, Nobuyuki; Kunitomo, Hiroshi; Hirose, Yasujiro; Shibamoto, Yuta

2016-09-01

An effective background field removal technique is desired for more accurate quantitative susceptibility mapping (QSM) prior to dipole inversion. The aim of this study was to evaluate the accuracy of regularization enabled sophisticated harmonic artifact reduction for phase data with varying spherical kernel sizes (REV-SHARP) method using a three-dimensional head phantom and human brain data. The proposed REV-SHARP method used the spherical mean value operation and Tikhonov regularization in the deconvolution process, with varying 2-14mm kernel sizes. The kernel sizes were gradually reduced, similar to the SHARP with varying spherical kernel (VSHARP) method. We determined the relative errors and relationships between the true local field and estimated local field in REV-SHARP, VSHARP, projection onto dipole fields (PDF), and regularization enabled SHARP (RESHARP). Human experiment was also conducted using REV-SHARP, VSHARP, PDF, and RESHARP. The relative errors in the numerical phantom study were 0.386, 0.448, 0.838, and 0.452 for REV-SHARP, VSHARP, PDF, and RESHARP. REV-SHARP result exhibited the highest correlation between the true local field and estimated local field. The linear regression slopes were 1.005, 1.124, 0.988, and 0.536 for REV-SHARP, VSHARP, PDF, and RESHARP in regions of interest on the three-dimensional head phantom. In human experiments, no obvious errors due to artifacts were present in REV-SHARP. The proposed REV-SHARP is a new method combined with variable spherical kernel size and Tikhonov regularization. This technique might make it possible to be more accurate backgroud field removal and help to achive better accuracy of QSM. Copyright © 2016 Elsevier Inc. All rights reserved.

The vertex operator for a generalization of MacMahon’s formula

NASA Astrophysics Data System (ADS)

Cai, Liqiang; Wang, Lifang; Wu, Ke; Yang, Jie

2015-10-01

We provide a vertex operator realization for a two-parameter generalization of MacMahon’s formula introduced by M. Vuletić [Trans. Amer. Math. Soc. 361, 2789 (2009)]. Since the generalized MacMahon function is the kernel function of some Macdonald symmetric function, we consider the action of two vertex operators on a state corresponding to a Macdonald symmetric function. It becomes evident that the vertex operators appear to be the creation and annihilation operators, respectively on the state.

Securely Partitioning Spacecraft Computing Resources: Validation of a Separation Kernel

NASA Astrophysics Data System (ADS)

Bremer, Leon; Schreutelkamp, Erwin

2011-08-01

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

A high performance parallel algorithm for 1-D FFT

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

Agarwal, R.C.; Gustavson, F.G.; Zubair, M.

1994-12-31

In this paper the authors propose a parallel high performance FFT algorithm based on a multi-dimensional formulation. They use this to solve a commonly encountered FFT based kernel on a distributed memory parallel machine, the IBM scalable parallel system, SP1. The kernel requires a forward FFT computation of an input sequence, multiplication of the transformed data by a coefficient array, and finally an inverse FFT computation of the resultant data. They show that the multi-dimensional formulation helps in reducing the communication costs and also improves the single node performance by effectively utilizing the memory system of the node. They implementedmore » this kernel on the IBM SP1 and observed a performance of 1.25 GFLOPS on a 64-node machine.« less

System characterization of neuronal excitability in the hippocampus and its relevance to observed dynamics of spontaneous seizure-like transitions.

PubMed

Zalay, Osbert C; Serletis, Demitre; Carlen, Peter L; Bardakjian, Berj L

2010-06-01

Most forms of epilepsy are marked by seizure episodes that arise spontaneously. The low-magnesium/high-potassium (low-Mg(2+)/high-K(+)) experimental model of epilepsy is an acute model that produces spontaneous, recurring seizure-like events (SLEs). To elucidate the nature of spontaneous seizure transitions and their relationship to neuronal excitability, whole-cell recordings from the intact hippocampus were undertaken in vitro, and the response of hippocampal CA3 neurons to Gaussian white noise injection was obtained before and after treatment with various concentrations of low-Mg(2+)/high-K(+) solution. A second-order Volterra kernel model was estimated for each of the input-output response pairs. The spectral energy of the responses was also computed, providing a quantitative measure of neuronal excitability. Changes in duration and amplitude of the first-order kernel correlated positively with the spectral energy increase following treatment with low-Mg(2+)/high-K(+) solution, suggesting that variations in neuronal excitability are coded by the system kernels, in part by differences to the profile of the first-order kernel. In particular, kernel duration was more sensitive than amplitude to changes in spectral energy, and correlated more strongly with kernel area. An oscillator network model of the hippocampal CA3 was constructed to investigate the relationship of kernel duration to network excitability, and the model was able to generate spontaneous, recurrent SLEs by increasing the duration of a mode function analogous to the first-order kernel. Results from the model indicated that disruption to the dynamic balance of feedback was responsible for seizure-like transitions and the observed intermittency of SLEs. A physiological candidate for feedback imbalance consistent with the network model is the destabilizing interaction of extracellular potassium and paroxysmal neuronal activation. Altogether, these results (1) validate a mathematical model for epileptiform activity in the hippocampus by quantifying and subsequently correlating its behavior with an experimental, in vitro model of epilepsy; (2) elucidate a possible mechanism for epileptogenesis; and (3) pave the way for control studies in epilepsy utilizing the herein proposed experimental and mathematical setup.

System characterization of neuronal excitability in the hippocampus and its relevance to observed dynamics of spontaneous seizure-like transitions

NASA Astrophysics Data System (ADS)

Zalay, Osbert C.; Serletis, Demitre; Carlen, Peter L.; Bardakjian, Berj L.

2010-06-01

Most forms of epilepsy are marked by seizure episodes that arise spontaneously. The low-magnesium/high-potassium (low-Mg2+/high-K+) experimental model of epilepsy is an acute model that produces spontaneous, recurring seizure-like events (SLEs). To elucidate the nature of spontaneous seizure transitions and their relationship to neuronal excitability, whole-cell recordings from the intact hippocampus were undertaken in vitro, and the response of hippocampal CA3 neurons to Gaussian white noise injection was obtained before and after treatment with various concentrations of low-Mg2+/high-K+ solution. A second-order Volterra kernel model was estimated for each of the input-output response pairs. The spectral energy of the responses was also computed, providing a quantitative measure of neuronal excitability. Changes in duration and amplitude of the first-order kernel correlated positively with the spectral energy increase following treatment with low-Mg2+/high-K+ solution, suggesting that variations in neuronal excitability are coded by the system kernels, in part by differences to the profile of the first-order kernel. In particular, kernel duration was more sensitive than amplitude to changes in spectral energy, and correlated more strongly with kernel area. An oscillator network model of the hippocampal CA3 was constructed to investigate the relationship of kernel duration to network excitability, and the model was able to generate spontaneous, recurrent SLEs by increasing the duration of a mode function analogous to the first-order kernel. Results from the model indicated that disruption to the dynamic balance of feedback was responsible for seizure-like transitions and the observed intermittency of SLEs. A physiological candidate for feedback imbalance consistent with the network model is the destabilizing interaction of extracellular potassium and paroxysmal neuronal activation. Altogether, these results (1) validate a mathematical model for epileptiform activity in the hippocampus by quantifying and subsequently correlating its behavior with an experimental, in vitro model of epilepsy; (2) elucidate a possible mechanism for epileptogenesis; and (3) pave the way for control studies in epilepsy utilizing the herein proposed experimental and mathematical setup.

The distal portion of the short arm of wheat (Triticum aestivum L.) chromosome 5D controls endosperm vitreosity and grain hardness.

PubMed

Morris, Craig F; Beecher, Brian S

2012-07-01

Kernel vitreosity is an important trait of wheat grain, but its developmental control is not completely known. We developed back-cross seven (BC(7)) near-isogenic lines in the soft white spring wheat cultivar Alpowa that lack the distal portion of chromosome 5D short arm. From the final back-cross, 46 BC(7)F(2) plants were isolated. These plants exhibited a complete and perfect association between kernel vitreosity (i.e. vitreous, non-vitreous or mixed) and Single Kernel Characterization System (SKCS) hardness. Observed segregation of 10:28:7 fit a 1:2:1 Chi-square. BC(7)F(2) plants classified as heterozygous for both SKCS hardness and kernel vitreosity (n = 29) were selected and a single vitreous and non-vitreous kernel were selected, and grown to maturity and subjected to SKCS analysis. The resultant phenotypic ratios were, from non-vitreous kernels, 23:6:0, and from vitreous kernels, 0:1:28, soft:heterozygous:hard, respectively. Three of these BC(7)F(2) heterozygous plants were selected and 40 kernels each drawn at random, grown to maturity and subjected to SKCS analysis. Phenotypic segregation ratios were 7:27:6, 11:20:9, and 3:28:9, soft:heterozygous:hard. Chi-square analysis supported a 1:2:1 segregation for one plant but not the other two, in which cases the two homozygous classes were under-represented. Twenty-two paired BC(7)F(2):F(3) full sibs were compared for kernel hardness, weight, size, density and protein content. SKCS hardness index differed markedly, 29.4 for the lines with a complete 5DS, and 88.6 for the lines possessing the deletion. The soft non-vitreous kernels were on average significantly heavier, by nearly 20%, and were slightly larger. Density and protein contents were similar, however. The results provide strong genetic evidence that gene(s) on distal 5DS control not only kernel hardness but also the manner in which the endosperm develops, viz. whether it is vitreous or non-vitreous.

Quantum kinetic expansion in the spin-boson model: Matrix formulation and system-bath factorized initial state.

PubMed

Gong, Zhihao; Tang, Zhoufei; Wang, Haobin; Wu, Jianlan

2017-12-28

Within the framework of the hierarchy equation of motion (HEOM), the quantum kinetic expansion (QKE) method of the spin-boson model is reformulated in the matrix representation. The equivalence between the two formulations (HEOM matrices and quantum operators) is numerically verified from the calculation of the time-integrated QKE rates. The matrix formulation of the QKE is extended to the system-bath factorized initial state. Following a one-to-one mapping between HEOM matrices and quantum operators, a quantum kinetic equation is rederived. The rate kernel is modified by an extra term following a systematic expansion over the site-site coupling. This modified QKE is numerically tested for its reliability by calculating the time-integrated rate and non-Markovian population kinetics. For an intermediate-to-strong dissipation strength and a large site-site coupling, the population transfer is found to be significantly different when the initial condition is changed from the local equilibrium to system-bath factorized state.

Telescope Array Control System Based on Wireless Touch Screen Platform

NASA Astrophysics Data System (ADS)

Fu, Xia-nan; Huang, Lei; Wei, Jian-yan

2017-10-01

Ground-based Wide Angle Cameras (GMAC) are the ground-based observational facility for the SVOM (Space Variable Object Monitor) astronomical satellite of Sino-French cooperation, and Mini-GWAC is the pathfinder and supplement of GWAC. In the context of the Mini-GWAC telescope array, this paper introduces the design and implementation of a kind of telescope array control system based on the wireless touch screen platform. We describe the development and implementation of the system in detail in terms of control system principle, system hardware structure, software design, experiment, and test etc. The system uses a touch-control PC which is based on the Windows CE system as the upper computer, while the wireless transceiver module and PLC (Programmable Logic Controller) are taken as the system kernel. It has the advantages of low cost, reliable data transmission, and simple operation. And the control system has been applied to the Mini-GWAC successfully.

a Gsa-Svm Hybrid System for Classification of Binary Problems

NASA Astrophysics Data System (ADS)

Sarafrazi, Soroor; Nezamabadi-pour, Hossein; Barahman, Mojgan

2011-06-01

This paperhybridizesgravitational search algorithm (GSA) with support vector machine (SVM) and made a novel GSA-SVM hybrid system to improve the classification accuracy in binary problems. GSA is an optimization heuristic toolused to optimize the value of SVM kernel parameter (in this paper, radial basis function (RBF) is chosen as the kernel function). The experimental results show that this newapproach can achieve high classification accuracy and is comparable to or better than the particle swarm optimization (PSO)-SVM and genetic algorithm (GA)-SVM, which are two hybrid systems for classification.

The Design and Implementation of Persistence in the Annex System

DTIC Science & Technology

2009-08-01

MONADS [Rosenberg 1990], Grasshopper [Dearle, di Bona et al. 1993], the Walnut kernel [Castro 1996], the L3 microkernel [Liedtke 1993] and its...successor L4 [Skoglund, Ceelen & Liedtke 2000], KeyKOS [Hardy 1985] and Charm [Dearle & Hulse 2000]. There is a great difference in design between these...persistence are Annex and the L4 micro-kernel [Skoglund, Ceelen & Liedtke 2000]. In either case, to implement some form of persistence a system must have a

Dynamic characteristics of oxygen consumption.

PubMed

Ye, Lin; Argha, Ahmadreza; Yu, Hairong; Celler, Branko G; Nguyen, Hung T; Su, Steven

2018-04-23

Previous studies have indicated that oxygen uptake ([Formula: see text]) is one of the most accurate indices for assessing the cardiorespiratory response to exercise. In most existing studies, the response of [Formula: see text] is often roughly modelled as a first-order system due to the inadequate stimulation and low signal to noise ratio. To overcome this difficulty, this paper proposes a novel nonparametric kernel-based method for the dynamic modelling of [Formula: see text] response to provide a more robust estimation. Twenty healthy non-athlete participants conducted treadmill exercises with monotonous stimulation (e.g., single step function as input). During the exercise, [Formula: see text] was measured and recorded by a popular portable gas analyser ([Formula: see text], COSMED). Based on the recorded data, a kernel-based estimation method was proposed to perform the nonparametric modelling of [Formula: see text]. For the proposed method, a properly selected kernel can represent the prior modelling information to reduce the dependence of comprehensive stimulations. Furthermore, due to the special elastic net formed by [Formula: see text] norm and kernelised [Formula: see text] norm, the estimations are smooth and concise. Additionally, the finite impulse response based nonparametric model which estimated by the proposed method can optimally select the order and fit better in terms of goodness-of-fit comparing to classical methods. Several kernels were introduced for the kernel-based [Formula: see text] modelling method. The results clearly indicated that the stable spline (SS) kernel has the best performance for [Formula: see text] modelling. Particularly, based on the experimental data from 20 participants, the estimated response from the proposed method with SS kernel was significantly better than the results from the benchmark method [i.e., prediction error method (PEM)] ([Formula: see text] vs [Formula: see text]). The proposed nonparametric modelling method is an effective method for the estimation of the impulse response of VO 2 -Speed system. Furthermore, the identified average nonparametric model method can dynamically predict [Formula: see text] response with acceptable accuracy during treadmill exercise.

Application of stochastic weighted algorithms to a multidimensional silica particle model

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

Menz, William J.; Patterson, Robert I.A.; Wagner, Wolfgang

2013-09-01

Highlights: •Stochastic weighted algorithms (SWAs) are developed for a detailed silica model. •An implementation of SWAs with the transition kernel is presented. •The SWAs’ solutions converge to the direct simulation algorithm’s (DSA) solution. •The efficiency of SWAs is evaluated for this multidimensional particle model. •It is shown that SWAs can be used for coagulation problems in industrial systems. -- Abstract: This paper presents a detailed study of the numerical behaviour of stochastic weighted algorithms (SWAs) using the transition regime coagulation kernel and a multidimensional silica particle model. The implementation in the SWAs of the transition regime coagulation kernel and associatedmore » majorant rates is described. The silica particle model of Shekar et al. [S. Shekar, A.J. Smith, W.J. Menz, M. Sander, M. Kraft, A multidimensional population balance model to describe the aerosol synthesis of silica nanoparticles, Journal of Aerosol Science 44 (2012) 83–98] was used in conjunction with this coagulation kernel to study the convergence properties of SWAs with a multidimensional particle model. High precision solutions were calculated with two SWAs and also with the established direct simulation algorithm. These solutions, which were generated using large number of computational particles, showed close agreement. It was thus demonstrated that SWAs can be successfully used with complex coagulation kernels and high dimensional particle models to simulate real-world systems.« less

A Software Architecture for Adaptive Modular Sensing Systems

PubMed Central

Lyle, Andrew C.; Naish, Michael D.

2010-01-01

By combining a number of simple transducer modules, an arbitrarily complex sensing system may be produced to accommodate a wide range of applications. This work outlines a novel software architecture and knowledge representation scheme that has been developed to support this type of flexible and reconfigurable modular sensing system. Template algorithms are used to embed intelligence within each module. As modules are added or removed, the composite sensor is able to automatically determine its overall geometry and assume an appropriate collective identity. A virtual machine-based middleware layer runs on top of a real-time operating system with a pre-emptive kernel, enabling platform-independent template algorithms to be written once and run on any module, irrespective of its underlying hardware architecture. Applications that may benefit from easily reconfigurable modular sensing systems include flexible inspection, mobile robotics, surveillance, and space exploration. PMID:22163614

A software architecture for adaptive modular sensing systems.

PubMed

Lyle, Andrew C; Naish, Michael D

2010-01-01

By combining a number of simple transducer modules, an arbitrarily complex sensing system may be produced to accommodate a wide range of applications. This work outlines a novel software architecture and knowledge representation scheme that has been developed to support this type of flexible and reconfigurable modular sensing system. Template algorithms are used to embed intelligence within each module. As modules are added or removed, the composite sensor is able to automatically determine its overall geometry and assume an appropriate collective identity. A virtual machine-based middleware layer runs on top of a real-time operating system with a pre-emptive kernel, enabling platform-independent template algorithms to be written once and run on any module, irrespective of its underlying hardware architecture. Applications that may benefit from easily reconfigurable modular sensing systems include flexible inspection, mobile robotics, surveillance, and space exploration.

SPICE for ESA Planetary Missions

NASA Astrophysics Data System (ADS)

Costa, M.

2018-04-01

The ESA SPICE Service leads the SPICE operations for ESA missions and is responsible for the generation of the SPICE Kernel Dataset for ESA missions. This contribution will describe the status of these datasets and outline the future developments.

Graph Theoretic Foundations of Multibody Dynamics Part I: Structural Properties

PubMed Central

Jain, Abhinandan

2011-01-01

This is the first part of two papers that use concepts from graph theory to obtain a deeper understanding of the mathematical foundations of multibody dynamics. The key contribution is the development of a unifying framework that shows that key analytical results and computational algorithms in multibody dynamics are a direct consequence of structural properties and require minimal assumptions about the specific nature of the underlying multibody system. This first part focuses on identifying the abstract graph theoretic structural properties of spatial operator techniques in multibody dynamics. The second part paper exploits these structural properties to develop a broad spectrum of analytical results and computational algorithms. Towards this, we begin with the notion of graph adjacency matrices and generalize it to define block-weighted adjacency (BWA) matrices and their 1-resolvents. Previously developed spatial operators are shown to be special cases of such BWA matrices and their 1-resolvents. These properties are shown to hold broadly for serial and tree topology multibody systems. Specializations of the BWA and 1-resolvent matrices are referred to as spatial kernel operators (SKO) and spatial propagation operators (SPO). These operators and their special properties provide the foundation for the analytical and algorithmic techniques developed in the companion paper. We also use the graph theory concepts to study the topology induced sparsity structure of these operators and the system mass matrix. Similarity transformations of these operators are also studied. While the detailed development is done for the case of rigid-link multibody systems, the extension of these techniques to a broader class of systems (e.g. deformable links) are illustrated. PMID:22102790

AGR-5/6/7 LEUCO Kernel Fabrication Readiness Review

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

Marshall, Douglas W.; Bailey, Kirk W.

2015-02-01

In preparation for forming low-enriched uranium carbide/oxide (LEUCO) fuel kernels for the Advanced Gas Reactor (AGR) fuel development and qualification program, Idaho National Laboratory conducted an operational readiness review of the Babcock & Wilcox Nuclear Operations Group – Lynchburg (B&W NOG-L) procedures, processes, and equipment from January 14 – January 16, 2015. The readiness review focused on requirements taken from the American Society Mechanical Engineers (ASME) Nuclear Quality Assurance Standard (NQA-1-2008, 1a-2009), a recent occurrence at the B&W NOG-L facility related to preparation of acid-deficient uranyl nitrate solution (ADUN), and a relook at concerns noted in a previous review. Topicmore » areas open for the review were communicated to B&W NOG-L in advance of the on-site visit to facilitate the collection of objective evidences attesting to the state of readiness.« less

Kernel-based least squares policy iteration for reinforcement learning.

PubMed

Xu, Xin; Hu, Dewen; Lu, Xicheng

2007-07-01

In this paper, we present a kernel-based least squares policy iteration (KLSPI) algorithm for reinforcement learning (RL) in large or continuous state spaces, which can be used to realize adaptive feedback control of uncertain dynamic systems. By using KLSPI, near-optimal control policies can be obtained without much a priori knowledge on dynamic models of control plants. In KLSPI, Mercer kernels are used in the policy evaluation of a policy iteration process, where a new kernel-based least squares temporal-difference algorithm called KLSTD-Q is proposed for efficient policy evaluation. To keep the sparsity and improve the generalization ability of KLSTD-Q solutions, a kernel sparsification procedure based on approximate linear dependency (ALD) is performed. Compared to the previous works on approximate RL methods, KLSPI makes two progresses to eliminate the main difficulties of existing results. One is the better convergence and (near) optimality guarantee by using the KLSTD-Q algorithm for policy evaluation with high precision. The other is the automatic feature selection using the ALD-based kernel sparsification. Therefore, the KLSPI algorithm provides a general RL method with generalization performance and convergence guarantee for large-scale Markov decision problems (MDPs). Experimental results on a typical RL task for a stochastic chain problem demonstrate that KLSPI can consistently achieve better learning efficiency and policy quality than the previous least squares policy iteration (LSPI) algorithm. Furthermore, the KLSPI method was also evaluated on two nonlinear feedback control problems, including a ship heading control problem and the swing up control of a double-link underactuated pendulum called acrobot. Simulation results illustrate that the proposed method can optimize controller performance using little a priori information of uncertain dynamic systems. It is also demonstrated that KLSPI can be applied to online learning control by incorporating an initial controller to ensure online performance.

Biologically-Inspired Spike-Based Automatic Speech Recognition of Isolated Digits Over a Reproducing Kernel Hilbert Space

PubMed Central

Li, Kan; Príncipe, José C.

2018-01-01

This paper presents a novel real-time dynamic framework for quantifying time-series structure in spoken words using spikes. Audio signals are converted into multi-channel spike trains using a biologically-inspired leaky integrate-and-fire (LIF) spike generator. These spike trains are mapped into a function space of infinite dimension, i.e., a Reproducing Kernel Hilbert Space (RKHS) using point-process kernels, where a state-space model learns the dynamics of the multidimensional spike input using gradient descent learning. This kernelized recurrent system is very parsimonious and achieves the necessary memory depth via feedback of its internal states when trained discriminatively, utilizing the full context of the phoneme sequence. A main advantage of modeling nonlinear dynamics using state-space trajectories in the RKHS is that it imposes no restriction on the relationship between the exogenous input and its internal state. We are free to choose the input representation with an appropriate kernel, and changing the kernel does not impact the system nor the learning algorithm. Moreover, we show that this novel framework can outperform both traditional hidden Markov model (HMM) speech processing as well as neuromorphic implementations based on spiking neural network (SNN), yielding accurate and ultra-low power word spotters. As a proof of concept, we demonstrate its capabilities using the benchmark TI-46 digit corpus for isolated-word automatic speech recognition (ASR) or keyword spotting. Compared to HMM using Mel-frequency cepstral coefficient (MFCC) front-end without time-derivatives, our MFCC-KAARMA offered improved performance. For spike-train front-end, spike-KAARMA also outperformed state-of-the-art SNN solutions. Furthermore, compared to MFCCs, spike trains provided enhanced noise robustness in certain low signal-to-noise ratio (SNR) regime. PMID:29666568

Biologically-Inspired Spike-Based Automatic Speech Recognition of Isolated Digits Over a Reproducing Kernel Hilbert Space.

PubMed

Li, Kan; Príncipe, José C

2018-01-01

This paper presents a novel real-time dynamic framework for quantifying time-series structure in spoken words using spikes. Audio signals are converted into multi-channel spike trains using a biologically-inspired leaky integrate-and-fire (LIF) spike generator. These spike trains are mapped into a function space of infinite dimension, i.e., a Reproducing Kernel Hilbert Space (RKHS) using point-process kernels, where a state-space model learns the dynamics of the multidimensional spike input using gradient descent learning. This kernelized recurrent system is very parsimonious and achieves the necessary memory depth via feedback of its internal states when trained discriminatively, utilizing the full context of the phoneme sequence. A main advantage of modeling nonlinear dynamics using state-space trajectories in the RKHS is that it imposes no restriction on the relationship between the exogenous input and its internal state. We are free to choose the input representation with an appropriate kernel, and changing the kernel does not impact the system nor the learning algorithm. Moreover, we show that this novel framework can outperform both traditional hidden Markov model (HMM) speech processing as well as neuromorphic implementations based on spiking neural network (SNN), yielding accurate and ultra-low power word spotters. As a proof of concept, we demonstrate its capabilities using the benchmark TI-46 digit corpus for isolated-word automatic speech recognition (ASR) or keyword spotting. Compared to HMM using Mel-frequency cepstral coefficient (MFCC) front-end without time-derivatives, our MFCC-KAARMA offered improved performance. For spike-train front-end, spike-KAARMA also outperformed state-of-the-art SNN solutions. Furthermore, compared to MFCCs, spike trains provided enhanced noise robustness in certain low signal-to-noise ratio (SNR) regime.

Semi-supervised learning for ordinal Kernel Discriminant Analysis.

PubMed

Pérez-Ortiz, M; Gutiérrez, P A; Carbonero-Ruz, M; Hervás-Martínez, C

2016-12-01

Ordinal classification considers those classification problems where the labels of the variable to predict follow a given order. Naturally, labelled data is scarce or difficult to obtain in this type of problems because, in many cases, ordinal labels are given by a user or expert (e.g. in recommendation systems). Firstly, this paper develops a new strategy for ordinal classification where both labelled and unlabelled data are used in the model construction step (a scheme which is referred to as semi-supervised learning). More specifically, the ordinal version of kernel discriminant learning is extended for this setting considering the neighbourhood information of unlabelled data, which is proposed to be computed in the feature space induced by the kernel function. Secondly, a new method for semi-supervised kernel learning is devised in the context of ordinal classification, which is combined with our developed classification strategy to optimise the kernel parameters. The experiments conducted compare 6 different approaches for semi-supervised learning in the context of ordinal classification in a battery of 30 datasets, showing (1) the good synergy of the ordinal version of discriminant analysis and the use of unlabelled data and (2) the advantage of computing distances in the feature space induced by the kernel function. Copyright © 2016 Elsevier Ltd. All rights reserved.

Error analysis of stochastic gradient descent ranking.

PubMed

Chen, Hong; Tang, Yi; Li, Luoqing; Yuan, Yuan; Li, Xuelong; Tang, Yuanyan

2013-06-01

Ranking is always an important task in machine learning and information retrieval, e.g., collaborative filtering, recommender systems, drug discovery, etc. A kernel-based stochastic gradient descent algorithm with the least squares loss is proposed for ranking in this paper. The implementation of this algorithm is simple, and an expression of the solution is derived via a sampling operator and an integral operator. An explicit convergence rate for leaning a ranking function is given in terms of the suitable choices of the step size and the regularization parameter. The analysis technique used here is capacity independent and is novel in error analysis of ranking learning. Experimental results on real-world data have shown the effectiveness of the proposed algorithm in ranking tasks, which verifies the theoretical analysis in ranking error.

Identification of Fourier transform infrared photoacoustic spectral features for detection of Aspergillus flavus infection in corn.

PubMed

Gordon, S H; Schudy, R B; Wheeler, B C; Wicklow, D T; Greene, R V

1997-04-01

Aspergillus flavus and other pathogenic fungi display typical infrared spectra which differ significantly from spectra of substrate materials such as corn. On this basis, specific spectral features have been identified which permit detection of fungal infection on the surface of corn kernels by photoacoustic infrared spectroscopy. In a blind study, ten corn kernels showing bright greenish yellow fluorescence (BGYF) in the germ or endosperm and ten BGYF-negative kernels were correctly classified as infected or not infected by Fourier transform infrared photoacoustic spectroscopy. Earlier studies have shown that BGYF-positive kernels contain the bulk of the aflatoxin contaminating grain at harvest. Ten major spectral features, identified by visual inspection of the photoacoustic spectra of A. flavus mycelium grown in culture versus uninfected corn, were interpreted and assigned by theoretical comparisons of the relative chemical compositions of fungi and corn. The spectral features can be built into either empirical or knowledge-based computer models (expert systems) for automatic infrared detection and segregation of grains or kernels containing aflatoxin from the food and feed supply.

Characterization of non-diffusive transport in plasma turbulence by means of flux-gradient integro-differential kernels

NASA Astrophysics Data System (ADS)

Alcuson, J. A.; Reynolds-Barredo, J. M.; Mier, J. A.; Sanchez, Raul; Del-Castillo-Negrete, Diego; Newman, David E.; Tribaldos, V.

2015-11-01

A method to determine fractional transport exponents in systems dominated by fluid or plasma turbulence is proposed. The method is based on the estimation of the integro-differential kernel that relates values of the fluxes and gradients of the transported field, and its comparison with the family of analytical kernels of the linear fractional transport equation. Although use of this type of kernels has been explored before in this context, the methodology proposed here is rather unique since the connection with specific fractional equations is exploited from the start. The procedure has been designed to be particularly well-suited for application in experimental setups, taking advantage of the fact that kernel determination only requires temporal data of the transported field measured on an Eulerian grid. The simplicity and robustness of the method is tested first by using fabricated data from continuous-time random walk models built with prescribed transport characteristics. Its strengths are then illustrated on numerical Eulerian data gathered from simulations of a magnetically confined turbulent plasma in a near-critical regime, that is known to exhibit superdiffusive radial transport

Intelligent Control of a Sensor-Actuator System via Kernelized Least-Squares Policy Iteration

PubMed Central

Liu, Bo; Chen, Sanfeng; Li, Shuai; Liang, Yongsheng

2012-01-01

In this paper a new framework, called Compressive Kernelized Reinforcement Learning (CKRL), for computing near-optimal policies in sequential decision making with uncertainty is proposed via incorporating the non-adaptive data-independent Random Projections and nonparametric Kernelized Least-squares Policy Iteration (KLSPI). Random Projections are a fast, non-adaptive dimensionality reduction framework in which high-dimensionality data is projected onto a random lower-dimension subspace via spherically random rotation and coordination sampling. KLSPI introduce kernel trick into the LSPI framework for Reinforcement Learning, often achieving faster convergence and providing automatic feature selection via various kernel sparsification approaches. In this approach, policies are computed in a low-dimensional subspace generated by projecting the high-dimensional features onto a set of random basis. We first show how Random Projections constitute an efficient sparsification technique and how our method often converges faster than regular LSPI, while at lower computational costs. Theoretical foundation underlying this approach is a fast approximation of Singular Value Decomposition (SVD). Finally, simulation results are exhibited on benchmark MDP domains, which confirm gains both in computation time and in performance in large feature spaces. PMID:22736969

Develop, Build, and Test a Virtual Lab to Support a Vulnerability Training System

DTIC Science & Technology

2004-09-01

docs.us.dell.com/support/edocs/systems/pe1650/ en /it/index.htm> (20 August 2004) “HOWTO: Installing Web Services with Linux /Tomcat/Apache/Struts...configured as host machines with VMware and VNC running on a Linux RedHat 9 Kernel. An Apache-Tomcat web server was configured as the external interface to...1650, dual processor, blade servers were configured as host machines with VMware and VNC running on a Linux RedHat 9 Kernel. An Apache-Tomcat web

Variation and oscillation for the multilinear singular integrals satisfying Hörmander type conditions.

PubMed

Xia, Yinhong

2018-01-01

Suppose that the kernel K satisfies a certain Hörmander type condition. Let b be a function satisfying [Formula: see text] for [Formula: see text], and let [Formula: see text] be a family of multilinear singular integral operators, i.e., [Formula: see text] The main purpose of this paper is to establish the weighted [Formula: see text]-boundedness of the variation operator and the oscillation operator for [Formula: see text].

Analysis and Implementation of Particle-to-Particle (P2P) Graphics Processor Unit (GPU) Kernel for Black-Box Adaptive Fast Multipole Method

DTIC Science & Technology

2015-06-01

5110P and 16 dx360M4 nodes each with one NVIDIA Kepler K20M/K40M GPU. Each node contained dual Intel Xeon E5-2670 (Sandy Bridge) central processing...kernel and as such does not employ multiple processors. This work makes use of a single processing core and a single NVIDIA Kepler K40 GK110...bandwidth (2 × 16 slot), 7.877 GFloat/s; Kepler K40 peak, 4,290 × 1 billion floating-point operations (GFLOPs), and 288 GB/s Kepler K40 memory

A thermodynamic approach for advanced fuels of gas-cooled reactors

NASA Astrophysics Data System (ADS)

Guéneau, C.; Chatain, S.; Gossé, S.; Rado, C.; Rapaud, O.; Lechelle, J.; Dumas, J. C.; Chatillon, C.

2005-09-01

For both high temperature reactor (HTR) and gas cooled fast reactor (GFR) systems, the high operating temperature in normal and accidental conditions necessitates the assessment of the thermodynamic data and associated phase diagrams for the complex system constituted of the fuel kernel, the inert materials and the fission products. A classical CALPHAD approach, coupling experiments and thermodynamic calculations, is proposed. Some examples of studies are presented leading with the CO and CO 2 gas formation during the chemical interaction of [UO 2± x/C] in the HTR particle, and the chemical compatibility of the couples [UN/SiC], [(U, Pu)N/SiC], [(U, Pu)N/TiN] for the GFR system. A project of constitution of a thermodynamic database for advanced fuels of gas-cooled reactors is proposed.

LoCoH: Non-parameteric kernel methods for constructing home ranges and utilization distributions

USGS Publications Warehouse

Getz, Wayne M.; Fortmann-Roe, Scott; Cross, Paul C.; Lyons, Andrew J.; Ryan, Sadie J.; Wilmers, Christopher C.

2007-01-01

Parametric kernel methods currently dominate the literature regarding the construction of animal home ranges (HRs) and utilization distributions (UDs). These methods frequently fail to capture the kinds of hard boundaries common to many natural systems. Recently a local convex hull (LoCoH) nonparametric kernel method, which generalizes the minimum convex polygon (MCP) method, was shown to be more appropriate than parametric kernel methods for constructing HRs and UDs, because of its ability to identify hard boundaries (e.g., rivers, cliff edges) and convergence to the true distribution as sample size increases. Here we extend the LoCoH in two ways: ‘‘fixed sphere-of-influence,’’ or r -LoCoH (kernels constructed from all points within a fixed radius r of each reference point), and an ‘‘adaptive sphere-of-influence,’’ or a -LoCoH (kernels constructed from all points within a radius a such that the distances of all points within the radius to the reference point sum to a value less than or equal to a ), and compare them to the original ‘‘fixed-number-of-points,’’ or k -LoCoH (all kernels constructed from k -1 nearest neighbors of root points). We also compare these nonparametric LoCoH to parametric kernel methods using manufactured data and data collected from GPS collars on African buffalo in the Kruger National Park, South Africa. Our results demonstrate that LoCoH methods are superior to parametric kernel methods in estimating areas used by animals, excluding unused areas (holes) and, generally, in constructing UDs and HRs arising from the movement of animals influenced by hard boundaries and irregular structures (e.g., rocky outcrops). We also demonstrate that a -LoCoH is generally superior to k - and r -LoCoH (with software for all three methods available at http://locoh.cnr.berkeley.edu).

The effect of CT technical factors on quantification of lung fissure integrity

NASA Astrophysics Data System (ADS)

Chong, D.; Brown, M. S.; Ochs, R.; Abtin, F.; Brown, M.; Ordookhani, A.; Shaw, G.; Kim, H. J.; Gjertson, D.; Goldin, J. G.

2009-02-01

A new emphysema treatment uses endobronchial valves to perform lobar volume reduction. The degree of fissure completeness may predict treatment efficacy. This study investigated the behavior of a semiautomated algorithm for quantifying lung fissure integrity in CT with respect to reconstruction kernel and dose. Raw CT data was obtained for six asymptomatic patients from a high-risk population for lung cancer. The patients were scanned on either a Siemens Sensation 16 or 64, using a low-dose protocol of 120 kVp, 25 mAs. Images were reconstructed using kernels ranging from smooth to sharp (B10f, B30f, B50f, B70f). Research software was used to simulate an even lower-dose acquisition of 15 mAs, and images were generated at the same kernels resulting in 8 series per patient. The left major fissure was manually contoured axially at regular intervals, yielding 37 contours across all patients. These contours were read into an image analysis and pattern classification system which computed a Fissure Integrity Score (FIS) for each kernel and dose. FIS values were analyzed using a mixed-effects model with kernel and dose as fixed effects and patient as random effect to test for difference due to kernel and dose. Analysis revealed no difference in FIS between the smooth kernels (B10f, B30f) nor between sharp kernels (B50f, B70f), but there was a significant difference between the sharp and smooth groups (p = 0.020). There was no significant difference in FIS between the two low-dose reconstructions (p = 0.882). Using a cutoff of 90%, the number of incomplete fissures increased from 5 to 10 when the imaging protocol changed from B50f to B30f. Reconstruction kernel has a significant effect on quantification of fissure integrity in CT. This has potential implications when selecting patients for endobronchial valve therapy.

Architectural development of an advanced EVA Electronic System

NASA Technical Reports Server (NTRS)

Lavelle, Joseph

1992-01-01

An advanced electronic system for future EVA missions (including zero gravity, the lunar surface, and the surface of Mars) is under research and development within the Advanced Life Support Division at NASA Ames Research Center. As a first step in the development, an optimum system architecture has been derived from an analysis of the projected requirements for these missions. The open, modular architecture centers around a distributed multiprocessing concept where the major subsystems independently process their own I/O functions and communicate over a common bus. Supervision and coordination of the subsystems is handled by an embedded real-time operating system kernel employing multitasking software techniques. A discussion of how the architecture most efficiently meets the electronic system functional requirements, maximizes flexibility for future development and mission applications, and enhances the reliability and serviceability of the system in these remote, hostile environments is included.

Quadcopter Control Using Speech Recognition

NASA Astrophysics Data System (ADS)

Malik, H.; Darma, S.; Soekirno, S.

2018-04-01

This research reported a comparison from a success rate of speech recognition systems that used two types of databases they were existing databases and new databases, that were implemented into quadcopter as motion control. Speech recognition system was using Mel frequency cepstral coefficient method (MFCC) as feature extraction that was trained using recursive neural network method (RNN). MFCC method was one of the feature extraction methods that most used for speech recognition. This method has a success rate of 80% - 95%. Existing database was used to measure the success rate of RNN method. The new database was created using Indonesian language and then the success rate was compared with results from an existing database. Sound input from the microphone was processed on a DSP module with MFCC method to get the characteristic values. Then, the characteristic values were trained using the RNN which result was a command. The command became a control input to the single board computer (SBC) which result was the movement of the quadcopter. On SBC, we used robot operating system (ROS) as the kernel (Operating System).

Near Theoretical Gigabit Link Efficiency for Distributed Data Acquisition Systems.

PubMed

Abu-Nimeh, Faisal T; Choong, Woon-Seng

2017-03-01

Link efficiency, data integrity, and continuity for high-throughput and real-time systems is crucial. Most of these applications require specialized hardware and operating systems as well as extensive tuning in order to achieve high efficiency. Here, we present an implementation of gigabit Ethernet data streaming which can achieve 99.26% link efficiency while maintaining no packet losses. The design and implementation are built on OpenPET, an opensource data acquisition platform for nuclear medical imaging, where (a) a crate hosting multiple OpenPET detector boards uses a User Datagram Protocol over Internet Protocol (UDP/IP) Ethernet soft-core, that is capable of understanding PAUSE frames, to stream data out to a computer workstation; (b) the receiving computer uses Netmap to allow the processing software (i.e., user space), which is written in Python, to directly receive and manage the network card's ring buffers, bypassing the operating system kernel's networking stack; and (c) a multi-threaded application using synchronized queues is implemented in the processing software (Python) to free up the ring buffers as quickly as possible while preserving data integrity and flow continuity.

Kernel abortion in maize : I. Carbohydrate concentration patterns and Acid invertase activity of maize kernels induced to abort in vitro.

PubMed

Hanft, J M; Jones, R J

1986-06-01

Kernels cultured in vitro were induced to abort by high temperature (35 degrees C) and by culturing six kernels/cob piece. Aborting kernels failed to enter a linear phase of dry mass accumulation and had a final mass that was less than 6% of nonaborting field-grown kernels. Kernels induced to abort by high temperature failed to synthesize starch in the endosperm and had elevated sucrose concentrations and low fructose and glucose concentrations in the pedicel during early growth compared to nonaborting kernels. Kernels induced to abort by high temperature also had much lower pedicel soluble acid invertase activities than did nonaborting kernels. These results suggest that high temperature during the lag phase of kernel growth may impair the process of sucrose unloading in the pedicel by indirectly inhibiting soluble acid invertase activity and prevent starch synthesis in the endosperm. Kernels induced to abort by culturing six kernels/cob piece had reduced pedicel fructose, glucose, and sucrose concentrations compared to kernels from field-grown ears. These aborting kernels also had a lower pedicel soluble acid invertase activity compared to nonaborting kernels from the same cob piece and from field-grown ears. The low invertase activity in pedicel tissue of the aborting kernels was probably caused by a lack of substrate (sucrose) for the invertase to cleave due to the intense competition for available assimilates. In contrast to kernels cultured at 35 degrees C, aborting kernels from cob pieces containing all six kernels accumulated starch in a linear fashion. These results indicate that kernels cultured six/cob piece abort because of an inadequate supply of sugar and are similar to apical kernels from field-grown ears that often abort prior to the onset of linear growth.

Gaussian process regression for geometry optimization

NASA Astrophysics Data System (ADS)

Denzel, Alexander; Kästner, Johannes

2018-03-01

We implemented a geometry optimizer based on Gaussian process regression (GPR) to find minimum structures on potential energy surfaces. We tested both a two times differentiable form of the Matérn kernel and the squared exponential kernel. The Matérn kernel performs much better. We give a detailed description of the optimization procedures. These include overshooting the step resulting from GPR in order to obtain a higher degree of interpolation vs. extrapolation. In a benchmark against the Limited-memory Broyden-Fletcher-Goldfarb-Shanno optimizer of the DL-FIND library on 26 test systems, we found the new optimizer to generally reduce the number of required optimization steps.

A field operational test on valve-regulated lead-acid absorbent-glass-mat batteries in micro-hybrid electric vehicles. Part I. Results based on kernel density estimation

NASA Astrophysics Data System (ADS)

Schaeck, S.; Karspeck, T.; Ott, C.; Weckler, M.; Stoermer, A. O.

2011-03-01

In March 2007 the BMW Group has launched the micro-hybrid functions brake energy regeneration (BER) and automatic start and stop function (ASSF). Valve-regulated lead-acid (VRLA) batteries in absorbent glass mat (AGM) technology are applied in vehicles with micro-hybrid power system (MHPS). In both part I and part II of this publication vehicles with MHPS and AGM batteries are subject to a field operational test (FOT). Test vehicles with conventional power system (CPS) and flooded batteries were used as a reference. In the FOT sample batteries were mounted several times and electrically tested in the laboratory intermediately. Vehicle- and battery-related diagnosis data were read out for each test run and were matched with laboratory data in a data base. The FOT data were analyzed by the use of two-dimensional, nonparametric kernel estimation for clear data presentation. The data show that capacity loss in the MHPS is comparable to the CPS. However, the influence of mileage performance, which cannot be separated, suggests that battery stress is enhanced in the MHPS although a battery refresh function is applied. Anyway, the FOT demonstrates the unsuitability of flooded batteries for the MHPS because of high early capacity loss due to acid stratification and because of vanishing cranking performance due to increasing internal resistance. Furthermore, the lack of dynamic charge acceptance for high energy regeneration efficiency is illustrated. Under the presented FOT conditions charge acceptance of lead-acid (LA) batteries decreases to less than one third for about half of the sample batteries compared to new battery condition. In part II of this publication FOT data are presented by multiple regression analysis (Schaeck et al., submitted for publication [1]).

Emotion Recognition from Single-Trial EEG Based on Kernel Fisher's Emotion Pattern and Imbalanced Quasiconformal Kernel Support Vector Machine

PubMed Central

Liu, Yi-Hung; Wu, Chien-Te; Cheng, Wei-Teng; Hsiao, Yu-Tsung; Chen, Po-Ming; Teng, Jyh-Tong

2014-01-01

Electroencephalogram-based emotion recognition (EEG-ER) has received increasing attention in the fields of health care, affective computing, and brain-computer interface (BCI). However, satisfactory ER performance within a bi-dimensional and non-discrete emotional space using single-trial EEG data remains a challenging task. To address this issue, we propose a three-layer scheme for single-trial EEG-ER. In the first layer, a set of spectral powers of different EEG frequency bands are extracted from multi-channel single-trial EEG signals. In the second layer, the kernel Fisher's discriminant analysis method is applied to further extract features with better discrimination ability from the EEG spectral powers. The feature vector produced by layer 2 is called a kernel Fisher's emotion pattern (KFEP), and is sent into layer 3 for further classification where the proposed imbalanced quasiconformal kernel support vector machine (IQK-SVM) serves as the emotion classifier. The outputs of the three layer EEG-ER system include labels of emotional valence and arousal. Furthermore, to collect effective training and testing datasets for the current EEG-ER system, we also use an emotion-induction paradigm in which a set of pictures selected from the International Affective Picture System (IAPS) are employed as emotion induction stimuli. The performance of the proposed three-layer solution is compared with that of other EEG spectral power-based features and emotion classifiers. Results on 10 healthy participants indicate that the proposed KFEP feature performs better than other spectral power features, and IQK-SVM outperforms traditional SVM in terms of the EEG-ER accuracy. Our findings also show that the proposed EEG-ER scheme achieves the highest classification accuracies of valence (82.68%) and arousal (84.79%) among all testing methods. PMID:25061837

Emotion recognition from single-trial EEG based on kernel Fisher's emotion pattern and imbalanced quasiconformal kernel support vector machine.

PubMed

Liu, Yi-Hung; Wu, Chien-Te; Cheng, Wei-Teng; Hsiao, Yu-Tsung; Chen, Po-Ming; Teng, Jyh-Tong

2014-07-24

Electroencephalogram-based emotion recognition (EEG-ER) has received increasing attention in the fields of health care, affective computing, and brain-computer interface (BCI). However, satisfactory ER performance within a bi-dimensional and non-discrete emotional space using single-trial EEG data remains a challenging task. To address this issue, we propose a three-layer scheme for single-trial EEG-ER. In the first layer, a set of spectral powers of different EEG frequency bands are extracted from multi-channel single-trial EEG signals. In the second layer, the kernel Fisher's discriminant analysis method is applied to further extract features with better discrimination ability from the EEG spectral powers. The feature vector produced by layer 2 is called a kernel Fisher's emotion pattern (KFEP), and is sent into layer 3 for further classification where the proposed imbalanced quasiconformal kernel support vector machine (IQK-SVM) serves as the emotion classifier. The outputs of the three layer EEG-ER system include labels of emotional valence and arousal. Furthermore, to collect effective training and testing datasets for the current EEG-ER system, we also use an emotion-induction paradigm in which a set of pictures selected from the International Affective Picture System (IAPS) are employed as emotion induction stimuli. The performance of the proposed three-layer solution is compared with that of other EEG spectral power-based features and emotion classifiers. Results on 10 healthy participants indicate that the proposed KFEP feature performs better than other spectral power features, and IQK-SVM outperforms traditional SVM in terms of the EEG-ER accuracy. Our findings also show that the proposed EEG-ER scheme achieves the highest classification accuracies of valence (82.68%) and arousal (84.79%) among all testing methods.

Electrochemical Immunosensor for the Detection of Aflatoxin B₁ in Palm Kernel Cake and Feed Samples.

PubMed

Azri, Farah Asilah; Selamat, Jinap; Sukor, Rashidah

2017-11-30

Palm kernel cake (PKC) is the solid residue following oil extraction of palm kernels and useful to fatten animals either as a single feed with only minerals and vitamins supplementation, or mixed with other feedstuffs such as corn kernels or soy beans. The occurrence of mycotoxins (aflatoxins, ochratoxins, zearalenone, and fumonisins) in feed samples affects the animal's health and also serves as a secondary contamination to humans via consumption of eggs, milk and meats. Of these, aflatoxin B₁ (AFB₁) is the most toxically potent and a confirmed carcinogen to both humans and animals. Methods such as High Performance Liquid Chromatography (HPLC) and Liquid Chromatography-Mass Spectrometry (LC-MS/MS) are common in the determination of mycotoxins. However, these methods usually require sample pre-treatment, extensive cleanup and skilled operator. Therefore, in the present work, a rapid method of electrochemical immunosensor for the detection of AFB₁ was developed based on an indirect competitive enzyme-linked immunosorbent assay (ELISA). Multi-walled carbon nanotubes (MWCNT) and chitosan (CS) were used as the electrode modifier for signal enhancement. N -ethyl- N '-(3-dimethylaminopropyl)-carbodiimide (EDC) and N -hydroxysuccinimide (NHS) activated the carboxyl groups at the surface of nanocomposite for the attachment of AFB₁-BSA antigen by covalent bonding. An indirect competitive reaction occurred between AFB₁-BSA and free AFB₁ for the binding site of a fixed amount of anti-AFB₁ antibody. A catalytic signal based on horseradish peroxidase (HRP) in the presence of hydrogen peroxide (H₂O₂) and 3,3',5,5'-tetramethylbenzidine (TMB) mediator was observed as a result of attachment of the secondary antibody to the immunoassay system. As a result, the reduction peak of TMB (Ox) was measured by using differential pulse voltammetry (DPV) analysis. Based on the results, the electrochemical surface area was increased from 0.396 cm² to 1.298 cm² due to the electrode modification with MWCNT/CS. At the optimal conditions, the working range of the electrochemical immunosensor was from 0.0001 to 10 ng/mL with limit of detection of 0.1 pg/mL. Good recoveries were obtained for the detection of spiked feed samples (PKC, corn kernels, soy beans). The developed method could be used for the screening of AFB₁ in real samples.

Impact of iron source and concentration on rice flavor using a simulated rice kernel micronutrient delivery system.

USDA-ARS?s Scientific Manuscript database

An extruded grain designed to look like a rice kernel fortified with one of two sources of iron (elemental iron and ferrous sulfate), with and without multiple fortificant (zinc, thiamin and folic acid) was mixed with milled Calrose rice at low (1:200), medium (1:100) and high (1:50) concentrations....

Exploring microwave resonant multi-point ignition using high-speed schlieren imaging

NASA Astrophysics Data System (ADS)

Liu, Cheng; Zhang, Guixin; Xie, Hong; Deng, Lei; Wang, Zhi

2018-03-01

Microwave plasma offers a potential method to achieve rapid combustion in a high-speed combustor. In this paper, microwave resonant multi-point ignition and its control method have been studied via high-speed schlieren imaging. The experiment was conducted with the microwave resonant ignition system and the schlieren optical system. The microwave pulse in 2.45 GHz with 2 ms width and 3 kW peak power was employed as an ignition energy source to produce initial flame kernels in the combustion chamber. A reflective schlieren method was designed to illustrate the flame development process with a high-speed camera. The bottom of the combustion chamber was made of a quartz glass coated with indium tin oxide, which ensures sufficient microwave reflection and light penetration. Ignition experiments were conducted at 2 bars of stoichiometric methane-air mixtures. Schlieren images show that flame kernels were generated at more than one location simultaneously and flame propagated with different speeds in different flame kernels. Ignition kernels were discussed in three types according to their appearances. Pressure curves and combustion duration also show that multi-point ignition plays a significant role in accelerating combustion.

Exploring microwave resonant multi-point ignition using high-speed schlieren imaging.

PubMed

Liu, Cheng; Zhang, Guixin; Xie, Hong; Deng, Lei; Wang, Zhi

2018-03-01

Microwave plasma offers a potential method to achieve rapid combustion in a high-speed combustor. In this paper, microwave resonant multi-point ignition and its control method have been studied via high-speed schlieren imaging. The experiment was conducted with the microwave resonant ignition system and the schlieren optical system. The microwave pulse in 2.45 GHz with 2 ms width and 3 kW peak power was employed as an ignition energy source to produce initial flame kernels in the combustion chamber. A reflective schlieren method was designed to illustrate the flame development process with a high-speed camera. The bottom of the combustion chamber was made of a quartz glass coated with indium tin oxide, which ensures sufficient microwave reflection and light penetration. Ignition experiments were conducted at 2 bars of stoichiometric methane-air mixtures. Schlieren images show that flame kernels were generated at more than one location simultaneously and flame propagated with different speeds in different flame kernels. Ignition kernels were discussed in three types according to their appearances. Pressure curves and combustion duration also show that multi-point ignition plays a significant role in accelerating combustion.

7 CFR 810.602 - Definition of other terms.

Code of Federal Regulations, 2010 CFR

2010-01-01

...) Damaged kernels. Kernels and pieces of flaxseed kernels that are badly ground-damaged, badly weather... instructions. Also, underdeveloped, shriveled, and small pieces of flaxseed kernels removed in properly... recleaning. (c) Heat-damaged kernels. Kernels and pieces of flaxseed kernels that are materially discolored...

Kernel Abortion in Maize 1

PubMed Central

Hanft, Jonathan M.; Jones, Robert J.

1986-01-01

Kernels cultured in vitro were induced to abort by high temperature (35°C) and by culturing six kernels/cob piece. Aborting kernels failed to enter a linear phase of dry mass accumulation and had a final mass that was less than 6% of nonaborting field-grown kernels. Kernels induced to abort by high temperature failed to synthesize starch in the endosperm and had elevated sucrose concentrations and low fructose and glucose concentrations in the pedicel during early growth compared to nonaborting kernels. Kernels induced to abort by high temperature also had much lower pedicel soluble acid invertase activities than did nonaborting kernels. These results suggest that high temperature during the lag phase of kernel growth may impair the process of sucrose unloading in the pedicel by indirectly inhibiting soluble acid invertase activity and prevent starch synthesis in the endosperm. Kernels induced to abort by culturing six kernels/cob piece had reduced pedicel fructose, glucose, and sucrose concentrations compared to kernels from field-grown ears. These aborting kernels also had a lower pedicel soluble acid invertase activity compared to nonaborting kernels from the same cob piece and from field-grown ears. The low invertase activity in pedicel tissue of the aborting kernels was probably caused by a lack of substrate (sucrose) for the invertase to cleave due to the intense competition for available assimilates. In contrast to kernels cultured at 35°C, aborting kernels from cob pieces containing all six kernels accumulated starch in a linear fashion. These results indicate that kernels cultured six/cob piece abort because of an inadequate supply of sugar and are similar to apical kernels from field-grown ears that often abort prior to the onset of linear growth. PMID:16664846

Out-of-Sample Extensions for Non-Parametric Kernel Methods.

PubMed

Pan, Binbin; Chen, Wen-Sheng; Chen, Bo; Xu, Chen; Lai, Jianhuang

2017-02-01

Choosing suitable kernels plays an important role in the performance of kernel methods. Recently, a number of studies were devoted to developing nonparametric kernels. Without assuming any parametric form of the target kernel, nonparametric kernel learning offers a flexible scheme to utilize the information of the data, which may potentially characterize the data similarity better. The kernel methods using nonparametric kernels are referred to as nonparametric kernel methods. However, many nonparametric kernel methods are restricted to transductive learning, where the prediction function is defined only over the data points given beforehand. They have no straightforward extension for the out-of-sample data points, and thus cannot be applied to inductive learning. In this paper, we show how to make the nonparametric kernel methods applicable to inductive learning. The key problem of out-of-sample extension is how to extend the nonparametric kernel matrix to the corresponding kernel function. A regression approach in the hyper reproducing kernel Hilbert space is proposed to solve this problem. Empirical results indicate that the out-of-sample performance is comparable to the in-sample performance in most cases. Experiments on face recognition demonstrate the superiority of our nonparametric kernel method over the state-of-the-art parametric kernel methods.

7 CFR 810.1202 - Definition of other terms.

Code of Federal Regulations, 2010 CFR

2010-01-01

... kernels. Kernels, pieces of rye kernels, and other grains that are badly ground-damaged, badly weather.... Also, underdeveloped, shriveled, and small pieces of rye kernels removed in properly separating the...-damaged kernels. Kernels, pieces of rye kernels, and other grains that are materially discolored and...

The Genetic Basis of Natural Variation in Kernel Size and Related Traits Using a Four-Way Cross Population in Maize.

PubMed

Chen, Jiafa; Zhang, Luyan; Liu, Songtao; Li, Zhimin; Huang, Rongrong; Li, Yongming; Cheng, Hongliang; Li, Xiantang; Zhou, Bo; Wu, Suowei; Chen, Wei; Wu, Jianyu; Ding, Junqiang

2016-01-01

Kernel size is an important component of grain yield in maize breeding programs. To extend the understanding on the genetic basis of kernel size traits (i.e., kernel length, kernel width and kernel thickness), we developed a set of four-way cross mapping population derived from four maize inbred lines with varied kernel sizes. In the present study, we investigated the genetic basis of natural variation in seed size and other components of maize yield (e.g., hundred kernel weight, number of rows per ear, number of kernels per row). In total, ten QTL affecting kernel size were identified, three of which (two for kernel length and one for kernel width) had stable expression in other components of maize yield. The possible genetic mechanism behind the trade-off of kernel size and yield components was discussed.

The Genetic Basis of Natural Variation in Kernel Size and Related Traits Using a Four-Way Cross Population in Maize

PubMed Central

Liu, Songtao; Li, Zhimin; Huang, Rongrong; Li, Yongming; Cheng, Hongliang; Li, Xiantang; Zhou, Bo; Wu, Suowei; Chen, Wei; Wu, Jianyu; Ding, Junqiang

2016-01-01

Kernel size is an important component of grain yield in maize breeding programs. To extend the understanding on the genetic basis of kernel size traits (i.e., kernel length, kernel width and kernel thickness), we developed a set of four-way cross mapping population derived from four maize inbred lines with varied kernel sizes. In the present study, we investigated the genetic basis of natural variation in seed size and other components of maize yield (e.g., hundred kernel weight, number of rows per ear, number of kernels per row). In total, ten QTL affecting kernel size were identified, three of which (two for kernel length and one for kernel width) had stable expression in other components of maize yield. The possible genetic mechanism behind the trade-off of kernel size and yield components was discussed. PMID:27070143

Spherical integral transforms of second-order gravitational tensor components onto third-order gravitational tensor components

NASA Astrophysics Data System (ADS)

Šprlák, Michal; Novák, Pavel

2017-02-01

New spherical integral formulas between components of the second- and third-order gravitational tensors are formulated in this article. First, we review the nomenclature and basic properties of the second- and third-order gravitational tensors. Initial points of mathematical derivations, i.e., the second- and third-order differential operators defined in the spherical local North-oriented reference frame and the analytical solutions of the gradiometric boundary-value problem, are also summarized. Secondly, we apply the third-order differential operators to the analytical solutions of the gradiometric boundary-value problem which gives 30 new integral formulas transforming (1) vertical-vertical, (2) vertical-horizontal and (3) horizontal-horizontal second-order gravitational tensor components onto their third-order counterparts. Using spherical polar coordinates related sub-integral kernels can efficiently be decomposed into azimuthal and isotropic parts. Both spectral and closed forms of the isotropic kernels are provided and their limits are investigated. Thirdly, numerical experiments are performed to test the consistency of the new integral transforms and to investigate properties of the sub-integral kernels. The new mathematical apparatus is valid for any harmonic potential field and may be exploited, e.g., when gravitational/magnetic second- and third-order tensor components become available in the future. The new integral formulas also extend the well-known Meissl diagram and enrich the theoretical apparatus of geodesy.

On the non-stationary generalized Langevin equation

NASA Astrophysics Data System (ADS)

Meyer, Hugues; Voigtmann, Thomas; Schilling, Tanja

2017-12-01

In molecular dynamics simulations and single molecule experiments, observables are usually measured along dynamic trajectories and then averaged over an ensemble ("bundle") of trajectories. Under stationary conditions, the time-evolution of such averages is described by the generalized Langevin equation. By contrast, if the dynamics is not stationary, it is not a priori clear which form the equation of motion for an averaged observable has. We employ the formalism of time-dependent projection operator techniques to derive the equation of motion for a non-equilibrium trajectory-averaged observable as well as for its non-stationary auto-correlation function. The equation is similar in structure to the generalized Langevin equation but exhibits a time-dependent memory kernel as well as a fluctuating force that implicitly depends on the initial conditions of the process. We also derive a relation between this memory kernel and the autocorrelation function of the fluctuating force that has a structure similar to a fluctuation-dissipation relation. In addition, we show how the choice of the projection operator allows us to relate the Taylor expansion of the memory kernel to data that are accessible in MD simulations and experiments, thus allowing us to construct the equation of motion. As a numerical example, the procedure is applied to Brownian motion initialized in non-equilibrium conditions and is shown to be consistent with direct measurements from simulations.

The NASA Mission Operations and Control Architecture Program

NASA Technical Reports Server (NTRS)

Ondrus, Paul J.; Carper, Richard D.; Jeffries, Alan J.

1994-01-01

The conflict between increases in space mission complexity and rapidly declining space mission budgets has created strong pressures to radically reduce the costs of designing and operating spacecraft. A key approach to achieving such reductions is through reducing the development and operations costs of the supporting mission operations systems. One of the efforts which the Communications and Data Systems Division at NASA Headquarters is using to meet this challenge is the Mission Operations Control Architecture (MOCA) project. Technical direction of this effort has been delegated to the Mission Operations Division (MOD) of the Goddard Space Flight Center (GSFC). MOCA is to develop a mission control and data acquisition architecture, and supporting standards, to guide the development of future spacecraft and mission control facilities at GSFC. The architecture will reduce the need for around-the-clock operations staffing, obtain a high level of reuse of flight and ground software elements from mission to mission, and increase overall system flexibility by enabling the migration of appropriate functions from the ground to the spacecraft. The end results are to be an established way of designing the spacecraft-ground system interface for GSFC's in-house developed spacecraft, and a specification of the end to end spacecraft control process, including data structures, interfaces, and protocols, suitable for inclusion in solicitation documents for future flight spacecraft. A flight software kernel may be developed and maintained in a condition that it can be offered as Government Furnished Equipment in solicitations. This paper describes the MOCA project, its current status, and the results to date.

Real-time operating system timing jitter and its impact on motor control

NASA Astrophysics Data System (ADS)

Proctor, Frederick M.; Shackleford, William P.

2001-12-01

General-purpose microprocessors are increasingly being used for control applications due to their widespread availability and software support for non-control functions like networking and operator interfaces. Two classes of real-time operating systems (RTOS) exist for these systems. The traditional RTOS serves as the sole operating system, and provides all OS services. Examples include ETS, LynxOS, QNX, Windows CE and VxWorks. RTOS extensions add real-time scheduling capabilities to non-real-time OSes, and provide minimal services needed for the time-critical portions of an application. Examples include RTAI and RTL for Linux, and HyperKernel, OnTime and RTX for Windows NT. Timing jitter is an issue in these systems, due to hardware effects such as bus locking, caches and pipelines, and software effects from mutual exclusion resource locks, non-preemtible critical sections, disabled interrupts, and multiple code paths in the scheduler. Jitter is typically on the order of a microsecond to a few tens of microseconds for hard real-time operating systems, and ranges from milliseconds to seconds in the worst case for soft real-time operating systems. The question of its significance on the performance of a controller arises. Naturally, the smaller the scheduling period required for a control task, the more significant is the impact of timing jitter. Aside from this intuitive relationship is the greater significance of timing on open-loop control, such as for stepper motors, than for closed-loop control, such as for servo motors. Techniques for measuring timing jitter are discussed, and comparisons between various platforms are presented. Techniques to reduce jitter or mitigate its effects are presented. The impact of jitter on stepper motor control is analyzed.

All-paths graph kernel for protein-protein interaction extraction with evaluation of cross-corpus learning.

PubMed

Airola, Antti; Pyysalo, Sampo; Björne, Jari; Pahikkala, Tapio; Ginter, Filip; Salakoski, Tapio

2008-11-19

Automated extraction of protein-protein interactions (PPI) is an important and widely studied task in biomedical text mining. We propose a graph kernel based approach for this task. In contrast to earlier approaches to PPI extraction, the introduced all-paths graph kernel has the capability to make use of full, general dependency graphs representing the sentence structure. We evaluate the proposed method on five publicly available PPI corpora, providing the most comprehensive evaluation done for a machine learning based PPI-extraction system. We additionally perform a detailed evaluation of the effects of training and testing on different resources, providing insight into the challenges involved in applying a system beyond the data it was trained on. Our method is shown to achieve state-of-the-art performance with respect to comparable evaluations, with 56.4 F-score and 84.8 AUC on the AImed corpus. We show that the graph kernel approach performs on state-of-the-art level in PPI extraction, and note the possible extension to the task of extracting complex interactions. Cross-corpus results provide further insight into how the learning generalizes beyond individual corpora. Further, we identify several pitfalls that can make evaluations of PPI-extraction systems incomparable, or even invalid. These include incorrect cross-validation strategies and problems related to comparing F-score results achieved on different evaluation resources. Recommendations for avoiding these pitfalls are provided.

7 CFR 810.802 - Definition of other terms.

Code of Federal Regulations, 2010 CFR

2010-01-01

...) Damaged kernels. Kernels and pieces of grain kernels for which standards have been established under the.... (d) Heat-damaged kernels. Kernels and pieces of grain kernels for which standards have been...

Survivability of Deterministic Dynamical Systems

PubMed Central

Hellmann, Frank; Schultz, Paul; Grabow, Carsten; Heitzig, Jobst; Kurths, Jürgen

2016-01-01

The notion of a part of phase space containing desired (or allowed) states of a dynamical system is important in a wide range of complex systems research. It has been called the safe operating space, the viability kernel or the sunny region. In this paper we define the notion of survivability: Given a random initial condition, what is the likelihood that the transient behaviour of a deterministic system does not leave a region of desirable states. We demonstrate the utility of this novel stability measure by considering models from climate science, neuronal networks and power grids. We also show that a semi-analytic lower bound for the survivability of linear systems allows a numerically very efficient survivability analysis in realistic models of power grids. Our numerical and semi-analytic work underlines that the type of stability measured by survivability is not captured by common asymptotic stability measures. PMID:27405955

7 CFR 981.408 - Inedible kernel.

Code of Federal Regulations, 2014 CFR

2014-01-01

... kernel is modified to mean a kernel, piece, or particle of almond kernel with any defect scored as... purposes of determining inedible kernels, pieces, or particles of almond kernels. [59 FR 39419, Aug. 3...

7 CFR 981.408 - Inedible kernel.

Code of Federal Regulations, 2011 CFR

2011-01-01

... kernel is modified to mean a kernel, piece, or particle of almond kernel with any defect scored as... purposes of determining inedible kernels, pieces, or particles of almond kernels. [59 FR 39419, Aug. 3...

7 CFR 981.408 - Inedible kernel.

Code of Federal Regulations, 2012 CFR

2012-01-01

... kernel is modified to mean a kernel, piece, or particle of almond kernel with any defect scored as... purposes of determining inedible kernels, pieces, or particles of almond kernels. [59 FR 39419, Aug. 3...

7 CFR 981.408 - Inedible kernel.

Code of Federal Regulations, 2013 CFR

2013-01-01

... kernel is modified to mean a kernel, piece, or particle of almond kernel with any defect scored as... purposes of determining inedible kernels, pieces, or particles of almond kernels. [59 FR 39419, Aug. 3...

Aflatoxin contamination of developing corn kernels.

PubMed

Amer, M A

2005-01-01

Preharvest of corn and its contamination with aflatoxin is a serious problem. Some environmental and cultural factors responsible for infection and subsequent aflatoxin production were investigated in this study. Stage of growth and location of kernels on corn ears were found to be one of the important factors in the process of kernel infection with A. flavus & A. parasiticus. The results showed positive correlation between the stage of growth and kernel infection. Treatment of corn with aflatoxin reduced germination, protein and total nitrogen contents. Total and reducing soluble sugar was increase in corn kernels as response to infection. Sucrose and protein content were reduced in case of both pathogens. Shoot system length, seeding fresh weigh and seedling dry weigh was also affected. Both pathogens induced reduction of starch content. Healthy corn seedlings treated with aflatoxin solution were badly affected. Their leaves became yellow then, turned brown with further incubation. Moreover, their total chlorophyll and protein contents showed pronounced decrease. On the other hand, total phenolic compounds were increased. Histopathological studies indicated that A. flavus & A. parasiticus could colonize corn silks and invade developing kernels. Germination of A. flavus spores was occurred and hyphae spread rapidly across the silk, producing extensive growth and lateral branching. Conidiophores and conidia had formed in and on the corn silk. Temperature and relative humidity greatly influenced the growth of A. flavus & A. parasiticus and aflatoxin production.

Cross-domain question classification in community question answering via kernel mapping

NASA Astrophysics Data System (ADS)

Su, Lei; Hu, Zuoliang; Yang, Bin; Li, Yiyang; Chen, Jun

2015-10-01

An increasingly popular method for retrieving information is via the community question answering (CQA) systems such as Yahoo! Answers and Baidu Knows. In CQA, question classification plays an important role to find the answers. However, the labeled training examples for statistical question classifier are fairly expensive to obtain, as they require the experienced human efforts. Meanwhile, unlabeled data are readily available. This paper employs the method of domain adaptation via kernel mapping to solve this problem. In detail, the kernel approach is utilized to map the target-domain data and the source-domain data into a common space, where the question classifiers are trained under the closer conditional probabilities. The kernel mapping function is constructed by domain knowledge. Therefore, domain knowledge could be transferred from the labeled examples in the source domain to the unlabeled ones in the targeted domain. The statistical training model can be improved by using a large number of unlabeled data. Meanwhile, the Hadoop Platform is used to construct the mapping mechanism to reduce the time complexity. Map/Reduce enable kernel mapping for domain adaptation in parallel in the Hadoop Platform. Experimental results show that the accuracy of question classification could be improved by the method of kernel mapping. Furthermore, the parallel method in the Hadoop Platform could effective schedule the computing resources to reduce the running time.

Evaluation of the OpenCL AES Kernel using the Intel FPGA SDK for OpenCL

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

Jin, Zheming; Yoshii, Kazutomo; Finkel, Hal

The OpenCL standard is an open programming model for accelerating algorithms on heterogeneous computing system. OpenCL extends the C-based programming language for developing portable codes on different platforms such as CPU, Graphics processing units (GPUs), Digital Signal Processors (DSPs) and Field Programmable Gate Arrays (FPGAs). The Intel FPGA SDK for OpenCL is a suite of tools that allows developers to abstract away the complex FPGA-based development flow for a high-level software development flow. Users can focus on the design of hardware-accelerated kernel functions in OpenCL and then direct the tools to generate the low-level FPGA implementations. The approach makes themore » FPGA-based development more accessible to software users as the needs for hybrid computing using CPUs and FPGAs are increasing. It can also significantly reduce the hardware development time as users can evaluate different ideas with high-level language without deep FPGA domain knowledge. In this report, we evaluate the performance of the kernel using the Intel FPGA SDK for OpenCL and Nallatech 385A FPGA board. Compared to the M506 module, the board provides more hardware resources for a larger design exploration space. The kernel performance is measured with the compute kernel throughput, an upper bound to the FPGA throughput. The report presents the experimental results in details. The Appendix lists the kernel source code.« less

Learning Circulant Sensing Kernels

DTIC Science & Technology

2014-03-01

Furthermore, we test learning the circulant sensing matrix/operator and the nonparametric dictionary altogether and obtain even better performance. We...scale. Furthermore, we test learning the circulant sensing matrix/operator and the nonparametric dictionary altogether and obtain even better performance...matrices, Tropp et al.[28] de - scribes a random filter for acquiring a signal x̄; Haupt et al.[12] describes a channel estimation problem to identify a

The FAST-T approach for operational, real time, short term hydrological forecasting: Results from the Betania Hydropower Reservoir case study

NASA Astrophysics Data System (ADS)

Domínguez, Efraín; Angarita, Hector; Rosmann, Thomas; Mendez, Zulma; Angulo, Gustavo

2013-04-01

A viable quantitative hydrological forecasting service is a combination of technological elements, personnel and knowledge, working together to establish a stable operational cycle of forecasts emission, dissemination and assimilation; hence, the process for establishing such system usually requires significant resources and time to reach an adequate development and integration in order to produce forecasts with acceptable levels of performance. Here are presented the results of this process for the recently implemented Operational Forecast Service for the Betania's Hydropower Reservoir - or SPHEB, located at the Upper-Magdalena River Basin (Colombia). The current scope of the SPHEB includes forecasting of water levels and discharge for the three main streams affluent to the reservoir, for lead times between +1 to +57 hours, and +1 to +10 days. The core of the SPHEB is the Flexible, Adaptive, Simple and Transient Time forecasting approach, namely FAST-T. This comprises of a set of data structures, mathematical kernel, distributed computing and network infrastructure designed to provide seamless real-time operational forecast and automatic model adjustment in case of failures in data transmission or assimilation. Among FAST-T main features are: an autonomous evaluation and detection of the most relevant information for the later configuration of forecasting models; an adaptively linearized mathematical kernel, the optimal adaptive linear combination or OALC, which provides a computationally simple and efficient algorithm for real-time applications; and finally, a meta-model catalog, containing prioritized forecast models at given stream conditions. The SPHEB is at present feed by the fraction of hydrological monitoring network installed at the basin that has telemetric capabilities via NOAA-GOES satellites (8 stages, approximately 47%) with data availability of about a 90% at one hour intervals. However, there is a dense network of 'conventional' hydro-meteorological stages -read manually once or twice per day - that, despite not ideal in the context of real-time system, improve model performance significantly, and therefore are entered into the system by manual input. At its current configuration, the SPHEB performance objectives are fulfilled for 90% of the forecasts with lead times up to +2 days and +15 hours (using the predictability criteria of the Russian Hydrometeorological Center S/?Δ) and the average accuracy is in the range 70-99% ( r2 criteria). However, longer lead times are at present not satisfactory in terms of forecasts accuracy.

A Novel Extreme Learning Machine Classification Model for e-Nose Application Based on the Multiple Kernel Approach.

PubMed

Jian, Yulin; Huang, Daoyu; Yan, Jia; Lu, Kun; Huang, Ying; Wen, Tailai; Zeng, Tanyue; Zhong, Shijie; Xie, Qilong

2017-06-19

A novel classification model, named the quantum-behaved particle swarm optimization (QPSO)-based weighted multiple kernel extreme learning machine (QWMK-ELM), is proposed in this paper. Experimental validation is carried out with two different electronic nose (e-nose) datasets. Being different from the existing multiple kernel extreme learning machine (MK-ELM) algorithms, the combination coefficients of base kernels are regarded as external parameters of single-hidden layer feedforward neural networks (SLFNs). The combination coefficients of base kernels, the model parameters of each base kernel, and the regularization parameter are optimized by QPSO simultaneously before implementing the kernel extreme learning machine (KELM) with the composite kernel function. Four types of common single kernel functions (Gaussian kernel, polynomial kernel, sigmoid kernel, and wavelet kernel) are utilized to constitute different composite kernel functions. Moreover, the method is also compared with other existing classification methods: extreme learning machine (ELM), kernel extreme learning machine (KELM), k-nearest neighbors (KNN), support vector machine (SVM), multi-layer perceptron (MLP), radical basis function neural network (RBFNN), and probabilistic neural network (PNN). The results have demonstrated that the proposed QWMK-ELM outperforms the aforementioned methods, not only in precision, but also in efficiency for gas classification.

Extending Automatic Parallelization to Optimize High-Level Abstractions for Multicore

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

Liao, C; Quinlan, D J; Willcock, J J

2008-12-12

Automatic introduction of OpenMP for sequential applications has attracted significant attention recently because of the proliferation of multicore processors and the simplicity of using OpenMP to express parallelism for shared-memory systems. However, most previous research has only focused on C and Fortran applications operating on primitive data types. C++ applications using high-level abstractions, such as STL containers and complex user-defined types, are largely ignored due to the lack of research compilers that are readily able to recognize high-level object-oriented abstractions and leverage their associated semantics. In this paper, we automatically parallelize C++ applications using ROSE, a multiple-language source-to-source compiler infrastructuremore » which preserves the high-level abstractions and gives us access to their semantics. Several representative parallelization candidate kernels are used to explore semantic-aware parallelization strategies for high-level abstractions, combined with extended compiler analyses. Those kernels include an array-base computation loop, a loop with task-level parallelism, and a domain-specific tree traversal. Our work extends the applicability of automatic parallelization to modern applications using high-level abstractions and exposes more opportunities to take advantage of multicore processors.« less

Neutronics Studies of Uranium-bearing Fully Ceramic Micro-encapsulated Fuel for PWRs

DOE PAGES

George, Nathan M.; Maldonado, G. Ivan; Terrani, Kurt A.; ...

2014-12-01

Our study evaluated the neutronics and some of the fuel cycle characteristics of using uranium-based fully ceramic microencapsulated (FCM) fuel in a pressurized water reactor (PWR). Specific PWR lattice designs with FCM fuel have been developed that are expected to achieve higher specific burnup levels in the fuel while also increasing the tolerance to reactor accidents. The SCALE software system was the primary analysis tool used to model the lattice designs. A parametric study was performed by varying tristructural isotropic particle design features (e.g., kernel diameter, coating layer thicknesses, and packing fraction) to understand the impact on reactivity and resultingmore » operating cycle length. Moreover, to match the lifetime of an 18-month PWR cycle, the FCM particle fuel design required roughly 10% additional fissile material at beginning of life compared with that of a standard uranium dioxide (UO 2) rod. Uranium mononitride proved to be a favorable fuel for the fuel kernel due to its higher heavy metal loading density compared with UO 2. The FCM fuel designs evaluated maintain acceptable neutronics design features for fuel lifetime, lattice peaking factors, and nonproliferation figure of merit.« less

Classification With Truncated Distance Kernel.

PubMed

Huang, Xiaolin; Suykens, Johan A K; Wang, Shuning; Hornegger, Joachim; Maier, Andreas

2018-05-01

This brief proposes a truncated distance (TL1) kernel, which results in a classifier that is nonlinear in the global region but is linear in each subregion. With this kernel, the subregion structure can be trained using all the training data and local linear classifiers can be established simultaneously. The TL1 kernel has good adaptiveness to nonlinearity and is suitable for problems which require different nonlinearities in different areas. Though the TL1 kernel is not positive semidefinite, some classical kernel learning methods are still applicable which means that the TL1 kernel can be directly used in standard toolboxes by replacing the kernel evaluation. In numerical experiments, the TL1 kernel with a pregiven parameter achieves similar or better performance than the radial basis function kernel with the parameter tuned by cross validation, implying the TL1 kernel a promising nonlinear kernel for classification tasks.

A Precise Drunk Driving Detection Using Weighted Kernel Based on Electrocardiogram.

PubMed

Wu, Chung Kit; Tsang, Kim Fung; Chi, Hao Ran; Hung, Faan Hei

2016-05-09

Globally, 1.2 million people die and 50 million people are injured annually due to traffic accidents. These traffic accidents cost $500 billion dollars. Drunk drivers are found in 40% of the traffic crashes. Existing drunk driving detection (DDD) systems do not provide accurate detection and pre-warning concurrently. Electrocardiogram (ECG) is a proven biosignal that accurately and simultaneously reflects human's biological status. In this letter, a classifier for DDD based on ECG is investigated in an attempt to reduce traffic accidents caused by drunk drivers. At this point, it appears that there is no known research or literature found on ECG classifier for DDD. To identify drunk syndromes, the ECG signals from drunk drivers are studied and analyzed. As such, a precise ECG-based DDD (ECG-DDD) using a weighted kernel is developed. From the measurements, 10 key features of ECG signals were identified. To incorporate the important features, the feature vectors are weighted in the customization of kernel functions. Four commonly adopted kernel functions are studied. Results reveal that weighted feature vectors improve the accuracy by 11% compared to the computation using the prime kernel. Evaluation shows that ECG-DDD improved the accuracy by 8% to 18% compared to prevailing methods.

Accelerating the Original Profile Kernel.

PubMed

Hamp, Tobias; Goldberg, Tatyana; Rost, Burkhard

2013-01-01

One of the most accurate multi-class protein classification systems continues to be the profile-based SVM kernel introduced by the Leslie group. Unfortunately, its CPU requirements render it too slow for practical applications of large-scale classification tasks. Here, we introduce several software improvements that enable significant acceleration. Using various non-redundant data sets, we demonstrate that our new implementation reaches a maximal speed-up as high as 14-fold for calculating the same kernel matrix. Some predictions are over 200 times faster and render the kernel as possibly the top contender in a low ratio of speed/performance. Additionally, we explain how to parallelize various computations and provide an integrative program that reduces creating a production-quality classifier to a single program call. The new implementation is available as a Debian package under a free academic license and does not depend on commercial software. For non-Debian based distributions, the source package ships with a traditional Makefile-based installer. Download and installation instructions can be found at https://rostlab.org/owiki/index.php/Fast_Profile_Kernel. Bugs and other issues may be reported at https://rostlab.org/bugzilla3/enter_bug.cgi?product=fastprofkernel.

Parametric classification of handvein patterns based on texture features

NASA Astrophysics Data System (ADS)

Al Mahafzah, Harbi; Imran, Mohammad; Supreetha Gowda H., D.

2018-04-01

In this paper, we have developed Biometric recognition system adopting hand based modality Handvein,which has the unique pattern for each individual and it is impossible to counterfeit and fabricate as it is an internal feature. We have opted in choosing feature extraction algorithms such as LBP-visual descriptor, LPQ-blur insensitive texture operator, Log-Gabor-Texture descriptor. We have chosen well known classifiers such as KNN and SVM for classification. We have experimented and tabulated results of single algorithm recognition rate for Handvein under different distance measures and kernel options. The feature level fusion is carried out which increased the performance level.

A Gaussian Processes Technique for Short-term Load Forecasting with Considerations of Uncertainty

NASA Astrophysics Data System (ADS)

Ohmi, Masataro; Mori, Hiroyuki

In this paper, an efficient method is proposed to deal with short-term load forecasting with the Gaussian Processes. Short-term load forecasting plays a key role to smooth power system operation such as economic load dispatching, unit commitment, etc. Recently, the deregulated and competitive power market increases the degree of uncertainty. As a result, it is more important to obtain better prediction results to save the cost. One of the most important aspects is that power system operator needs the upper and lower bounds of the predicted load to deal with the uncertainty while they require more accurate predicted values. The proposed method is based on the Bayes model in which output is expressed in a distribution rather than a point. To realize the model efficiently, this paper proposes the Gaussian Processes that consists of the Bayes linear model and kernel machine to obtain the distribution of the predicted value. The proposed method is successively applied to real data of daily maximum load forecasting.

OpenSHMEM-UCX : Evaluation of UCX for implementing OpenSHMEM Programming Model

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

Baker, Matthew B; Gorentla Venkata, Manjunath; Aderholdt, William Ferrol

2016-01-01

The OpenSHMEM reference implementation was developed towards the goal of developing an open source and high-performing Open- SHMEM implementation. To achieve portability and performance across various networks, the OpenSHMEM reference implementation uses GAS- Net and UCCS for network operations. Recently, new network layers have emerged with the promise of providing high-performance, scalabil- ity, and portability for HPC applications. In this paper, we implement the OpenSHMEM reference implementation to use the UCX framework for network operations. Then, we evaluate its performance and scalabil- ity on Cray XK systems to understand UCX s suitability for developing the OpenSHMEM programming model. Further, wemore » develop a bench- mark called SHOMS for evaluating the OpenSHMEM implementation. Our experimental results show that OpenSHMEM-UCX outperforms the vendor supplied OpenSHMEM implementation in most cases on the Cray XK system by up to 40% with respect to message rate and up to 70% for the execution of application kernels.« less

Assessment of NOAA NUCAPS upper air temperature profiles using COSMIC GPS radio occultation and ARM radiosondes

NASA Astrophysics Data System (ADS)

Feltz, M. L.; Borg, L.; Knuteson, R. O.; Tobin, D.; Revercomb, H.; Gambacorta, A.

2017-09-01

The U.S. National Oceanic and Atmospheric Administration (NOAA) recently began operational processing to derive vertical temperature profiles from two new sensors, Cross-Track Infrared Sounder and Advanced Technology Microwave Sounder, which were developed for the next generation of U.S. weather satellites. The NOAA-Unique Combined Atmospheric Processing System (NUCAPS) has been developed by NOAA to routinely process data from future Joint Polar Satellite System operational satellites and the preparatory Suomi-NPP satellite. This paper assesses the NUCAPS vertical temperature profile product from the upper troposphere into the middle stratosphere using radiosonde and GPS radio occultation (RO) data. Radiosonde data from the Department of Energy Atmospheric Radiation Measurement (ARM) program are=] compared to both the NUCAPS and GPS RO temperature products to evaluate bias and RMS errors. At all three fixed ARM sites for time periods investigated the NUCAPS temperature in the 100-40 hPa range is found to have an average bias to the radiosondes of less than 0.45 K and an RMS error of less than 1 K when temperature averaging kernels are applied. At a 95% confidence level, the radiosondes and RO were found to agree within 0.4 K at the North Slope of Alaska site and within 0.83 K at Southern Great Plains and Tropical Western Pacific. The GPS RO-derived dry temperatures, obtained from the University Corporation for Atmospheric Research Constellation Observing System for Meteorology, Ionosphere, and Climate (COSMIC) mission, are used as a common reference for the intercomparison of NUCAPS temperature products to similar products produced by NASA from Atmospheric Infrared Sounder (AIRS) and by European Organisation for the Exploitation of Meteorological Satellites from MetOp-B Infrared Atmospheric Sounding Interferometer (IASI). For seasonal and zonal scales, the NUCAPS agreement with AIRS and IASI is less than 0.5 K after application of averaging kernels.

A Novel Extreme Learning Machine Classification Model for e-Nose Application Based on the Multiple Kernel Approach

PubMed Central

Jian, Yulin; Huang, Daoyu; Yan, Jia; Lu, Kun; Huang, Ying; Wen, Tailai; Zeng, Tanyue; Zhong, Shijie; Xie, Qilong

2017-01-01

A novel classification model, named the quantum-behaved particle swarm optimization (QPSO)-based weighted multiple kernel extreme learning machine (QWMK-ELM), is proposed in this paper. Experimental validation is carried out with two different electronic nose (e-nose) datasets. Being different from the existing multiple kernel extreme learning machine (MK-ELM) algorithms, the combination coefficients of base kernels are regarded as external parameters of single-hidden layer feedforward neural networks (SLFNs). The combination coefficients of base kernels, the model parameters of each base kernel, and the regularization parameter are optimized by QPSO simultaneously before implementing the kernel extreme learning machine (KELM) with the composite kernel function. Four types of common single kernel functions (Gaussian kernel, polynomial kernel, sigmoid kernel, and wavelet kernel) are utilized to constitute different composite kernel functions. Moreover, the method is also compared with other existing classification methods: extreme learning machine (ELM), kernel extreme learning machine (KELM), k-nearest neighbors (KNN), support vector machine (SVM), multi-layer perceptron (MLP), radical basis function neural network (RBFNN), and probabilistic neural network (PNN). The results have demonstrated that the proposed QWMK-ELM outperforms the aforementioned methods, not only in precision, but also in efficiency for gas classification. PMID:28629202

Architectural requirements for the Red Storm computing system.

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

Camp, William J.; Tomkins, James Lee

This report is based on the Statement of Work (SOW) describing the various requirements for delivering 3 new supercomputer system to Sandia National Laboratories (Sandia) as part of the Department of Energy's (DOE) Accelerated Strategic Computing Initiative (ASCI) program. This system is named Red Storm and will be a distributed memory, massively parallel processor (MPP) machine built primarily out of commodity parts. The requirements presented here distill extensive architectural and design experience accumulated over a decade and a half of research, development and production operation of similar machines at Sandia. Red Storm will have an unusually high bandwidth, low latencymore » interconnect, specially designed hardware and software reliability features, a light weight kernel compute node operating system and the ability to rapidly switch major sections of the machine between classified and unclassified computing environments. Particular attention has been paid to architectural balance in the design of Red Storm, and it is therefore expected to achieve an atypically high fraction of its peak speed of 41 TeraOPS on real scientific computing applications. In addition, Red Storm is designed to be upgradeable to many times this initial peak capability while still retaining appropriate balance in key design dimensions. Installation of the Red Storm computer system at Sandia's New Mexico site is planned for 2004, and it is expected that the system will be operated for a minimum of five years following installation.« less

Gabor-based kernel PCA with fractional power polynomial models for face recognition.

PubMed

Liu, Chengjun

2004-05-01

This paper presents a novel Gabor-based kernel Principal Component Analysis (PCA) method by integrating the Gabor wavelet representation of face images and the kernel PCA method for face recognition. Gabor wavelets first derive desirable facial features characterized by spatial frequency, spatial locality, and orientation selectivity to cope with the variations due to illumination and facial expression changes. The kernel PCA method is then extended to include fractional power polynomial models for enhanced face recognition performance. A fractional power polynomial, however, does not necessarily define a kernel function, as it might not define a positive semidefinite Gram matrix. Note that the sigmoid kernels, one of the three classes of widely used kernel functions (polynomial kernels, Gaussian kernels, and sigmoid kernels), do not actually define a positive semidefinite Gram matrix either. Nevertheless, the sigmoid kernels have been successfully used in practice, such as in building support vector machines. In order to derive real kernel PCA features, we apply only those kernel PCA eigenvectors that are associated with positive eigenvalues. The feasibility of the Gabor-based kernel PCA method with fractional power polynomial models has been successfully tested on both frontal and pose-angled face recognition, using two data sets from the FERET database and the CMU PIE database, respectively. The FERET data set contains 600 frontal face images of 200 subjects, while the PIE data set consists of 680 images across five poses (left and right profiles, left and right half profiles, and frontal view) with two different facial expressions (neutral and smiling) of 68 subjects. The effectiveness of the Gabor-based kernel PCA method with fractional power polynomial models is shown in terms of both absolute performance indices and comparative performance against the PCA method, the kernel PCA method with polynomial kernels, the kernel PCA method with fractional power polynomial models, the Gabor wavelet-based PCA method, and the Gabor wavelet-based kernel PCA method with polynomial kernels.

Approximating the linear quadratic optimal control law for hereditary systems with delays in the control

NASA Technical Reports Server (NTRS)

Milman, Mark H.

1987-01-01

The fundamental control synthesis issue of establishing a priori convergence rates of approximation schemes for feedback controllers for a class of distributed parameter systems is addressed within the context of hereditary systems. Specifically, a factorization approach is presented for deriving approximations to the optimal feedback gains for the linear regulator-quadratic cost problem associated with time-varying functional differential equations with control delays. The approach is based on a discretization of the state penalty which leads to a simple structure for the feedback control law. General properties of the Volterra factors of Hilbert-Schmidt operators are then used to obtain convergence results for the controls, trajectories and feedback kernels. Two algorithms are derived from the basic approximation scheme, including a fast algorithm, in the time-invariant case. A numerical example is also considered.

Real-time networked control of an industrial robot manipulator via discrete-time second-order sliding modes

NASA Astrophysics Data System (ADS)

Massimiliano Capisani, Luca; Facchinetti, Tullio; Ferrara, Antonella

2010-08-01

This article presents the networked control of a robotic anthropomorphic manipulator based on a second-order sliding mode technique, where the control objective is to track a desired trajectory for the manipulator. The adopted control scheme allows an easy and effective distribution of the control algorithm over two networked machines. While the predictability of real-time tasks execution is achieved by the Soft Hard Real-Time Kernel (S.Ha.R.K.) real-time operating system, the communication is established via a standard Ethernet network. The performances of the control system are evaluated under different experimental system configurations using, to perform the experiments, a COMAU SMART3-S2 industrial robot, and the results are analysed to put into evidence the robustness of the proposed approach against possible network delays, packet losses and unmodelled effects.

Systemic Growth of F. graminearum in Wheat Plants and Related Accumulation of Deoxynivalenol

PubMed Central

Moretti, Antonio; Panzarini, Giuseppe; Somma, Stefania; Campagna, Claudio; Ravaglia, Stefano; Logrieco, Antonio F.; Solfrizzo, Michele

2014-01-01

Fusarium head blight (FHB) is an important disease of wheat worldwide caused mainly by Fusarium graminearum (syn. Gibberella zeae). This fungus can be highly aggressive and can produce several mycotoxins such as deoxynivalenol (DON), a well known harmful metabolite for humans, animals, and plants. The fungus can survive overwinter on wheat residues and on the soil, and can usually attack the wheat plant at their point of flowering, being able to infect the heads and to contaminate the kernels at the maturity. Contaminated kernels can be sometimes used as seeds for the cultivation of the following year. Poor knowledge on the ability of the strains of F. graminearum occurring on wheat seeds to be transmitted to the plant and to contribute to the final DON contamination of kernels is available. Therefore, this study had the goals of evaluating: (a) the capability of F. graminearum causing FHB of wheat to be transmitted from the seeds or soil to the kernels at maturity and the progress of the fungus within the plant at different growth stages; (b) the levels of DON contamination in both plant tissues and kernels. The study has been carried out for two years in a climatic chamber. The F. gramineraum strain selected for the inoculation was followed within the plant by using Vegetative Compatibility technique, and quantified by Real-Time PCR. Chemical analyses of DON were carried out by using immunoaffinity cleanup and HPLC/UV/DAD. The study showed that F. graminearum originated from seeds or soil can grow systemically in the plant tissues, with the exception of kernels and heads. There seems to be a barrier that inhibits the colonization of the heads by the fungus. High levels of DON and F. graminearum were found in crowns, stems, and straw, whereas low levels of DON and no detectable levels of F. graminearum were found in both heads and kernels. Finally, in all parts of the plant (heads, crowns, and stems at milk and vitreous ripening stages, and straw at vitreous ripening), also the accumulation of significant quantities of DON-3-glucoside (DON-3G), a product of DON glycosylation, was detected, with decreasing levels in straw, crown, stems and kernels. The presence of DON and DON-3G in heads and kernels without the occurrence of F. graminearum may be explained by their water solubility that could facilitate their translocation from stem to heads and kernels. The presence of DON-3G at levels 23 times higher than DON in the heads at milk stage without the occurrence of F. graminearum may indicate that an active glycosylation of DON also occurs in the head tissues. Finally, the high levels of DON accumulated in straws are worrisome since they represent additional sources of mycotoxin for livestock. PMID:24727554

A multi-label learning based kernel automatic recommendation method for support vector machine.

PubMed

Zhang, Xueying; Song, Qinbao

2015-01-01

Choosing an appropriate kernel is very important and critical when classifying a new problem with Support Vector Machine. So far, more attention has been paid on constructing new kernels and choosing suitable parameter values for a specific kernel function, but less on kernel selection. Furthermore, most of current kernel selection methods focus on seeking a best kernel with the highest classification accuracy via cross-validation, they are time consuming and ignore the differences among the number of support vectors and the CPU time of SVM with different kernels. Considering the tradeoff between classification success ratio and CPU time, there may be multiple kernel functions performing equally well on the same classification problem. Aiming to automatically select those appropriate kernel functions for a given data set, we propose a multi-label learning based kernel recommendation method built on the data characteristics. For each data set, the meta-knowledge data base is first created by extracting the feature vector of data characteristics and identifying the corresponding applicable kernel set. Then the kernel recommendation model is constructed on the generated meta-knowledge data base with the multi-label classification method. Finally, the appropriate kernel functions are recommended to a new data set by the recommendation model according to the characteristics of the new data set. Extensive experiments over 132 UCI benchmark data sets, with five different types of data set characteristics, eleven typical kernels (Linear, Polynomial, Radial Basis Function, Sigmoidal function, Laplace, Multiquadric, Rational Quadratic, Spherical, Spline, Wave and Circular), and five multi-label classification methods demonstrate that, compared with the existing kernel selection methods and the most widely used RBF kernel function, SVM with the kernel function recommended by our proposed method achieved the highest classification performance.

A Multi-Label Learning Based Kernel Automatic Recommendation Method for Support Vector Machine

PubMed Central

Zhang, Xueying; Song, Qinbao

2015-01-01

Choosing an appropriate kernel is very important and critical when classifying a new problem with Support Vector Machine. So far, more attention has been paid on constructing new kernels and choosing suitable parameter values for a specific kernel function, but less on kernel selection. Furthermore, most of current kernel selection methods focus on seeking a best kernel with the highest classification accuracy via cross-validation, they are time consuming and ignore the differences among the number of support vectors and the CPU time of SVM with different kernels. Considering the tradeoff between classification success ratio and CPU time, there may be multiple kernel functions performing equally well on the same classification problem. Aiming to automatically select those appropriate kernel functions for a given data set, we propose a multi-label learning based kernel recommendation method built on the data characteristics. For each data set, the meta-knowledge data base is first created by extracting the feature vector of data characteristics and identifying the corresponding applicable kernel set. Then the kernel recommendation model is constructed on the generated meta-knowledge data base with the multi-label classification method. Finally, the appropriate kernel functions are recommended to a new data set by the recommendation model according to the characteristics of the new data set. Extensive experiments over 132 UCI benchmark data sets, with five different types of data set characteristics, eleven typical kernels (Linear, Polynomial, Radial Basis Function, Sigmoidal function, Laplace, Multiquadric, Rational Quadratic, Spherical, Spline, Wave and Circular), and five multi-label classification methods demonstrate that, compared with the existing kernel selection methods and the most widely used RBF kernel function, SVM with the kernel function recommended by our proposed method achieved the highest classification performance. PMID:25893896

Receiver operating characteristic for a spectrogram correlator-based humpback whale detector-classifier.

PubMed

Abbot, Ted A; Premus, Vincent E; Abbot, Philip A; Mayer, Owen A

2012-09-01

This paper presents recent experimental results and a discussion of system enhancements made to the real-time autonomous humpback whale detector-classifier algorithm first presented by Abbot et al. [J. Acoust. Soc. Am. 127, 2894-2903 (2010)]. In February 2010, a second-generation system was deployed in an experiment conducted off of leeward Kauai during which 26 h of humpback vocalizations were recorded via sonobuoy and processed in real time. These data have been analyzed along with 40 h of humpbacks-absent data collected from the same location during July-August 2009. The extensive whales-absent data set in particular has enabled the quantification of system false alarm rates and the measurement of receiver operating characteristic curves. The performance impact of three enhancements incorporated into the second-generation system are discussed, including (1) a method to eliminate redundancy in the kernel library, (2) increased use of contextual analysis, and (3) the augmentation of the training data with more recent humpback vocalizations. It will be shown that the performance of the real-time system was improved to yield a probability of correct classification of 0.93 and a probability of false alarm of 0.004 over the 66 h of independent test data.

7 CFR 981.7 - Edible kernel.

Code of Federal Regulations, 2010 CFR

2010-01-01

... 7 Agriculture 8 2010-01-01 2010-01-01 false Edible kernel. 981.7 Section 981.7 Agriculture... Regulating Handling Definitions § 981.7 Edible kernel. Edible kernel means a kernel, piece, or particle of almond kernel that is not inedible. [41 FR 26852, June 30, 1976] ...

Kernel K-Means Sampling for Nyström Approximation.

PubMed

He, Li; Zhang, Hong

2018-05-01

A fundamental problem in Nyström-based kernel matrix approximation is the sampling method by which training set is built. In this paper, we suggest to use kernel -means sampling, which is shown in our works to minimize the upper bound of a matrix approximation error. We first propose a unified kernel matrix approximation framework, which is able to describe most existing Nyström approximations under many popular kernels, including Gaussian kernel and polynomial kernel. We then show that, the matrix approximation error upper bound, in terms of the Frobenius norm, is equal to the -means error of data points in kernel space plus a constant. Thus, the -means centers of data in kernel space, or the kernel -means centers, are the optimal representative points with respect to the Frobenius norm error upper bound. Experimental results, with both Gaussian kernel and polynomial kernel, on real-world data sets and image segmentation tasks show the superiority of the proposed method over the state-of-the-art methods.

SU-E-T-329: Dosimetric Impact of Implementing Metal Artifact Reduction Methods and Metal Energy Deposition Kernels for Photon Dose Calculations

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

Huang, J; Followill, D; Howell, R

2015-06-15

Purpose: To investigate two strategies for reducing dose calculation errors near metal implants: use of CT metal artifact reduction methods and implementation of metal-based energy deposition kernels in the convolution/superposition (C/S) method. Methods: Radiochromic film was used to measure the dose upstream and downstream of titanium and Cerrobend implants. To assess the dosimetric impact of metal artifact reduction methods, dose calculations were performed using baseline, uncorrected images and metal artifact reduction Methods: Philips O-MAR, GE’s monochromatic gemstone spectral imaging (GSI) using dual-energy CT, and GSI imaging with metal artifact reduction software applied (MARs).To assess the impact of metal kernels, titaniummore » and silver kernels were implemented into a commercial collapsed cone C/S algorithm. Results: The CT artifact reduction methods were more successful for titanium than Cerrobend. Interestingly, for beams traversing the metal implant, we found that errors in the dimensions of the metal in the CT images were more important for dose calculation accuracy than reduction of imaging artifacts. The MARs algorithm caused a distortion in the shape of the titanium implant that substantially worsened the calculation accuracy. In comparison to water kernel dose calculations, metal kernels resulted in better modeling of the increased backscatter dose at the upstream interface but decreased accuracy directly downstream of the metal. We also found that the success of metal kernels was dependent on dose grid size, with smaller calculation voxels giving better accuracy. Conclusion: Our study yielded mixed results, with neither the metal artifact reduction methods nor the metal kernels being globally effective at improving dose calculation accuracy. However, some successes were observed. The MARs algorithm decreased errors downstream of Cerrobend by a factor of two, and metal kernels resulted in more accurate backscatter dose upstream of metals. Thus, these two strategies do have the potential to improve accuracy for patients with metal implants in certain scenarios. This work was supported by Public Health Service grants CA 180803 and CA 10953 awarded by the National Cancer Institute, United States of Health and Human Services, and in part by Mobius Medical Systems.« less

Learning a peptide-protein binding affinity predictor with kernel ridge regression

PubMed Central

2013-01-01

Background The cellular function of a vast majority of proteins is performed through physical interactions with other biomolecules, which, most of the time, are other proteins. Peptides represent templates of choice for mimicking a secondary structure in order to modulate protein-protein interaction. They are thus an interesting class of therapeutics since they also display strong activity, high selectivity, low toxicity and few drug-drug interactions. Furthermore, predicting peptides that would bind to a specific MHC alleles would be of tremendous benefit to improve vaccine based therapy and possibly generate antibodies with greater affinity. Modern computational methods have the potential to accelerate and lower the cost of drug and vaccine discovery by selecting potential compounds for testing in silico prior to biological validation. Results We propose a specialized string kernel for small bio-molecules, peptides and pseudo-sequences of binding interfaces. The kernel incorporates physico-chemical properties of amino acids and elegantly generalizes eight kernels, comprised of the Oligo, the Weighted Degree, the Blended Spectrum, and the Radial Basis Function. We provide a low complexity dynamic programming algorithm for the exact computation of the kernel and a linear time algorithm for it’s approximation. Combined with kernel ridge regression and SupCK, a novel binding pocket kernel, the proposed kernel yields biologically relevant and good prediction accuracy on the PepX database. For the first time, a machine learning predictor is capable of predicting the binding affinity of any peptide to any protein with reasonable accuracy. The method was also applied to both single-target and pan-specific Major Histocompatibility Complex class II benchmark datasets and three Quantitative Structure Affinity Model benchmark datasets. Conclusion On all benchmarks, our method significantly (p-value ≤ 0.057) outperforms the current state-of-the-art methods at predicting peptide-protein binding affinities. The proposed approach is flexible and can be applied to predict any quantitative biological activity. Moreover, generating reliable peptide-protein binding affinities will also improve system biology modelling of interaction pathways. Lastly, the method should be of value to a large segment of the research community with the potential to accelerate the discovery of peptide-based drugs and facilitate vaccine development. The proposed kernel is freely available at http://graal.ift.ulaval.ca/downloads/gs-kernel/. PMID:23497081

TH-C-BRD-04: Beam Modeling and Validation with Triple and Double Gaussian Dose Kernel for Spot Scanning Proton Beams

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

Hirayama, S; Takayanagi, T; Fujii, Y

2014-06-15

Purpose: To present the validity of our beam modeling with double and triple Gaussian dose kernels for spot scanning proton beams in Nagoya Proton Therapy Center. This study investigates the conformance between the measurements and calculation results in absolute dose with two types of beam kernel. Methods: A dose kernel is one of the important input data required for the treatment planning software. The dose kernel is the 3D dose distribution of an infinitesimal pencil beam of protons in water and consists of integral depth doses and lateral distributions. We have adopted double and triple Gaussian model as lateral distributionmore » in order to take account of the large angle scattering due to nuclear reaction by fitting simulated inwater lateral dose profile for needle proton beam at various depths. The fitted parameters were interpolated as a function of depth in water and were stored as a separate look-up table for the each beam energy. The process of beam modeling is based on the method of MDACC [X.R.Zhu 2013]. Results: From the comparison results between the absolute doses calculated by double Gaussian model and those measured at the center of SOBP, the difference is increased up to 3.5% in the high-energy region because the large angle scattering due to nuclear reaction is not sufficiently considered at intermediate depths in the double Gaussian model. In case of employing triple Gaussian dose kernels, the measured absolute dose at the center of SOBP agrees with calculation within ±1% regardless of the SOBP width and maximum range. Conclusion: We have demonstrated the beam modeling results of dose distribution employing double and triple Gaussian dose kernel. Treatment planning system with the triple Gaussian dose kernel has been successfully verified and applied to the patient treatment with a spot scanning technique in Nagoya Proton Therapy Center.« less

Multi-PSF fusion in image restoration of range-gated systems

NASA Astrophysics Data System (ADS)

Wang, Canjin; Sun, Tao; Wang, Tingfeng; Miao, Xikui; Wang, Rui

2018-07-01

For the task of image restoration, an accurate estimation of degrading PSF/kernel is the premise of recovering a visually superior image. The imaging process of range-gated imaging system in atmosphere associates with lots of factors, such as back scattering, background radiation, diffraction limit and the vibration of the platform. On one hand, due to the difficulty of constructing models for all factors, the kernels from physical-model based methods are not strictly accurate and practical. On the other hand, there are few strong edges in images, which brings significant errors to most of image-feature-based methods. Since different methods focus on different formation factors of the kernel, their results often complement each other. Therefore, we propose an approach which combines physical model with image features. With an fusion strategy using GCRF (Gaussian Conditional Random Fields) framework, we get a final kernel which is closer to the actual one. Aiming at the problem that ground-truth image is difficult to obtain, we then propose a semi data-driven fusion method in which different data sets are used to train fusion parameters. Finally, a semi blind restoration strategy based on EM (Expectation Maximization) and RL (Richardson-Lucy) algorithm is proposed. Our methods not only models how the lasers transfer in the atmosphere and imaging in the ICCD (Intensified CCD) plane, but also quantifies other unknown degraded factors using image-based methods, revealing how multiple kernel elements interact with each other. The experimental results demonstrate that our method achieves better performance than state-of-the-art restoration approaches.

Modified Mahalanobis Taguchi System for Imbalance Data Classification

PubMed Central

2017-01-01

The Mahalanobis Taguchi System (MTS) is considered one of the most promising binary classification algorithms to handle imbalance data. Unfortunately, MTS lacks a method for determining an efficient threshold for the binary classification. In this paper, a nonlinear optimization model is formulated based on minimizing the distance between MTS Receiver Operating Characteristics (ROC) curve and the theoretical optimal point named Modified Mahalanobis Taguchi System (MMTS). To validate the MMTS classification efficacy, it has been benchmarked with Support Vector Machines (SVMs), Naive Bayes (NB), Probabilistic Mahalanobis Taguchi Systems (PTM), Synthetic Minority Oversampling Technique (SMOTE), Adaptive Conformal Transformation (ACT), Kernel Boundary Alignment (KBA), Hidden Naive Bayes (HNB), and other improved Naive Bayes algorithms. MMTS outperforms the benchmarked algorithms especially when the imbalance ratio is greater than 400. A real life case study on manufacturing sector is used to demonstrate the applicability of the proposed model and to compare its performance with Mahalanobis Genetic Algorithm (MGA). PMID:28811820

Implementing real-time robotic systems using CHIMERA II

NASA Technical Reports Server (NTRS)

Stewart, David B.; Schmitz, Donald E.; Khosla, Pradeep K.

1990-01-01

A description is given of the CHIMERA II programming environment and operating system, which was developed for implementing real-time robotic systems. Sensor-based robotic systems contain both general- and special-purpose hardware, and thus the development of applications tends to be a time-consuming task. The CHIMERA II environment is designed to reduce the development time by providing a convenient software interface between the hardware and the user. CHIMERA II supports flexible hardware configurations which are based on one or more VME-backplanes. All communication across multiple processors is transparent to the user through an extensive set of interprocessor communication primitives. CHIMERA II also provides a high-performance real-time kernel which supports both deadline and highest-priority-first scheduling. The flexibility of CHIMERA II allows hierarchical models for robot control, such as NASREM, to be implemented with minimal programming time and effort.

Measurement and Analysis of Failures in Computer Systems

NASA Technical Reports Server (NTRS)

Thakur, Anshuman

1997-01-01

This thesis presents a study of software failures spanning several different releases of Tandem's NonStop-UX operating system running on Tandem Integrity S2(TMR) systems. NonStop-UX is based on UNIX System V and is fully compliant with industry standards, such as the X/Open Portability Guide, the IEEE POSIX standards, and the System V Interface Definition (SVID) extensions. In addition to providing a general UNIX interface to the hardware, the operating system has built-in recovery mechanisms and audit routines that check the consistency of the kernel data structures. The analysis is based on data on software failures and repairs collected from Tandem's product report (TPR) logs for a period exceeding three years. A TPR log is created when a customer or an internal developer observes a failure in a Tandem Integrity system. This study concentrates primarily on those TPRs that report a UNIX panic that subsequently crashes the system. Approximately 200 of the TPRs fall into this category. Approximately 50% of the failures reported are from field systems, and the rest are from the testing and development sites. It has been observed by Tandem developers that fewer cases are encountered from the field than from the test centers. Thus, the data selection mechanism has introduced a slight skew.

Experimental study of turbulent flame kernel propagation

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

Mansour, Mohy; Peters, Norbert; Schrader, Lars-Uve

2008-07-15

Flame kernels in spark ignited combustion systems dominate the flame propagation and combustion stability and performance. They are likely controlled by the spark energy, flow field and mixing field. The aim of the present work is to experimentally investigate the structure and propagation of the flame kernel in turbulent premixed methane flow using advanced laser-based techniques. The spark is generated using pulsed Nd:YAG laser with 20 mJ pulse energy in order to avoid the effect of the electrodes on the flame kernel structure and the variation of spark energy from shot-to-shot. Four flames have been investigated at equivalence ratios, {phi}{submore » j}, of 0.8 and 1.0 and jet velocities, U{sub j}, of 6 and 12 m/s. A combined two-dimensional Rayleigh and LIPF-OH technique has been applied. The flame kernel structure has been collected at several time intervals from the laser ignition between 10 {mu}s and 2 ms. The data show that the flame kernel structure starts with spherical shape and changes gradually to peanut-like, then to mushroom-like and finally disturbed by the turbulence. The mushroom-like structure lasts longer in the stoichiometric and slower jet velocity. The growth rate of the average flame kernel radius is divided into two linear relations; the first one during the first 100 {mu}s is almost three times faster than that at the later stage between 100 and 2000 {mu}s. The flame propagation is slightly faster in leaner flames. The trends of the flame propagation, flame radius, flame cross-sectional area and mean flame temperature are related to the jet velocity and equivalence ratio. The relations obtained in the present work allow the prediction of any of these parameters at different conditions. (author)« less

Time-frequency Features for Impedance Cardiography Signals During Anesthesia Using Different Distribution Kernels.

PubMed

Muñoz, Jesús Escrivá; Gambús, Pedro; Jensen, Erik W; Vallverdú, Montserrat

2018-01-01

This works investigates the time-frequency content of impedance cardiography signals during a propofol-remifentanil anesthesia. In the last years, impedance cardiography (ICG) is a technique which has gained much attention. However, ICG signals need further investigation. Time-Frequency Distributions (TFDs) with 5 different kernels are used in order to analyze impedance cardiography signals (ICG) before the start of the anesthesia and after the loss of consciousness. In total, ICG signals from one hundred and thirty-one consecutive patients undergoing major surgery under general anesthesia were analyzed. Several features were extracted from the calculated TFDs in order to characterize the time-frequency content of the ICG signals. Differences between those features before and after the loss of consciousness were studied. The Extended Modified Beta Distribution (EMBD) was the kernel for which most features shows statistically significant changes between before and after the loss of consciousness. Among all analyzed features, those based on entropy showed a sensibility, specificity and area under the curve of the receiver operating characteristic above 60%. The anesthetic state of the patient is reflected on linear and non-linear features extracted from the TFDs of the ICG signals. Especially, the EMBD is a suitable kernel for the analysis of ICG signals and offers a great range of features which change according to the patient's anesthesia state in a statistically significant way. Schattauer GmbH.

Optimizing Blocking and Nonblocking Reduction Operations for Multicore Systems: Hierarchical Design and Implementation

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

Gorentla Venkata, Manjunath; Shamis, Pavel; Graham, Richard L

2013-01-01

Many scientific simulations, using the Message Passing Interface (MPI) programming model, are sensitive to the performance and scalability of reduction collective operations such as MPI Allreduce and MPI Reduce. These operations are the most widely used abstractions to perform mathematical operations over all processes that are part of the simulation. In this work, we propose a hierarchical design to implement the reduction operations on multicore systems. This design aims to improve the efficiency of reductions by 1) tailoring the algorithms and customizing the implementations for various communication mechanisms in the system 2) providing the ability to configure the depth ofmore » hierarchy to match the system architecture, and 3) providing the ability to independently progress each of this hierarchy. Using this design, we implement MPI Allreduce and MPI Reduce operations (and its nonblocking variants MPI Iallreduce and MPI Ireduce) for all message sizes, and evaluate on multiple architectures including InfiniBand and Cray XT5. We leverage and enhance our existing infrastructure, Cheetah, which is a framework for implementing hierarchical collective operations to implement these reductions. The experimental results show that the Cheetah reduction operations outperform the production-grade MPI implementations such as Open MPI default, Cray MPI, and MVAPICH2, demonstrating its efficiency, flexibility and portability. On Infini- Band systems, with a microbenchmark, a 512-process Cheetah nonblocking Allreduce and Reduce achieves a speedup of 23x and 10x, respectively, compared to the default Open MPI reductions. The blocking variants of the reduction operations also show similar performance benefits. A 512-process nonblocking Cheetah Allreduce achieves a speedup of 3x, compared to the default MVAPICH2 Allreduce implementation. On a Cray XT5 system, a 6144-process Cheetah Allreduce outperforms the Cray MPI by 145%. The evaluation with an application kernel, Conjugate Gradient solver, shows that the Cheetah reductions speeds up total time to solution by 195%, demonstrating the potential benefits for scientific simulations.« less

Source Code Analysis Laboratory (SCALe)

DTIC Science & Technology

2012-04-01

Versus Flagged Nonconformities (FNC) Software System TP/FNC Ratio Mozilla Firefox version 2.0 6/12 50% Linux kernel version 2.6.15 10/126 8...is inappropriately tuned for analysis of the Linux kernel, which has anomalous results. Customizing SCALe to work with software for a particular...servers support a collection of virtual machines (VMs) that can be configured to support analysis in various environments, such as Windows XP and Linux . A

Transient and asymptotic behaviour of the binary breakage problem

NASA Astrophysics Data System (ADS)

Mantzaris, Nikos V.

2005-06-01

The general binary breakage problem with power-law breakage functions and two families of symmetric and asymmetric breakage kernels is studied in this work. A useful transformation leads to an equation that predicts self-similar solutions in its asymptotic limit and offers explicit knowledge of the mean size and particle density at each point in dimensionless time. A novel moving boundary algorithm in the transformed coordinate system is developed, allowing the accurate prediction of the full transient behaviour of the system from the initial condition up to the point where self-similarity is achieved, and beyond if necessary. The numerical algorithm is very rapid and its results are in excellent agreement with known analytical solutions. In the case of the symmetric breakage kernels only unimodal, self-similar number density functions are obtained asymptotically for all parameter values and independent of the initial conditions, while in the case of asymmetric breakage kernels, bimodality appears for high degrees of asymmetry and sharp breakage functions. For symmetric and discrete breakage kernels, self-similarity is not achieved. The solution exhibits sustained oscillations with amplitude that depends on the initial condition and the sharpness of the breakage mechanism, while the period is always fixed and equal to ln 2 with respect to dimensionless time.

7 CFR 810.2202 - Definition of other terms.

Code of Federal Regulations, 2014 CFR

2014-01-01

... kernels, foreign material, and shrunken and broken kernels. The sum of these three factors may not exceed... the removal of dockage and shrunken and broken kernels. (g) Heat-damaged kernels. Kernels, pieces of... sample after the removal of dockage and shrunken and broken kernels. (h) Other grains. Barley, corn...

7 CFR 981.8 - Inedible kernel.

Code of Federal Regulations, 2010 CFR

2010-01-01

... 7 Agriculture 8 2010-01-01 2010-01-01 false Inedible kernel. 981.8 Section 981.8 Agriculture... Regulating Handling Definitions § 981.8 Inedible kernel. Inedible kernel means a kernel, piece, or particle of almond kernel with any defect scored as serious damage, or damage due to mold, gum, shrivel, or...

7 CFR 51.1415 - Inedible kernels.

Code of Federal Regulations, 2010 CFR

2010-01-01

... 7 Agriculture 2 2010-01-01 2010-01-01 false Inedible kernels. 51.1415 Section 51.1415 Agriculture... Standards for Grades of Pecans in the Shell 1 Definitions § 51.1415 Inedible kernels. Inedible kernels means that the kernel or pieces of kernels are rancid, moldy, decayed, injured by insects or otherwise...

An Approximate Approach to Automatic Kernel Selection.

PubMed

Ding, Lizhong; Liao, Shizhong

2016-02-02

Kernel selection is a fundamental problem of kernel-based learning algorithms. In this paper, we propose an approximate approach to automatic kernel selection for regression from the perspective of kernel matrix approximation. We first introduce multilevel circulant matrices into automatic kernel selection, and develop two approximate kernel selection algorithms by exploiting the computational virtues of multilevel circulant matrices. The complexity of the proposed algorithms is quasi-linear in the number of data points. Then, we prove an approximation error bound to measure the effect of the approximation in kernel matrices by multilevel circulant matrices on the hypothesis and further show that the approximate hypothesis produced with multilevel circulant matrices converges to the accurate hypothesis produced with kernel matrices. Experimental evaluations on benchmark datasets demonstrate the effectiveness of approximate kernel selection.

Nondestructive In Situ Measurement Method for Kernel Moisture Content in Corn Ear.

PubMed

Zhang, Han-Lin; Ma, Qin; Fan, Li-Feng; Zhao, Peng-Fei; Wang, Jian-Xu; Zhang, Xiao-Dong; Zhu, De-Hai; Huang, Lan; Zhao, Dong-Jie; Wang, Zhong-Yi

2016-12-20

Moisture content is an important factor in corn breeding and cultivation. A corn breed with low moisture at harvest is beneficial for mechanical operations, reduces drying and storage costs after harvesting and, thus, reduces energy consumption. Nondestructive measurement of kernel moisture in an intact corn ear allows us to select corn varieties with seeds that have high dehydration speeds in the mature period. We designed a sensor using a ring electrode pair for nondestructive measurement of the kernel moisture in a corn ear based on a high-frequency detection circuit. Through experiments using the effective scope of the electrodes' electric field, we confirmed that the moisture in the corn cob has little effect on corn kernel moisture measurement. Before the sensor was applied in practice, we investigated temperature and conductivity effects on the output impedance. Results showed that the temperature was linearly related to the output impedance (both real and imaginary parts) of the measurement electrodes and the detection circuit's output voltage. However, the conductivity has a non-monotonic dependence on the output impedance (both real and imaginary parts) of the measurement electrodes and the output voltage of the high-frequency detection circuit. Therefore, we reduced the effect of conductivity on the measurement results through measurement frequency selection. Corn moisture measurement results showed a quadric regression between corn ear moisture and the imaginary part of the output impedance, and there is also a quadric regression between corn kernel moisture and the high-frequency detection circuit output voltage at 100 MHz. In this study, two corn breeds were measured using our sensor and gave R ² values for the quadric regression equation of 0.7853 and 0.8496.

Fine tuning of process parameters for improving briquette production from palm kernel shell gasification waste.

PubMed

Bazargan, Alireza; Rough, Sarah L; McKay, Gordon

2018-04-01

Palm kernel shell biochars (PKSB) ejected as residues from a gasifier have been used for solid fuel briquette production. With this approach, palm kernel shells can be used for energy production twice: first, by producing rich syngas during gasification; second, by compacting the leftover residues from gasification into high calorific value briquettes. Herein, the process parameters for the manufacture of PKSB biomass briquettes via compaction are optimized. Two possible optimum process scenarios are considered. In the first, the compaction speed is increased from 0.5 to 10 mm/s, the compaction pressure is decreased from 80 Pa to 40 MPa, the retention time is reduced from 10 s to zero, and the starch binder content of the briquette is halved from 0.1 to 0.05 kg/kg. With these adjustments, the briquette production rate increases by more than 20-fold; hence capital and operational costs can be reduced and the service life of compaction equipment can be increased. The resulting product satisfactorily passes tensile (compressive) crushing strength and impact resistance tests. The second scenario involves reducing the starch weight content to 0.03 kg/kg, while reducing the compaction pressure to a value no lower than 60 MPa. Overall, in both cases, the PKSB biomass briquettes show excellent potential as a solid fuel with calorific values on par with good-quality coal. CHNS: carbon, hydrogen, nitrogen, sulfur; FFB: fresh fruit bunch(es); HHV: higher heating value [J/kg]; LHV: lower heating value [J/kg]; PKS: palm kernel shell(s); PKSB: palm kernel shell biochar(s); POME: palm oil mill effluent; RDF: refuse-derived fuel; TGA: thermogravimetric analysis.

Coupling individual kernel-filling processes with source-sink interactions into GREENLAB-Maize.

PubMed

Ma, Yuntao; Chen, Youjia; Zhu, Jinyu; Meng, Lei; Guo, Yan; Li, Baoguo; Hoogenboom, Gerrit

2018-02-13

Failure to account for the variation of kernel growth in a cereal crop simulation model may cause serious deviations in the estimates of crop yield. The goal of this research was to revise the GREENLAB-Maize model to incorporate source- and sink-limited allocation approaches to simulate the dry matter accumulation of individual kernels of an ear (GREENLAB-Maize-Kernel). The model used potential individual kernel growth rates to characterize the individual potential sink demand. The remobilization of non-structural carbohydrates from reserve organs to kernels was also incorporated. Two years of field experiments were conducted to determine the model parameter values and to evaluate the model using two maize hybrids with different plant densities and pollination treatments. Detailed observations were made on the dimensions and dry weights of individual kernels and other above-ground plant organs throughout the seasons. Three basic traits characterizing an individual kernel were compared on simulated and measured individual kernels: (1) final kernel size; (2) kernel growth rate; and (3) duration of kernel filling. Simulations of individual kernel growth closely corresponded to experimental data. The model was able to reproduce the observed dry weight of plant organs well. Then, the source-sink dynamics and the remobilization of carbohydrates for kernel growth were quantified to show that remobilization processes accompanied source-sink dynamics during the kernel-filling process. We conclude that the model may be used to explore options for optimizing plant kernel yield by matching maize management to the environment, taking into account responses at the level of individual kernels. © The Author(s) 2018. Published by Oxford University Press on behalf of the Annals of Botany Company. All rights reserved. For permissions, please e-mail: journals.permissions@oup.com.

Unconventional protein sources: apricot seed kernels.

PubMed

Gabrial, G N; El-Nahry, F I; Awadalla, M Z; Girgis, S M

1981-09-01

Hamawy apricot seed kernels (sweet), Amar apricot seed kernels (bitter) and treated Amar apricot kernels (bitterness removed) were evaluated biochemically. All kernels were found to be high in fat (42.2--50.91%), protein (23.74--25.70%) and fiber (15.08--18.02%). Phosphorus, calcium, and iron were determined in all experimental samples. The three different apricot seed kernels were used for extensive study including the qualitative determination of the amino acid constituents by acid hydrolysis, quantitative determination of some amino acids, and biological evaluation of the kernel proteins in order to use them as new protein sources. Weanling albino rats failed to grow on diets containing the Amar apricot seed kernels due to low food consumption because of its bitterness. There was no loss in weight in that case. The Protein Efficiency Ratio data and blood analysis results showed the Hamawy apricot seed kernels to be higher in biological value than treated apricot seed kernels. The Net Protein Ratio data which accounts for both weight, maintenance and growth showed the treated apricot seed kernels to be higher in biological value than both Hamawy and Amar kernels. The Net Protein Ratio for the last two kernels were nearly equal.

Bandlimited computerized improvements in characterization of nonlinear systems with memory

NASA Astrophysics Data System (ADS)

Nuttall, Albert H.; Katz, Richard A.; Hughes, Derke R.; Koch, Robert M.

2016-05-01

The present article discusses some inroads in nonlinear signal processing made by the prime algorithm developer, Dr. Albert H. Nuttall and co-authors, a consortium of research scientists from the Naval Undersea Warfare Center Division, Newport, RI. The algorithm, called the Nuttall-Wiener-Volterra 'NWV' algorithm is named for its principal contributors [1], [2],[ 3] over many years of developmental research. The NWV algorithm significantly reduces the computational workload for characterizing nonlinear systems with memory. Following this formulation, two measurement waveforms on the system are required in order to characterize a specified nonlinear system under consideration: (1) an excitation input waveform, x(t) (the transmitted signal); and, (2) a response output waveform, z(t) (the received signal). Given these two measurement waveforms for a given propagation channel, a 'kernel' or 'channel response', h= [h0,h1,h2,h3] between the two measurement points, is computed via a least squares approach that optimizes modeled kernel values by performing a best fit between measured response z(t) and a modeled response y(t). New techniques significantly diminish the exponential growth of the number of computed kernel coefficients at second and third order in order to combat and reasonably alleviate the curse of dimensionality.

Study of the mechanisms for flame stabilization in gas turbine model combustors using kHz laser diagnostics

NASA Astrophysics Data System (ADS)

Boxx, Isaac; Carter, Campbell D.; Stöhr, Michael; Meier, Wolfgang

2013-05-01

An image-processing routine was developed to autonomously identify and statistically characterize flame-kernel events, wherein OH (from a planar laser-induced fluorescence, PLIF, measurement) appears in the probe region away from the contiguous OH layer. This routine was applied to datasets from two gas turbine model combustors that consist of thousands of joint OH-velocity images from kHz framerate OH-PLIF and particle image velocimetry (PIV). Phase sorting of the kernel centroids with respect to the dominant fluid-dynamic structure of the combustors (a helical precessing vortex core, PVC) indicates through-plane transport of reacting fluid best explains their sudden appearance in the PLIF images. The concentration of flame-kernel events around the periphery of the mean location of the PVC indicates they are likely the result of wrinkling and/or breakup of the primary flame sheet associated with the passage of the PVC as it circumscribes the burner centerline. The prevailing through-plane velocity of the swirling flow-field transports these fragments into the imaging plane of the OH-PLIF system. The lack of flame-kernel events near the center of the PVC (in which there is lower strain and longer fluid-dynamic residence times) indicates that auto-ignition is not a likely explanation for these flame kernels in a majority of cases. The lack of flame-kernel centroid variation in one flame in which there is no PVC further supports this explanation.

Characteristics and composition of watermelon, pumpkin, and paprika seed oils and flours.

PubMed

El-Adawy, T A; Taha, K M

2001-03-01

The nutritional quality and functional properties of paprika seed flour and seed kernel flours of pumpkin and watermelon were studied, as were the characteristics and structure of their seed oils. Paprika seed and seed kernels of pumpkin and watermelon were rich in oil and protein. All flour samples contained considerable amounts of P, K, Mg, Mn, and Ca. Paprika seed flour was superior to watermelon and pumpkin seed kernel flours in content of lysine and total essential amino acids. Oil samples had high amounts of unsaturated fatty acids with linoleic and oleic acids as the major acids. All oil samples fractionated into seven classes including triglycerides as a major lipid class. Data obtained for the oils' characteristics compare well with those of other edible oils. Antinutritional compounds such as stachyose, raffinose, verbascose, trypsin inhibitor, phytic acid, and tannins were detected in all flours. Pumpkin seed kernel flour had higher values of chemical score, essential amino acid index, and in vitro protein digestibility than the other flours examined. The first limiting amino acid was lysine for both watermelon and pumpkin seed kernel flours, but it was leucine in paprika seed flour. Protein solubility index, water and fat absorption capacities, emulsification properties, and foam stability were excellent in watermelon and pumpkin seed kernel flours and fairly good in paprika seed flour. Flour samples could be potentially added to food systems such as bakery products and ground meat formulations not only as a nutrient supplement but also as a functional agent in these formulations.

An introduction to kernel-based learning algorithms.

PubMed

Müller, K R; Mika, S; Rätsch, G; Tsuda, K; Schölkopf, B

2001-01-01

This paper provides an introduction to support vector machines, kernel Fisher discriminant analysis, and kernel principal component analysis, as examples for successful kernel-based learning methods. We first give a short background about Vapnik-Chervonenkis theory and kernel feature spaces and then proceed to kernel based learning in supervised and unsupervised scenarios including practical and algorithmic considerations. We illustrate the usefulness of kernel algorithms by discussing applications such as optical character recognition and DNA analysis.

7 CFR 981.408 - Inedible kernel.

Code of Federal Regulations, 2010 CFR

2010-01-01

... 7 Agriculture 8 2010-01-01 2010-01-01 false Inedible kernel. 981.408 Section 981.408 Agriculture... Administrative Rules and Regulations § 981.408 Inedible kernel. Pursuant to § 981.8, the definition of inedible kernel is modified to mean a kernel, piece, or particle of almond kernel with any defect scored as...

Detection of Splice Sites Using Support Vector Machine

NASA Astrophysics Data System (ADS)

Varadwaj, Pritish; Purohit, Neetesh; Arora, Bhumika

Automatic identification and annotation of exon and intron region of gene, from DNA sequences has been an important research area in field of computational biology. Several approaches viz. Hidden Markov Model (HMM), Artificial Intelligence (AI) based machine learning and Digital Signal Processing (DSP) techniques have extensively and independently been used by various researchers to cater this challenging task. In this work, we propose a Support Vector Machine based kernel learning approach for detection of splice sites (the exon-intron boundary) in a gene. Electron-Ion Interaction Potential (EIIP) values of nucleotides have been used for mapping character sequences to corresponding numeric sequences. Radial Basis Function (RBF) SVM kernel is trained using EIIP numeric sequences. Furthermore this was tested on test gene dataset for detection of splice site by window (of 12 residues) shifting. Optimum values of window size, various important parameters of SVM kernel have been optimized for a better accuracy. Receiver Operating Characteristic (ROC) curves have been utilized for displaying the sensitivity rate of the classifier and results showed 94.82% accuracy for splice site detection on test dataset.

Continuity properties of the semi-group and its integral kernel in non-relativistic QED

NASA Astrophysics Data System (ADS)

Matte, Oliver

2016-07-01

Employing recent results on stochastic differential equations associated with the standard model of non-relativistic quantum electrodynamics by B. Güneysu, J. S. Møller, and the present author, we study the continuity of the corresponding semi-group between weighted vector-valued Lp-spaces, continuity properties of elements in the range of the semi-group, and the pointwise continuity of an operator-valued semi-group kernel. We further discuss the continuous dependence of the semi-group and its integral kernel on model parameters. All these results are obtained for Kato decomposable electrostatic potentials and the actual assumptions on the model are general enough to cover the Nelson model as well. As a corollary, we obtain some new pointwise exponential decay and continuity results on elements of low-energetic spectral subspaces of atoms or molecules that also take spin into account. In a simpler situation where spin is neglected, we explain how to verify the joint continuity of positive ground state eigenvectors with respect to spatial coordinates and model parameters. There are no smallness assumptions imposed on any model parameter.

Accurately estimating PSF with straight lines detected by Hough transform

NASA Astrophysics Data System (ADS)

Wang, Ruichen; Xu, Liangpeng; Fan, Chunxiao; Li, Yong

2018-04-01

This paper presents an approach to estimating point spread function (PSF) from low resolution (LR) images. Existing techniques usually rely on accurate detection of ending points of the profile normal to edges. In practice however, it is often a great challenge to accurately localize profiles of edges from a LR image, which hence leads to a poor PSF estimation of the lens taking the LR image. For precisely estimating the PSF, this paper proposes firstly estimating a 1-D PSF kernel with straight lines, and then robustly obtaining the 2-D PSF from the 1-D kernel by least squares techniques and random sample consensus. Canny operator is applied to the LR image for obtaining edges and then Hough transform is utilized to extract straight lines of all orientations. Estimating 1-D PSF kernel with straight lines effectively alleviates the influence of the inaccurate edge detection on PSF estimation. The proposed method is investigated on both natural and synthetic images for estimating PSF. Experimental results show that the proposed method outperforms the state-ofthe- art and does not rely on accurate edge detection.

Robust Pedestrian Classification Based on Hierarchical Kernel Sparse Representation.

PubMed

Sun, Rui; Zhang, Guanghai; Yan, Xiaoxing; Gao, Jun

2016-08-16

Vision-based pedestrian detection has become an active topic in computer vision and autonomous vehicles. It aims at detecting pedestrians appearing ahead of the vehicle using a camera so that autonomous vehicles can assess the danger and take action. Due to varied illumination and appearance, complex background and occlusion pedestrian detection in outdoor environments is a difficult problem. In this paper, we propose a novel hierarchical feature extraction and weighted kernel sparse representation model for pedestrian classification. Initially, hierarchical feature extraction based on a CENTRIST descriptor is used to capture discriminative structures. A max pooling operation is used to enhance the invariance of varying appearance. Then, a kernel sparse representation model is proposed to fully exploit the discrimination information embedded in the hierarchical local features, and a Gaussian weight function as the measure to effectively handle the occlusion in pedestrian images. Extensive experiments are conducted on benchmark databases, including INRIA, Daimler, an artificially generated dataset and a real occluded dataset, demonstrating the more robust performance of the proposed method compared to state-of-the-art pedestrian classification methods.

Robust Pedestrian Classification Based on Hierarchical Kernel Sparse Representation

PubMed Central

Sun, Rui; Zhang, Guanghai; Yan, Xiaoxing; Gao, Jun

2016-01-01

Vision-based pedestrian detection has become an active topic in computer vision and autonomous vehicles. It aims at detecting pedestrians appearing ahead of the vehicle using a camera so that autonomous vehicles can assess the danger and take action. Due to varied illumination and appearance, complex background and occlusion pedestrian detection in outdoor environments is a difficult problem. In this paper, we propose a novel hierarchical feature extraction and weighted kernel sparse representation model for pedestrian classification. Initially, hierarchical feature extraction based on a CENTRIST descriptor is used to capture discriminative structures. A max pooling operation is used to enhance the invariance of varying appearance. Then, a kernel sparse representation model is proposed to fully exploit the discrimination information embedded in the hierarchical local features, and a Gaussian weight function as the measure to effectively handle the occlusion in pedestrian images. Extensive experiments are conducted on benchmark databases, including INRIA, Daimler, an artificially generated dataset and a real occluded dataset, demonstrating the more robust performance of the proposed method compared to state-of-the-art pedestrian classification methods. PMID:27537888

Does money matter in inflation forecasting?

NASA Astrophysics Data System (ADS)

Binner, J. M.; Tino, P.; Tepper, J.; Anderson, R.; Jones, B.; Kendall, G.

2010-11-01

This paper provides the most fully comprehensive evidence to date on whether or not monetary aggregates are valuable for forecasting US inflation in the early to mid 2000s. We explore a wide range of different definitions of money, including different methods of aggregation and different collections of included monetary assets. In our forecasting experiment we use two nonlinear techniques, namely, recurrent neural networks and kernel recursive least squares regression-techniques that are new to macroeconomics. Recurrent neural networks operate with potentially unbounded input memory, while the kernel regression technique is a finite memory predictor. The two methodologies compete to find the best fitting US inflation forecasting models and are then compared to forecasts from a naïve random walk model. The best models were nonlinear autoregressive models based on kernel methods. Our findings do not provide much support for the usefulness of monetary aggregates in forecasting inflation. Beyond its economic findings, our study is in the tradition of physicists’ long-standing interest in the interconnections among statistical mechanics, neural networks, and related nonparametric statistical methods, and suggests potential avenues of extension for such studies.

Design of CT reconstruction kernel specifically for clinical lung imaging

NASA Astrophysics Data System (ADS)

Cody, Dianna D.; Hsieh, Jiang; Gladish, Gregory W.

2005-04-01

In this study we developed a new reconstruction kernel specifically for chest CT imaging. An experimental flat-panel CT scanner was used on large dogs to produce 'ground-truth" reference chest CT images. These dogs were also examined using a clinical 16-slice CT scanner. We concluded from the dog images acquired on the clinical scanner that the loss of subtle lung structures was due mostly to the presence of the background noise texture when using currently available reconstruction kernels. This qualitative evaluation of the dog CT images prompted the design of a new recon kernel. This new kernel consisted of the combination of a low-pass and a high-pass kernel to produce a new reconstruction kernel, called the 'Hybrid" kernel. The performance of this Hybrid kernel fell between the two kernels on which it was based, as expected. This Hybrid kernel was also applied to a set of 50 patient data sets; the analysis of these clinical images is underway. We are hopeful that this Hybrid kernel will produce clinical images with an acceptable tradeoff of lung detail, reliable HU, and image noise.

Evaluation of Statistical Downscaling Skill at Reproducing Extreme Events

NASA Astrophysics Data System (ADS)

McGinnis, S. A.; Tye, M. R.; Nychka, D. W.; Mearns, L. O.

2015-12-01

Climate model outputs usually have much coarser spatial resolution than is needed by impacts models. Although higher resolution can be achieved using regional climate models for dynamical downscaling, further downscaling is often required. The final resolution gap is often closed with a combination of spatial interpolation and bias correction, which constitutes a form of statistical downscaling. We use this technique to downscale regional climate model data and evaluate its skill in reproducing extreme events. We downscale output from the North American Regional Climate Change Assessment Program (NARCCAP) dataset from its native 50-km spatial resolution to the 4-km resolution of University of Idaho's METDATA gridded surface meterological dataset, which derives from the PRISM and NLDAS-2 observational datasets. We operate on the major variables used in impacts analysis at a daily timescale: daily minimum and maximum temperature, precipitation, humidity, pressure, solar radiation, and winds. To interpolate the data, we use the patch recovery method from the Earth System Modeling Framework (ESMF) regridding package. We then bias correct the data using Kernel Density Distribution Mapping (KDDM), which has been shown to exhibit superior overall performance across multiple metrics. Finally, we evaluate the skill of this technique in reproducing extreme events by comparing raw and downscaled output with meterological station data in different bioclimatic regions according to the the skill scores defined by Perkins et al in 2013 for evaluation of AR4 climate models. We also investigate techniques for improving bias correction of values in the tails of the distributions. These techniques include binned kernel density estimation, logspline kernel density estimation, and transfer functions constructed by fitting the tails with a generalized pareto distribution.

A new discriminative kernel from probabilistic models.

PubMed

Tsuda, Koji; Kawanabe, Motoaki; Rätsch, Gunnar; Sonnenburg, Sören; Müller, Klaus-Robert

2002-10-01

Recently, Jaakkola and Haussler (1999) proposed a method for constructing kernel functions from probabilistic models. Their so-called Fisher kernel has been combined with discriminative classifiers such as support vector machines and applied successfully in, for example, DNA and protein analysis. Whereas the Fisher kernel is calculated from the marginal log-likelihood, we propose the TOP kernel derived; from tangent vectors of posterior log-odds. Furthermore, we develop a theoretical framework on feature extractors from probabilistic models and use it for analyzing the TOP kernel. In experiments, our new discriminative TOP kernel compares favorably to the Fisher kernel.

Analysis of projectile motion: A comparative study using fractional operators with power law, exponential decay and Mittag-Leffler kernel

NASA Astrophysics Data System (ADS)

Gómez-Aguilar, J. F.; Escobar-Jiménez, R. F.; López-López, M. G.; Alvarado-Martínez, V. M.

2018-03-01

In this paper, the two-dimensional projectile motion was studied; for this study two cases were considered, for the first one, we considered that there is no air resistance and, for the second case, we considered a resisting medium k . The study was carried out by using fractional calculus. The solution to this study was obtained by using fractional operators with power law, exponential decay and Mittag-Leffler kernel in the range of γ \\in (0,1] . These operators were considered in the Liouville-Caputo sense to use physical initial conditions with a known physical interpretation. The range and the maximum height of the projectile were obtained using these derivatives. With the aim of exploring the validity of the obtained results, we compared our results with experimental data given in the literature. A multi-objective particle swarm optimization approach was used for generating Pareto-optimal solutions for the parameters k and γ for different fixed values of velocity v0 and angle θ . The results showed some relevant qualitative differences between the use of power law, exponential decay and Mittag-Leffler law.

Scalable and Power Efficient Data Analytics for Hybrid Exascale Systems

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

Choudhary, Alok; Samatova, Nagiza; Wu, Kesheng

This project developed a generic and optimized set of core data analytics functions. These functions organically consolidate a broad constellation of high performance analytical pipelines. As the architectures of emerging HPC systems become inherently heterogeneous, there is a need to design algorithms for data analysis kernels accelerated on hybrid multi-node, multi-core HPC architectures comprised of a mix of CPUs, GPUs, and SSDs. Furthermore, the power-aware trend drives the advances in our performance-energy tradeoff analysis framework which enables our data analysis kernels algorithms and software to be parameterized so that users can choose the right power-performance optimizations.

Mission and Safety Critical (MASC) plans for the MASC Kernel simulation

NASA Technical Reports Server (NTRS)

1991-01-01

This report discusses a prototype for Mission and Safety Critical (MASC) kernel simulation which explains the intended approach and how the simulation will be used. Smalltalk is chosen for the simulation because of usefulness in quickly building working models of the systems and its object-oriented approach to software. A scenario is also introduced to give details about how the simulation works. The eventual system will be a fully object-oriented one implemented in Ada via Dragoon. To implement the simulation, a scenario using elements typical of those in the Space Station, was created.

Implementing Kernel Methods Incrementally by Incremental Nonlinear Projection Trick.

PubMed

Kwak, Nojun

2016-05-20

Recently, the nonlinear projection trick (NPT) was introduced enabling direct computation of coordinates of samples in a reproducing kernel Hilbert space. With NPT, any machine learning algorithm can be extended to a kernel version without relying on the so called kernel trick. However, NPT is inherently difficult to be implemented incrementally because an ever increasing kernel matrix should be treated as additional training samples are introduced. In this paper, an incremental version of the NPT (INPT) is proposed based on the observation that the centerization step in NPT is unnecessary. Because the proposed INPT does not change the coordinates of the old data, the coordinates obtained by INPT can directly be used in any incremental methods to implement a kernel version of the incremental methods. The effectiveness of the INPT is shown by applying it to implement incremental versions of kernel methods such as, kernel singular value decomposition, kernel principal component analysis, and kernel discriminant analysis which are utilized for problems of kernel matrix reconstruction, letter classification, and face image retrieval, respectively.

Cut and join operator ring in tensor models

NASA Astrophysics Data System (ADS)

Itoyama, H.; Mironov, A.; Morozov, A.

2018-07-01

Recent advancement of rainbow tensor models based on their superintegrability (manifesting itself as the existence of an explicit expression for a generic Gaussian correlator) has allowed us to bypass the long-standing problem seen as the lack of eigenvalue/determinant representation needed to establish the KP/Toda integrability. As the mandatory next step, we discuss in this paper how to provide an adequate designation to each of the connected gauge-invariant operators that form a double coset, which is required to cleverly formulate a tree-algebra generalization of the Virasoro constraints. This problem goes beyond the enumeration problem per se tied to the permutation group, forcing us to introduce a few gauge fixing procedures to the coset. We point out that the permutation-based labeling, which has proven to be relevant for the Gaussian averages is, via interesting complexity, related to the one based on the keystone trees, whose algebra will provide the tensor counterpart of the Virasoro algebra for matrix models. Moreover, our simple analysis reveals the existence of nontrivial kernels and co-kernels for the cut operation and for the join operation respectively that prevent a straightforward construction of the non-perturbative RG-complete partition function and the identification of truly independent time variables. We demonstrate these problems by the simplest non-trivial Aristotelian RGB model with one complex rank-3 tensor, studying its ring of gauge-invariant operators, generated by the keystone triple with the help of four operations: addition, multiplication, cut and join.

Design, development and validation of software for modelling dietary exposure to food chemicals and nutrients.

PubMed

McNamara, C; Naddy, B; Rohan, D; Sexton, J

2003-10-01

The Monte Carlo computational system for stochastic modelling of dietary exposure to food chemicals and nutrients is presented. This system was developed through a European Commission-funded research project. It is accessible as a Web-based application service. The system allows and supports very significant complexity in the data sets used as the model input, but provides a simple, general purpose, linear kernel for model evaluation. Specific features of the system include the ability to enter (arbitrarily) complex mathematical or probabilistic expressions at each and every input data field, automatic bootstrapping on subjects and on subject food intake diaries, and custom kernels to apply brand information such as market share and loyalty to the calculation of food and chemical intake.

Application Characterization at Scale: Lessons learned from developing a distributed Open Community Runtime system for High Performance Computing

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

Landwehr, Joshua B.; Suetterlein, Joshua D.; Marquez, Andres

2016-05-16

Since 2012, the U.S. Department of Energy’s X-Stack program has been developing solutions including runtime systems, programming models, languages, compilers, and tools for the Exascale system software to address crucial performance and power requirements. Fine grain programming models and runtime systems show a great potential to efficiently utilize the underlying hardware. Thus, they are essential to many X-Stack efforts. An abundant amount of small tasks can better utilize the vast parallelism available on current and future machines. Moreover, finer tasks can recover faster and adapt better, due to a decrease in state and control. Nevertheless, current applications have been writtenmore » to exploit old paradigms (such as Communicating Sequential Processor and Bulk Synchronous Parallel processing). To fully utilize the advantages of these new systems, applications need to be adapted to these new paradigms. As part of the applications’ porting process, in-depth characterization studies, focused on both application characteristics and runtime features, need to take place to fully understand the application performance bottlenecks and how to resolve them. This paper presents a characterization study for a novel high performance runtime system, called the Open Community Runtime, using key HPC kernels as its vehicle. This study has the following contributions: one of the first high performance, fine grain, distributed memory runtime system implementing the OCR standard (version 0.99a); and a characterization study of key HPC kernels in terms of runtime primitives running on both intra and inter node environments. Running on a general purpose cluster, we have found up to 1635x relative speed-up for a parallel tiled Cholesky Kernels on 128 nodes with 16 cores each and a 1864x relative speed-up for a parallel tiled Smith-Waterman kernel on 128 nodes with 30 cores.« less

Increasing accuracy of dispersal kernels in grid-based population models

USGS Publications Warehouse

Slone, D.H.

2011-01-01

Dispersal kernels in grid-based population models specify the proportion, distance and direction of movements within the model landscape. Spatial errors in dispersal kernels can have large compounding effects on model accuracy. Circular Gaussian and Laplacian dispersal kernels at a range of spatial resolutions were investigated, and methods for minimizing errors caused by the discretizing process were explored. Kernels of progressively smaller sizes relative to the landscape grid size were calculated using cell-integration and cell-center methods. These kernels were convolved repeatedly, and the final distribution was compared with a reference analytical solution. For large Gaussian kernels (σ > 10 cells), the total kernel error was <10 &sup-11; compared to analytical results. Using an invasion model that tracked the time a population took to reach a defined goal, the discrete model results were comparable to the analytical reference. With Gaussian kernels that had σ ≤ 0.12 using the cell integration method, or σ ≤ 0.22 using the cell center method, the kernel error was greater than 10%, which resulted in invasion times that were orders of magnitude different than theoretical results. A goal-seeking routine was developed to adjust the kernels to minimize overall error. With this, corrections for small kernels were found that decreased overall kernel error to <10-11 and invasion time error to <5%.

Anthraquinones isolated from the browned Chinese chestnut kernels (Castanea mollissima blume)

NASA Astrophysics Data System (ADS)

Zhang, Y. L.; Qi, J. H.; Qin, L.; Wang, F.; Pang, M. X.

2016-08-01

Anthraquinones (AQS) represent a group of secondary metallic products in plants. AQS are often naturally occurring in plants and microorganisms. In a previous study, we found that AQS were produced by enzymatic browning reaction in Chinese chestnut kernels. To find out whether non-enzymatic browning reaction in the kernels could produce AQS too, AQS were extracted from three groups of chestnut kernels: fresh kernels, non-enzymatic browned kernels, and browned kernels, and the contents of AQS were determined. High performance liquid chromatography (HPLC) and nuclear magnetic resonance (NMR) methods were used to identify two compounds of AQS, rehein(1) and emodin(2). AQS were barely exists in the fresh kernels, while both browned kernel groups sample contained a high amount of AQS. Thus, we comfirmed that AQS could be produced during both enzymatic and non-enzymatic browning process. Rhein and emodin were the main components of AQS in the browned kernels.

Structured Kernel Subspace Learning for Autonomous Robot Navigation.

PubMed

Kim, Eunwoo; Choi, Sungjoon; Oh, Songhwai

2018-02-14

This paper considers two important problems for autonomous robot navigation in a dynamic environment, where the goal is to predict pedestrian motion and control a robot with the prediction for safe navigation. While there are several methods for predicting the motion of a pedestrian and controlling a robot to avoid incoming pedestrians, it is still difficult to safely navigate in a dynamic environment due to challenges, such as the varying quality and complexity of training data with unwanted noises. This paper addresses these challenges simultaneously by proposing a robust kernel subspace learning algorithm based on the recent advances in nuclear-norm and l 1 -norm minimization. We model the motion of a pedestrian and the robot controller using Gaussian processes. The proposed method efficiently approximates a kernel matrix used in Gaussian process regression by learning low-rank structured matrix (with symmetric positive semi-definiteness) to find an orthogonal basis, which eliminates the effects of erroneous and inconsistent data. Based on structured kernel subspace learning, we propose a robust motion model and motion controller for safe navigation in dynamic environments. We evaluate the proposed robust kernel learning in various tasks, including regression, motion prediction, and motion control problems, and demonstrate that the proposed learning-based systems are robust against outliers and outperform existing regression and navigation methods.

A Tensor-Product-Kernel Framework for Multiscale Neural Activity Decoding and Control

PubMed Central

Li, Lin; Brockmeier, Austin J.; Choi, John S.; Francis, Joseph T.; Sanchez, Justin C.; Príncipe, José C.

2014-01-01

Brain machine interfaces (BMIs) have attracted intense attention as a promising technology for directly interfacing computers or prostheses with the brain's motor and sensory areas, thereby bypassing the body. The availability of multiscale neural recordings including spike trains and local field potentials (LFPs) brings potential opportunities to enhance computational modeling by enriching the characterization of the neural system state. However, heterogeneity on data type (spike timing versus continuous amplitude signals) and spatiotemporal scale complicates the model integration of multiscale neural activity. In this paper, we propose a tensor-product-kernel-based framework to integrate the multiscale activity and exploit the complementary information available in multiscale neural activity. This provides a common mathematical framework for incorporating signals from different domains. The approach is applied to the problem of neural decoding and control. For neural decoding, the framework is able to identify the nonlinear functional relationship between the multiscale neural responses and the stimuli using general purpose kernel adaptive filtering. In a sensory stimulation experiment, the tensor-product-kernel decoder outperforms decoders that use only a single neural data type. In addition, an adaptive inverse controller for delivering electrical microstimulation patterns that utilizes the tensor-product kernel achieves promising results in emulating the responses to natural stimulation. PMID:24829569

Nonlocal kinetic energy functional from the jellium-with-gap model: Applications to orbital-free density functional theory

NASA Astrophysics Data System (ADS)

Constantin, Lucian A.; Fabiano, Eduardo; Della Sala, Fabio

2018-05-01

Orbital-free density functional theory (OF-DFT) promises to describe the electronic structure of very large quantum systems, being its computational cost linear with the system size. However, the OF-DFT accuracy strongly depends on the approximation made for the kinetic energy (KE) functional. To date, the most accurate KE functionals are nonlocal functionals based on the linear-response kernel of the homogeneous electron gas, i.e., the jellium model. Here, we use the linear-response kernel of the jellium-with-gap model to construct a simple nonlocal KE functional (named KGAP) which depends on the band-gap energy. In the limit of vanishing energy gap (i.e., in the case of metals), the KGAP is equivalent to the Smargiassi-Madden (SM) functional, which is accurate for metals. For a series of semiconductors (with different energy gaps), the KGAP performs much better than SM, and results are close to the state-of-the-art functionals with sophisticated density-dependent kernels.

Monte Carlo Perturbation Theory Estimates of Sensitivities to System Dimensions

DOE PAGES

Burke, Timothy P.; Kiedrowski, Brian C.

2017-12-11

Here, Monte Carlo methods are developed using adjoint-based perturbation theory and the differential operator method to compute the sensitivities of the k-eigenvalue, linear functions of the flux (reaction rates), and bilinear functions of the forward and adjoint flux (kinetics parameters) to system dimensions for uniform expansions or contractions. The calculation of sensitivities to system dimensions requires computing scattering and fission sources at material interfaces using collisions occurring at the interface—which is a set of events with infinitesimal probability. Kernel density estimators are used to estimate the source at interfaces using collisions occurring near the interface. The methods for computing sensitivitiesmore » of linear and bilinear ratios are derived using the differential operator method and adjoint-based perturbation theory and are shown to be equivalent to methods previously developed using a collision history–based approach. The methods for determining sensitivities to system dimensions are tested on a series of fast, intermediate, and thermal critical benchmarks as well as a pressurized water reactor benchmark problem with iterated fission probability used for adjoint-weighting. The estimators are shown to agree within 5% and 3σ of reference solutions obtained using direct perturbations with central differences for the majority of test problems.« less

Broken rice kernels and the kinetics of rice hydration and texture during cooking.

PubMed

Saleh, Mohammed; Meullenet, Jean-Francois

2013-05-01

During rice milling and processing, broken kernels are inevitably present, although to date it has been unclear as to how the presence of broken kernels affects rice hydration and cooked rice texture. Therefore, this work intended to study the effect of broken kernels in a rice sample on rice hydration and texture during cooking. Two medium-grain and two long-grain rice cultivars were harvested, dried and milled, and the broken kernels were separated from unbroken kernels. Broken rice kernels were subsequently combined with unbroken rice kernels forming treatments of 0, 40, 150, 350 or 1000 g kg(-1) broken kernels ratio. Rice samples were then cooked and the moisture content of the cooked rice, the moisture uptake rate, and rice hardness and stickiness were measured. As the amount of broken rice kernels increased, rice sample texture became increasingly softer (P < 0.05) but the unbroken kernels became significantly harder. Moisture content and moisture uptake rate were positively correlated, and cooked rice hardness was negatively correlated to the percentage of broken kernels in rice samples. Differences in the proportions of broken rice in a milled rice sample play a major role in determining the texture properties of cooked rice. Variations in the moisture migration kinetics between broken and unbroken kernels caused faster hydration of the cores of broken rice kernels, with greater starch leach-out during cooking affecting the texture of the cooked rice. The texture of cooked rice can be controlled, to some extent, by varying the proportion of broken kernels in milled rice. © 2012 Society of Chemical Industry.

Advanced Operating System Technologies

NASA Astrophysics Data System (ADS)

Cittolin, Sergio; Riccardi, Fabio; Vascotto, Sandro

In this paper we describe an R&D effort to define an OS architecture suitable for the requirements of the Data Acquisition and Control of an LHC experiment. Large distributed computing systems are foreseen to be the core part of the DAQ and Control system of the future LHC experiments. Neworks of thousands of processors, handling dataflows of several gigaBytes per second, with very strict timing constraints (microseconds), will become a common experience in the following years. Problems like distributyed scheduling, real-time communication protocols, failure-tolerance, distributed monitoring and debugging will have to be faced. A solid software infrastructure will be required to manage this very complicared environment, and at this moment neither CERN has the necessary expertise to build it, nor any similar commercial implementation exists. Fortunately these problems are not unique to the particle and high energy physics experiments, and the current research work in the distributed systems field, especially in the distributed operating systems area, is trying to address many of the above mentioned issues. The world that we are going to face in the next ten years will be quite different and surely much more interconnected than the one we see now. Very ambitious projects exist, planning to link towns, nations and the world in a single "Data Highway". Teleconferencing, Video on Demend, Distributed Multimedia Applications are just a few examples of the very demanding tasks to which the computer industry is committing itself. This projects are triggering a great research effort in the distributed, real-time micro-kernel based operating systems field and in the software enginering areas. The purpose of our group is to collect the outcame of these different research efforts, and to establish a working environment where the different ideas and techniques can be tested, evaluated and possibly extended, to address the requirements of a DAQ and Control System suitable for LHC. Our work started in the second half of 1994, with a research agreement between CERN and Chorus Systemes (France), world leader in the micro-kernel OS technology. The Chorus OS is targeted to distributed real-time applications, and it can very efficiently support different "OS personalities" in the same environment, like Posix, UNIX, and a CORBA compliant distributed object architecture. Projects are being set-up to verify the suitability of our work for LHC applications, we are building a scaled-down prototype of the DAQ system foreseen for the CMS experiment at LHC, where we will directly test our protocols and where we will be able to make measurements and benchmarks, guiding our development and allowing us to build an analytical model of the system, suitable for simulation and large scale verification.

Soft Real-Time PID Control on a VME Computer

NASA Technical Reports Server (NTRS)

Karayan, Vahag; Sander, Stanley; Cageao, Richard

2007-01-01

microPID (uPID) is a computer program for real-time proportional + integral + derivative (PID) control of a translation stage in a Fourier-transform ultraviolet spectrometer. microPID implements a PID control loop over a position profile at sampling rate of 8 kHz (sampling period 125microseconds). The software runs in a strippeddown Linux operating system on a VersaModule Eurocard (VME) computer operating in real-time priority queue using an embedded controller, a 16-bit digital-to-analog converter (D/A) board, and a laser-positioning board (LPB). microPID consists of three main parts: (1) VME device-driver routines, (2) software that administers a custom protocol for serial communication with a control computer, and (3) a loop section that obtains the current position from an LPB-driver routine, calculates the ideal position from the profile, and calculates a new voltage command by use of an embedded PID routine all within each sampling period. The voltage command is sent to the D/A board to control the stage. microPID uses special kernel headers to obtain microsecond timing resolution. Inasmuch as microPID implements a single-threaded process and all other processes are disabled, the Linux operating system acts as a soft real-time system.

GKS. Minimal Graphical Kernel System C Binding

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

Simons, R.W.

1985-10-01

GKS (the Graphical Kernel System) is both an American National Standard (ANS) and an ISO international standard graphics package. It conforms to ANS X3.124-1985 and to the May 1985 draft proposal for the GKS C Language Binding standard under development by the X3H3 Technical Committee. This implementation includes level ma (the lowest level of the ANS) and some routines from level mb. The following graphics capabilities are supported: two-dimensional lines, markers, text, and filled areas; control over color, line type, and character height and alignment; multiple simultaneous workstations and multiple transformations; and locator and choice input. Tektronix 4014 and 4115more » terminals are supported, and support for other devices may be added. Since this implementation was developed under UNIX, it uses makefiles, C shell scripts, the ar library maintainer, editor scripts, and other UNIX utilities. Therefore, implementing it under another operating system may require considerable effort. Also included with GKS is the small plot package (SPP), a direct descendant of the WEASEL plot package developed at Sandia. SPP is built on the GKS; therefore, all of the capabilities of GKS are available. It is not necessary to use GKS functions, since entire plots can be produced using only SPP functions, but the addition of GKS will give the programmer added power and flexibility. SPP provides single-call plot commands, linear and logarithmic axis commands, control for optional plotting of tick marks and tick mark labels, and permits plotting of data with or without markers and connecting lines.« less

Developing infrared array controller with software real time operating system

NASA Astrophysics Data System (ADS)

Sako, Shigeyuki; Miyata, Takashi; Nakamura, Tomohiko; Motohara, Kentaro; Uchimoto, Yuka Katsuno; Onaka, Takashi; Kataza, Hirokazu

2008-07-01

Real-time capabilities are required for a controller of a large format array to reduce a dead-time attributed by readout and data transfer. The real-time processing has been achieved by dedicated processors including DSP, CPLD, and FPGA devices. However, the dedicated processors have problems with memory resources, inflexibility, and high cost. Meanwhile, a recent PC has sufficient resources of CPUs and memories to control the infrared array and to process a large amount of frame data in real-time. In this study, we have developed an infrared array controller with a software real-time operating system (RTOS) instead of the dedicated processors. A Linux PC equipped with a RTAI extension and a dual-core CPU is used as a main computer, and one of the CPU cores is allocated to the real-time processing. A digital I/O board with DMA functions is used for an I/O interface. The signal-processing cores are integrated in the OS kernel as a real-time driver module, which is composed of two virtual devices of the clock processor and the frame processor tasks. The array controller with the RTOS realizes complicated operations easily, flexibly, and at a low cost.

Improving INPE'S balloon ground facilities for operation of the protoMIRAX experiment

NASA Astrophysics Data System (ADS)

Mattiello-Francisco, F.; Rinke, E.; Fernandes, J. O.; Cardoso, L.; Cardoso, P.; Braga, J.

2014-10-01

The system requirements for reusing the scientific balloon ground facilities available at INPE were a challenge to the ground system engineers involved in the protoMIRAX X-ray astronomy experiment. A significant effort on software updating was required for the balloon ground station. Considering that protoMIRAX is a pathfinder for the MIRAX satellite mission, a ground infrastructure compatible with INPE's satellite operation approach would be useful and highly recommended to control and monitor the experiment during the balloon flights. This approach will make use of the SATellite Control System (SATCS), a software-based architecture developed at INPE for satellite commanding and monitoring. SATCS complies with particular operational requirements of different satellites by using several customized object-oriented software elements and frameworks. We present the ground solution designed for protoMIRAX operation, the Control and Reception System (CRS). A new server computer, properly configured with Ethernet, has extended the existing ground station facilities with switch, converters and new software (OPS/SERVER) in order to support the available uplink and downlink channels being mapped to TCP/IP gateways required by SATCS. Currently, the CRS development is customizing the SATCS for the kernel functions of protoMIRAX command and telemetry processing. Design-patterns, component-based libraries and metadata are widely used in the SATCS in order to extend the frameworks to address the Packet Utilization Standard (PUS) for ground-balloon communication, in compliance with the services provided by the data handling computer onboard the protoMIRAX balloon.

GPU-accelerated iterative reconstruction for limited-data tomography in CBCT systems.

PubMed

de Molina, Claudia; Serrano, Estefania; Garcia-Blas, Javier; Carretero, Jesus; Desco, Manuel; Abella, Monica

2018-05-15

Standard cone-beam computed tomography (CBCT) involves the acquisition of at least 360 projections rotating through 360 degrees. Nevertheless, there are cases in which only a few projections can be taken in a limited angular span, such as during surgery, where rotation of the source-detector pair is limited to less than 180 degrees. Reconstruction of limited data with the conventional method proposed by Feldkamp, Davis and Kress (FDK) results in severe artifacts. Iterative methods may compensate for the lack of data by including additional prior information, although they imply a high computational burden and memory consumption. We present an accelerated implementation of an iterative method for CBCT following the Split Bregman formulation, which reduces computational time through GPU-accelerated kernels. The implementation enables the reconstruction of large volumes (>1024 3 pixels) using partitioning strategies in forward- and back-projection operations. We evaluated the algorithm on small-animal data for different scenarios with different numbers of projections, angular span, and projection size. Reconstruction time varied linearly with the number of projections and quadratically with projection size but remained almost unchanged with angular span. Forward- and back-projection operations represent 60% of the total computational burden. Efficient implementation using parallel processing and large-memory management strategies together with GPU kernels enables the use of advanced reconstruction approaches which are needed in limited-data scenarios. Our GPU implementation showed a significant time reduction (up to 48 ×) compared to a CPU-only implementation, resulting in a total reconstruction time from several hours to few minutes.

Nonlinear Deep Kernel Learning for Image Annotation.

PubMed

Jiu, Mingyuan; Sahbi, Hichem

2017-02-08

Multiple kernel learning (MKL) is a widely used technique for kernel design. Its principle consists in learning, for a given support vector classifier, the most suitable convex (or sparse) linear combination of standard elementary kernels. However, these combinations are shallow and often powerless to capture the actual similarity between highly semantic data, especially for challenging classification tasks such as image annotation. In this paper, we redefine multiple kernels using deep multi-layer networks. In this new contribution, a deep multiple kernel is recursively defined as a multi-layered combination of nonlinear activation functions, each one involves a combination of several elementary or intermediate kernels, and results into a positive semi-definite deep kernel. We propose four different frameworks in order to learn the weights of these networks: supervised, unsupervised, kernel-based semisupervised and Laplacian-based semi-supervised. When plugged into support vector machines (SVMs), the resulting deep kernel networks show clear gain, compared to several shallow kernels for the task of image annotation. Extensive experiments and analysis on the challenging ImageCLEF photo annotation benchmark, the COREL5k database and the Banana dataset validate the effectiveness of the proposed method.

Multineuron spike train analysis with R-convolution linear combination kernel.

PubMed

Tezuka, Taro

2018-06-01

A spike train kernel provides an effective way of decoding information represented by a spike train. Some spike train kernels have been extended to multineuron spike trains, which are simultaneously recorded spike trains obtained from multiple neurons. However, most of these multineuron extensions were carried out in a kernel-specific manner. In this paper, a general framework is proposed for extending any single-neuron spike train kernel to multineuron spike trains, based on the R-convolution kernel. Special subclasses of the proposed R-convolution linear combination kernel are explored. These subclasses have a smaller number of parameters and make optimization tractable when the size of data is limited. The proposed kernel was evaluated using Gaussian process regression for multineuron spike trains recorded from an animal brain. It was compared with the sum kernel and the population Spikernel, which are existing ways of decoding multineuron spike trains using kernels. The results showed that the proposed approach performs better than these kernels and also other commonly used neural decoding methods. Copyright © 2018 Elsevier Ltd. All rights reserved.

Predicting complex traits using a diffusion kernel on genetic markers with an application to dairy cattle and wheat data

PubMed Central

2013-01-01

Background Arguably, genotypes and phenotypes may be linked in functional forms that are not well addressed by the linear additive models that are standard in quantitative genetics. Therefore, developing statistical learning models for predicting phenotypic values from all available molecular information that are capable of capturing complex genetic network architectures is of great importance. Bayesian kernel ridge regression is a non-parametric prediction model proposed for this purpose. Its essence is to create a spatial distance-based relationship matrix called a kernel. Although the set of all single nucleotide polymorphism genotype configurations on which a model is built is finite, past research has mainly used a Gaussian kernel. Results We sought to investigate the performance of a diffusion kernel, which was specifically developed to model discrete marker inputs, using Holstein cattle and wheat data. This kernel can be viewed as a discretization of the Gaussian kernel. The predictive ability of the diffusion kernel was similar to that of non-spatial distance-based additive genomic relationship kernels in the Holstein data, but outperformed the latter in the wheat data. However, the difference in performance between the diffusion and Gaussian kernels was negligible. Conclusions It is concluded that the ability of a diffusion kernel to capture the total genetic variance is not better than that of a Gaussian kernel, at least for these data. Although the diffusion kernel as a choice of basis function may have potential for use in whole-genome prediction, our results imply that embedding genetic markers into a non-Euclidean metric space has very small impact on prediction. Our results suggest that use of the black box Gaussian kernel is justified, given its connection to the diffusion kernel and its similar predictive performance. PMID:23763755

WE-AB-303-09: Rapid Projection Computations for On-Board Digital Tomosynthesis in Radiation Therapy

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

Iliopoulos, AS; Sun, X; Pitsianis, N

2015-06-15

Purpose: To facilitate fast and accurate iterative volumetric image reconstruction from limited-angle on-board projections. Methods: Intrafraction motion hinders the clinical applicability of modern radiotherapy techniques, such as lung stereotactic body radiation therapy (SBRT). The LIVE system may impact clinical practice by recovering volumetric information via Digital Tomosynthesis (DTS), thus entailing low time and radiation dose for image acquisition during treatment. The DTS is estimated as a deformation of prior CT via iterative registration with on-board images; this shifts the challenge to the computational domain, owing largely to repeated projection computations across iterations. We address this issue by composing efficient digitalmore » projection operators from their constituent parts. This allows us to separate the static (projection geometry) and dynamic (volume/image data) parts of projection operations by means of pre-computations, enabling fast on-board processing, while also relaxing constraints on underlying numerical models (e.g. regridding interpolation kernels). Further decoupling the projectors into simpler ones ensures the incurred memory overhead remains low, within the capacity of a single GPU. These operators depend only on the treatment plan and may be reused across iterations and patients. The dynamic processing load is kept to a minimum and maps well to the GPU computational model. Results: We have integrated efficient, pre-computable modules for volumetric ray-casting and FDK-based back-projection with the LIVE processing pipeline. Our results show a 60x acceleration of the DTS computations, compared to the previous version, using a single GPU; presently, reconstruction is attained within a couple of minutes. The present implementation allows for significant flexibility in terms of the numerical and operational projection model; we are investigating the benefit of further optimizations and accurate digital projection sub-kernels. Conclusion: Composable projection operators constitute a versatile research tool which can greatly accelerate iterative registration algorithms and may be conducive to the clinical applicability of LIVE. National Institutes of Health Grant No. R01-CA184173; GPU donation by NVIDIA Corporation.« less

7 CFR 981.9 - Kernel weight.

Code of Federal Regulations, 2010 CFR

2010-01-01

... 7 Agriculture 8 2010-01-01 2010-01-01 false Kernel weight. 981.9 Section 981.9 Agriculture Regulations of the Department of Agriculture (Continued) AGRICULTURAL MARKETING SERVICE (Marketing Agreements... Regulating Handling Definitions § 981.9 Kernel weight. Kernel weight means the weight of kernels, including...

An SVM model with hybrid kernels for hydrological time series

NASA Astrophysics Data System (ADS)

Wang, C.; Wang, H.; Zhao, X.; Xie, Q.

2017-12-01

Support Vector Machine (SVM) models have been widely applied to the forecast of climate/weather and its impact on other environmental variables such as hydrologic response to climate/weather. When using SVM, the choice of the kernel function plays the key role. Conventional SVM models mostly use one single type of kernel function, e.g., radial basis kernel function. Provided that there are several featured kernel functions available, each having its own advantages and drawbacks, a combination of these kernel functions may give more flexibility and robustness to SVM approach, making it suitable for a wide range of application scenarios. This paper presents such a linear combination of radial basis kernel and polynomial kernel for the forecast of monthly flowrate in two gaging stations using SVM approach. The results indicate significant improvement in the accuracy of predicted series compared to the approach with either individual kernel function, thus demonstrating the feasibility and advantages of such hybrid kernel approach for SVM applications.

Spectrofluorimetric determination of melatonin in kernels of four different Pistacia varieties after ultrasound-assisted solid-liquid extraction.

PubMed

Oladi, Elham; Mohamadi, Maryam; Shamspur, Tayebeh; Mostafavi, Ali

2014-11-11

Melatonin is normally consumed to regulate the body's biological cycle. However it also has therapeutic properties, such as anti-tumor, anti-aging and protects the immune system. There are some reports on the presence of melatonin in edible kernels such as walnuts, but the extraction of melatonin from pistachio kernels is reported here for the first time. For this, the methanolic extract of pistachio kernels was exposed to gas chromatography/mass spectrometry analysis which confirmed the presence of melatonin. A fluorescence-based method was applied for the determination of melatonin in different extracts. When excited at λ=275 nm, the fluorescence emission intensity of melatonin was measured at λ=366 nm. Ultrasound-assisted solid-liquid extraction was used for the extraction of melatonin from pistachio kernels prior to fluorimetric determination. To achieve the highest extraction recovery, the main parameters affecting the extraction efficiency such as extracting solvent type and volume, temperature, sonication time and pH were evaluated. Under the optimized conditions, a linear dependence of fluorescence intensity on melatonin concentration was observed in the range of 0.0040-0.160 μg mL(-1), with a detection limit of 0.0036 μg mL(-1). This method was applied successfully for measuring and comparing the melatonin content in the kernels of four different varieties of Pistacia including Ahmad Aghaei, Akbari, Kalle Qouchi and Fandoghi. In addition, the results obtained were compared with those obtained using GC/MS. A good agreement was observed indicating the reliability of the proposed method. Copyright © 2014 Elsevier B.V. All rights reserved.

Spectrofluorimetric determination of melatonin in kernels of four different Pistacia varieties after ultrasound-assisted solid-liquid extraction

NASA Astrophysics Data System (ADS)

Oladi, Elham; Mohamadi, Maryam; Shamspur, Tayebeh; Mostafavi, Ali

2014-11-01

Melatonin is normally consumed to regulate the body's biological cycle. However it also has therapeutic properties, such as anti-tumor, anti-aging and protects the immune system. There are some reports on the presence of melatonin in edible kernels such as walnuts, but the extraction of melatonin from pistachio kernels is reported here for the first time. For this, the methanolic extract of pistachio kernels was exposed to gas chromatography/mass spectrometry analysis which confirmed the presence of melatonin. A fluorescence-based method was applied for the determination of melatonin in different extracts. When excited at λ = 275 nm, the fluorescence emission intensity of melatonin was measured at λ = 366 nm. Ultrasound-assisted solid-liquid extraction was used for the extraction of melatonin from pistachio kernels prior to fluorimetric determination. To achieve the highest extraction recovery, the main parameters affecting the extraction efficiency such as extracting solvent type and volume, temperature, sonication time and pH were evaluated. Under the optimized conditions, a linear dependence of fluorescence intensity on melatonin concentration was observed in the range of 0.0040-0.160 μg mL-1, with a detection limit of 0.0036 μg mL-1. This method was applied successfully for measuring and comparing the melatonin content in the kernels of four different varieties of Pistacia including Ahmad Aghaei, Akbari, Kalle Qouchi and Fandoghi. In addition, the results obtained were compared with those obtained using GC/MS. A good agreement was observed indicating the reliability of the proposed method.

Improved scatter correction using adaptive scatter kernel superposition

NASA Astrophysics Data System (ADS)

Sun, M.; Star-Lack, J. M.

2010-11-01

Accurate scatter correction is required to produce high-quality reconstructions of x-ray cone-beam computed tomography (CBCT) scans. This paper describes new scatter kernel superposition (SKS) algorithms for deconvolving scatter from projection data. The algorithms are designed to improve upon the conventional approach whose accuracy is limited by the use of symmetric kernels that characterize the scatter properties of uniform slabs. To model scatter transport in more realistic objects, nonstationary kernels, whose shapes adapt to local thickness variations in the projection data, are proposed. Two methods are introduced: (1) adaptive scatter kernel superposition (ASKS) requiring spatial domain convolutions and (2) fast adaptive scatter kernel superposition (fASKS) where, through a linearity approximation, convolution is efficiently performed in Fourier space. The conventional SKS algorithm, ASKS, and fASKS, were tested with Monte Carlo simulations and with phantom data acquired on a table-top CBCT system matching the Varian On-Board Imager (OBI). All three models accounted for scatter point-spread broadening due to object thickening, object edge effects, detector scatter properties and an anti-scatter grid. Hounsfield unit (HU) errors in reconstructions of a large pelvis phantom with a measured maximum scatter-to-primary ratio over 200% were reduced from -90 ± 58 HU (mean ± standard deviation) with no scatter correction to 53 ± 82 HU with SKS, to 19 ± 25 HU with fASKS and to 13 ± 21 HU with ASKS. HU accuracies and measured contrast were similarly improved in reconstructions of a body-sized elliptical Catphan phantom. The results show that the adaptive SKS methods offer significant advantages over the conventional scatter deconvolution technique.

Approximate kernel competitive learning.

PubMed

Wu, Jian-Sheng; Zheng, Wei-Shi; Lai, Jian-Huang

2015-03-01

Kernel competitive learning has been successfully used to achieve robust clustering. However, kernel competitive learning (KCL) is not scalable for large scale data processing, because (1) it has to calculate and store the full kernel matrix that is too large to be calculated and kept in the memory and (2) it cannot be computed in parallel. In this paper we develop a framework of approximate kernel competitive learning for processing large scale dataset. The proposed framework consists of two parts. First, it derives an approximate kernel competitive learning (AKCL), which learns kernel competitive learning in a subspace via sampling. We provide solid theoretical analysis on why the proposed approximation modelling would work for kernel competitive learning, and furthermore, we show that the computational complexity of AKCL is largely reduced. Second, we propose a pseudo-parallelled approximate kernel competitive learning (PAKCL) based on a set-based kernel competitive learning strategy, which overcomes the obstacle of using parallel programming in kernel competitive learning and significantly accelerates the approximate kernel competitive learning for large scale clustering. The empirical evaluation on publicly available datasets shows that the proposed AKCL and PAKCL can perform comparably as KCL, with a large reduction on computational cost. Also, the proposed methods achieve more effective clustering performance in terms of clustering precision against related approximate clustering approaches. Copyright © 2014 Elsevier Ltd. All rights reserved.

Multiple kernels learning-based biological entity relationship extraction method.

PubMed

Dongliang, Xu; Jingchang, Pan; Bailing, Wang

2017-09-20

Automatic extracting protein entity interaction information from biomedical literature can help to build protein relation network and design new drugs. There are more than 20 million literature abstracts included in MEDLINE, which is the most authoritative textual database in the field of biomedicine, and follow an exponential growth over time. This frantic expansion of the biomedical literature can often be difficult to absorb or manually analyze. Thus efficient and automated search engines are necessary to efficiently explore the biomedical literature using text mining techniques. The P, R, and F value of tag graph method in Aimed corpus are 50.82, 69.76, and 58.61%, respectively. The P, R, and F value of tag graph kernel method in other four evaluation corpuses are 2-5% higher than that of all-paths graph kernel. And The P, R and F value of feature kernel and tag graph kernel fuse methods is 53.43, 71.62 and 61.30%, respectively. The P, R and F value of feature kernel and tag graph kernel fuse methods is 55.47, 70.29 and 60.37%, respectively. It indicated that the performance of the two kinds of kernel fusion methods is better than that of simple kernel. In comparison with the all-paths graph kernel method, the tag graph kernel method is superior in terms of overall performance. Experiments show that the performance of the multi-kernels method is better than that of the three separate single-kernel method and the dual-mutually fused kernel method used hereof in five corpus sets.

The role of fractional time-derivative operators on anomalous diffusion

NASA Astrophysics Data System (ADS)

Tateishi, Angel A.; Ribeiro, Haroldo V.; Lenzi, Ervin K.

2017-10-01

The generalized diffusion equations with fractional order derivatives have shown be quite efficient to describe the diffusion in complex systems, with the advantage of producing exact expressions for the underlying diffusive properties. Recently, researchers have proposed different fractional-time operators (namely: the Caputo-Fabrizio and Atangana-Baleanu) which, differently from the well-known Riemann-Liouville operator, are defined by non-singular memory kernels. Here we proposed to use these new operators to generalize the usual diffusion equation. By analyzing the corresponding fractional diffusion equations within the continuous time random walk framework, we obtained waiting time distributions characterized by exponential, stretched exponential, and power-law functions, as well as a crossover between two behaviors. For the mean square displacement, we found crossovers between usual and confined diffusion, and between usual and sub-diffusion. We obtained the exact expressions for the probability distributions, where non-Gaussian and stationary distributions emerged. This former feature is remarkable because the fractional diffusion equation is solved without external forces and subjected to the free diffusion boundary conditions. We have further shown that these new fractional diffusion equations are related to diffusive processes with stochastic resetting, and to fractional diffusion equations with derivatives of distributed order. Thus, our results suggest that these new operators may be a simple and efficient way for incorporating different structural aspects into the system, opening new possibilities for modeling and investigating anomalous diffusive processes.

Predicting spatial patterns of plant recruitment using animal-displacement kernels.

PubMed

Santamaría, Luis; Rodríguez-Pérez, Javier; Larrinaga, Asier R; Pias, Beatriz

2007-10-10

For plants dispersed by frugivores, spatial patterns of recruitment are primarily influenced by the spatial arrangement and characteristics of parent plants, the digestive characteristics, feeding behaviour and movement patterns of animal dispersers, and the structure of the habitat matrix. We used an individual-based, spatially-explicit framework to characterize seed dispersal and seedling fate in an endangered, insular plant-disperser system: the endemic shrub Daphne rodriguezii and its exclusive disperser, the endemic lizard Podarcis lilfordi. Plant recruitment kernels were chiefly determined by the disperser's patterns of space utilization (i.e. the lizard's displacement kernels), the position of the various plant individuals in relation to them, and habitat structure (vegetation cover vs. bare soil). In contrast to our expectations, seed gut-passage rate and its effects on germination, and lizard speed-of-movement, habitat choice and activity rhythm were of minor importance. Predicted plant recruitment kernels were strongly anisotropic and fine-grained, preventing their description using one-dimensional, frequency-distance curves. We found a general trade-off between recruitment probability and dispersal distance; however, optimal recruitment sites were not necessarily associated to sites of maximal adult-plant density. Conservation efforts aimed at enhancing the regeneration of endangered plant-disperser systems may gain in efficacy by manipulating the spatial distribution of dispersers (e.g. through the creation of refuges and feeding sites) to create areas favourable to plant recruitment.

A dual-input nonlinear system analysis of autonomic modulation of heart rate

NASA Technical Reports Server (NTRS)

Chon, K. H.; Mullen, T. J.; Cohen, R. J.

1996-01-01

Linear analyses of fluctuations in heart rate and other hemodynamic variables have been used to elucidate cardiovascular regulatory mechanisms. The role of nonlinear contributions to fluctuations in hemodynamic variables has not been fully explored. This paper presents a nonlinear system analysis of the effect of fluctuations in instantaneous lung volume (ILV) and arterial blood pressure (ABP) on heart rate (HR) fluctuations. To successfully employ a nonlinear analysis based on the Laguerre expansion technique (LET), we introduce an efficient procedure for broadening the spectral content of the ILV and ABP inputs to the model by adding white noise. Results from computer simulations demonstrate the effectiveness of broadening the spectral band of input signals to obtain consistent and stable kernel estimates with the use of the LET. Without broadening the band of the ILV and ABP inputs, the LET did not provide stable kernel estimates. Moreover, we extend the LET to the case of multiple inputs in order to accommodate the analysis of the combined effect of ILV and ABP effect on heart rate. Analyzes of data based on the second-order Volterra-Wiener model reveal an important contribution of the second-order kernels to the description of the effect of lung volume and arterial blood pressure on heart rate. Furthermore, physiological effects of the autonomic blocking agents propranolol and atropine on changes in the first- and second-order kernels are also discussed.

Scoliosis curve type classification using kernel machine from 3D trunk image

NASA Astrophysics Data System (ADS)

Adankon, Mathias M.; Dansereau, Jean; Parent, Stefan; Labelle, Hubert; Cheriet, Farida

2012-03-01

Adolescent idiopathic scoliosis (AIS) is a deformity of the spine manifested by asymmetry and deformities of the external surface of the trunk. Classification of scoliosis deformities according to curve type is used to plan management of scoliosis patients. Currently, scoliosis curve type is determined based on X-ray exam. However, cumulative exposure to X-rays radiation significantly increases the risk for certain cancer. In this paper, we propose a robust system that can classify the scoliosis curve type from non invasive acquisition of 3D trunk surface of the patients. The 3D image of the trunk is divided into patches and local geometric descriptors characterizing the surface of the back are computed from each patch and forming the features. We perform the reduction of the dimensionality by using Principal Component Analysis and 53 components were retained. In this work a multi-class classifier is built with Least-squares support vector machine (LS-SVM) which is a kernel classifier. For this study, a new kernel was designed in order to achieve a robust classifier in comparison with polynomial and Gaussian kernel. The proposed system was validated using data of 103 patients with different scoliosis curve types diagnosed and classified by an orthopedic surgeon from the X-ray images. The average rate of successful classification was 93.3% with a better rate of prediction for the major thoracic and lumbar/thoracolumbar types.

Initial Kernel Timing Using a Simple PIM Performance Model

NASA Technical Reports Server (NTRS)

Katz, Daniel S.; Block, Gary L.; Springer, Paul L.; Sterling, Thomas; Brockman, Jay B.; Callahan, David

2005-01-01

This presentation will describe some initial results of paper-and-pencil studies of 4 or 5 application kernels applied to a processor-in-memory (PIM) system roughly similar to the Cascade Lightweight Processor (LWP). The application kernels are: * Linked list traversal * Sun of leaf nodes on a tree * Bitonic sort * Vector sum * Gaussian elimination The intent of this work is to guide and validate work on the Cascade project in the areas of compilers, simulators, and languages. We will first discuss the generic PIM structure. Then, we will explain the concepts needed to program a parallel PIM system (locality, threads, parcels). Next, we will present a simple PIM performance model that will be used in the remainder of the presentation. For each kernel, we will then present a set of codes, including codes for a single PIM node, and codes for multiple PIM nodes that move data to threads and move threads to data. These codes are written at a fairly low level, between assembly and C, but much closer to C than to assembly. For each code, we will present some hand-drafted timing forecasts, based on the simple PIM performance model. Finally, we will conclude by discussing what we have learned from this work, including what programming styles seem to work best, from the point-of-view of both expressiveness and performance.

7 CFR 51.2295 - Half kernel.

Code of Federal Regulations, 2010 CFR

2010-01-01

... 7 Agriculture 2 2010-01-01 2010-01-01 false Half kernel. 51.2295 Section 51.2295 Agriculture... Standards for Shelled English Walnuts (Juglans Regia) Definitions § 51.2295 Half kernel. Half kernel means the separated half of a kernel with not more than one-eighth broken off. ...

7 CFR 810.206 - Grades and grade requirements for barley.

Code of Federal Regulations, 2010 CFR

2010-01-01

... weight per bushel (pounds) Sound barley (percent) Maximum Limits of— Damaged kernels 1 (percent) Heat damaged kernels (percent) Foreign material (percent) Broken kernels (percent) Thin barley (percent) U.S... or otherwise of distinctly low quality. 1 Includes heat-damaged kernels. Injured-by-frost kernels and...

Parametric and non-parametric modeling of short-term synaptic plasticity. Part I: computational study

PubMed Central

Marmarelis, Vasilis Z.; Berger, Theodore W.

2009-01-01

Parametric and non-parametric modeling methods are combined to study the short-term plasticity (STP) of synapses in the central nervous system (CNS). The nonlinear dynamics of STP are modeled by means: (1) previously proposed parametric models based on mechanistic hypotheses and/or specific dynamical processes, and (2) non-parametric models (in the form of Volterra kernels) that transforms the presynaptic signals into postsynaptic signals. In order to synergistically use the two approaches, we estimate the Volterra kernels of the parametric models of STP for four types of synapses using synthetic broadband input–output data. Results show that the non-parametric models accurately and efficiently replicate the input–output transformations of the parametric models. Volterra kernels provide a general and quantitative representation of the STP. PMID:18506609

MCViNE- An object oriented Monte Carlo neutron ray tracing simulation package

DOE PAGES

Lin, J. Y. Y.; Smith, Hillary L.; Granroth, Garrett E.; ...

2015-11-28

MCViNE (Monte-Carlo VIrtual Neutron Experiment) is an open-source Monte Carlo (MC) neutron ray-tracing software for performing computer modeling and simulations that mirror real neutron scattering experiments. We exploited the close similarity between how instrument components are designed and operated and how such components can be modeled in software. For example we used object oriented programming concepts for representing neutron scatterers and detector systems, and recursive algorithms for implementing multiple scattering. Combining these features together in MCViNE allows one to handle sophisticated neutron scattering problems in modern instruments, including, for example, neutron detection by complex detector systems, and single and multiplemore » scattering events in a variety of samples and sample environments. In addition, MCViNE can use simulation components from linear-chain-based MC ray tracing packages which facilitates porting instrument models from those codes. Furthermore it allows for components written solely in Python, which expedites prototyping of new components. These developments have enabled detailed simulations of neutron scattering experiments, with non-trivial samples, for time-of-flight inelastic instruments at the Spallation Neutron Source. Examples of such simulations for powder and single-crystal samples with various scattering kernels, including kernels for phonon and magnon scattering, are presented. As a result, with simulations that closely reproduce experimental results, scattering mechanisms can be turned on and off to determine how they contribute to the measured scattering intensities, improving our understanding of the underlying physics.« less

ZmDof3, a maize endosperm-specific Dof protein gene, regulates starch accumulation and aleurone development in maize endosperm.

PubMed

Qi, Xin; Li, Shixue; Zhu, Yaxi; Zhao, Qian; Zhu, Dengyun; Yu, Jingjuan

2017-01-01

To explore the function of Dof transcription factors during kernel development in maize, we first identified Dof genes in the maize genome. We found that ZmDof3 was exclusively expressed in the endosperm of maize kernel and had the features of a Dof transcription factor. Suppression of ZmDof3 resulted in a defective kernel phenotype with reduced starch content and a partially patchy aleurone layer. The expression levels of starch synthesis-related genes and aleurone differentiation-associated genes were down-regulated in ZmDof3 knockdown kernels, indicating that ZmDof3 plays an important role in maize endosperm development. The maize endosperm, occupying a large proportion of the kernel, plays an important role in seed development and germination. Current knowledge regarding the regulation of endosperm development is limited. Dof proteins, a family of plant-specific transcription factors, play critical roles in diverse biological processes. In this study, an endosperm-specific Dof protein gene, ZmDof3, was identified in maize through genome-wide screening. Suppression of ZmDof3 resulted in a defective kernel phenotype. The endosperm of ZmDof3 knockdown kernels was loosely packed with irregular starch granules observed by electronic microscope. Through genome-wide expression profiling, we found that down-regulated genes were enriched in GO terms related to carbohydrate metabolism. Moreover, ZmDof3 could bind to the Dof core element in the promoter of starch biosynthesis genes Du1 and Su2 in vitro and in vivo. In addition, the aleurone at local position in mature ZmDof3 knockdown kernels varied from one to three layers, which consisted of smaller and irregular cells. Further analyses showed that knockdown of ZmDof3 reduced the expression of Nkd1, which is involved in aleurone cell differentiation, and that ZmDof3 could bind to the Dof core element in the Nkd1 promoter. Our study reveals that ZmDof3 functions in maize endosperm development as a positive regulator in the signaling system controlling starch accumulation and aleurone development.

7 CFR 51.1449 - Damage.

Code of Federal Regulations, 2014 CFR

2014-01-01

...) Kernel which is “dark amber” or darker color; (e) Kernel having more than one dark kernel spot, or one dark kernel spot more than one-eighth inch in greatest dimension; (f) Shriveling when the surface of the kernel is very conspicuously wrinkled; (g) Internal flesh discoloration of a medium shade of gray...

7 CFR 51.1449 - Damage.

Code of Federal Regulations, 2013 CFR

2013-01-01

...) Kernel which is “dark amber” or darker color; (e) Kernel having more than one dark kernel spot, or one dark kernel spot more than one-eighth inch in greatest dimension; (f) Shriveling when the surface of the kernel is very conspicuously wrinkled; (g) Internal flesh discoloration of a medium shade of gray...

7 CFR 51.2125 - Split or broken kernels.

Code of Federal Regulations, 2010 CFR

2010-01-01

... 7 Agriculture 2 2010-01-01 2010-01-01 false Split or broken kernels. 51.2125 Section 51.2125 Agriculture Regulations of the Department of Agriculture AGRICULTURAL MARKETING SERVICE (Standards... kernels. Split or broken kernels means seven-eighths or less of complete whole kernels but which will not...

7 CFR 51.2296 - Three-fourths half kernel.

Code of Federal Regulations, 2010 CFR

2010-01-01

... 7 Agriculture 2 2010-01-01 2010-01-01 false Three-fourths half kernel. 51.2296 Section 51.2296 Agriculture Regulations of the Department of Agriculture AGRICULTURAL MARKETING SERVICE (Standards...-fourths half kernel. Three-fourths half kernel means a portion of a half of a kernel which has more than...

The Classification of Diabetes Mellitus Using Kernel k-means

NASA Astrophysics Data System (ADS)

Alamsyah, M.; Nafisah, Z.; Prayitno, E.; Afida, A. M.; Imah, E. M.

2018-01-01

Diabetes Mellitus is a metabolic disorder which is characterized by chronicle hypertensive glucose. Automatics detection of diabetes mellitus is still challenging. This study detected diabetes mellitus by using kernel k-Means algorithm. Kernel k-means is an algorithm which was developed from k-means algorithm. Kernel k-means used kernel learning that is able to handle non linear separable data; where it differs with a common k-means. The performance of kernel k-means in detecting diabetes mellitus is also compared with SOM algorithms. The experiment result shows that kernel k-means has good performance and a way much better than SOM.

UNICOS Kernel Internals Application Development

NASA Technical Reports Server (NTRS)

Caredo, Nicholas; Craw, James M. (Technical Monitor)

1995-01-01

Having an understanding of UNICOS Kernel Internals is valuable information. However, having the knowledge is only half the value. The second half comes with knowing how to use this information and apply it to the development of tools. The kernel contains vast amounts of useful information that can be utilized. This paper discusses the intricacies of developing utilities that utilize kernel information. In addition, algorithms, logic, and code will be discussed for accessing kernel information. Code segments will be provided that demonstrate how to locate and read kernel structures. Types of applications that can utilize kernel information will also be discussed.

Detection of maize kernels breakage rate based on K-means clustering

NASA Astrophysics Data System (ADS)

Yang, Liang; Wang, Zhuo; Gao, Lei; Bai, Xiaoping

2017-04-01

In order to optimize the recognition accuracy of maize kernels breakage detection and improve the detection efficiency of maize kernels breakage, this paper using computer vision technology and detecting of the maize kernels breakage based on K-means clustering algorithm. First, the collected RGB images are converted into Lab images, then the original images clarity evaluation are evaluated by the energy function of Sobel 8 gradient. Finally, the detection of maize kernels breakage using different pixel acquisition equipments and different shooting angles. In this paper, the broken maize kernels are identified by the color difference between integrity kernels and broken kernels. The original images clarity evaluation and different shooting angles are taken to verify that the clarity and shooting angles of the images have a direct influence on the feature extraction. The results show that K-means clustering algorithm can distinguish the broken maize kernels effectively.

Modeling adaptive kernels from probabilistic phylogenetic trees.

PubMed

Nicotra, Luca; Micheli, Alessio

2009-01-01

Modeling phylogenetic interactions is an open issue in many computational biology problems. In the context of gene function prediction we introduce a class of kernels for structured data leveraging on a hierarchical probabilistic modeling of phylogeny among species. We derive three kernels belonging to this setting: a sufficient statistics kernel, a Fisher kernel, and a probability product kernel. The new kernels are used in the context of support vector machine learning. The kernels adaptivity is obtained through the estimation of the parameters of a tree structured model of evolution using as observed data phylogenetic profiles encoding the presence or absence of specific genes in a set of fully sequenced genomes. We report results obtained in the prediction of the functional class of the proteins of the budding yeast Saccharomyces cerevisae which favorably compare to a standard vector based kernel and to a non-adaptive tree kernel function. A further comparative analysis is performed in order to assess the impact of the different components of the proposed approach. We show that the key features of the proposed kernels are the adaptivity to the input domain and the ability to deal with structured data interpreted through a graphical model representation.

Aflatoxin and nutrient contents of peanut collected from local market and their processed foods

NASA Astrophysics Data System (ADS)

Ginting, E.; Rahmianna, A. A.; Yusnawan, E.

2018-01-01

Peanut is succeptable to aflatoxin contamination and the sources of peanut as well as processing methods considerably affect aflatoxin content of the products. Therefore, the study on aflatoxin and nutrient contents of peanut collected from local market and their processed foods were performed. Good kernels of peanut were prepared into fried peanut, pressed-fried peanut, peanut sauce, peanut press cake, fermented peanut press cake (tempe) and fried tempe, while blended kernels (good and poor kernels) were processed into peanut sauce and tempe and poor kernels were only processed into tempe. The results showed that good and blended kernels which had high number of sound/intact kernels (82,46% and 62,09%), contained 9.8-9.9 ppb of aflatoxin B1, while slightly higher level was seen in poor kernels (12.1 ppb). However, the moisture, ash, protein, and fat contents of the kernels were similar as well as the products. Peanut tempe and fried tempe showed the highest increase in protein content, while decreased fat contents were seen in all products. The increase in aflatoxin B1 of peanut tempe prepared from poor kernels > blended kernels > good kernels. However, it averagely decreased by 61.2% after deep-fried. Excluding peanut tempe and fried tempe, aflatoxin B1 levels in all products derived from good kernels were below the permitted level (15 ppb). This suggests that sorting peanut kernels as ingredients and followed by heat processing would decrease the aflatoxin content in the products.

Partial Deconvolution with Inaccurate Blur Kernel.

PubMed

Ren, Dongwei; Zuo, Wangmeng; Zhang, David; Xu, Jun; Zhang, Lei

2017-10-17

Most non-blind deconvolution methods are developed under the error-free kernel assumption, and are not robust to inaccurate blur kernel. Unfortunately, despite the great progress in blind deconvolution, estimation error remains inevitable during blur kernel estimation. Consequently, severe artifacts such as ringing effects and distortions are likely to be introduced in the non-blind deconvolution stage. In this paper, we tackle this issue by suggesting: (i) a partial map in the Fourier domain for modeling kernel estimation error, and (ii) a partial deconvolution model for robust deblurring with inaccurate blur kernel. The partial map is constructed by detecting the reliable Fourier entries of estimated blur kernel. And partial deconvolution is applied to wavelet-based and learning-based models to suppress the adverse effect of kernel estimation error. Furthermore, an E-M algorithm is developed for estimating the partial map and recovering the latent sharp image alternatively. Experimental results show that our partial deconvolution model is effective in relieving artifacts caused by inaccurate blur kernel, and can achieve favorable deblurring quality on synthetic and real blurry images.Most non-blind deconvolution methods are developed under the error-free kernel assumption, and are not robust to inaccurate blur kernel. Unfortunately, despite the great progress in blind deconvolution, estimation error remains inevitable during blur kernel estimation. Consequently, severe artifacts such as ringing effects and distortions are likely to be introduced in the non-blind deconvolution stage. In this paper, we tackle this issue by suggesting: (i) a partial map in the Fourier domain for modeling kernel estimation error, and (ii) a partial deconvolution model for robust deblurring with inaccurate blur kernel. The partial map is constructed by detecting the reliable Fourier entries of estimated blur kernel. And partial deconvolution is applied to wavelet-based and learning-based models to suppress the adverse effect of kernel estimation error. Furthermore, an E-M algorithm is developed for estimating the partial map and recovering the latent sharp image alternatively. Experimental results show that our partial deconvolution model is effective in relieving artifacts caused by inaccurate blur kernel, and can achieve favorable deblurring quality on synthetic and real blurry images.

Computational prediction of virus-human protein-protein interactions using embedding kernelized heterogeneous data.

PubMed

Nourani, Esmaeil; Khunjush, Farshad; Durmuş, Saliha

2016-05-24

Pathogenic microorganisms exploit host cellular mechanisms and evade host defense mechanisms through molecular pathogen-host interactions (PHIs). Therefore, comprehensive analysis of these PHI networks should be an initial step for developing effective therapeutics against infectious diseases. Computational prediction of PHI data is gaining increasing demand because of scarcity of experimental data. Prediction of protein-protein interactions (PPIs) within PHI systems can be formulated as a classification problem, which requires the knowledge of non-interacting protein pairs. This is a restricting requirement since we lack datasets that report non-interacting protein pairs. In this study, we formulated the "computational prediction of PHI data" problem using kernel embedding of heterogeneous data. This eliminates the abovementioned requirement and enables us to predict new interactions without randomly labeling protein pairs as non-interacting. Domain-domain associations are used to filter the predicted results leading to 175 novel PHIs between 170 human proteins and 105 viral proteins. To compare our results with the state-of-the-art studies that use a binary classification formulation, we modified our settings to consider the same formulation. Detailed evaluations are conducted and our results provide more than 10 percent improvements for accuracy and AUC (area under the receiving operating curve) results in comparison with state-of-the-art methods.

Dynamic extension of the Simulation Problem Analysis Kernel (SPANK)

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

Sowell, E.F.; Buhl, W.F.

1988-07-15

The Simulation Problem Analysis Kernel (SPANK) is an object-oriented simulation environment for general simulation purposes. Among its unique features is use of the directed graph as the primary data structure, rather than the matrix. This allows straightforward use of graph algorithms for matching variables and equations, and reducing the problem graph for efficient numerical solution. The original prototype implementation demonstrated the principles for systems of algebraic equations, allowing simulation of steady-state, nonlinear systems (Sowell 1986). This paper describes how the same principles can be extended to include dynamic objects, allowing simulation of general dynamic systems. The theory is developed andmore » an implementation is described. An example is taken from the field of building energy system simulation. 2 refs., 9 figs.« less

Novel procedure for characterizing nonlinear systems with memory: 2017 update

NASA Astrophysics Data System (ADS)

Nuttall, Albert H.; Katz, Richard A.; Hughes, Derke R.; Koch, Robert M.

2017-05-01

The present article discusses novel improvements in nonlinear signal processing made by the prime algorithm developer, Dr. Albert H. Nuttall and co-authors, a consortium of research scientists from the Naval Undersea Warfare Center Division, Newport, RI. The algorithm, called the Nuttall-Wiener-Volterra or 'NWV' algorithm is named for its principal contributors [1], [2],[ 3] . The NWV algorithm significantly reduces the computational workload for characterizing nonlinear systems with memory. Following this formulation, two measurement waveforms are required in order to characterize a specified nonlinear system under consideration: (1) an excitation input waveform, x(t) (the transmitted signal); and, (2) a response output waveform, z(t) (the received signal). Given these two measurement waveforms for a given propagation channel, a 'kernel' or 'channel response', h= [h0,h1,h2,h3] between the two measurement points, is computed via a least squares approach that optimizes modeled kernel values by performing a best fit between measured response z(t) and a modeled response y(t). New techniques significantly diminish the exponential growth of the number of computed kernel coefficients at second and third order and alleviate the Curse of Dimensionality (COD) in order to realize practical nonlinear solutions of scientific and engineering interest.

Real-time digital signal recovery for a multi-pole low-pass transfer function system.

PubMed

Lee, Jhinhwan

2017-08-01

In order to solve the problems of waveform distortion and signal delay by many physical and electrical systems with multi-pole linear low-pass transfer characteristics, a simple digital-signal-processing (DSP)-based method of real-time recovery of the original source waveform from the distorted output waveform is proposed. A mathematical analysis on the convolution kernel representation of the single-pole low-pass transfer function shows that the original source waveform can be accurately recovered in real time using a particular moving average algorithm applied on the input stream of the distorted waveform, which can also significantly reduce the overall delay time constant. This method is generalized for multi-pole low-pass systems and has noise characteristics of the inverse of the low-pass filter characteristics. This method can be applied to most sensors and amplifiers operating close to their frequency response limits to improve the overall performance of data acquisition systems and digital feedback control systems.

A Prototype SSVEP Based Real Time BCI Gaming System

PubMed Central

Martišius, Ignas

2016-01-01

Although brain-computer interface technology is mainly designed with disabled people in mind, it can also be beneficial to healthy subjects, for example, in gaming or virtual reality systems. In this paper we discuss the typical architecture, paradigms, requirements, and limitations of electroencephalogram-based gaming systems. We have developed a prototype three-class brain-computer interface system, based on the steady state visually evoked potentials paradigm and the Emotiv EPOC headset. An online target shooting game, implemented in the OpenViBE environment, has been used for user feedback. The system utilizes wave atom transform for feature extraction, achieving an average accuracy of 78.2% using linear discriminant analysis classifier, 79.3% using support vector machine classifier with a linear kernel, and 80.5% using a support vector machine classifier with a radial basis function kernel. PMID:27051414

A Prototype SSVEP Based Real Time BCI Gaming System.

PubMed

Martišius, Ignas; Damaševičius, Robertas

2016-01-01

Although brain-computer interface technology is mainly designed with disabled people in mind, it can also be beneficial to healthy subjects, for example, in gaming or virtual reality systems. In this paper we discuss the typical architecture, paradigms, requirements, and limitations of electroencephalogram-based gaming systems. We have developed a prototype three-class brain-computer interface system, based on the steady state visually evoked potentials paradigm and the Emotiv EPOC headset. An online target shooting game, implemented in the OpenViBE environment, has been used for user feedback. The system utilizes wave atom transform for feature extraction, achieving an average accuracy of 78.2% using linear discriminant analysis classifier, 79.3% using support vector machine classifier with a linear kernel, and 80.5% using a support vector machine classifier with a radial basis function kernel.

Comparative Implementation of High Performance Computing for Power System Dynamic Simulations

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

Jin, Shuangshuang; Huang, Zhenyu; Diao, Ruisheng

Dynamic simulation for transient stability assessment is one of the most important, but intensive, computations for power system planning and operation. Present commercial software is mainly designed for sequential computation to run a single simulation, which is very time consuming with a single processer. The application of High Performance Computing (HPC) to dynamic simulations is very promising in accelerating the computing process by parallelizing its kernel algorithms while maintaining the same level of computation accuracy. This paper describes the comparative implementation of four parallel dynamic simulation schemes in two state-of-the-art HPC environments: Message Passing Interface (MPI) and Open Multi-Processing (OpenMP).more » These implementations serve to match the application with dedicated multi-processor computing hardware and maximize the utilization and benefits of HPC during the development process.« less

Approximating the linear quadratic optimal control law for hereditary systems with delays in the control

NASA Technical Reports Server (NTRS)

Milman, Mark H.

1988-01-01

The fundamental control synthesis issue of establishing a priori convergence rates of approximation schemes for feedback controllers for a class of distributed parameter systems is addressed within the context of hereditary schemes. Specifically, a factorization approach is presented for deriving approximations to the optimal feedback gains for the linear regulator-quadratic cost problem associated with time-varying functional differential equations with control delays. The approach is based on a discretization of the state penalty which leads to a simple structure for the feedback control law. General properties of the Volterra factors of Hilbert-Schmidt operators are then used to obtain convergence results for the controls, trajectories and feedback kernels. Two algorithms are derived from the basic approximation scheme, including a fast algorithm, in the time-invariant case. A numerical example is also considered.

7 CFR 981.401 - Adjusted kernel weight.

Code of Federal Regulations, 2012 CFR

2012-01-01

... based on the analysis of a 1,000 gram sample taken from a lot of almonds weighing 10,000 pounds with less than 95 percent kernels, and a 1,000 gram sample taken from a lot of almonds weighing 10,000... percent kernels containing the following: Edible kernels, 530 grams; inedible kernels, 120 grams; foreign...

7 CFR 981.401 - Adjusted kernel weight.

Code of Federal Regulations, 2011 CFR

2011-01-01

... based on the analysis of a 1,000 gram sample taken from a lot of almonds weighing 10,000 pounds with less than 95 percent kernels, and a 1,000 gram sample taken from a lot of almonds weighing 10,000... percent kernels containing the following: Edible kernels, 530 grams; inedible kernels, 120 grams; foreign...